diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5d551d4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,411 @@ +pred_figs/ +processed/ +public-code/ +.idea/ + +## Core latex/pdflatex auxiliary files: +*.aux +*.lof +*.log +*.lot +*.fls +*.out +*.toc +*.fmt +*.fot +*.cb +*.cb2 +.*.lb + +## Intermediate documents: +*.dvi +*.xdv +*-converted-to.* +# these rules might exclude image files for figures etc. +# *.ps +# *.eps +# *.pdf + +## Generated if empty string is given at "Please type another file name for output:" +.pdf + +## Bibliography auxiliary files (bibtex/biblatex/biber): +*.bbl +*.bcf +*.blg +*-blx.aux +*-blx.bib +*.run.xml + +## Build tool auxiliary files: +*.fdb_latexmk +*.synctex +*.synctex(busy) +*.synctex.gz +*.synctex.gz(busy) +*.pdfsync + +## Build tool directories for auxiliary files +# latexrun +latex.out/ + +## Auxiliary and intermediate files from other packages: +# algorithms +*.alg +*.loa + +# achemso +acs-*.bib + +# amsthm +*.thm + +# beamer +*.nav +*.pre +*.snm +*.vrb + +# changes +*.soc + +# comment +*.cut + +# cprotect +*.cpt + +# elsarticle (documentclass of Elsevier journals) +*.spl + +# endnotes +*.ent + +# fixme +*.lox + +# feynmf/feynmp +*.mf +*.mp +*.t[1-9] +*.t[1-9][0-9] +*.tfm + +#(r)(e)ledmac/(r)(e)ledpar +*.end +*.?end +*.[1-9] +*.[1-9][0-9] +*.[1-9][0-9][0-9] +*.[1-9]R +*.[1-9][0-9]R +*.[1-9][0-9][0-9]R +*.eledsec[1-9] +*.eledsec[1-9]R +*.eledsec[1-9][0-9] +*.eledsec[1-9][0-9]R +*.eledsec[1-9][0-9][0-9] +*.eledsec[1-9][0-9][0-9]R + +# glossaries +*.acn +*.acr +*.glg +*.glo +*.gls +*.glsdefs +*.lzo +*.lzs + +# uncomment this for glossaries-extra (will ignore makeindex's style files!) +# *.ist + +# gnuplottex +*-gnuplottex-* + +# gregoriotex +*.gaux +*.gtex + +# htlatex +*.4ct +*.4tc +*.idv +*.lg +*.trc +*.xref + +# hyperref +*.brf + +# knitr +*-concordance.tex +# TODO Comment the next line if you want to keep your tikz graphics files +*.tikz +*-tikzDictionary + +# listings +*.lol + +# luatexja-ruby +*.ltjruby + +# makeidx +*.idx +*.ilg +*.ind + +# minitoc +*.maf +*.mlf +*.mlt +*.mtc[0-9]* +*.slf[0-9]* +*.slt[0-9]* +*.stc[0-9]* + +# minted +_minted* +*.pyg + +# morewrites +*.mw + +# nomencl +*.nlg +*.nlo +*.nls + +# pax +*.pax + +# pdfpcnotes +*.pdfpc + +# sagetex +*.sagetex.sage +*.sagetex.py +*.sagetex.scmd + +# scrwfile +*.wrt + +# sympy +*.sout +*.sympy +sympy-plots-for-*.tex/ + +# pdfcomment +*.upa +*.upb + +# pythontex +*.pytxcode +pythontex-files-*/ + +# tcolorbox +*.listing + +# thmtools +*.loe + +# TikZ & PGF +*.dpth +*.md5 +*.auxlock + +# todonotes +*.tdo + +# vhistory +*.hst +*.ver + +# easy-todo +*.lod + +# xcolor +*.xcp + +# xmpincl +*.xmpi + +# xindy +*.xdy + +# xypic precompiled matrices and outlines +*.xyc +*.xyd + +# endfloat +*.ttt +*.fff + +# Latexian +TSWLatexianTemp* + +## Editors: +# WinEdt +*.bak +*.sav + +# Texpad +.texpadtmp + +# LyX +*.lyx~ + +# Kile +*.backup + +# gummi +.*.swp + +# KBibTeX +*~[0-9]* + +# TeXnicCenter +*.tps + +# auto folder when using emacs and auctex +./auto/* +*.el + +# expex forward references with \gathertags +*-tags.tex + +# standalone packages +*.sta + +# Makeindex log files +*.lpz + + +logs/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + diff --git a/.gitmodules b/.gitmodules index 019b8e4..ca57f9c 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,3 @@ -[submodule "data/nuScenes/nuscenes-devkit"] - path = data/nuScenes/nuscenes-devkit +[submodule "experiments/nuScenes/devkit"] + path = experiments/nuScenes/devkit url = https://github.com/nutonomy/nuscenes-devkit diff --git a/README.md b/README.md index 3f44d4b..abc001c 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,15 @@ -# Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control # +

-This repository contains the code for [Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control](https://arxiv.org/abs/2001.03093) by Tim Salzmann\*, Boris Ivanovic\*, Punarjay Chakravarty, and Marco Pavone (\* denotes equal contribution). +Things to fix/add: +- keep pedestrians branch as pedestrians_old and make this one master. -Specifically, this branch is for the Trajectron++ applied to the nuScenes autonomous driving dataset. +# Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data # +This repository contains the code for [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](img/paper.pdf) by Tim Salzmann\*, Boris Ivanovic\*, Punarjay Chakravarty, and Marco Pavone (\* denotes equal contribution). ## Installation ## -### Note about Submodules ### -When cloning this branch, make sure you clone the submodules as well, with the following command: +### Cloning ### +When cloning this repository, make sure you clone the submodules as well, with the following command: ``` git clone --recurse-submodules ``` @@ -18,7 +20,6 @@ git submodule update # Fetching all of the data from the submodules at the speci ``` ### Environment Setup ### - First, we'll create a conda environment to hold the dependencies. ``` conda create --name trajectron++ python=3.6 -y @@ -28,29 +29,116 @@ pip install -r requirements.txt Then, since this project uses IPython notebooks, we'll install this conda environment as a kernel. ``` -python -m ipykernel install --user --name trajectron++ --display-name "Python 3.6 (Trajectron++)" +python -m ipykernel install --user --name trajectronpp --display-name "Python 3.6 (Trajectron++)" ``` -Now, you can start a Jupyter session and view/run all the notebooks in `code/notebooks` with +### Data Setup ### +#### Pedestrian Datasets #### +We've already included preprocessed data splits for the ETH and UCY Pedestrian datasets in this repository, you can see them in `experiments/pedestrians/raw`. In order to process them into a data format that our model can work with, execute the follwing. ``` -jupyter notebook +cd experiments/pedestrians +python process_data.py # This will take around 10-15 minutes, depending on your computer. ``` -When you're done, don't forget to deactivate the conda environment with +#### nuScenes Dataset #### +Download the nuScenes dataset (this requires signing up on [their website](https://www.nuscenes.org/)). Note that the full dataset is very large, so if you only wish to test out the codebase and model then you can just download the nuScenes "mini" dataset which only requires around 4 GB of space. Extract the downloaded zip file's contents and place them in the `experiments/nuScenes` directory. Then, download the map expansion pack (v1.1) and copy the contents of the extracted `maps` folder into the `experiments/nuScenes/v1.0-mini/maps` folder. Finally, process them into a data format that our model can work with. ``` -source deactivate +cd experiments/nuScenes + +# For the mini nuScenes dataset, use the following +python process_data.py --data=./v1.0-mini --version="v1.0-mini" --output_path=../processed + +# For the full nuScenes dataset, use the following +python process_data.py --data=./v1.0 --version="v1.0" --output_path=../processed +``` +In case you also want a validation set generated (by default this will just produce the training and test sets), replace line 406 in `process_data.py` with: +``` + val_scene_names = val_scenes ``` -## Scripts ## +## Model Training ## +### Pedestrian Dataset ### +To train a model on the ETH and UCY Pedestrian datasets, you can execute a version of the following command from within the `trajectron/` directory. +``` +python train.py --eval_every 10 --vis_every 1 --train_data_dict _train.pkl --eval_data_dict _val.pkl --offline_scene_graph yes --preprocess_workers 5 --log_dir ../experiments/pedestrians/models --log_tag --train_epochs 100 --augment --conf +``` -Run any of these with a `-h` or `--help` flag to see all available command arguments. -* `code/train.py` - Trains a new Trajectron++ model. -* `code/notebooks/run_eval.bash` - Evaluates the performance of the Trajectron++. This script mainly collects evaluation data, which can then be visualized with `code/notebooks/NuScenes Quantitative.ipynb`. -* `data/nuScenes/process_nuScenes.py` - Processes the nuScenes dataset into a format that the Trajectron++ can directly work with, following our internal structures for handling data (see `code/data` for more information). -* `code/notebooks/NuScenes Qualitative.ipynb` - Visualizes the predictions that the Trajectron++ makes. +For example, a fully-fleshed out version of this command to train a model without dynamics integration for evaluation on the ETH - University scene would look like: +``` +python train.py --eval_every 10 --vis_every 1 --train_data_dict eth_train.pkl --eval_data_dict eth_val.pkl --offline_scene_graph yes --preprocess_workers 5 --log_dir ../experiments/pedestrians/models --log_tag _eth_vel_ar3 --train_epochs 100 --augment --conf ../experiments/pedestrians/models/eth_vel/config.json +``` +What this means is to train a new Trajectron++ model which will be evaluated every 10 epochs, have a few outputs visualized in Tensorboard every 1 epoch, use the `eth_train.pkl` file as the source of training data (which actually contains the four other datasets, since we train using a leave-one-out scheme), and evaluate the partially-trained models on the data within `eth_val.pkl`. Further options specify that we want to perform a bit of preprocessing to make training as fast as possible (`--offline_scene_graph yes`), use 5 threads to parallelize data loading, save trained models and Tensorboard logs to `../experiments/pedestrians/models`, mark the created log directory with an additional `_eth_vel_ar3` at the end, run training for 100 epochs, augment the dataset with rotations (`--augment`), and use the same model configuration as in the model we previously trained for the ETH dataset without any dynamics integration (`--conf ../experiments/pedestrians/models/eth_vel/config.json`). + +If you wanted to train a model _with_ dynamics integration for the ETH - University scene, then you would instead run: +``` +python train.py --eval_every 10 --vis_every 1 --train_data_dict eth_train.pkl --eval_data_dict eth_val.pkl --offline_scene_graph yes --preprocess_workers 5 --log_dir ../experiments/pedestrians/models --log_tag _eth_ar3 --train_epochs 100 --augment --conf ../experiments/pedestrians/models/eth_attention_radius_3/config.json +``` +where the only difference is the sourced model configuration (now from `../experiments/pedestrians/models/eth_attention_radius_3/config.json`). Our codebase is set up such that hyperparameters are saved in a json file every time a model is trained, so that you don't have to remember what settings you use when you end up training many models in parallel! + +Commands like these would be used for all of the scenes in the ETH and UCY datasets (the options being `eth`, `hotel`, `univ`, `zara1`, and `zara2`). The only change would be what `train_data_dict`, `eval_data_dict`, `log_tag`, and configuration file (`conf`) you wish to use. + +### nuScenes Dataset ### +To train a model on the nuScenes dataset, you can execute one of the following commands from within the `trajectron/` directory, depending on the model version you desire. + +| Model | Command | +|-------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Base | `python train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScenes/models/vel_ee/config.json --train_data_dict nuScenes_train_full.pkl --eval_data_dict nuScenes_val_full.pkl --offline_scene_graph yes --preprocess_workers 10 --batch_size 256 --log_dir ../experiments/nuScenes/models --train_epochs 20 --node_freq_mult_train --log_tag _vel_ee --augment` | +| +Dynamics Integration | `python train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScenes/models/int_ee/config.json --train_data_dict nuScenes_train_full.pkl --eval_data_dict nuScenes_val_full.pkl --offline_scene_graph yes --preprocess_workers 10 --batch_size 256 --log_dir ../experiments/nuScenes/models --train_epochs 20 --node_freq_mult_train --log_tag _int_ee --augment` | +| +Dynamics Integration, Maps | `python train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScenes/models/int_ee_me/config.json --train_data_dict nuScenes_train_full.pkl --eval_data_dict nuScenes_val_full.pkl --offline_scene_graph yes --preprocess_workers 10 --batch_size 256 --log_dir ../experiments/nuScenes/models --train_epochs 20 --node_freq_mult_train --log_tag _int_ee_me --map_encoding --augment` | +| +Dynamics Integration, Maps, Robot Future | `python train.py --eval_every 1 --vis_every 1 --conf ../experiments/nuScenes/models/robot/config.json --train_data_dict nuScenes_train_full.pkl --eval_data_dict nuScenes_val_full.pkl --offline_scene_graph yes --preprocess_workers 10 --batch_size 256 --log_dir ../experiments/nuScenes/models --train_epochs 20 --node_freq_mult_train --log_tag _robot --incl_robot_node --map_encoding` | + +In case you also want to produce the version of our model that was trained without the ego-vehicle (first row of Table 4 (b) in the paper), then run the command from the third row of the table above, but change line 132 of `train.py` to: +``` + return_robot=False) +``` + +### CPU Training ### +By default, our training script assumes access to a GPU. If you want to train on a CPU, comment out line 38 in `train.py` and add `--device cpu` to the training command. + +## Model Evaluation ## +### Pedestrian Datasets ### +To evaluate a trained model, you can execute a version of the following command from within the `experiments/pedestrians` directory. +``` +python evaluate.py --model --checkpoint --data ../processed/_test.pkl --output_path results --output_tag __12 --node_type PEDESTRIAN +``` + +For example, a fully-fleshed out version of this command to evaluate a model without dynamics integration for evaluation on the ETH - University scene would look like: +``` +python evaluate.py --model models/eth_vel --checkpoint 100 --data ../processed/eth_test.pkl --output_path results --output_tag eth_vel_12 --node_type PEDESTRIAN +``` +The same for a model with dynamics integration would look like: +``` +python evaluate.py --model models/eth_attention_radius_3 --checkpoint 100 --data ../processed/eth_test.pkl --output_path results --output_tag eth_12 --node_type PEDESTRIAN +``` +These scripts will produce csv files in the `results` directory which can then be analyzed in the `Result Analysis.ipynb` notebook. + +### nuScenes Dataset ### +If you just want to use a trained model to generate trajectories and plot them, you can do this in the `NuScenes Qualitative.ipynb` notebook. + +To evaluate a trained model's performance on forecasting vehicles, you can execute a one of the following commands from within the `experiments/nuScenes` directory. + +| Model | Command | +|-------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Base | `python evaluate.py --model models/vel_ee --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag vel_ee --node_type VEHICLE --prediction_horizon 6` | +| +Dynamics Integration | `python evaluate.py --model models/int_ee --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag int_ee --node_type VEHICLE --prediction_horizon 6` | +| +Dynamics Integration, Maps | `python evaluate.py --model models/int_ee_me --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag int_ee_me --node_type VEHICLE --prediction_horizon 6` | +| +Dynamics Integration, Maps, Robot Future | `python evaluate.py --model models/robot --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag robot --node_type VEHICLE --prediction_horizon 6` | + +If you instead wanted to evaluate a trained model's performance on forecasting pedestrians, you can execute a one of the following. + +| Model | Command | +|-----------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Base | `python evaluate.py --model models/ee_vel --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag vel_ee_ped --node_type PEDESTRIAN --prediction_horizon 6` | +| +Dynamics Integration, Maps | `python evaluate.py --model models/int_ee_me --checkpoint=12 --data ../processed/nuScenes_test_full.pkl --output_path results --output_tag int_ee_me_ped --node_type PEDESTRIAN --prediction_horizon 6` | + +These scripts will produce csv files in the `results` directory which can then be analyzed in the `NuScenes Quantitative.ipynb` notebook. ## Datasets ## -A sample of fully-processed scenes from the nuScenes dataset are available in this repository, in `data/processed`. +### ETH and UCY Pedestrian Datasets ### +Preprocessed ETH and UCY datasets are available in this repository, under `experiments/pedestrians/raw` (e.g., `raw/eth/train`). The train/validation/test splits are the same as those found in [Social GAN](https://github.com/agrimgupta92/sgan). -If you want the *original* nuScenes dataset, you can find it here: [nuScenes Dataset](https://www.nuscenes.org/). +If you want the *original* ETH or UCY datasets, you can find them here: [ETH Dataset](http://www.vision.ee.ethz.ch/en/datasets/) and [UCY Dataset](https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data). + +### nuScenes Dataset ### +If you only want to evaluate models (e.g., produce trajectories and plot them), then the nuScenes mini dataset should be fine. If you want to train a model, then the full nuScenes dataset is required. In either case, you can find them on the [dataset website](https://www.nuscenes.org/). diff --git a/code/config.json b/code/config.json deleted file mode 100644 index 758add6..0000000 --- a/code/config.json +++ /dev/null @@ -1,108 +0,0 @@ -{ - - "batch_size": 256, - "grad_clip": 1.0, - - "learning_rate_style": "exp", - "learning_rate": 0.002, - "min_learning_rate": 0.0005, - "learning_decay_rate": 0.9995, - - "prediction_horizon": 6, - "minimum_history_length": 1, - "maximum_history_length": 8, - - "map_context": 120, - "map_enc_num_layers": 4, - "map_enc_hidden_size": 512, - "map_enc_output_size": 512, - "map_enc_dropout": 0.5, - - "alpha": 1, - "k": 30, - "k_eval": 200, - "use_iwae": false, - "kl_exact": true, - - "kl_min": 0.07, - "kl_weight": 5.0, - "kl_weight_start": 0, - "kl_decay_rate": 0.99995, - "kl_crossover": 500, - "kl_sigmoid_divisor": 4, - - "inf_warmup": 1.0, - "inf_warmup_start": 1.0, - "inf_warmup_crossover": 1500, - "inf_warmup_sigmoid_divisor": 4, - - "rnn_kwargs": { - "dropout_keep_prob": 0.5 - }, - "MLP_dropout_keep_prob": 0.9, - "rnn_io_dropout_keep_prob": 1.0, - "enc_rnn_dim_multiple_inputs": 8, - "enc_rnn_dim_edge": 8, - "enc_rnn_dim_edge_influence": 8, - "enc_rnn_dim_history": 32, - "enc_rnn_dim_future": 32, - "dec_rnn_dim": 512, - "dec_GMM_proj_MLP_dims": null, - "sample_model_during_dec": true, - "dec_sample_model_prob_start": 1.00, - "dec_sample_model_prob_final": 1.00, - "dec_sample_model_prob_crossover": 200, - "dec_sample_model_prob_divisor": 4, - "q_z_xy_MLP_dims": null, - "p_z_x_MLP_dims": 32, - "fuzz_factor": 0.05, - "GMM_components": 12, - "log_sigma_min": -10, - "log_sigma_max": 10, - "log_p_yt_xz_max": 50, - "N": 2, - "K": 5, - "tau_init": 2.0, - "tau_final": 0.05, - "tau_decay_rate": 0.997, - "use_z_logit_clipping": true, - "z_logit_clip_start": 0.05, - "z_logit_clip_final": 5.0, - "z_logit_clip_crossover": 500, - "z_logit_clip_divisor": 5, - - "state": { - "PEDESTRIAN": { - "position": ["x", "y"], - "velocity": ["x", "y"], - "acceleration": ["x", "y"], - "heading": ["value"] - }, - "BICYCLE": { - "position": ["x", "y"], - "velocity": ["x", "y", "m"], - "acceleration": ["x", "y", "m"], - "heading": ["value"] - }, - "VEHICLE": { - "position": ["x", "y"], - "velocity": ["x", "y", "m"], - "acceleration": ["x", "y", "m"], - "heading": ["value"] - } - }, - - "pred_state": { - "PEDESTRIAN": { - "velocity": ["x", "y"] - }, - "BICYCLE": { - "velocity": ["x", "y"] - }, - "VEHICLE": { - "velocity": ["x", "y"] - } - }, - - "log_histograms": false -} \ No newline at end of file diff --git a/code/data/__init__.py b/code/data/__init__.py deleted file mode 100644 index 663be5b..0000000 --- a/code/data/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .data_structures import Position, Velocity, Acceleration, Orientation, Map, ActuatorAngle, Scalar -from .scene import Scene -from .node import Node, BicycleNode -from .scene_graph import TemporalSceneGraph -from .environment import Environment diff --git a/code/data/data_structures.py b/code/data/data_structures.py deleted file mode 100644 index d8a4b72..0000000 --- a/code/data/data_structures.py +++ /dev/null @@ -1,163 +0,0 @@ -import numpy as np -from scipy.ndimage.interpolation import rotate - - -class MotionEntity(object): - def __init__(self, x, y, z=None): - self.x = x - self.y = y - self.z = z - self.m = None - - @property - def l(self): - if self.z is not None: - return np.linalg.norm(np.vstack((self.x, self.y, self.z)), axis=0) - else: - return np.linalg.norm(np.vstack((self.x, self.y)), axis=0) - - -class Position(MotionEntity): - pass - - -class Velocity(MotionEntity): - @staticmethod - def from_position(position, dt=1): - dx = np.zeros_like(position.x) * np.nan - dx[~np.isnan(position.x)] = np.gradient(position.x[~np.isnan(position.x)], dt) - - dy = np.zeros_like(position.y) * np.nan - dy[~np.isnan(position.y)] = np.gradient(position.y[~np.isnan(position.y)], dt) - - if position.z is not None: - dz = np.zeros_like(position.z) * np.nan - dz[~np.isnan(position.z)] = np.gradient(position.z[~np.isnan(position.z)], dt) - else: - dz = None - - return Velocity(dx, dy, dz) - - -class Acceleration(MotionEntity): - @staticmethod - def from_velocity(velocity, dt=1): - ddx = np.zeros_like(velocity.x) * np.nan - ddx[~np.isnan(velocity.x)] = np.gradient(velocity.x[~np.isnan(velocity.x)], dt) - - ddy = np.zeros_like(velocity.y) * np.nan - ddy[~np.isnan(velocity.y)] = np.gradient(velocity.y[~np.isnan(velocity.y)], dt) - - if velocity.z is not None: - ddz = np.zeros_like(velocity.z) * np.nan - ddz[~np.isnan(velocity.z)] = np.gradient(velocity.z[~np.isnan(velocity.z)], dt) - else: - ddz = None - - return Acceleration(ddx, ddy, ddz) - - -class ActuatorAngle(object): - def __init__(self): - pass - - -class Scalar(object): - def __init__(self, value): - self.value = value - self.derivative = None - -# TODO Finish -class Orientation(object): - def __init__(self, x, y, z, w): - self.x = x - self.y = y - self.z = z - self.w = w - - -class Map(object): - def __init__(self, data=None, homography=None, description=None, data_file=""): - self.data = data - self.homography = homography - self.description = description - self.uint = False - self.data_file = data_file - self.rotated_maps_origin = None - self.rotated_maps = None - if self.data.dtype == np.uint8: - self.uint = True - - @property - def fdata(self): - if self.uint: - return self.data / 255. - else: - return self.data - - def to_map_points(self, world_pts): - org_shape = None - if len(world_pts.shape) > 2: - org_shape = world_pts.shape - world_pts = world_pts.reshape((-1, 2)) - N, dims = world_pts.shape - points_with_one = np.ones((dims + 1, N)) - points_with_one[:dims] = world_pts.T - map_points = (self.homography @ points_with_one).T[..., :dims] # TODO There was np.fliplr here for pedestrian dataset. WHY? - if org_shape is not None: - map_points = map_points.reshape(org_shape) - return map_points - - def to_rotated_map_points(self, world_pts, rotation_angle): - rotation_rad = -rotation_angle * np.pi / 180 - rot_mat = np.array([[np.cos(rotation_rad), np.sin(rotation_rad), 0.], - [-np.sin(rotation_rad), np.cos(rotation_rad), 0.], - [0., 0., 1.]]) - org_map_points = self.to_map_points(world_pts) + 1 - - org_shape = None - if len(org_map_points.shape) > 2: - org_shape = org_map_points.shape - org_map_points = org_map_points.reshape((-1, 2)) - N, dims = org_map_points.shape - points_with_one = np.ones((dims + 1, N)) - points_with_one[:dims] = org_map_points.T - org_map_pts_rot = (rot_mat @ points_with_one).T[..., :dims] - if org_shape is not None: - org_map_pts_rot = org_map_pts_rot.reshape(org_shape) - - map_pts_rot = self.rotated_maps_origin + org_map_pts_rot - return map_pts_rot - - def calculate_rotations(self): - org_shape = self.data.shape - l = (np.ceil(np.sqrt(org_shape[0]**2 + org_shape[1]**2)) * 2).astype(int) + 1 - rotated_maps = np.zeros((360, l, l, org_shape[2]), dtype=np.uint8) - o = np.array([l // 2, l // 2]) - rotated_maps[0, o[0]+1:o[0]+org_shape[0]+1, o[1]+1:o[1]+org_shape[1]+1] = self.data - for i in range(1, 360): - rotated_maps[i] = rotate(rotated_maps[0], reshape=False, angle=i, prefilter=False) - rotated_maps[0] = rotate(rotated_maps[0], reshape=False, angle=0, prefilter=False) - self.rotated_maps_origin = o - self.rotated_maps = rotated_maps - - # def __getstate__(self): - # with open(self.data_file, 'w') as f: - # np.save(f, self.rotated_maps) - # self.rotated_maps = None - # state = self.__dict__.copy() - # return state - # - # def __setstate__(self, state): - # self.__dict__.update(state) - # with open(self.data_file, 'r') as f: - # self.rotated_maps = np.load(f) - -if __name__ == "__main__": - img = np.zeros((103, 107, 3)) - img[57, 84] = 255. - homography = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) - m = Map(data=img, homography=homography) - m.calculate_rotations() - t = m.to_rotated_map_points(np.array([[57, 84]]), 0).astype(int) - print(m.rotated_maps[0, t[0, 0], t[0, 1]]) \ No newline at end of file diff --git a/code/data/node.py b/code/data/node.py deleted file mode 100644 index c1d8cff..0000000 --- a/code/data/node.py +++ /dev/null @@ -1,264 +0,0 @@ -import numpy as np -from pyquaternion import Quaternion -from . import ActuatorAngle -from scipy.interpolate import splrep, splev, CubicSpline -from scipy.integrate import cumtrapz - - -class Node(object): - def __init__(self, type, position=None, velocity=None, acceleration=None, heading=None, orientation=None, - length=None, width=None, height=None, first_timestep=0, is_robot=False): - self.type = type - self.position = position - self.heading = heading - self.length = length - self.wdith = width - self.height = height - self.orientation = orientation - self.velocity = velocity - self.acceleration = acceleration - self.first_timestep = first_timestep - self.dimensions = ['x', 'y', 'z'] - self.is_robot = is_robot - self._last_timestep = None - self.description = "" - - def __repr__(self): - return self.type.name - - def scene_ts_to_node_ts(self, scene_ts): - """ - Transforms timestamp from scene into timeframe of node data. - :param scene_ts: Scene timesteps - :return: ts: Transformed timesteps, paddingl: Number of timesteps in scene range which are not available in - node data before data is available. paddingu: Number of timesteps in scene range which are not - available in node data after data is available. - """ - paddingl = (self.first_timestep - scene_ts[0]).clip(0) - paddingu = (scene_ts[1] - self.last_timestep).clip(0) - ts = np.array(scene_ts).clip(min=self.first_timestep, max=self.last_timestep) - self.first_timestep - return ts, paddingl, paddingu - - def history_points_at(self, ts): - """ - Number of history points in trajectory. Timestep is exclusive. - :param ts: Scene timestep where the number of history points are queried. - :return: Number of history timesteps. - """ - return ts - self.first_timestep - - def get_entity(self, ts_scene, entity, dims, padding=np.nan): - if ts_scene.size == 1: - ts_scene = np.array([ts_scene, ts_scene]) - length = ts_scene[1] - ts_scene[0] + 1 # ts is inclusive - entity_array = np.zeros((length, len(dims))) * padding - ts, paddingl, paddingu = self.scene_ts_to_node_ts(ts_scene) - entity_array[paddingl:length - paddingu] = np.array([getattr(getattr(self, entity), d)[ts[0]:ts[1]+1] for d in dims]).T - return entity_array - - def get(self, ts_scene, state, padding=np.nan): - return np.hstack([self.get_entity(ts_scene, entity, dims, padding) for entity, dims in state.items()]) - - - @property - def timesteps(self): - return self.position.x.size - - @property - def last_timestep(self): - if self._last_timestep is None: - self._last_timestep = self.first_timestep + self.timesteps - 1 - return self._last_timestep - - -class BicycleNode(Node): - def __init__(self, type, position=None, velocity=None, acceleration=None, heading=None, orientation=None, - length=None, width=None, height=None, first_timestep=0, actuator_angle=None): - super().__init__(type, position=position, velocity=velocity, acceleration=acceleration, heading=heading, - orientation=orientation, length=length, width=width, height=height, - first_timestep=first_timestep) - self.actuator_angle = actuator_angle - - # TODO Probably wrong. Differential of magnitude is not euqal to the the magnitude of the differentials - def calculate_steering_angle_old(self, vel_tresh=0.0): - vel = np.linalg.norm(np.hstack((np.expand_dims(self.velocity.x, 1), np.expand_dims(self.velocity.y, 1))), axis=1) - - beta = np.arctan2(self.velocity.y, self.velocity.x) - self.heading.value - beta[vel < vel_tresh] = 0. - steering_angle = np.arctan2(2 * np.sin(beta), np.cos(beta)) - steering_angle[np.abs(steering_angle) > np.pi / 2] = 0 # Velocity Outlier - - aa = ActuatorAngle() - aa.steering_angle = np.zeros_like(np.arctan2(2 * np.sin(beta), np.cos(beta))) - self.actuator_angle = aa - - def calculate_steering_angle(self, dt, steering_tresh=0.0, vel_tresh=0.0): - t = np.arange(0, self.timesteps * dt, dt) - s = 0.01 * len(t) - #c_pos_x_g_x_tck = CubicSpline(t, np.array(pos_x_filtert)) - #c_pos_y_g_x_tck = CubicSpline(t, np.array(pos_y_filtert)) - #c_pos_x_g_x_tck = splrep(t, self.position.x, s=s) - #c_pos_y_g_x_tck = splrep(t, self.position.y, s=s) - - #vel_x_g = c_pos_x_g_x_tck(t, 1) - #vel_y_g = c_pos_y_g_x_tck(t, 1) - #vel_x_g = splev(t, c_pos_x_g_x_tck, der=1) - #vel_y_g = splev(t, c_pos_y_g_x_tck, der=1) - - vel_x_g = self.velocity.x - vel_y_g = self.velocity.y - - v_x_ego = [] - h = [] - for t in range(self.timesteps): - dh_max = 1.2 / self.length * (np.linalg.norm(np.array([vel_x_g[t], vel_y_g[t]]))) - heading = np.arctan2(vel_y_g[t], vel_x_g[t]) - #if len(h) > 0 and np.abs(heading - h[-1]) > dh_max: - # heading = h[-1] - h.append(heading) - q = Quaternion(axis=(0.0, 0.0, 1.0), radians=heading) - v_x_ego_t = q.inverse.rotate(np.array([vel_x_g[t], vel_y_g[t], 1]))[0] - if v_x_ego_t < 0.0: - v_x_ego_t = 0. - v_x_ego.append(v_x_ego_t) - - v_x_ego = np.stack(v_x_ego, axis=0) - h = np.stack(h, axis=0) - - dh = np.gradient(h, dt) - - sa = np.arctan2(dh * self.length, v_x_ego) - sa[(dh == 0.) | (v_x_ego == 0.)] = 0. - sa = sa.clip(min=-steering_tresh, max=steering_tresh) - - a = np.gradient(v_x_ego, dt) - # int = self.integrate_bicycle_model(np.array([a]), - # sa, - # np.array([h[0]]), - # np.array([self.position.x[0], - # self.position.y[0]]), - # v_x_ego[0], - # self.length, 0.5) - # p = np.stack((self.position.x, self.position.y), axis=1) - # - # #assert ((int[0] - p) < 1.0).all() - - aa = ActuatorAngle() - aa.steering_angle = sa - self.acceleration.m = a - self.actuator_angle = aa - - def inverse_np_gradient(self, f, dx, F0=0.): - N = f.shape[0] - l = f.shape[-1] - l2 = np.ceil(l / 2).astype(int) - return (F0 + - ((2 * dx) * - np.c_['-1', - np.r_['-1', np.zeros((N, 1)), f[..., 1:-1:2].cumsum(axis=-1)], - f[..., ::2].cumsum(axis=-1) - f[..., [0]] / 2] - ).reshape((N, 2, l2)).reshape(N, 2 * l2, order='F')[:, :l] - ) - - def integrate_trajectory(self, v, x0, dt): - xd_ = self.inverse_np_gradient(v[..., 0], dx=dt, F0=x0[0]) - yd_ = self.inverse_np_gradient(v[..., 1], dx=dt, F0=x0[1]) - integrated = np.stack([xd_, yd_], axis=2) - return integrated - - def integrate_bicycle_model(self, a, sa, h0, x0, v0, l, dt): - v_m = self.inverse_np_gradient(a, dx=0.5, F0=v0) - - dh = (np.tan(sa) / l) * v_m[0] - h = self.inverse_np_gradient(np.array([dh]), dx=dt, F0=h0) - - vx = np.cos(h) * v_m - vy = np.sin(h) * v_m - - v = np.stack((vx, vy), axis=2) - return self.integrate_trajectory(v, x0, dt) - - def calculate_steering_angle_keep(self, dt, steering_tresh=0.0, vel_tresh=0.0): - - vel_approx = np.linalg.norm(np.stack((self.velocity.x, self.velocity.y), axis=0), axis=0) - mask = np.ones_like(vel_approx) - mask[vel_approx < vel_tresh] = 0 - - t = np.arange(0, self.timesteps * dt, dt) - pos_x_filtert = [] - pos_y_filtert = [] - s = None - for i in range(mask.size): - if mask[i] == 0 and s is None: - s = i - elif mask[i] != 0 and s is not None: - t_start = t[s-1] - pos_x_start = self.position.x[s-1] - pos_y_start = self.position.y[s-1] - t_mean = t[s:i].mean() - pos_x_mean = self.position.x[s:i].mean() - pos_y_mean = self.position.y[s:i].mean() - t_end = t[i] - pos_x_end = self.position.x[i] - pos_y_end = self.position.y[i] - for step in range(s, i+1): - if t[step] <= t_mean: - pos_x_filtert.append(pos_x_start + ((t[step] - t_start) / (t_mean - t_start)) * (pos_x_mean - pos_x_start)) - pos_y_filtert.append(pos_y_start + ((t[step] - t_start) / (t_mean - t_start)) * (pos_y_mean - pos_y_start)) - else: - pos_x_filtert.append(pos_x_mean + ((t[step] - t_end) / (t_end - t_mean)) * (pos_x_end - pos_x_mean)) - pos_y_filtert.append(pos_y_mean + ((t[step] - t_end) / (t_end - t_mean)) * (pos_y_end - pos_y_mean)) - s = None - elif mask[i] != 0 and s is None: - pos_x_filtert.append(self.position.x[i].mean()) - pos_y_filtert.append(self.position.y[i].mean()) - if s is not None: - t_start = t[s - 1] - pos_x_start = self.position.x[s - 1] - pos_y_start = self.position.y[s - 1] - t_mean = t[s:i].max() - pos_x_mean = self.position.x[s:i].mean() - pos_y_mean = self.position.y[s:i].mean() - for step in range(s, i+1): - pos_x_filtert.append( - pos_x_start + ((t[step] - t_start) / (t_mean - t_start)) * (pos_x_mean - pos_x_start)) - pos_y_filtert.append( - pos_y_start + ((t[step] - t_start) / (t_mean - t_start)) * (pos_y_mean - pos_y_start)) - - s = 0.001 * len(t) - #c_pos_x_g_x_tck = CubicSpline(t, np.array(pos_x_filtert)) - #c_pos_y_g_x_tck = CubicSpline(t, np.array(pos_y_filtert)) - c_pos_x_g_x_tck = splrep(t, np.array(pos_x_filtert), s=s) - c_pos_y_g_x_tck = splrep(t, np.array(pos_y_filtert), s=s) - - #vel_x_g = c_pos_x_g_x_tck(t, 1) - #vel_y_g = c_pos_y_g_x_tck(t, 1) - vel_x_g = splev(t, c_pos_x_g_x_tck, der=1) - vel_y_g = splev(t, c_pos_y_g_x_tck, der=1) - - v_x_ego = [] - h = [] - for t in range(self.timesteps): - dh_max = 1.19 / self.length * (np.linalg.norm(np.array([vel_x_g[t], vel_y_g[t]]))) - heading = np.arctan2(vel_y_g[t], vel_x_g[t]) - if len(h) > 0 and np.abs(heading - h[-1]) > dh_max: - heading = h[-1] - h.append(heading) - q = Quaternion(axis=(0.0, 0.0, 1.0), radians=heading) - v_x_ego_t = q.inverse.rotate(np.array([vel_x_g[t], vel_y_g[t], 1]))[0] - if v_x_ego_t < 0.0: - v_x_ego_t = 0. - v_x_ego.append(v_x_ego_t) - - v_x_ego = np.stack(v_x_ego, axis=0) - h = np.stack(h, axis=0) - - dh = np.gradient(h, dt) - - sa = np.arctan2(dh * self.length, v_x_ego) - sa[dh == 0.] = 0. - - aa = ActuatorAngle() - aa.steering_angle = sa - self.actuator_angle = aa - diff --git a/code/data/scene.py b/code/data/scene.py deleted file mode 100644 index db4d4b9..0000000 --- a/code/data/scene.py +++ /dev/null @@ -1,102 +0,0 @@ -import numpy as np -from .scene_graph import TemporalSceneGraph - - -class Scene(object): - def __init__(self, map=None, timesteps=0, dt=1, name=""): - self.map = map - self.timesteps = timesteps - self.dt = dt - self.name = name - - self.nodes = [] - - self.robot = None - - self.temporal_scene_graph = None - - self.description = "" - - def get_scene_graph(self, timestep, attention_radius=None, edge_addition_filter=None, edge_removal_filter=None): - if self.temporal_scene_graph is None: - timestep_range = np.array([timestep - len(edge_addition_filter), timestep + len(edge_removal_filter)]) - node_pos_dict = dict() - present_nodes = self.present_nodes(np.array([timestep])) - - for node in present_nodes[timestep]: - node_pos_dict[node] = np.squeeze(node.get(timestep_range, {'position': ['x', 'y']})) - tsg = TemporalSceneGraph.create_from_temp_scene_dict(node_pos_dict, - attention_radius, - duration=(len(edge_addition_filter) + - len(edge_removal_filter) + 1), - edge_addition_filter=edge_addition_filter, - edge_removal_filter=edge_removal_filter - ) - - return tsg.to_scene_graph(t=len(edge_addition_filter), - t_hist=len(edge_addition_filter), - t_fut=len(edge_removal_filter)) - else: - return self.temporal_scene_graph.to_scene_graph(timestep, - len(edge_addition_filter), - len(edge_removal_filter)) - - def calculate_scene_graph(self, attention_radius, state, edge_addition_filter=None, edge_removal_filter=None): - timestep_range = np.array([0, self.timesteps-1]) - node_pos_dict = dict() - - for node in self.nodes: - node_pos_dict[node] = np.squeeze(node.get(timestep_range, {'position': ['x', 'y']})) - - self.temporal_scene_graph = TemporalSceneGraph.create_from_temp_scene_dict(node_pos_dict, - attention_radius, - duration=self.timesteps, - edge_addition_filter=edge_addition_filter, - edge_removal_filter=edge_removal_filter) - - def length(self): - return self.timesteps * self.dt - - def present_nodes(self, timesteps, type=None, min_history_timesteps=0, min_future_timesteps=0, include_robot=True, max_nodes=None, curve=False): # TODO REMOVE - present_nodes = {} - - picked_nodes = 0 - - rand_idx = np.random.choice(len(self.nodes), len(self.nodes), replace=False) - - for i in rand_idx: - node = self.nodes[i] - if node.is_robot and not include_robot: - continue - if type is None or node.type == type: - if curve and node.type.name == 'VEHICLE': - if 'curve' not in node.description and np.random.rand() > 0.1: - continue - lower_bound = timesteps - min_history_timesteps - upper_bound = timesteps + min_future_timesteps - mask = (node.first_timestep <= lower_bound) & (upper_bound <= node.last_timestep) - if mask.any(): - timestep_indices_present = np.nonzero(mask)[0] - for timestep_index_present in timestep_indices_present: - if timesteps[timestep_index_present] in present_nodes.keys(): - present_nodes[timesteps[timestep_index_present]].append(node) - else: - present_nodes[timesteps[timestep_index_present]] = [node] - picked_nodes += 1 - if max_nodes is not None and picked_nodes >= max_nodes: - break - - if max_nodes is not None and picked_nodes >= max_nodes: - break - - return present_nodes - - def sample_timesteps(self, batch_size, min_future_timesteps=0): - if batch_size > self.timesteps: - batch_size = self.timesteps - return np.random.choice(np.arange(0, self.timesteps-min_future_timesteps), size=batch_size, replace=False) - - def __repr__(self): - return f"Scene: Duration: {self.length()}s," \ - f" Nodes: {len(self.nodes)}," \ - f" Map: {'Yes' if self.map is not None else 'No'}." diff --git a/code/data/scene_graph.py b/code/data/scene_graph.py deleted file mode 100644 index fef1ba6..0000000 --- a/code/data/scene_graph.py +++ /dev/null @@ -1,173 +0,0 @@ -import numpy as np -from scipy.spatial.distance import pdist, squareform -import scipy.signal as ss -from collections import defaultdict -import warnings - - -class TemporalSceneGraph(object): - def __init__(self, - edge_radius, - nodes=None, - adj_cube=np.zeros((1, 0, 0)), - weight_cube=np.zeros((1, 0, 0)), - node_type_mat=np.zeros((0, 0)), - edge_scaling=None): - self.edge_radius = edge_radius - self.nodes = nodes - if nodes is None: - self.nodes = np.array([]) - self.adj_cube = adj_cube - self.weight_cube = weight_cube - self.node_type_mat = node_type_mat - self.adj_mat = np.max(self.adj_cube, axis=0).clip(max=1.0) - self.edge_scaling = edge_scaling - self.node_index_lookup = None - self.calculate_node_index_lookup() - - def calculate_node_index_lookup(self): - node_index_lookup = dict() - for i, node in enumerate(self.nodes): - node_index_lookup[node] = i - - self.node_index_lookup = node_index_lookup - - def get_num_edges(self, t=0): - return np.sum(self.adj_cube[t]) // 2 - - def get_index(self, node): - return self.node_index_lookup[node] - - @staticmethod - def get_edge_type(n1, n2): - return '-'.join(sorted([str(n1), str(n2)])) - - @classmethod - def create_from_temp_scene_dict(cls, - scene_temp_dict, - attention_radius, - duration=1, - edge_addition_filter=None, - edge_removal_filter=None): - """ - Construct a spatiotemporal graph from agent positions in a dataset. - - returns: sg: An aggregate SceneGraph of the dataset. - """ - nodes = scene_temp_dict.keys() - N = len(nodes) - total_timesteps = duration - - position_cube = np.zeros((total_timesteps, N, 2)) - - adj_cube = np.zeros((total_timesteps, N, N), dtype=np.int8) - dist_cube = np.zeros((total_timesteps, N, N), dtype=np.float) - - node_type_mat = np.zeros((N, N), dtype=np.int8) - node_attention_mat = np.zeros((N, N), dtype=np.float) - - for node_idx, node in enumerate(nodes): - position_cube[:, node_idx] = scene_temp_dict[node] - node_type_mat[:, node_idx] = node.type.value - for node_idx_from, node_from in enumerate(nodes): - node_attention_mat[node_idx_from, node_idx] = attention_radius[(node_from.type, node.type)] - - np.fill_diagonal(node_type_mat, 0) - agg_adj_matrix = np.zeros((N, N), dtype=np.int8) - - for timestep in range(position_cube.shape[0]): - dists = squareform(pdist(position_cube[timestep], metric='euclidean')) - - # Put a 1 for all agent pairs which are closer than the edge_radius. - # Can produce a warning as dists can be nan if no data for node is available. - # This is accepted as nan <= x evaluates to False - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - adj_matrix = (dists <= node_attention_mat).astype(np.int8) * node_type_mat - - # Remove self-loops. - np.fill_diagonal(adj_matrix, 0) - - agg_adj_matrix |= adj_matrix - - adj_cube[timestep] = adj_matrix - dist_cube[timestep] = dists - - dist_cube[np.isnan(dist_cube)] = 0. - weight_cube = np.divide(1., - dist_cube, - out=np.zeros_like(dist_cube), - where=(dist_cube > 0.)) - edge_scaling = None - if edge_addition_filter is not None and edge_removal_filter is not None: - edge_scaling = cls.calculate_edge_scaling(adj_cube, edge_addition_filter, edge_removal_filter) - sg = cls(attention_radius, np.array(list(nodes)), adj_cube, weight_cube, node_type_mat, edge_scaling=edge_scaling) - return sg - - @staticmethod - def calculate_edge_scaling(adj_cube, edge_addition_filter, edge_removal_filter): - new_edges = np.minimum( - ss.convolve(adj_cube, np.reshape(edge_addition_filter, (-1, 1, 1)), 'same'), 1. - ) - - old_edges = np.minimum( - ss.convolve(adj_cube, np.reshape(edge_removal_filter, (-1, 1, 1)), 'same'), 1. - ) - - return np.minimum(new_edges + old_edges, 1.) - - def to_scene_graph(self, t, t_hist=0, t_fut=0): - lower_t = np.clip(t-t_hist, a_min=0, a_max=None) - higher_t = np.clip(t + t_fut + 1, a_min=None, a_max=self.adj_cube.shape[0] + 1) - adj_mat = np.max(self.adj_cube[lower_t:higher_t], axis=0) - weight_mat = np.max(self.weight_cube[lower_t:higher_t], axis=0) - return SceneGraph(self.edge_radius, - self.nodes, - adj_mat, - weight_mat, - self.node_type_mat, - self.node_index_lookup, - edge_scaling=self.edge_scaling[t]) - - -class SceneGraph(object): - def __init__(self, - edge_radius, - nodes=None, - adj_mat=np.zeros((0, 0)), - weight_mat=np.zeros((0, 0)), - node_type_mat=np.zeros((0, 0)), - node_index_lookup=None, - edge_scaling=None): - self.edge_radius = edge_radius - self.nodes = nodes - if nodes is None: - self.nodes = np.array([]) - self.node_type_mat = node_type_mat - self.adj_mat = adj_mat - self.weight_mat = weight_mat - self.edge_scaling = edge_scaling - self.node_index_lookup = node_index_lookup - - def get_index(self, node): - return self.node_index_lookup[node] - - def get_neighbors(self, node, type): - node_index = self.get_index(node) - connection_mask = self.adj_mat[node_index].astype(bool) - mask = ((self.node_type_mat[node_index] == type.value) * connection_mask) - return self.nodes[mask] - - def get_edge_scaling(self, node=None): - if node is None: - return self.edge_scaling - else: - node_index = self.get_index(node) - return self.edge_scaling[node_index, self.adj_mat[node_index] > 0.] - - def get_edge_weight(self, node=None): - if node is None: - return self.weight_mat - else: - node_index = self.get_index(node) - return self.weight_mat[node_index, self.adj_mat[node_index] > 0.] \ No newline at end of file diff --git a/code/model/components/gmm2d.py b/code/model/components/gmm2d.py deleted file mode 100644 index 1bc5b8a..0000000 --- a/code/model/components/gmm2d.py +++ /dev/null @@ -1,61 +0,0 @@ -import torch -import torch.distributions as td -import numpy as np -from model.model_utils import to_one_hot - - -class GMM2D(object): - def __init__(self, log_pis, mus, log_sigmas, corrs, pred_state_length, device, - clip_lo=-10, clip_hi=10): - self.device = device - self.pred_state_length = pred_state_length - - # input shapes - # pis: [..., GMM_c] - # mus: [..., GMM_c*2] - # sigmas: [..., GMM_c*2] - # corrs: [..., GMM_c] - GMM_c = log_pis.shape[-1] - - # Sigma = [s1^2 p*s1*s2 L = [s1 0 - # p*s1*s2 s2^2 ] p*s2 sqrt(1-p^2)*s2] - log_pis = log_pis - torch.logsumexp(log_pis, dim=-1, keepdim=True) - mus = self.reshape_to_components(mus, GMM_c) # [..., GMM_c, 2] - log_sigmas = self.reshape_to_components(torch.clamp(log_sigmas, min=clip_lo, max=clip_hi), GMM_c) - sigmas = torch.exp(log_sigmas) # [..., GMM_c, 2] - one_minus_rho2 = 1 - corrs**2 # [..., GMM_c] - - self.L1 = sigmas*torch.stack([torch.ones_like(corrs, device=self.device), corrs], dim=-1) - self.L2 = sigmas*torch.stack([torch.zeros_like(corrs, device=self.device), torch.sqrt(one_minus_rho2)], dim=-1) - - self.batch_shape = log_pis.shape[:-1] - self.GMM_c = GMM_c - self.log_pis = log_pis # [..., GMM_c] - self.mus = mus # [..., GMM_c, 2] - self.log_sigmas = log_sigmas # [..., GMM_c, 2] - self.sigmas = sigmas # [..., GMM_c, 2] - self.corrs = corrs # [..., GMM_c] - self.one_minus_rho2 = one_minus_rho2 # [..., GMM_c] - self.cat = td.Categorical(logits=log_pis) - - def sample(self): - MVN_samples = (self.mus - + self.L1*torch.unsqueeze(torch.randn_like(self.corrs, device=self.device), dim=-1) # [..., GMM_c, 2] - + self.L2*torch.unsqueeze(torch.randn_like(self.corrs, device=self.device), dim=-1)) # (manual 2x2 matmul) - cat_samples = self.cat.sample() # [...] - selector = torch.unsqueeze(to_one_hot(cat_samples, self.GMM_c, self.device), dim=-1) - return torch.sum(MVN_samples*selector, dim=-2) - - def log_prob(self, x): - # x: [..., 2] - x = torch.unsqueeze(x, dim=-2) # [..., 1, 2] - dx = x - self.mus # [..., GMM_c, 2] - z = (torch.sum((dx/self.sigmas)**2, dim=-1) - - 2*self.corrs*torch.prod(dx, dim=-1)/torch.prod(self.sigmas, dim=-1)) # [..., GMM_c] - component_log_p = -(torch.log(self.one_minus_rho2) + 2*torch.sum(self.log_sigmas, dim=-1) + - z/self.one_minus_rho2 + - 2*np.log(2*np.pi))/2 - return torch.logsumexp(self.log_pis + component_log_p, dim=-1) - - def reshape_to_components(self, tensor, GMM_c): - return torch.reshape(tensor, list(tensor.shape[:-1]) + [GMM_c, self.pred_state_length]) \ No newline at end of file diff --git a/code/model/components/map_encoder.py b/code/model/components/map_encoder.py deleted file mode 100644 index 6562d83..0000000 --- a/code/model/components/map_encoder.py +++ /dev/null @@ -1,20 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -class CNNMapEncoder(nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super(CNNMapEncoder, self).__init__() - self.conv1 = nn.Conv2d(3, 128, 5, stride=2) - self.conv2 = nn.Conv2d(128, 256, 5, stride=3) - self.conv3 = nn.Conv2d(256, 64, 5, stride=2) - self.fc = nn.Linear(7 * 7 * 64, 512) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = F.relu(self.conv2(x)) - x = F.relu(self.conv3(x)) - x = x.view(-1, 7 * 7 * 64) - x = F.relu(self.fc(x)) - return x diff --git a/code/model/dyn_stg.py b/code/model/dyn_stg.py deleted file mode 100644 index d9b3358..0000000 --- a/code/model/dyn_stg.py +++ /dev/null @@ -1,454 +0,0 @@ -import numpy as np -import torch -from model.node_model import MultimodalGenerativeCVAE - - -class SpatioTemporalGraphCVAEModel(object): - def __init__(self, model_registrar, - hyperparams, log_writer, - device): - super(SpatioTemporalGraphCVAEModel, self).__init__() - self.hyperparams = hyperparams - self.log_writer = log_writer - self.device = device - self.curr_iter = 0 - - self.model_registrar = model_registrar - self.node_models_dict = dict() - self.nodes = set() - - self.env = None - - self.min_hl = self.hyperparams['minimum_history_length'] - self.max_hl = self.hyperparams['maximum_history_length'] - self.ph = self.hyperparams['prediction_horizon'] - self.state = self.hyperparams['state'] - self.state_length = dict() - for type in self.state.keys(): - self.state_length[type] = int(np.sum([len(entity_dims) for entity_dims in self.state[type].values()])) - self.pred_state = self.hyperparams['pred_state'] - - def set_scene_graph(self, env): - self.env = env - - self.node_models_dict.clear() - - edge_types = env.get_edge_types() - - for node_type in env.NodeType: - self.node_models_dict[node_type] = MultimodalGenerativeCVAE(env, - node_type, - self.model_registrar, - self.hyperparams, - self.device, - edge_types, - log_writer=self.log_writer) - - def set_curr_iter(self, curr_iter): - self.curr_iter = curr_iter - for node_str, model in self.node_models_dict.items(): - model.set_curr_iter(curr_iter) - - def set_annealing_params(self): - for node_str, model in self.node_models_dict.items(): - model.set_annealing_params() - - def step_annealers(self): - for node in self.node_models_dict: - self.node_models_dict[node].step_annealers() - - def get_input(self, scene, timesteps, node_type, min_future_timesteps, max_nodes=None, curve=False): # Curve is there to resample during training - inputs = list() - labels = list() - first_history_indices = list() - nodes = list() - node_scene_graph_batched = list() - timesteps_in_scene = list() - nodes_per_ts = scene.present_nodes(timesteps, - type=node_type, - min_history_timesteps=self.min_hl, - min_future_timesteps=min_future_timesteps, - include_robot=not self.hyperparams['incl_robot_node'], - max_nodes=max_nodes, - curve=curve) - - # Get Inputs for each node present in Scene - for timestep in timesteps: - if timestep in nodes_per_ts.keys(): - present_nodes = nodes_per_ts[timestep] - timestep_range = np.array([timestep - self.max_hl, timestep + min_future_timesteps]) - scene_graph_t = scene.get_scene_graph(timestep, - self.env.attention_radius, - self.hyperparams['edge_addition_filter'], - self.hyperparams['edge_removal_filter']) - - for node in present_nodes: - timesteps_in_scene.append(timestep) - input = node.get(timestep_range, self.state[node.type.name]) - label = node.get(timestep_range, self.pred_state[node.type.name]) - first_history_index = (self.max_hl - node.history_points_at(timestep)).clip(0) - inputs.append(input) - labels.append(label) - first_history_indices.append(first_history_index) - nodes.append(node) - - node_scene_graph_batched.append((node, scene_graph_t)) - - return inputs, labels, first_history_indices, timesteps_in_scene, node_scene_graph_batched, nodes - - def train_loss(self, scene, timesteps, max_nodes=None): - losses = dict() - for node_type in self.env.NodeType: - losses[node_type] = [] - - for node_type in self.env.NodeType: - # Get Input data for node type and given timesteps - (inputs, - labels, - first_history_indices, - timesteps_in_scene, - node_scene_graph_batched, _) = self.get_input(scene, - timesteps, - node_type, - self.ph, - max_nodes=max_nodes, - curve=True) # Curve is there to resample during training - - # There are no nodes of type present for timestep - if len(inputs) == 0: - continue - - uniform_t = self.max_hl - - inputs = np.array(inputs) - labels = np.array(labels) - - # Vehicles are rotated such that the x axis is lateral - if node_type == self.env.NodeType.VEHICLE: - # transform x y to ego. - pos = inputs[..., 0:2] - pos_org = pos.copy() - vel = inputs[..., 2:4] - acc = inputs[..., 5:7] - heading = inputs[:, uniform_t, -1] - rot_mat = np.zeros((pos.shape[0], pos.shape[1], 3, 3)) - rot_mat[:, :, 0, 0] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 0, 1] = np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 0] = -np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 1] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 2, 2] = 1. - - pos = pos - pos[:, uniform_t, np.newaxis, :] - - pos_with_one = np.ones((pos.shape[0], pos.shape[1], 3, 1)) - pos_with_one[:, :, :2] = pos[..., np.newaxis] - pos_rot = np.squeeze(rot_mat @ pos_with_one, axis=-1)[..., :2] - - vel_with_one = np.ones((vel.shape[0], vel.shape[1], 3, 1)) - vel_with_one[:, :, :2] = vel[..., np.newaxis] - vel_rot = np.squeeze(rot_mat @ vel_with_one, axis=-1)[..., :2] - - acc_with_one = np.ones((acc.shape[0], acc.shape[1], 3, 1)) - acc_with_one[:, :, :2] = acc[..., np.newaxis] - acc_rot = np.squeeze(rot_mat @ acc_with_one, axis=-1)[..., :2] - - inputs[..., 0:2] = pos_rot - inputs[..., 2:4] = vel_rot - inputs[..., 5:7] = acc_rot - - l_vel_with_one = np.ones((labels.shape[0], labels.shape[1], 3, 1)) - l_vel_with_one[:, :, :2] = labels[..., np.newaxis] - labels = np.squeeze(rot_mat @ l_vel_with_one, axis=-1)[..., :2] - - # Standardize, Position is standardized relative to current pos and attention_radius for node_type-node_type - _, std = self.env.get_standardize_params(self.state[node_type.name], node_type=node_type) - # std[0:2] = self.env.attention_radius[(node_type, node_type)] - rel_state = np.array(inputs)[:, uniform_t] - rel_state = np.hstack((rel_state, np.zeros_like(rel_state))) - rel_state = np.expand_dims(rel_state, 1) - std = np.tile(std, 2) - inputs = np.tile(inputs, 2) - inputs[..., self.state_length[node_type.name]:self.state_length[node_type.name]+2] = 0. - inputs_st = self.env.standardize(inputs, - self.state[node_type.name], - mean=rel_state, - std=std, - node_type=node_type) - labels_st = self.env.standardize(labels, self.pred_state[node_type.name], node_type=node_type) - - if node_type == self.env.NodeType.VEHICLE: - inputs[..., 0:2] = pos_org - - # Convert to torch tensors - inputs = torch.tensor(inputs).float().to(self.device) - inputs_st = torch.tensor(inputs_st).float().to(self.device) - first_history_indices = torch.tensor(first_history_indices).float().to(self.device).long() - labels = torch.tensor(labels).float().to(self.device) - labels_st = torch.tensor(labels_st).float().to(self.device) - - # Run forward pass - model = self.node_models_dict[node_type] - loss = model.train_loss(inputs, - inputs_st, - first_history_indices, - labels, - labels_st, - scene, - node_scene_graph_batched, - timestep=uniform_t, - timesteps_in_scene=timesteps_in_scene, - prediction_horizon=self.ph) - - losses[node_type].append(loss) - - for node_type in self.env.NodeType: - losses[node_type] = torch.mean(torch.stack(losses[node_type])) if len(losses[node_type]) > 0 else None - return losses - - def eval_loss(self, scene, timesteps, max_nodes=None): - losses = dict() - for node_type in self.env.NodeType: - losses[node_type] = {'nll_q_is': list(), 'nll_p': list(), 'nll_exact': list(), 'nll_sampled': list()} - for node_type in self.env.NodeType: - # Get Input data for node type and given timesteps - (inputs, - labels, - first_history_indices, - timesteps_in_scene, - node_scene_graph_batched, _) = self.get_input(scene, timesteps, node_type, self.ph, max_nodes=max_nodes) - - # There are no nodes of type present for timestep - if len(inputs) == 0: - continue - - uniform_t = self.max_hl - - inputs = np.array(inputs) - labels = np.array(labels) - - # Vehicles are rotated such that the x axis is lateral - if node_type == self.env.NodeType.VEHICLE: - # transform x y to ego. - pos = inputs[..., 0:2] - pos_org = pos.copy() - vel = inputs[..., 2:4] - acc = inputs[..., 5:7] - heading = inputs[:, uniform_t, -1] - rot_mat = np.zeros((pos.shape[0], pos.shape[1], 3, 3)) - rot_mat[:, :, 0, 0] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 0, 1] = np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 0] = -np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 1] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 2, 2] = 1. - - pos = pos - pos[:, uniform_t, np.newaxis, :] - - pos_with_one = np.ones((pos.shape[0], pos.shape[1], 3, 1)) - pos_with_one[:, :, :2] = pos[..., np.newaxis] - pos_rot = np.squeeze(rot_mat @ pos_with_one, axis=-1)[..., :2] - - vel_with_one = np.ones((vel.shape[0], vel.shape[1], 3, 1)) - vel_with_one[:, :, :2] = vel[..., np.newaxis] - vel_rot = np.squeeze(rot_mat @ vel_with_one, axis=-1)[..., :2] - - acc_with_one = np.ones((acc.shape[0], acc.shape[1], 3, 1)) - acc_with_one[:, :, :2] = acc[..., np.newaxis] - acc_rot = np.squeeze(rot_mat @ acc_with_one, axis=-1)[..., :2] - - inputs[..., 0:2] = pos_rot - inputs[..., 2:4] = vel_rot - inputs[..., 5:7] = acc_rot - - l_vel_with_one = np.ones((labels.shape[0], labels.shape[1], 3, 1)) - l_vel_with_one[:, :, :2] = labels[..., np.newaxis] - labels = np.squeeze(rot_mat @ l_vel_with_one, axis=-1)[..., :2] - - # Standardize, Position is standardized relative to current pos and attention_radius for node_type-node_type - _, std = self.env.get_standardize_params(self.state[node_type.name], node_type=node_type) - rel_state = np.array(inputs)[:, uniform_t] - rel_state = np.hstack((rel_state, np.zeros_like(rel_state))) - rel_state = np.expand_dims(rel_state, 1) - std = np.tile(std, 2) - inputs = np.tile(inputs, 2) - inputs[..., self.state_length[node_type.name]:self.state_length[node_type.name]+2] = 0. - inputs_st = self.env.standardize(inputs, - self.state[node_type.name], - mean=rel_state, - std=std, - node_type=node_type) - labels_st = self.env.standardize(labels, self.pred_state[node_type.name], node_type=node_type) - - if node_type == self.env.NodeType.VEHICLE: - inputs[..., 0:2] = pos_org - - # Convert to torch tensors - inputs = torch.tensor(inputs).float().to(self.device) - inputs_st = torch.tensor(inputs_st).float().to(self.device) - first_history_indices = torch.tensor(first_history_indices).float().to(self.device).long() - labels = torch.tensor(labels).float().to(self.device) - labels_st = torch.tensor(labels_st).float().to(self.device) - - # Run forward pass - model = self.node_models_dict[node_type] - (nll_q_is, nll_p, nll_exact, nll_sampled) = model.eval_loss(inputs, - inputs_st, - first_history_indices, - labels, - labels_st, - scene, - node_scene_graph_batched, - timestep=uniform_t, - timesteps_in_scene=timesteps_in_scene, - prediction_horizon=self.ph) - - if nll_q_is is not None: - losses[node_type]['nll_q_is'].append(nll_q_is.cpu().numpy()) - losses[node_type]['nll_p'].append(nll_p.cpu().numpy()) - losses[node_type]['nll_exact'].append(nll_exact.cpu().numpy()) - losses[node_type]['nll_sampled'].append(nll_sampled.cpu().numpy()) - - return losses - - def predict(self, - scene, - timesteps, - ph, - num_samples_z=1, - num_samples_gmm=1, - min_future_timesteps=0, - most_likely_z=False, - most_likely_gmm=False, - all_z=False, - max_nodes=None): - - predictions_dict = {} - for node_type in self.env.NodeType: - # Get Input data for node type and given timesteps - (inputs, - labels, - first_history_indices, - timesteps_in_scene, - node_scene_graph_batched, - nodes) = self.get_input(scene, timesteps, node_type, min_future_timesteps, max_nodes=max_nodes) - - # There are no nodes of type present for timestep - if len(inputs) == 0: - continue - - uniform_t = self.max_hl - - inputs = np.array(inputs) - labels = np.array(labels) - - # Vehicles are rotated such that the x axis is lateral - if node_type == self.env.NodeType.VEHICLE: - # transform x y to ego. - pos = inputs[..., 0:2] - pos_org = pos.copy() - vel = inputs[..., 2:4] - acc = inputs[..., 5:7] - heading = inputs[:, uniform_t, -1] - rot_mat = np.zeros((pos.shape[0], pos.shape[1], 3, 3)) - rot_mat[:, :, 0, 0] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 0, 1] = np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 0] = -np.sin(heading)[:, np.newaxis] - rot_mat[:, :, 1, 1] = np.cos(heading)[:, np.newaxis] - rot_mat[:, :, 2, 2] = 1. - - pos = pos - pos[:, uniform_t, np.newaxis, :] - - pos_with_one = np.ones((pos.shape[0], pos.shape[1], 3, 1)) - pos_with_one[:, :, :2] = pos[..., np.newaxis] - pos_rot = np.squeeze(rot_mat @ pos_with_one, axis=-1)[..., :2] - - vel_with_one = np.ones((vel.shape[0], vel.shape[1], 3, 1)) - vel_with_one[:, :, :2] = vel[..., np.newaxis] - vel_rot = np.squeeze(rot_mat @ vel_with_one, axis=-1)[..., :2] - - acc_with_one = np.ones((acc.shape[0], acc.shape[1], 3, 1)) - acc_with_one[:, :, :2] = acc[..., np.newaxis] - acc_rot = np.squeeze(rot_mat @ acc_with_one, axis=-1)[..., :2] - - inputs[..., 0:2] = pos_rot - inputs[..., 2:4] = vel_rot - inputs[..., 5:7] = acc_rot - - l_vel_with_one = np.ones((labels.shape[0], labels.shape[1], 3, 1)) - l_vel_with_one[:, :, :2] = labels[..., np.newaxis] - labels = np.squeeze(rot_mat @ l_vel_with_one, axis=-1)[..., :2] - - # Standardize, Position is standardized relative to current pos and attention_radius for node_type-node_type - _, std = self.env.get_standardize_params(self.state[node_type.name], node_type=node_type) - rel_state = np.array(inputs)[:, uniform_t] - rel_state = np.hstack((rel_state, np.zeros_like(rel_state))) - rel_state = np.expand_dims(rel_state, 1) - std = np.tile(std, 2) - inputs = np.tile(inputs, 2) - inputs[..., self.state_length[node_type.name]:self.state_length[node_type.name]+2] = 0. - inputs_st = self.env.standardize(inputs, - self.state[node_type.name], - mean=rel_state, - std=std, - node_type=node_type) - labels_st = self.env.standardize(labels, self.pred_state[node_type.name], node_type=node_type) - - if node_type == self.env.NodeType.VEHICLE: - inputs[..., 0:2] = pos_org - - # Convert to torch tensors - inputs = torch.tensor(inputs).float().to(self.device) - inputs_st = torch.tensor(inputs_st).float().to(self.device) - first_history_indices = torch.tensor(first_history_indices).float().to(self.device).long() - labels = torch.tensor(labels).float().to(self.device) - labels_st = torch.tensor(labels_st).float().to(self.device) - - # Run forward pass - model = self.node_models_dict[node_type] - predictions = model.predict(inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched, - timestep=uniform_t, - timesteps_in_scene=timesteps_in_scene, - prediction_horizon=ph, - num_samples_z=num_samples_z, - num_samples_gmm=num_samples_gmm, - most_likely_z=most_likely_z, - most_likely_gmm=most_likely_gmm, - all_z=all_z) - - predictions_uns = self.env.unstandardize(predictions.cpu().detach().numpy(), - self.pred_state[node_type.name], - node_type) - - # Vehicles are rotated such that the x axis is lateral. For output rotation has to be reversed - if node_type == self.env.NodeType.VEHICLE: - heading = inputs.cpu().detach().numpy()[:, uniform_t, -1] - rot_mat = np.zeros((predictions_uns.shape[0], - predictions_uns.shape[1], - predictions_uns.shape[2], - predictions_uns.shape[3], 3, 3)) - rot_mat[:, :, :, :, 0, 0] = np.cos(-heading)[:, np.newaxis] - rot_mat[:, :, :, :, 0, 1] = np.sin(-heading)[:, np.newaxis] - rot_mat[:, :, :, :, 1, 0] = -np.sin(-heading)[:, np.newaxis] - rot_mat[:, :, :, :, 1, 1] = np.cos(-heading)[:, np.newaxis] - rot_mat[:, :, :, :, 2, 2] = 1. - - p_vel_with_one = np.ones((predictions_uns.shape[0], - predictions_uns.shape[1], - predictions_uns.shape[2], - predictions_uns.shape[3], 3, 1)) - p_vel_with_one[:, :, :, :, :2] = predictions_uns[..., np.newaxis] - predictions_uns = np.squeeze(rot_mat @ p_vel_with_one, axis=-1)[..., :2] - - # Assign predictions to node - for i, ts in enumerate(timesteps_in_scene): - if not ts in predictions_dict.keys(): - predictions_dict[ts] = dict() - predictions_dict[ts][nodes[i]] = predictions_uns[:, :, i] - - return predictions_dict - diff --git a/code/model/node_model.py b/code/model/node_model.py deleted file mode 100644 index 69b3591..0000000 --- a/code/model/node_model.py +++ /dev/null @@ -1,1200 +0,0 @@ -import warnings -import torch.distributions as td -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from model.components import * -from model.model_utils import * - - -class MultimodalGenerativeCVAE(object): - def __init__(self, - env, - node_type, - model_registrar, - hyperparams, - device, - edge_types, - log_writer=None): - self.env = env - self.node_type = node_type.name - self.model_registrar = model_registrar - self.log_writer = log_writer - self.device = device - self.edge_types = [edge_type for edge_type in edge_types if edge_type[0] is node_type] - self.curr_iter = 0 - - self.node_modules = dict() - self.hyperparams = hyperparams - - self.min_hl = self.hyperparams['minimum_history_length'] - self.max_hl = self.hyperparams['maximum_history_length'] - self.ph = self.hyperparams['prediction_horizon'] - self.state = self.hyperparams['state'] - self.pred_state = self.hyperparams['pred_state'][node_type.name] - self.state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[node_type.name].values()])) * 2 # We have the relative and absolute state - self.robot_state_length = int(np.sum([len(entity_dims) for entity_dims in self.state['VEHICLE'].values()])) # TODO VEHICLE is hard coded for now - self.pred_state_length = int(np.sum([len(entity_dims) for entity_dims in self.pred_state.values()])) - - edge_types_str = [env.edge_type_str(edge_type) for edge_type in self.edge_types] - - self.create_graphical_model(edge_types_str) - - def set_curr_iter(self, curr_iter): - self.curr_iter = curr_iter - - def add_submodule(self, name, model_if_absent): - self.node_modules[name] = self.model_registrar.get_model(name, model_if_absent) - - def clear_submodules(self): - self.node_modules.clear() - - def create_graphical_model(self, edge_types): - self.clear_submodules() - - ############################ - # Node History Encoder # - ############################ - self.add_submodule(self.node_type + '/node_history_encoder', - model_if_absent=nn.LSTM(input_size=self.state_length, - hidden_size=self.hyperparams['enc_rnn_dim_history'], - batch_first=True)) - - ########################### - # Node Future Encoder # - ########################### - # We'll create this here, but then later check if in training mode. - # Based on that, we'll factor this into the computation graph (or not). - self.add_submodule(self.node_type + '/node_future_encoder', - model_if_absent=nn.LSTM(input_size=self.pred_state_length, - hidden_size=self.hyperparams['enc_rnn_dim_future'], - bidirectional=True, - batch_first=True)) - # These are related to how you initialize states for the node future encoder. - self.add_submodule(self.node_type + '/node_future_encoder/initial_h', - model_if_absent=nn.Linear(self.state_length, - self.hyperparams['enc_rnn_dim_future'])) - self.add_submodule(self.node_type + '/node_future_encoder/initial_c', - model_if_absent=nn.Linear(self.state_length, - self.hyperparams['enc_rnn_dim_future'])) - - ############################ - # Robot Future Encoder # - ############################ - # We'll create this here, but then later check if we're next to the robot. - # Based on that, we'll factor this into the computation graph (or not). - if self.hyperparams['incl_robot_node']: - self.add_submodule('robot_future_encoder', - model_if_absent=nn.LSTM(input_size=self.robot_state_length , - hidden_size=self.hyperparams['enc_rnn_dim_future'], - bidirectional=True, - batch_first=True)) - # These are related to how you initialize states for the robot future encoder. - self.add_submodule('robot_future_encoder/initial_h', - model_if_absent=nn.Linear(self.robot_state_length, - self.hyperparams['enc_rnn_dim_future'])) - self.add_submodule('robot_future_encoder/initial_c', - model_if_absent=nn.Linear(self.robot_state_length, - self.hyperparams['enc_rnn_dim_future'])) - - ##################### - # Edge Encoders # - ##################### - # print('create_graphical_model', self.node) - # print('create_graphical_model', self.neighbors_via_edge_type) - for edge_type in edge_types: - neighbor_state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[edge_type.split('-')[1]].values()])) - if self.hyperparams['edge_state_combine_method'] == 'pointnet': - self.add_submodule(edge_type + '/pointnet_encoder', - model_if_absent=nn.Sequential( - nn.Linear(self.state_length, 2 * self.state_length), - nn.ReLU(), - nn.Linear(2 * self.state_length, 2 * self.state_length), - nn.ReLU())) - - edge_encoder_input_size = 2 * self.state_length + self.state_length - - elif self.hyperparams['edge_state_combine_method'] == 'attention': - self.add_submodule(self.node_type + '/edge_attention_combine', - model_if_absent=TemporallyBatchedAdditiveAttention( - encoder_hidden_state_dim=self.state_length, - decoder_hidden_state_dim=self.state_length)) - edge_encoder_input_size = self.state_length + neighbor_state_length - - else: - edge_encoder_input_size = self.state_length + neighbor_state_length - - self.add_submodule(edge_type + '/edge_encoder', - model_if_absent=nn.LSTM(input_size=edge_encoder_input_size, - hidden_size=self.hyperparams['enc_rnn_dim_edge'], - batch_first=True)) - - ############################## - # Edge Influence Encoder # - ############################## - # NOTE: The edge influence encoding happens during calls - # to forward or incremental_forward, so we don't create - # a model for it here for the max and sum variants. - if self.hyperparams['edge_influence_combine_method'] == 'bi-rnn': - self.add_submodule(self.node_type + '/edge_influence_encoder', - model_if_absent=nn.LSTM(input_size=self.hyperparams['enc_rnn_dim_edge'], - hidden_size=self.hyperparams['enc_rnn_dim_edge_influence'], - bidirectional=True, - batch_first=True)) - - # Four times because we're trying to mimic a bi-directional - # LSTM's output (which, here, is c and h from both ends). - self.eie_output_dims = 4 * self.hyperparams['enc_rnn_dim_edge_influence'] - - elif self.hyperparams['edge_influence_combine_method'] == 'attention': - # Chose additive attention because of https://arxiv.org/pdf/1703.03906.pdf - # We calculate an attention context vector using the encoded edges as the "encoder" (that we attend _over_) - # and the node history encoder representation as the "decoder state" (that we attend _on_). - self.add_submodule(self.node_type + '/edge_influence_encoder', - model_if_absent=AdditiveAttention( - encoder_hidden_state_dim=self.hyperparams['enc_rnn_dim_edge_influence'], - decoder_hidden_state_dim=self.hyperparams['enc_rnn_dim_history'])) - - self.eie_output_dims = self.hyperparams['enc_rnn_dim_edge_influence'] - - ################### - # Map Encoder # - ################### - if self.hyperparams['use_map_encoding']: - self.add_submodule(self.node_type + '/map_encoder', - model_if_absent=CNNMapEncoder(input_size=self.hyperparams['map_context'], - hidden_size=self.hyperparams['map_enc_hidden_size'], - output_size=self.hyperparams['map_enc_output_size'])) - - ################################ - # Discrete Latent Variable # - ################################ - self.latent = DiscreteLatent(self.hyperparams, self.device) - - ###################################################################### - # Various Fully-Connected Layers from Encoder to Latent Variable # - ###################################################################### - # Edge Influence Encoder Node History Encoder - x_size = self.eie_output_dims + self.hyperparams['enc_rnn_dim_history'] - if self.hyperparams['incl_robot_node']: - # Future Conditional Encoder - x_size += 4 * self.hyperparams['enc_rnn_dim_future'] - - if self.hyperparams['use_map_encoding']: - # Map Encoder - x_size += self.hyperparams['map_enc_output_size'] - - z_size = self.hyperparams['N'] * self.hyperparams['K'] - - if self.hyperparams['p_z_x_MLP_dims'] is not None: - self.add_submodule(self.node_type + '/p_z_x', - model_if_absent=nn.Linear(x_size, self.hyperparams['p_z_x_MLP_dims'])) - hx_size = self.hyperparams['p_z_x_MLP_dims'] - else: - hx_size = x_size - - self.add_submodule(self.node_type + '/hx_to_z', - model_if_absent=nn.Linear(hx_size, self.latent.z_dim)) - - if self.hyperparams['q_z_xy_MLP_dims'] is not None: - self.add_submodule(self.node_type + '/q_z_xy', - # Node Future Encoder - model_if_absent=nn.Linear(x_size + 4 * self.hyperparams['enc_rnn_dim_future'], - self.hyperparams['q_z_xy_MLP_dims'])) - hxy_size = self.hyperparams['q_z_xy_MLP_dims'] - else: - # Node Future Encoder - hxy_size = x_size + 4 * self.hyperparams['enc_rnn_dim_future'] - - self.add_submodule(self.node_type + '/hxy_to_z', - model_if_absent=nn.Linear(hxy_size, self.latent.z_dim)) - - #################### - # Decoder LSTM # - #################### - if self.hyperparams['incl_robot_node']: - decoder_input_dims = self.pred_state_length + self.robot_state_length + z_size + x_size - else: - decoder_input_dims = self.pred_state_length + z_size + x_size - - self.add_submodule(self.node_type + '/decoder/lstm_cell', - model_if_absent=nn.GRUCell(decoder_input_dims, self.hyperparams['dec_rnn_dim'])) - self.add_submodule(self.node_type + '/decoder/initial_h', - model_if_absent=nn.Linear(z_size + x_size, self.hyperparams['dec_rnn_dim'])) - self.add_submodule(self.node_type + '/decoder/initial_c', - model_if_absent=nn.Linear(z_size + x_size, self.hyperparams['dec_rnn_dim'])) - - - ################### - # Decoder GMM # - ################### - self.add_submodule(self.node_type + '/decoder/proj_to_GMM_log_pis', - model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], - self.hyperparams['GMM_components'])) - self.add_submodule(self.node_type + '/decoder/proj_to_GMM_mus', - model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], - self.hyperparams['GMM_components'] * self.pred_state_length)) - self.add_submodule(self.node_type + '/decoder/proj_to_GMM_log_sigmas', - model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], - self.hyperparams['GMM_components'] * self.pred_state_length)) - self.add_submodule(self.node_type + '/decoder/proj_to_GMM_corrs', - model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], - self.hyperparams['GMM_components'])) - - for name, module in self.node_modules.items(): - module.to(self.device) - - def create_new_scheduler(self, name, annealer, annealer_kws, creation_condition=True): - value_scheduler = None - rsetattr(self, name + '_scheduler', value_scheduler) - if creation_condition: - annealer_kws['device'] = self.device - value_annealer = annealer(annealer_kws) - rsetattr(self, name + '_annealer', value_annealer) - - # This is the value that we'll update on each call of - # step_annealers(). - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - rsetattr(self, name, torch.tensor(value_annealer(0), device=self.device)) - dummy_optimizer = optim.Optimizer([rgetattr(self, name)], - {'lr': torch.tensor(value_annealer(0), device=self.device)}) - rsetattr(self, name + '_optimizer', dummy_optimizer) - - value_scheduler = CustomLR(dummy_optimizer, - value_annealer) - rsetattr(self, name + '_scheduler', value_scheduler) - - self.schedulers.append(value_scheduler) - self.annealed_vars.append(name) - - def set_annealing_params(self): - self.schedulers = list() - self.annealed_vars = list() - - self.create_new_scheduler(name='kl_weight', - annealer=sigmoid_anneal, - annealer_kws={ - 'start': self.hyperparams['kl_weight_start'], - 'finish': self.hyperparams['kl_weight'], - 'center_step': self.hyperparams['kl_crossover'], - 'steps_lo_to_hi': self.hyperparams['kl_crossover'] / self.hyperparams[ - 'kl_sigmoid_divisor'] - }, - creation_condition=((np.abs(self.hyperparams['alpha'] - 1.0) < 1e-3) - and (not self.hyperparams['use_iwae']))) - - self.create_new_scheduler(name='dec_sample_model_prob', - annealer=sigmoid_anneal, - annealer_kws={ - 'start': self.hyperparams['dec_sample_model_prob_start'], - 'finish': self.hyperparams['dec_sample_model_prob_final'], - 'center_step': self.hyperparams['dec_sample_model_prob_crossover'], - 'steps_lo_to_hi': self.hyperparams['dec_sample_model_prob_crossover'] / - self.hyperparams['dec_sample_model_prob_divisor'] - }, - creation_condition=self.hyperparams['sample_model_during_dec']) - - self.create_new_scheduler(name='latent.temp', - annealer=exp_anneal, - annealer_kws={ - 'start': self.hyperparams['tau_init'], - 'finish': self.hyperparams['tau_final'], - 'rate': self.hyperparams['tau_decay_rate'] - }) - - self.create_new_scheduler(name='latent.z_logit_clip', - annealer=sigmoid_anneal, - annealer_kws={ - 'start': self.hyperparams['z_logit_clip_start'], - 'finish': self.hyperparams['z_logit_clip_final'], - 'center_step': self.hyperparams['z_logit_clip_crossover'], - 'steps_lo_to_hi': self.hyperparams['z_logit_clip_crossover'] / self.hyperparams[ - 'z_logit_clip_divisor'] - }, - creation_condition=self.hyperparams['use_z_logit_clipping']) - - self.create_new_scheduler(name='warmup_dropout_keep', - annealer=sigmoid_anneal, - annealer_kws={ - 'start': self.hyperparams['inf_warmup_start'], - 'finish': self.hyperparams['inf_warmup'], - 'center_step': self.hyperparams['inf_warmup_crossover'], - 'steps_lo_to_hi': self.hyperparams['inf_warmup_crossover'] / self.hyperparams[ - 'inf_warmup_sigmoid_divisor'] - }) - - def step_annealers(self): - # This should manage all of the step-wise changed - # parameters automatically. - for idx, annealed_var in enumerate(self.annealed_vars): - if rgetattr(self, annealed_var + '_scheduler') is not None: - # First we step the scheduler. - rgetattr(self, annealed_var + '_scheduler').step() - - # Then we set the annealed vars' value. - rsetattr(self, annealed_var, rgetattr(self, annealed_var + '_optimizer').param_groups[0]['lr']) - - self.summarize_annealers() - - def summarize_annealers(self): - if self.log_writer is not None: - for annealed_var in self.annealed_vars: - if rgetattr(self, annealed_var) is not None: - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), annealed_var.replace('.', '/')), - rgetattr(self, annealed_var), self.curr_iter) - - def obtain_encoded_tensor_dict(self, - mode, - timestep, - timesteps_in_scene, - inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched): - - tensor_dict = dict() # tensor_dict - batch_size = inputs.shape[0] - - ######################################### - # Provide basic information to encoders # - ######################################### - node_traj = inputs - node_history = inputs[:, :timestep + 1] - node_present_state = inputs[:, timestep] - node_pos = inputs[:, timestep, 0:2] - node_vel = inputs[:, timestep, 2:4] - - node_traj_st = inputs_st - node_history_st = inputs_st[:, :timestep + 1] - node_present_state_st = inputs_st[:, timestep] - node_pos_st = inputs_st[:, timestep, 0:2] - node_vel_st = inputs_st[:, timestep, 2:4] - - if self.hyperparams['incl_robot_node'] and scene.robot is not None: - robot_traj_list = [] - for timestep_s in timesteps_in_scene: - timestep_range = np.array([timestep_s - self.max_hl, timestep_s + node_traj.shape[1] - self.max_hl - 1]) - robot_traj_list.append(scene.robot.get(timestep_range, self.state[scene.robot.type.name])) - robot_traj_np = np.array(robot_traj_list) - - # Make Robot State relative to node - _, std = self.env.get_standardize_params(self.state[scene.robot.type.name], node_type=scene.robot.type) - std[0:2] = 40 - rel_state = np.zeros_like(robot_traj_np) - rel_state[..., :6] = node_traj[..., :6].cpu() - robot_traj_np_st = self.env.standardize(robot_traj_np, - self.state[scene.robot.type.name], - node_type=scene.robot.type, - mean=rel_state, - std=std) - robot_traj_st = torch.tensor(robot_traj_np_st).float().to(self.device) - robot_present_state_st = robot_traj_st[:, timestep] - robot_future_st = robot_traj_st[:, timestep+1:] - - tensor_dict['robot_present'] = robot_present_state_st - tensor_dict['robot_future'] = robot_future_st - - ################## - # Encode History # - ################## - tensor_dict['node_history_encoded'] = self.encode_node_history(mode, - node_history_st, - first_history_indices, - timestep) - - ################## - # Encode Present # - ################## - tensor_dict['node_present'] = node_present_state_st # [bs, state_dim] - - ################## - # Encode Future # - ################## - if mode != ModeKeys.PREDICT: - tensor_dict['node_future'] = labels_st[:, timestep + 1:timestep + self.ph + 1] # [bs, ph, state_dim] - - ####################################### - # Encode Joint Present (Robot + Node) # - ####################################### - if self.warmup_dropout_keep < 0.5: - if self.hyperparams['incl_robot_node'] and scene.robot is not None: - tensor_dict['joint_present'] = torch.zeros_like(torch.cat([robot_present_state_st, - labels_st[:, timestep]], dim=1)) - else: - tensor_dict['joint_present'] = torch.zeros_like(labels_st[:, timestep]) - else: - if self.hyperparams['incl_robot_node'] and scene.robot is not None: - tensor_dict['joint_present'] = torch.cat([robot_present_state_st, labels_st[:, timestep]], dim=1) - else: - tensor_dict['joint_present'] = labels_st[:, timestep] - - ############################## - # Encode Node Edges per Type # - ############################## - tensor_dict["node_edges_encoded"] = list() - for edge_type in self.edge_types: - connected_nodes_batched = list() - edge_masks_batched = list() - for i, (node, scene_graph) in enumerate(node_scene_graph_batched): - # We get all nodes which are connected to the current node for the current timestep - - connected_nodes_batched.append(scene_graph.get_neighbors(node, edge_type[1])) - - if self.hyperparams['dynamic_edges'] == 'yes': - # We get the edge masks for the current node at the current timestep - edge_masks_for_node = scene_graph.get_edge_scaling(node) - edge_masks_batched.append(torch.tensor(edge_masks_for_node).float().to(self.device)) - - # Encode edges for given edge type - encoded_edges_type = self.encode_edge(mode, - node_history, - node_history_st, - edge_type, - connected_nodes_batched, - edge_masks_batched, - first_history_indices, - timestep, - timesteps_in_scene, - scene) - tensor_dict["node_edges_encoded"].append(encoded_edges_type) # List of [bs/nbs, enc_rnn_dim] - ##################### - # Encode Node Edges # - ##################### - tensor_dict["total_edge_influence"] = self.encode_total_edge_influence(mode, - tensor_dict["node_edges_encoded"], - tensor_dict["node_history_encoded"], - batch_size) # [bs/nbs, 4*enc_rnn_dim] - #print(time.time() - t) - ############## - # Encode Map # - ############## - if mode == ModeKeys.TRAIN: - rand_heading = (2 * np.random.rand(node_present_state.shape[0]) - 1) * 5 * np.pi / 180 # outside if because seeding - else: - rand_heading = 0. - if self.hyperparams['use_map_encoding']: - heading = node_present_state.cpu().numpy()[:, -1] + rand_heading - node_pos_cpu = node_pos.cpu().numpy() - if self.node_type == 'VEHICLE': - node_pos_cpu = node_pos_cpu + 20 * np.array([np.cos(heading), np.sin(heading)]).T - cropped_maps_np = get_cropped_maps_heading_exact(world_pts=node_pos_cpu, - map=scene.map[self.node_type], - context_size=self.hyperparams['map_context'], - heading=heading) - cropped_maps_np = np.swapaxes(cropped_maps_np, -1, 1) - cropped_maps = torch.from_numpy(cropped_maps_np).to(self.device) - del cropped_maps_np - encoded_map = self.node_modules[self.node_type + '/map_encoder'](cropped_maps) - - encoded_map = F.dropout(encoded_map, 0.5, training=(mode == ModeKeys.TRAIN)) - - tensor_dict["encoded_maps"] = encoded_map - - if self.log_writer is not None and mode != ModeKeys.PREDICT: - context_size = self.hyperparams['map_context'] - #cropped_maps = cropped_maps.clone() - #cropped_maps[:, :, context_size // 2 - 3:context_size // 2 + 3, context_size // 2 - 3:context_size // 2 + 3] = 1. - self.log_writer.add_images('%s/cropped_maps' % str(self.node_type), - cropped_maps, - self.curr_iter) - - img_pts = scene.map[self.node_type].to_map_points(node_pos_cpu) - - box_arr = np.empty((img_pts.shape[0], 4)) - box_arr[:, 0] = img_pts[:, 0] - context_size // 2 - box_arr[:, 1] = img_pts[:, 1] - context_size // 2 - box_arr[:, 2] = img_pts[:, 0] + context_size // 2 - box_arr[:, 3] = img_pts[:, 1] + context_size // 2 - - self.log_writer.add_image_with_boxes('%s/cropped_locs' % str(self.node_type), - np.swapaxes(scene.map[self.node_type].fdata, -1, 0).astype(float), - box_arr, - self.curr_iter) - - ###################################### - # Concatenate Encoder Outputs into x # - ###################################### - concat_list = list() - if self.hyperparams['use_map_encoding']: - concat_list.append(tensor_dict["encoded_maps"]) # [bs/nbs, map_enc_output_size] - - # Every node has an edge-influence encoder (which could just be zero). - if self.warmup_dropout_keep < 0.5: - concat_list.append(torch.zeros_like(tensor_dict["total_edge_influence"])) - else: - concat_list.append(tensor_dict["total_edge_influence"]) # [bs/nbs, 4*enc_rnn_dim] - - # Every node has a history encoder. - if self.warmup_dropout_keep < 0.5: - concat_list.append(torch.zeros_like(tensor_dict["node_history_encoded"])) - else: - concat_list.append(tensor_dict["node_history_encoded"]) # [bs/nbs, enc_rnn_dim_history] - - if self.hyperparams['incl_robot_node'] and scene.robot is not None: - tensor_dict[scene.robot.type.name + "_robot_future_encoder"] = self.encode_robot_future( - tensor_dict['robot_present'], - tensor_dict['robot_future'], - mode, - scene.robot.type.name + '_robot') - # [bs/nbs, 4*enc_rnn_dim_future] - concat_list.append(tensor_dict[scene.robot.type.name + "_robot_future_encoder"]) - - elif self.hyperparams['incl_robot_node']: - # Four times because we're trying to mimic a bi-directional RNN's output (which is c and h from both ends). - concat_list.append( - torch.zeros([batch_size, 4 * self.hyperparams['enc_rnn_dim_future']], device=self.device)) - - tensor_dict["x"] = torch.cat(concat_list, dim=1) - - if mode == ModeKeys.TRAIN or mode == ModeKeys.EVAL: - tensor_dict[self.node_type + "_future_encoder"] = self.encode_node_future(tensor_dict['node_present'], - tensor_dict['node_future'], - mode, - self.node_type) - - return tensor_dict - - def encode_node_history(self, mode, node_traj, first_history_indices, timestep): - outputs, _ = run_lstm_on_variable_length_seqs(self.node_modules[self.node_type + '/node_history_encoder'], - node_traj, - torch.ones_like(first_history_indices) * timestep, - first_history_indices, - self.hyperparams[ - 'maximum_history_length'] + 1) # history + current - - outputs = F.dropout(outputs, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) # [bs, max_time, enc_rnn_dim] - - last_index_per_sequence = timestep - first_history_indices - - return outputs[torch.arange(first_history_indices.shape[0]), last_index_per_sequence] - - def encode_edge(self, - mode, - node_history, - node_history_st, - edge_type, - connected_nodes, - edge_masks, - first_history_indices, - timestep, - timesteps_in_scene, - scene): - - max_hl = self.hyperparams['maximum_history_length'] - - edge_states_list = list() # list of [#of neighbors, max_hl, state_dim] - for i, timestep_in_scene in enumerate(timesteps_in_scene): # Get neighbors for timestep in batch - neighbor_states = list() - for node in connected_nodes[i]: - neighbor_state_np = node.get(np.array([timestep_in_scene - max_hl, timestep_in_scene]), - self.state[node.type.name], - padding=0.0) - - # Make State relative to node - _, std = self.env.get_standardize_params(self.state[node.type.name], node_type=node.type) - std[0:2] = self.env.attention_radius[edge_type] - rel_state = np.zeros_like(neighbor_state_np) - rel_state[:, :6] = node_history[i, -1, :6].cpu() - neighbor_state_np_st = self.env.standardize(neighbor_state_np, - self.state[node.type.name], - node_type=node.type, - mean=rel_state, - std=std) - - neighbor_state = torch.tensor(neighbor_state_np_st).float().to(self.device) - neighbor_states.append(neighbor_state) - if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? - neighbor_state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1].name].values()])) - edge_states_list.append(torch.zeros((1, max_hl + 1, neighbor_state_length), device=self.device)) - else: - edge_states_list.append(torch.stack(neighbor_states, dim=0)) - - if self.hyperparams['edge_state_combine_method'] == 'sum': - # Used in Structural-RNN to combine edges as well. - op_applied_edge_states_list = list() - for neighbors_state in edge_states_list: - op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0)) - combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) - if self.hyperparams['dynamic_edges'] == 'yes': - # Should now be (bs, time, 1) - op_applied_edge_mask_list = list() - for edge_mask in edge_masks: - op_applied_edge_mask_list.append(torch.clamp(torch.sum(edge_mask, dim=0, keepdim=True), max=1.)) - combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) - - elif self.hyperparams['edge_state_combine_method'] == 'max': - # Used in NLP, e.g. max over word embeddings in a sentence. - op_applied_edge_states_list = list() - for neighbors_state in edge_states_list: - op_applied_edge_states_list.append(torch.max(neighbors_state, dim=0)) - combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) - if self.hyperparams['dynamic_edges'] == 'yes': - # Should now be (bs, time, 1) - op_applied_edge_mask_list = list() - for edge_mask in edge_masks: - op_applied_edge_mask_list.append(torch.clamp(torch.max(edge_mask, dim=0, keepdim=True), max=1.)) - combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) - - elif self.hyperparams['edge_state_combine_method'] == 'mean': - # Used in NLP, e.g. mean over word embeddings in a sentence. - op_applied_edge_states_list = list() - for neighbors_state in edge_states_list: - op_applied_edge_states_list.append(torch.mean(neighbors_state, dim=0)) - combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) - if self.hyperparams['dynamic_edges'] == 'yes': - # Should now be (bs, time, 1) - op_applied_edge_mask_list = list() - for edge_mask in edge_masks: - op_applied_edge_mask_list.append(torch.clamp(torch.mean(edge_mask, dim=0, keepdim=True), max=1.)) - combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) - - joint_history = torch.cat([combined_neighbors, node_history_st], dim=-1) - - outputs, _ = run_lstm_on_variable_length_seqs(self.node_modules[self.env.edge_type_str(edge_type) + '/edge_encoder'], - joint_history, - torch.ones_like(first_history_indices) * timestep, - first_history_indices, - self.hyperparams[ - 'maximum_history_length'] + 1) # Add prediction timestep - - outputs = F.dropout(outputs, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) # [bs, max_time, enc_rnn_dim] - - last_index_per_sequence = timestep - first_history_indices - ret = outputs[torch.arange(last_index_per_sequence.shape[0]), last_index_per_sequence] - if self.hyperparams['dynamic_edges'] == 'yes': - return ret * combined_edge_masks - else: - return ret - - def encode_total_edge_influence(self, mode, encoded_edges, node_history_encoder, batch_size): - if self.hyperparams['edge_influence_combine_method'] == 'sum': - stacked_encoded_edges = torch.stack(encoded_edges, dim=0) - combined_edges = torch.sum(stacked_encoded_edges, dim=0) - - elif self.hyperparams['edge_influence_combine_method'] == 'mean': - stacked_encoded_edges = torch.stack(encoded_edges, dim=0) - combined_edges = torch.mean(stacked_encoded_edges, dim=0) - - elif self.hyperparams['edge_influence_combine_method'] == 'max': - stacked_encoded_edges = torch.stack(encoded_edges, dim=0) - combined_edges = torch.max(stacked_encoded_edges, dim=0) - - elif self.hyperparams['edge_influence_combine_method'] == 'bi-rnn': - if len(encoded_edges) == 0: - combined_edges = torch.zeros((batch_size, self.eie_output_dims), device=self.device) - - else: - # axis=1 because then we get size [batch_size, max_time, depth] - encoded_edges = torch.stack(encoded_edges, dim=1) - - _, state = self.node_modules[self.node_type + '/edge_influence_encoder'](encoded_edges) - combined_edges = unpack_RNN_state(state) - combined_edges = F.dropout(combined_edges, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - elif self.hyperparams['edge_influence_combine_method'] == 'attention': - # Used in Social Attention (https://arxiv.org/abs/1710.04689) - if len(encoded_edges) == 0: - combined_edges = torch.zeros((batch_size, self.eie_output_dims), device=self.device) - - else: - # axis=1 because then we get size [batch_size, max_time, depth] - encoded_edges = torch.stack(encoded_edges, dim=1) - combined_edges, _ = self.node_modules[self.node_type + '/edge_influence_encoder'](encoded_edges, - node_history_encoder) - combined_edges = F.dropout(combined_edges, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - return combined_edges - - def encode_node_future(self, node_present, node_future, mode, scope): - initial_h_model = self.node_modules[self.node_type + '/node_future_encoder/initial_h'] - initial_c_model = self.node_modules[self.node_type + '/node_future_encoder/initial_c'] - - # Here we're initializing the forward hidden states, - # but zeroing the backward ones. - initial_h = initial_h_model(node_present) - initial_h = torch.stack([initial_h, torch.zeros_like(initial_h, device=self.device)], dim=0) - - initial_c = initial_c_model(node_present) - initial_c = torch.stack([initial_c, torch.zeros_like(initial_c, device=self.device)], dim=0) - - initial_state = (initial_h, initial_c) - - _, state = self.node_modules[self.node_type + '/node_future_encoder'](node_future, initial_state) - state = unpack_RNN_state(state) - state = F.dropout(state, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - return state - - def encode_robot_future(self, robot_present, robot_future, mode, scope): - initial_h_model = self.node_modules['robot_future_encoder/initial_h'] - initial_c_model = self.node_modules['robot_future_encoder/initial_c'] - - # Here we're initializing the forward hidden states, - # but zeroing the backward ones. - initial_h = initial_h_model(robot_present) - initial_h = torch.stack([initial_h, torch.zeros_like(initial_h, device=self.device)], dim=0) - - initial_c = initial_c_model(robot_present) - initial_c = torch.stack([initial_c, torch.zeros_like(initial_c, device=self.device)], dim=0) - - initial_state = (initial_h, initial_c) - - _, state = self.node_modules['robot_future_encoder'](robot_future, initial_state) - state = unpack_RNN_state(state) - state = F.dropout(state, - p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - return state - - def q_z_xy(self, x, y, mode): - xy = torch.cat([x, y], dim=1) - - if self.hyperparams['q_z_xy_MLP_dims'] is not None: - dense = self.node_modules[self.node_type + '/q_z_xy'] - h = F.dropout(F.relu(dense(xy)), - p=1. - self.hyperparams['MLP_dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - else: - h = xy - - to_latent = self.node_modules[self.node_type + '/hxy_to_z'] - return self.latent.dist_from_h(to_latent(h), mode) - - def p_z_x(self, x, mode): - if self.hyperparams['p_z_x_MLP_dims'] is not None: - dense = self.node_modules[self.node_type + '/p_z_x'] - h = F.dropout(F.relu(dense(x)), - p=1. - self.hyperparams['MLP_dropout_keep_prob'], - training=(mode == ModeKeys.TRAIN)) - - else: - h = x - - to_latent = self.node_modules[self.node_type + '/hx_to_z'] - return self.latent.dist_from_h(to_latent(h), mode) - - def project_to_GMM_params(self, tensor): - log_pis = self.node_modules[self.node_type + '/decoder/proj_to_GMM_log_pis'](tensor) - mus = self.node_modules[self.node_type + '/decoder/proj_to_GMM_mus'](tensor) - log_sigmas = self.node_modules[self.node_type + '/decoder/proj_to_GMM_log_sigmas'](tensor) - corrs = torch.tanh(self.node_modules[self.node_type + '/decoder/proj_to_GMM_corrs'](tensor)) - return log_pis, mus, log_sigmas, corrs - - def p_y_xz(self, x, z_stacked, tensor_dict, mode, - num_predicted_timesteps, num_samples_z, num_samples_gmm=1, most_likely_gmm=False): - ph = num_predicted_timesteps - - our_future = "node_future" - robot_future = "robot_future" - - k = num_samples_z * num_samples_gmm - GMM_c, pred_dim = self.hyperparams['GMM_components'], self.pred_state_length - - z = torch.reshape(z_stacked, (-1, self.latent.z_dim)) - zx = torch.cat([z, x.repeat(k, 1)], dim=1) - - cell = self.node_modules[self.node_type + '/decoder/lstm_cell'] - initial_h_model = self.node_modules[self.node_type + '/decoder/initial_h'] - - initial_state = initial_h_model(zx) - - log_pis, mus, log_sigmas, corrs = [], [], [], [] - if mode in [ModeKeys.TRAIN, ModeKeys.EVAL]: - state = initial_state - if self.hyperparams['sample_model_during_dec'] and mode == ModeKeys.TRAIN: - input_ = torch.cat([zx, tensor_dict['joint_present'].repeat(k, 1)], dim=1) - for j in range(ph): - h_state = cell(input_, state) - log_pi_t, mu_t, log_sigma_t, corr_t = self.project_to_GMM_params(h_state) - y_t = GMM2D(log_pi_t, mu_t, log_sigma_t, corr_t, self.pred_state_length, self.device, - self.hyperparams['log_sigma_min'], - self.hyperparams['log_sigma_max']).sample() # [k;bs, pred_dim] - - # This is where we pick our output y_t or the true output - # our_future to pass into the next cell (we do this with - # probability self.dec_sample_model_prob and is only done - # during training). - mask = td.Bernoulli(probs=self.dec_sample_model_prob).sample((y_t.size()[0], 1)) - y_t = mask * y_t + (1 - mask) * (tensor_dict[our_future][:, j, :].repeat(k, 1)) - - log_pis.append(log_pi_t) - mus.append(mu_t) - log_sigmas.append(log_sigma_t) - corrs.append(corr_t) - - if self.hyperparams['incl_robot_node']: - dec_inputs = torch.cat([tensor_dict[robot_future][:, j, :].repeat(k, 1), y_t], dim=1) - else: - dec_inputs = y_t - - input_ = torch.cat([zx, dec_inputs], dim=1) - state = h_state - - log_pis = torch.stack(log_pis, dim=1) - mus = torch.stack(mus, dim=1) - log_sigmas = torch.stack(log_sigmas, dim=1) - corrs = torch.stack(corrs, dim=1) - - else: - zx_with_time_dim = zx.unsqueeze(dim=1) # [k;bs/nbs, 1, N*K + 2*enc_rnn_dim] - zx_time_tiled = zx_with_time_dim.repeat(1, ph, 1) - if self.hyperparams['incl_robot_node']: - dec_inputs = torch.cat([ - tensor_dict["joint_present"].unsqueeze(dim=1), - torch.cat([tensor_dict[robot_future][:, :ph - 1, :], tensor_dict[our_future][:, :ph - 1, :]], - dim=2) - ], dim=1) - else: - dec_inputs = torch.cat([ - tensor_dict["joint_present"].unsqueeze(dim=1), - tensor_dict[our_future][:, :ph - 1, :] - ], dim=1) - - outputs = list() - for j in range(ph): - inputs = torch.cat([zx_time_tiled, dec_inputs.repeat(k, 1, 1)], - dim=2) - h_state = cell(inputs[:, j, :], state) - outputs.append(h_state) - state = h_state - - outputs = torch.stack(outputs, dim=1) - log_pis, mus, log_sigmas, corrs = self.project_to_GMM_params(outputs) - - if self.hyperparams['log_histograms'] and self.log_writer is not None: - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'GMM_log_pis'), log_pis, self.curr_iter) - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'GMM_mus'), mus, self.curr_iter) - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'GMM_log_sigmas'), log_sigmas, - self.curr_iter) - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'GMM_corrs'), corrs, self.curr_iter) - - elif mode == ModeKeys.PREDICT: - input_ = torch.cat([zx, tensor_dict["joint_present"].repeat(k, 1)], dim=1) - state = initial_state - - log_pis, mus, log_sigmas, corrs, y = [], [], [], [], [] - for j in range(ph): - h_state = cell(input_, state) - log_pi_t, mu_t, log_sigma_t, corr_t = self.project_to_GMM_params(h_state) - - gmm = GMM2D(log_pi_t, mu_t, log_sigma_t, corr_t, self.pred_state_length, self.device, - self.hyperparams['log_sigma_min'], - self.hyperparams['log_sigma_max']) # [k;bs, pred_dim] - - if most_likely_gmm: - y_t_list = [] - for i in range(gmm.mus.shape[0]): - gmm_i = GMM2D(log_pi_t[i], mu_t[i], log_sigma_t[i], corr_t[i], self.pred_state_length, - self.device, - self.hyperparams['log_sigma_min'], - self.hyperparams['log_sigma_max']) # [k;bs, pred_dim] - x_min = gmm.mus[i, ..., 0].min() - x_max = gmm.mus[i, ..., 0].max() - y_min = gmm.mus[i, ..., 1].min() - y_max = gmm.mus[i, ..., 1].max() - x_min = x_min - 0.5 * torch.abs(x_min) - x_max = x_max + 0.5 * torch.abs(x_max) - y_min = y_min - 0.5 * torch.abs(y_min) - y_max = y_max + 0.5 * torch.abs(y_max) - search_grid = torch.stack(torch.meshgrid([torch.arange(x_min, x_max, 0.01), - torch.arange(y_min, y_max, 0.01)]), dim=2 - ).view(1, -1, 2).float().to( - self.device) - - ll_score = gmm_i.log_prob(search_grid).squeeze() - y_t_list.append(search_grid[0, torch.argmax(ll_score, dim=0)]) - - y_t = torch.stack(y_t_list, dim=0) - else: - y_t = gmm.sample() - - log_pis.append(log_pi_t) - mus.append(mu_t) - log_sigmas.append(log_sigma_t) - corrs.append(corr_t) - y.append(y_t) - - if self.hyperparams['incl_robot_node']: - dec_inputs = torch.cat([tensor_dict[robot_future][:, j, :].repeat(k, 1), y_t], dim=1) - else: - dec_inputs = y_t - - input_ = torch.cat([zx, dec_inputs],dim=1) - state = h_state - - log_pis = torch.stack(log_pis, dim=1) - mus = torch.stack(mus, dim=1) - log_sigmas = torch.stack(log_sigmas, dim=1) - corrs = torch.stack(corrs, dim=1) - sampled_future = torch.reshape(torch.stack(y, dim=1), (num_samples_z, num_samples_gmm, -1, ph, pred_dim)) - - y_dist = GMM2D(torch.reshape(log_pis, [k, -1, ph, GMM_c]), - torch.reshape(mus, [k, -1, ph, GMM_c * pred_dim]), - torch.reshape(log_sigmas, [k, -1, ph, GMM_c * pred_dim]), - torch.reshape(corrs, [k, -1, ph, GMM_c]), - self.pred_state_length, self.device, - self.hyperparams['log_sigma_min'], self.hyperparams['log_sigma_max']) - - if mode == ModeKeys.PREDICT: - return y_dist, sampled_future - else: - return y_dist - - def encoder(self, x, y, mode, num_samples=None): - if mode == ModeKeys.TRAIN: - sample_ct = self.hyperparams['k'] - elif mode == ModeKeys.EVAL: - sample_ct = self.hyperparams['k_eval'] - elif mode == ModeKeys.PREDICT: - sample_ct = num_samples - if num_samples is None: - raise ValueError("num_samples cannot be None with mode == PREDICT.") - - self.latent.q_dist = self.q_z_xy(x, y, mode) - self.latent.p_dist = self.p_z_x(x, mode) - - z = self.latent.sample_q(sample_ct, mode) - - if mode == ModeKeys.TRAIN and self.hyperparams['kl_exact']: - kl_obj = self.latent.kl_q_p(self.log_writer, '%s' % str(self.node_type), self.curr_iter) - if self.log_writer is not None: - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'kl'), kl_obj, self.curr_iter) - else: - kl_obj = None - - return z, kl_obj - - def decoder(self, x, y, z, tensor_dict, mode, num_predicted_timesteps, num_samples): - y_dist = self.p_y_xz(x, z, tensor_dict, mode, num_predicted_timesteps, num_samples) - log_p_yt_xz = torch.clamp(y_dist.log_prob(y), max=self.hyperparams['log_p_yt_xz_max']) - if self.hyperparams['log_histograms'] and self.log_writer is not None: - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'log_p_yt_xz'), log_p_yt_xz, self.curr_iter) - - log_p_y_xz = torch.sum(log_p_yt_xz, dim=2) - return log_p_y_xz - - def mutual_inf_mc(self, x_dist): - dist = x_dist.__class__ - H_y = dist(probs=x_dist.probs.mean(dim=0)).entropy() - return (H_y - x_dist.entropy().mean(dim=0)).sum() - - def train_loss(self, - inputs, - inputs_st, - first_history_indices, - labels, - labels_st, - scene, - node_scene_graph_batched, - timestep, - timesteps_in_scene, - prediction_horizon): - - mode = ModeKeys.TRAIN - - tensor_dict = self.obtain_encoded_tensor_dict(mode, - timestep, - timesteps_in_scene, - inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched) - - z, kl = self.encoder(tensor_dict["x"], tensor_dict[self.node_type + "_future_encoder"], mode) - log_p_y_xz = self.decoder(tensor_dict["x"], tensor_dict["node_future"], z, tensor_dict, mode, - prediction_horizon, - self.hyperparams['k']) - - if np.abs(self.hyperparams['alpha'] - 1.0) < 1e-3 and not self.hyperparams['use_iwae']: - log_p_y_xz_mean = torch.mean(log_p_y_xz, dim=0) # [nbs] - log_likelihood = torch.mean(log_p_y_xz_mean) - - mutual_inf_q = self.mutual_inf_mc(self.latent.q_dist) - mutual_inf_p = self.mutual_inf_mc(self.latent.p_dist) - - ELBO = log_likelihood - self.kl_weight * kl + 1. * mutual_inf_p - loss = -ELBO - - if self.hyperparams['log_histograms'] and self.log_writer is not None: - self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'log_p_y_xz'), - log_p_y_xz_mean, - self.curr_iter) - - else: - log_q_z_xy = self.latent.q_log_prob(z) # [k, nbs] - log_p_z_x = self.latent.p_log_prob(z) # [k, nbs] - a = self.hyperparams['alpha'] - log_pp_over_q = log_p_y_xz + log_p_z_x - log_q_z_xy - log_likelihood = (torch.mean(torch.logsumexp(log_pp_over_q * (1. - a), dim=0)) - - torch.log(self.hyperparams['k'])) / (1. - a) - loss = -log_likelihood - - if self.log_writer is not None: - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'mutual_information_q'), - mutual_inf_q, - self.curr_iter) - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'mutual_information_p'), - mutual_inf_p, - self.curr_iter) - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'log_likelihood'), - log_likelihood, - self.curr_iter) - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'loss'), - loss, - self.curr_iter) - if self.hyperparams['log_histograms']: - self.latent.summarize_for_tensorboard(self.log_writer, str(self.node_type), self.curr_iter) - - return loss - - def eval_loss(self, - inputs, - inputs_st, - first_history_indices, - labels, - labels_st, - scene, - node_scene_graph_batched, - timestep, - timesteps_in_scene, - prediction_horizon, - compute_naive=True, - compute_exact=True, - compute_sample=True): - mode = ModeKeys.EVAL - - tensor_dict = self.obtain_encoded_tensor_dict(mode, - timestep, - timesteps_in_scene, - inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched) - - ### Importance sampled NLL estimate - z, _ = self.encoder(tensor_dict["x"], tensor_dict[self.node_type + "_future_encoder"], - mode) # [k_eval, nbs, N*K] - log_p_y_xz = self.decoder(tensor_dict["x"], tensor_dict['node_future'], z, tensor_dict, mode, - prediction_horizon, - self.hyperparams['k_eval']) # [k_eval, nbs] - log_q_z_xy = self.latent.q_log_prob(z) # [k_eval, nbs] - log_p_z_x = self.latent.p_log_prob(z) # [k_eval, nbs] - log_likelihood = torch.mean(torch.logsumexp(log_p_y_xz + log_p_z_x - log_q_z_xy, dim=0)) - \ - torch.log(torch.tensor(self.hyperparams['k_eval'], dtype=torch.float, device=self.device)) - nll_q_is = -log_likelihood - - ### Naive sampled NLL estimate - nll_p = torch.tensor(np.nan) - if compute_naive: - z = self.latent.sample_p(self.hyperparams['k_eval'], mode) - log_p_y_xz = self.decoder(tensor_dict["x"], tensor_dict['node_future'], z, tensor_dict, mode, - prediction_horizon, - self.hyperparams['k_eval']) - log_likelihood_p = torch.mean(torch.logsumexp(log_p_y_xz, dim=0)) - \ - torch.log( - torch.tensor(self.hyperparams['k_eval'], dtype=torch.float, device=self.device)) - nll_p = -log_likelihood_p - - ### Exact NLL - nll_exact = torch.tensor(np.nan) - if compute_exact: - K, N = self.hyperparams['K'], self.hyperparams['N'] - if K ** N < 50: - nbs = tensor_dict["x"].size()[0] - z_raw = torch.from_numpy( - DiscreteLatent.all_one_hot_combinations(N, K).astype(np.float32) - ).to(self.device).repeat(1, nbs) # [K**N, nbs*N*K] - - z = torch.reshape(z_raw, (K ** N, -1, N * K)) # [K**N, nbs, N*K] - log_p_y_xz = self.decoder(tensor_dict["x"], tensor_dict['node_future'], z, tensor_dict, mode, - prediction_horizon, - K ** N) # [K**N, nbs] - log_p_z_x = self.latent.p_log_prob(z) # [K**N, nbs] - exact_log_likelihood = torch.mean(torch.logsumexp(log_p_y_xz + log_p_z_x, dim=0)) - - nll_exact = -exact_log_likelihood - - nll_sampled = torch.tensor(np.nan) - if compute_sample: - z = self.latent.sample_p(self.hyperparams['k_eval'], mode) - y_dist, _ = self.p_y_xz(tensor_dict["x"], z, tensor_dict, ModeKeys.PREDICT, prediction_horizon, - self.hyperparams['k_eval']) - log_p_yt_xz = torch.clamp(y_dist.log_prob(tensor_dict['node_future']), - max=self.hyperparams['log_p_yt_xz_max']) - log_p_y_xz = torch.sum(log_p_yt_xz, dim=2) - log_p_y_xz_mean = torch.mean(log_p_y_xz, dim=0) # [nbs] - log_likelihood = torch.mean(log_p_y_xz_mean) - nll_sampled = -log_likelihood - - if self.log_writer is not None: - self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'log_likelihood_eval'), - log_likelihood, - self.curr_iter) - - return nll_q_is, nll_p, nll_exact, nll_sampled - - def predict(self, - inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched, - timestep, - timesteps_in_scene, - prediction_horizon, - num_samples_z, - num_samples_gmm, - most_likely_z=False, - most_likely_gmm=False, - all_z=False): - mode = ModeKeys.PREDICT - - tensor_dict = self.obtain_encoded_tensor_dict(mode, - timestep, - timesteps_in_scene, - inputs, - inputs_st, - labels, - labels_st, - first_history_indices, - scene, - node_scene_graph_batched) - - self.latent.p_dist = self.p_z_x(tensor_dict["x"], mode) - z, num_samples_z = self.latent.sample_p(num_samples_z, - mode, - num_samples_gmm=num_samples_gmm, - most_likely=most_likely_z, - all_z=all_z) - - y_dist, our_sampled_future = self.p_y_xz(tensor_dict["x"], z, tensor_dict, mode, - prediction_horizon, - num_samples_z, - num_samples_gmm, - most_likely_gmm) # y_dist.mean is [k, bs, ph*state_dim] - - return our_sampled_future diff --git a/code/notebooks/NuScenes Qualitative.ipynb b/code/notebooks/NuScenes Qualitative.ipynb deleted file mode 100644 index a0c0b6f..0000000 --- a/code/notebooks/NuScenes Qualitative.ipynb +++ /dev/null @@ -1,675 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import sys\n", - "sys.path.append('../../code')\n", - "import os\n", - "import numpy as np\n", - "import torch\n", - "import pickle\n", - "import json\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as ticker\n", - "import matplotlib.patheffects as pe\n", - "from helper import *\n", - "from data import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load nuScenes SDK and data" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "nuScenes_data_path = # Data Path to nuScenes data set \n", - "nuScenes_devkit_path = '../../data/nuScenes/nuscenes-devkit/python-sdk/'\n", - "sys.path.append(nuScenes_devkit_path)\n", - "from nuscenes.map_expansion.map_api import NuScenesMap\n", - "nusc_map = NuScenesMap(dataroot=nuScenes_data_path, map_name='singapore-queenstown')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "line_colors = ['#375397','#80CBE5','#ABCB51','#F05F78', '#C8B0B0']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Map Encoding Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../../data/processed/nuScenes_samples.pkl', 'rb') as f:\n", - " eval_env = pickle.load(f, encoding='latin1')\n", - "eval_scenes = eval_env.scenes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading from ../../data/nuScenes/models/me_demo/model_registrar-1.pt\n", - "Loaded!\n", - "\n" - ] - } - ], - "source": [ - "ph = 6\n", - "log_dir = '../../data/nuScenes/models'\n", - "model_dir = os.path.join(log_dir, 'me_demo') \n", - "eval_stg, hyp = load_model(model_dir, eval_env, ts=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Load Scene\n", - "for s_scene in eval_scenes:\n", - " if s_scene.name == \"1002\":\n", - " scene = s_scene" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# Define ROI in nuScenes Map\n", - "x_min = 262.0\n", - "x_max = 560.0\n", - "y_min = 1864.0\n", - "y_max = 2071.0" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "layers = ['drivable_area',\n", - " 'road_segment',\n", - " 'lane',\n", - " 'ped_crossing',\n", - " 'walkway',\n", - " 'stop_line',\n", - " 'road_divider',\n", - " 'lane_divider']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prediction including Map Encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ph = 7\n", - "with torch.no_grad():\n", - " timestep = np.array([32])\n", - " predictions = eval_stg.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=500,\n", - " most_likely_z=False,\n", - " all_z=False,\n", - " most_likely_gmm=False)\n", - "\n", - " predictions_mm = eval_stg.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=1,\n", - " most_likely_z=True,\n", - " all_z=False,\n", - " most_likely_gmm=True)\n", - "\n", - " # Plot predicted timestep for random scene in map\n", - " my_patch = (x_min, y_min, x_max, y_max)\n", - " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10, 22.4), alpha=0.1, render_egoposes_range=False)\n", - "\n", - " ax.plot([], [], 'ko-',\n", - " zorder=620,\n", - " markersize=4,\n", - " linewidth=2, alpha=0.7, label='Ours (MM)')\n", - "\n", - " ax.plot([],\n", - " [],\n", - " 'w--o', label='Ground Truth',\n", - " linewidth=3,\n", - " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", - "\n", - " plot_vehicle_nice(ax,\n", - " predictions,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " plot_vehicle_mm(ax,\n", - " predictions_mm,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " ax.set_ylim((1965, 2020))\n", - " ax.set_xlim((370, 405))\n", - " leg = ax.legend(loc='center left', fontsize=28, frameon=True)\n", - " ax.axis('off')\n", - " for lh in leg.legendHandles:\n", - " lh.set_alpha(.5)\n", - " fig.show()\n", - " # fig.savefig('plots/qual_nuScenes_map.pdf', dpi=300, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prediction without Map Encoding" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading from ../../data/nuScenes/models/baseline/model_registrar-1.pt\n", - "Loaded!\n", - "\n" - ] - } - ], - "source": [ - "model_dir = os.path.join(log_dir, 'baseline') \n", - "eval_stg_nm, hyp = load_model(model_dir, eval_env, ts=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with torch.no_grad():\n", - " timestep = np.array([32])\n", - " predictions = eval_stg_nm.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=500,\n", - " most_likely_z=False,\n", - " all_z=False,\n", - " most_likely_gmm=False)\n", - "\n", - " predictions_mm = eval_stg_nm.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=1,\n", - " most_likely_z=True,\n", - " all_z=False,\n", - " most_likely_gmm=True)\n", - "\n", - " # Plot predicted timestep for random scene in map\n", - " my_patch = (x_min, y_min, x_max, y_max)\n", - " fig, ax = nusc_map.render_map_patch(my_patch, nusc_map.non_geometric_layers[:-1], figsize=(10, 22.4), alpha=0.1,\n", - " render_egoposes_range=False)\n", - "\n", - " ax.plot([], [], 'ko-',\n", - " zorder=620,\n", - " markersize=4,\n", - " linewidth=2, alpha=0.7, label='Ours (MM)')\n", - "\n", - " ax.plot([],\n", - " [],\n", - " 'w--',\n", - " linewidth=4,\n", - " zorder=650,\n", - " path_effects=[pe.Stroke(linewidth=2, foreground='k'), pe.Normal()], label=\"Ground Truth\")\n", - "\n", - " plot_vehicle_nice(ax,\n", - " predictions,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - " plot_vehicle_mm(ax,\n", - " predictions_mm,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " ax.set_ylim((1965, 2020))\n", - " ax.set_xlim((370, 405))\n", - " ax.axis('off')\n", - " ax.get_legend().remove()\n", - " fig.show()\n", - " # fig.savefig('plots/qual_nuScenes_no_map.pdf', dpi=300, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Robot Future Influence Demo" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../../data/processed/nuScenes_samples.pkl', 'rb') as f:\n", - " eval_env = pickle.load(f, encoding='latin1')\n", - "eval_scenes = eval_env.scenes" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Loading from ../../data/nuScenes/models/robot_demo/model_registrar-1.pt\n", - "Loaded!\n", - "\n" - ] - } - ], - "source": [ - "ph = 6\n", - "model_dir = os.path.join(log_dir, 'robot_demo') \n", - "eval_stg_nm, hyp = load_model(model_dir, eval_env, ts=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Define ROI in nuScenes map\n", - "x_min = 277\n", - "x_max = 523\n", - "y_min = 1515\n", - "y_max = 1783" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "nusc_map = NuScenesMap(dataroot=nuScenes_data_path, map_name='boston-seaport')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found\n" - ] - } - ], - "source": [ - "for s_scene in eval_scenes:\n", - " if s_scene.name == \"234\":\n", - " scene = s_scene\n", - " print('Found')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "keep_nodes = []\n", - "for i in range(len(scene.nodes)):\n", - " if scene.nodes[i].position.x[0] > 140 and scene.nodes[i].position.x[0] < 160:\n", - " if scene.nodes[i].position.y[0] > 104 and scene.nodes[i].position.y[0] < 120:\n", - " keep_nodes.append(scene.nodes[i])\n", - "scene.nodes = keep_nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "layers = ['drivable_area',\n", - " 'lane',\n", - " 'ped_crossing',\n", - " 'walkway',\n", - " 'road_divider',\n", - " 'lane_divider']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Robot normal acceleration" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with torch.no_grad():\n", - " timestep = np.array([9])\n", - " predictions = eval_stg_nm.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=100,\n", - " most_likely_z=False,\n", - " all_z=False,\n", - " min_future_timesteps=ph,\n", - " most_likely_gmm=False)\n", - "\n", - " my_patch = (x_min, y_min, x_max, y_max)\n", - " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10.8, 9), alpha=0.1, render_egoposes_range=False,\n", - " render_legend=False)\n", - " hand, labl = ax.get_legend_handles_labels()\n", - " handout = []\n", - " lablout = []\n", - " ax.legend(handout, lablout)\n", - " fig.legend(handout, lablout)\n", - " ax.plot([],\n", - " [],\n", - " '--o',\n", - " c='#F05F78',\n", - " linewidth=4,\n", - " markersize=3,\n", - " zorder=650,\n", - " path_effects=[pe.Stroke(linewidth=2, foreground='k'), pe.Normal()], label=\"Ego Plan\")\n", - "\n", - " plot_vehicle_nice_mv(ax,\n", - " predictions,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " test = {'u': 2}\n", - " p_robot = dict()\n", - " p_robot[timestep[0]] = {scene.robot: np.zeros((1, 1, ph, 2))}\n", - "\n", - " plot_vehicle_nice_mv_robot(ax,\n", - " p_robot,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " ax.set_ylim((1615, 1630))\n", - " ax.set_xlim((418, 435))\n", - " ax.axis('off')\n", - " leg = ax.legend(loc='lower left', fontsize=31, frameon=True)\n", - " ax.axis('off')\n", - " for lh in leg.legendHandles:\n", - " lh.set_alpha(.5)\n", - " # leg.remove()\n", - " fig.show()\n", - " #fig.savefig('plots/qual_nuScenes_robot_vel.pdf', dpi=300, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Robot decrease acceleration" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "breaking_at_t = 10\n", - "breaking_sequence = np.arange(1, scene.timesteps - breaking_at_t + 1)\n", - "scene.robot.velocity.x[breaking_at_t:] = 0.25 * scene.robot.velocity.x[breaking_at_t:]\n", - "scene.robot.velocity.y[breaking_at_t:] = 0.25 * scene.robot.velocity.y[breaking_at_t:]\n", - "scene.robot.position.x[breaking_at_t:] = np.squeeze(integrate(scene.robot.velocity.x[np.newaxis, breaking_at_t:], 0.5, scene.robot.position.x[breaking_at_t]))\n", - "scene.robot.position.y[breaking_at_t:] = np.squeeze(integrate(scene.robot.velocity.y[np.newaxis, breaking_at_t:], 0.5, scene.robot.position.y[breaking_at_t]))\n", - "scene.robot.acceleration.x = np.gradient(scene.robot.velocity.x[breaking_at_t:], 0.5)\n", - "scene.robot.acceleration.y = np.gradient(scene.robot.velocity.y[breaking_at_t:], 0.5)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "with torch.no_grad():\n", - " timestep = np.array([9])\n", - " predictions = eval_stg_nm.predict(scene,\n", - " timestep,\n", - " ph,\n", - " num_samples_z=100,\n", - " most_likely_z=False,\n", - " all_z=False,\n", - " min_future_timesteps=ph,\n", - " most_likely_gmm=False)\n", - "\n", - " my_patch = (x_min, y_min, x_max, y_max)\n", - " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10.8, 9), alpha=0.1, render_egoposes_range=False,\n", - " render_legend=False)\n", - " hand, labl = ax.get_legend_handles_labels()\n", - " handout = []\n", - " lablout = []\n", - " ax.legend(handout, lablout)\n", - " fig.legend(handout, lablout)\n", - " ax.plot([],\n", - " [],\n", - " '--o',\n", - " c='#F05F78',\n", - " linewidth=4,\n", - " markersize=3,\n", - " zorder=650,\n", - " path_effects=[pe.Stroke(linewidth=2, foreground='k'), pe.Normal()], label=\"Ego Plan\")\n", - "\n", - " plot_vehicle_nice_mv(ax,\n", - " predictions,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " test = {'u': 2}\n", - " p_robot = dict()\n", - " p_robot[timestep[0]] = {scene.robot: np.zeros((1, 1, ph, 2))}\n", - "\n", - " plot_vehicle_nice_mv_robot(ax,\n", - " p_robot,\n", - " scene.dt,\n", - " max_hl=10,\n", - " ph=ph,\n", - " map=None, x_min=x_min, y_min=y_min)\n", - "\n", - " ax.set_ylim((1615, 1630))\n", - " ax.set_xlim((418, 435))\n", - " ax.axis('off')\n", - " leg = ax.legend(loc='lower left', fontsize=31, frameon=True)\n", - " ax.axis('off')\n", - " for lh in leg.legendHandles:\n", - " lh.set_alpha(.5)\n", - " # leg.remove()\n", - " fig.show()\n", - " #fig.savefig('plots/qual_nuScenes_robot_no_vel.pdf', dpi=300, bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:trajectron] *", - "language": "python", - "name": "conda-env-trajectron-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/code/notebooks/NuScenes Quantitative.ipynb b/code/notebooks/NuScenes Quantitative.ipynb deleted file mode 100644 index c5d87c2..0000000 --- a/code/notebooks/NuScenes Quantitative.ipynb +++ /dev/null @@ -1,357 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import glob\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as ticker" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vehicles" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDE Results for: full\n", - "-----------------PH: 2 -------------------\n", - "FDE Mean @1.0s: 0.20380615197870672\n", - "RB Viols @1.0s: 0.009331367081367082\n", - "FDE @1.0s: 0.159987192509441\n", - "----------------------------------------------\n", - "-----------------PH: 4 -------------------\n", - "FDE Mean @2.0s: 0.7089245446368799\n", - "RB Viols @2.0s: 0.01796110396110396\n", - "FDE @2.0s: 0.5775874097014032\n", - "----------------------------------------------\n", - "-----------------PH: 6 -------------------\n", - "FDE Mean @3.0s: 1.4580818527562975\n", - "RB Viols @3.0s: 0.03552634777634778\n", - "FDE @3.0s: 1.2355597794019821\n", - "----------------------------------------------\n", - "-----------------PH: 8 -------------------\n", - "FDE Mean @4.0s: 2.4471793265269386\n", - "RB Viols @4.0s: 0.060967467467467465\n", - "FDE @4.0s: 2.1380248632492256\n", - "----------------------------------------------\n", - "\n", - "FDE Results for: me\n", - "-----------------PH: 2 -------------------\n", - "FDE Mean @1.0s: 0.18777519200515588\n", - "RB Viols @1.0s: 0.009766516516516516\n", - "FDE @1.0s: 0.1417875049716435\n", - "----------------------------------------------\n", - "-----------------PH: 4 -------------------\n", - "FDE Mean @2.0s: 0.7073491425264654\n", - "RB Viols @2.0s: 0.020398362648362648\n", - "FDE @2.0s: 0.5714762070457313\n", - "----------------------------------------------\n", - "-----------------PH: 6 -------------------\n", - "FDE Mean @3.0s: 1.4959927599549832\n", - "RB Viols @3.0s: 0.04249910624910625\n", - "FDE @3.0s: 1.246918581584546\n", - "----------------------------------------------\n", - "-----------------PH: 8 -------------------\n", - "FDE Mean @4.0s: 2.5390342440589615\n", - "RB Viols @4.0s: 0.0770950235950236\n", - "FDE @4.0s: 2.179967698272057\n", - "----------------------------------------------\n", - "\n", - "FDE Results for: edge\n", - "-----------------PH: 2 -------------------\n", - "FDE Mean @1.0s: 0.15595462204927724\n", - "RB Viols @1.0s: 0.010491312741312741\n", - "FDE @1.0s: 0.1078299341842368\n", - "----------------------------------------------\n", - "-----------------PH: 4 -------------------\n", - "FDE Mean @2.0s: 0.7240192573370534\n", - "RB Viols @2.0s: 0.02703835978835979\n", - "FDE @2.0s: 0.5440453973645435\n", - "----------------------------------------------\n", - "-----------------PH: 6 -------------------\n", - "FDE Mean @3.0s: 1.6096807601302014\n", - "RB Viols @3.0s: 0.05722472472472472\n", - "FDE @3.0s: 1.2470658003652806\n", - "----------------------------------------------\n", - "-----------------PH: 8 -------------------\n", - "FDE Mean @4.0s: 2.8022766895142093\n", - "RB Viols @4.0s: 0.09920749320749321\n", - "FDE @4.0s: 2.2312648038365013\n", - "----------------------------------------------\n", - "\n", - "FDE Results for: baseline\n", - "-----------------PH: 2 -------------------\n", - "FDE Mean @1.0s: 0.1570008548474456\n", - "RB Viols @1.0s: 0.010126090376090377\n", - "FDE @1.0s: 0.1244030330443721\n", - "----------------------------------------------\n", - "-----------------PH: 4 -------------------\n", - "FDE Mean @2.0s: 0.7250004386936006\n", - "RB Viols @2.0s: 0.028963248963248964\n", - "FDE @2.0s: 0.55775542534863\n", - "----------------------------------------------\n", - "-----------------PH: 6 -------------------\n", - "FDE Mean @3.0s: 1.6166743345433776\n", - "RB Viols @3.0s: 0.060814421564421565\n", - "FDE @3.0s: 1.2488000332764773\n", - "----------------------------------------------\n", - "-----------------PH: 8 -------------------\n", - "FDE Mean @4.0s: 2.8218295359569936\n", - "RB Viols @4.0s: 0.1029012584012584\n", - "FDE @4.0s: 2.2299034558777775\n", - "----------------------------------------------\n", - "\n" - ] - } - ], - "source": [ - "for model in ['full', 'me', 'edge', 'baseline']:\n", - " print(f\"FDE Results for: {model}\")\n", - " for ph in [2, 4, 6, 8]:\n", - " print(f\"-----------------PH: {ph} -------------------\")\n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_veh_fde_full*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " \n", - " print(f\"FDE Mean @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'fde'].mean()}\")\n", - " del perf_df \n", - " \n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_veh_obs_full*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"RB Viols @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'obs'].sum() / (len(perf_df['error_value'][perf_df['error_type'] == 'obs'].index)*2000)}\")\n", - " del perf_df\n", - "\n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_veh_fde_mm*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"FDE @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'fde'].mean()}\") \n", - " print(\"----------------------------------------------\")\n", - " del perf_df\n", - " print(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "line_colors = ['#375397','#80CBE5','#ABCB51','#F05F78', '#C8B0B0']" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "t = [0, 1, 3]\n", - "t_ours = [0, 1, 2, 3, 4]\n", - "SLSTM_l2 = np.array([0.24, 0.71, 1.85])\n", - "CSP_l2 = np.array([0.24, 0.70, 1.74])\n", - "CARNET_l2 = np.array([0.23, 0.61, 1.58])\n", - "SPAGNN_l2 = np.array([0.22, 0.58, 1.45])\n", - "OURS_l2 = np.array([0., 0.159987192509441, 0.5775874097014032, 1.2355597794019826, 2.138024863249226])\n", - "LINEAR_l2 = np.array([0., 0.32, 0.89, 1.7, 2.73])" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(4, 2), dpi=300)\n", - "ax.plot(t_ours[1:], LINEAR_l2[1:], '--o', linewidth=2, markersize=6 , label='Const. Velocity', c='#E79675', zorder=-10, markeredgecolor='#545454')\n", - "ax.scatter(t[1:], SPAGNN_l2[1:], 50, marker='s', label='SpAGNN', c=line_colors[2], edgecolor='#545454', zorder=2)\n", - "ax.scatter(t[1:], CSP_l2[1:], 50, marker='o' , label='CSP', c=line_colors[4], edgecolor='#545454')\n", - "ax.scatter(t[1:], CARNET_l2[1:], 50, marker='D' , label='CAR-Net', c=line_colors[1], edgecolor='#545454', zorder=1)\n", - "ax.scatter(t[1:], SLSTM_l2[1:], 50, marker='*', label='S-LSTM', c=line_colors[0], edgecolor='#545454')\n", - "ax.plot(t_ours[1:], OURS_l2[1:], '-X', linewidth=3 , label='Ours (MM)', markersize=8, c=line_colors[3], markeredgecolor='#545454')\n", - "\n", - "ax.legend(loc='lower right')\n", - "ax.set_xlabel('Time (s)')\n", - "ax.set_ylabel('Final Displacement Error (m)', fontsize=8)\n", - "\n", - "ax.set_ylim((0, 2.8))\n", - "ax.xaxis.grid(False)\n", - "ax.xaxis.set_major_locator(ticker.MultipleLocator(1.0))\n", - "\n", - "leg = ax.legend(loc='best', fontsize=7, frameon=True)\n", - "\n", - "fig.savefig('plots/nuscenes_fde_time.pdf', dpi=300, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pedestrians" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FDE Results for: full\n", - "-----------------PH: 2 -------------------\n", - "FDE Mean @1.0s: 0.05230925689054953\n", - "ADE Mean @1.0s: 0.04856795664591188\n", - "KDE Mean @1.0s: -0.3251187847736798\n", - "FDE @1.0s: 0.04016995675203458\n", - "ADE @1.0s: 0.041850598781567855\n", - "----------------------------------------------\n", - "-----------------PH: 4 -------------------\n", - "FDE Mean @2.0s: 0.22040387111879736\n", - "ADE Mean @2.0s: 0.11191537726981232\n", - "KDE Mean @2.0s: -1.0563039331643547\n", - "FDE @2.0s: 0.17426690322281393\n", - "ADE @2.0s: 0.08985219625873946\n", - "----------------------------------------------\n", - "-----------------PH: 6 -------------------\n", - "FDE Mean @3.0s: 0.4262051877411372\n", - "ADE Mean @3.0s: 0.19877290465482894\n", - "KDE Mean @3.0s: -0.736257085075441\n", - "FDE @3.0s: 0.34659922052319353\n", - "ADE @3.0s: 0.16031131517313374\n", - "----------------------------------------------\n", - "-----------------PH: 8 -------------------\n", - "FDE Mean @4.0s: 0.6871485975784108\n", - "ADE Mean @4.0s: 0.30302359859532546\n", - "KDE Mean @4.0s: -0.1948802425435838\n", - "FDE @4.0s: 0.5796212036776903\n", - "ADE @4.0s: 0.24894417052873324\n", - "----------------------------------------------\n", - "\n" - ] - } - ], - "source": [ - "for model in ['full']:\n", - " print(f\"FDE Results for: {model}\")\n", - " for ph in [2, 4, 6, 8]:\n", - " print(f\"-----------------PH: {ph} -------------------\")\n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_ped_fde_full*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"FDE Mean @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'fde'].mean()}\")\n", - " del perf_df \n", - " \n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_ped_ade_full*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"ADE Mean @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'ade'].mean()}\")\n", - " del perf_df\n", - " \n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_ped_kde_full*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"KDE Mean @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'kde'].mean()}\")\n", - " del perf_df \n", - "\n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_ped_fde_mm*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"FDE @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'fde'].mean()}\") \n", - " del perf_df\n", - " \n", - " perf_df = pd.DataFrame()\n", - " for f in glob.glob(f\"csv/{model}_ped_ade_mm*{ph}ph.csv\"):\n", - " dataset_df = pd.read_csv(f)\n", - " dataset_df['model'] = model\n", - " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", - " del perf_df['Unnamed: 0']\n", - " print(f\"ADE @{ph*0.5}s: {perf_df['error_value'][perf_df['error_type'] == 'ade'].mean()}\") \n", - " del perf_df\n", - " print(\"----------------------------------------------\")\n", - " print(\"\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:trajectron] *", - "language": "python", - "name": "conda-env-trajectron-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/code/notebooks/model_to_metric_nuScenes.py b/code/notebooks/model_to_metric_nuScenes.py deleted file mode 100644 index 7693010..0000000 --- a/code/notebooks/model_to_metric_nuScenes.py +++ /dev/null @@ -1,182 +0,0 @@ -import sys -sys.path.append('../../code') -import os -import pickle -import json -import argparse -import torch -import numpy as np -import pandas as pd -from tqdm import tqdm -from model.model_registrar import ModelRegistrar -from model.dyn_stg import SpatioTemporalGraphCVAEModel -import evaluation -from utils import prediction_output_to_trajectories -from scipy.interpolate import RectBivariateSpline - -parser = argparse.ArgumentParser() -parser.add_argument("--model", help="model full path", type=str) -parser.add_argument("--checkpoint", help="model checkpoint to evaluate", type=int) -parser.add_argument("--data", help="full path to data file", type=str) -parser.add_argument("--output", help="full path to output csv file", type=str) -parser.add_argument("--node_type", help="Node Type to evaluate", type=str) -parser.add_argument("--prediction_horizon", nargs='+', help="prediction horizon", type=int, default=None) -args = parser.parse_args() - - -def compute_obs_violations(predicted_trajs, map): - obs_map = 1 - map.fdata[..., 0] - - interp_obs_map = RectBivariateSpline(range(obs_map.shape[0]), - range(obs_map.shape[1]), - obs_map, - kx=1, ky=1) - - old_shape = predicted_trajs.shape - pred_trajs_map = map.to_map_points(predicted_trajs.reshape((-1, 2))) - - traj_obs_values = interp_obs_map(pred_trajs_map[:, 0], pred_trajs_map[:, 1], grid=False) - traj_obs_values = traj_obs_values.reshape((old_shape[0], old_shape[1])) - num_viol_trajs = np.sum(traj_obs_values.max(axis=1) > 0, dtype=float) - - return num_viol_trajs - -def compute_heading_error(prediction_output_dict, dt, max_hl, ph, node_type_enum, kde=True, obs=False, map=None): - - heading_error = list() - - for t in prediction_output_dict.keys(): - for node in prediction_output_dict[t].keys(): - if node.type.name == 'VEHICLE': - gt_vel = node.get(t + ph - 1, {'velocity': ['x', 'y']})[0] - gt_heading = np.arctan2(gt_vel[1], gt_vel[0]) - our_heading = np.arctan2(prediction_output_dict[t][node][..., -2, 1], prediction_output_dict[t][node][..., -2, 0]) - he = np.mean(np.abs(gt_heading - our_heading)) % (2 * np.pi) - heading_error.append(he) - - return heading_error - - -def load_model(model_dir, env, ts=99): - model_registrar = ModelRegistrar(model_dir, 'cpu') - model_registrar.load_models(ts) - with open(os.path.join(model_dir, 'config.json'), 'r') as config_json: - hyperparams = json.load(config_json) - - stg = SpatioTemporalGraphCVAEModel(model_registrar, - hyperparams, - None, 'cuda:0') - hyperparams['incl_robot_node'] = False - - stg.set_scene_graph(env) - stg.set_annealing_params() - return stg, hyperparams - - -if __name__ == "__main__": - with open(args.data, 'rb') as f: - env = pickle.load(f, encoding='latin1') - scenes = env.scenes - - eval_stg, hyperparams = load_model(args.model, env, ts=args.checkpoint) - - print("-- Preparing Node Graph") - for scene in tqdm(scenes): - scene.calculate_scene_graph(hyperparams['edge_radius'], - hyperparams['state'], - hyperparams['edge_addition_filter'], - hyperparams['edge_removal_filter']) - - if args.prediction_horizon is None: - args.prediction_horizon = [hyperparams['prediction_horizon']] - - for ph in args.prediction_horizon: - print(f"Prediction Horizon: {ph}") - max_hl = hyperparams['maximum_history_length'] - node_type = env.NodeType[args.node_type] - print(f"Node Type: {node_type.name}") - print(f"Edge Radius: {hyperparams['edge_radius']}") - - with torch.no_grad(): - eval_ade_batch_errors = np.array([]) - eval_fde_batch_errors = np.array([]) - eval_kde_nll = np.array([]) - eval_obs_viols = np.array([]) - print("-- Evaluating Full") - for i, scene in enumerate(tqdm(scenes)): - for timestep in range(scene.timesteps): - predictions = eval_stg.predict(scene, - np.array([timestep]), - ph, - num_samples_z=2000, - most_likely_z=False, - min_future_timesteps=8) - - if not predictions: - continue - - eval_error_dict = evaluation.compute_batch_statistics(predictions, - scene.dt, - node_type_enum=env.NodeType, - max_hl=max_hl, - ph=ph, - map=scene.map[node_type.name], - obs=True) - - eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, eval_error_dict[node_type]['ade'])) - eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, eval_error_dict[node_type]['fde'])) - eval_kde_nll = np.hstack((eval_kde_nll, eval_error_dict[node_type]['kde'])) - eval_obs_viols = np.hstack((eval_obs_viols, eval_error_dict[node_type]['obs_viols'])) - - del predictions - del eval_error_dict - - print(f"Final Mean Displacement Error @{ph * scene.dt}s: {np.mean(eval_fde_batch_errors)}") - print(f"Road Violations @{ph * scene.dt}s: {100 * np.sum(eval_obs_viols) / (eval_obs_viols.shape[0] * 2000)}%") - pd.DataFrame({'error_value': eval_ade_batch_errors, 'error_type': 'ade', 'type': 'full', 'ph': ph}).to_csv(args.output + '_ade_full_' + str(ph)+'ph' + '.csv') - pd.DataFrame({'error_value': eval_fde_batch_errors, 'error_type': 'fde', 'type': 'full', 'ph': ph}).to_csv(args.output + '_fde_full' + str(ph)+'ph' + '.csv') - pd.DataFrame({'error_value': eval_kde_nll, 'error_type': 'kde', 'type': 'full', 'ph': ph}).to_csv(args.output + '_kde_full' + str(ph)+'ph' + '.csv') - pd.DataFrame({'error_value': eval_obs_viols, 'error_type': 'obs', 'type': 'full', 'ph': ph}).to_csv(args.output + '_obs_full' + str(ph)+'ph' + '.csv') - - eval_ade_batch_errors = np.array([]) - eval_fde_batch_errors = np.array([]) - eval_heading_err = np.array([]) - eval_obs_viols = np.array([]) - print("-- Evaluating most likely Z and GMM") - for i, scene in enumerate(scenes): - print(f"---- Evaluating Scene {i+1}/{len(scenes)}") - for t in np.arange(0, scene.timesteps, 20): - timesteps = np.arange(t, t+20) - - predictions = eval_stg.predict(scene, - timesteps, - ph, - num_samples_z=1, - most_likely_z=True, - most_likely_gmm=True, - min_future_timesteps=8) - - eval_error_dict = evaluation.compute_batch_statistics(predictions, - scene.dt, - node_type_enum=env.NodeType, - max_hl=max_hl, - ph=ph, - map=1 - scene.map[node_type.name].fdata[..., 0], - kde=False) - eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, eval_error_dict[node_type]['ade'])) - eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, eval_error_dict[node_type]['fde'])) - eval_obs_viols = np.hstack((eval_obs_viols, eval_error_dict[node_type]['obs_viols'])) - - heading_error = compute_heading_error(predictions, - scene.dt, - node_type_enum=env.NodeType, - max_hl=max_hl, - ph=ph, - map=1 - scene.map[node_type.name].fdata[..., 0], - kde=False) - eval_heading_err = np.hstack((eval_heading_err, heading_error)) - - print(f"Final Displacement Error @{ph * scene.dt}s: {np.mean(eval_fde_batch_errors)}") - pd.DataFrame({'error_value': eval_ade_batch_errors, 'error_type': 'ade', 'type': 'mm', 'ph': ph}).to_csv(args.output + '_ade_mm' + str(ph)+'ph' + '.csv') - pd.DataFrame({'error_value': eval_fde_batch_errors, 'error_type': 'fde', 'type': 'mm', 'ph': ph}).to_csv(args.output + '_fde_mm' + str(ph)+'ph' + '.csv') - pd.DataFrame({'error_value': eval_obs_viols, 'error_type': 'obs', 'type': 'mm', 'ph': ph}).to_csv( args.output + '_obs_mm' + str(ph)+'ph' + '.csv') diff --git a/code/notebooks/run_eval.bash b/code/notebooks/run_eval.bash deleted file mode 100644 index 64d300f..0000000 --- a/code/notebooks/run_eval.bash +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -python model_to_metric_nuScenes.py --model "../../data/nuScenes/models/full" --checkpoint 1 --data "../../data/processed/nuScenes_test.pkl" --output "./csv/full_veh" --node_type "VEHICLE" --prediction_horizon 2 4 6 8 > full_out.txt -python model_to_metric_nuScenes.py --model "../../data/nuScenes/models/me_demo" --checkpoint 1 --data "../../data/processed/nuScenes_test.pkl" --output "./csv/me_veh" --node_type "VEHICLE" --prediction_horizon 2 4 6 8 > me_out.txt -python model_to_metric_nuScenes.py --model "../../data/nuScenes/models/edge" --checkpoint 1 --data "../../data/processed/nuScenes_test.pkl" --output "./csv/edge_veh" --node_type "VEHICLE" --prediction_horizon 2 4 6 8 > edge_out.txt -python model_to_metric_nuScenes.py --model "../../data/nuScenes/models/baseline" --checkpoint 1 --data "../../data/processed/nuScenes_test.pkl" --output "./csv/baseline_veh" --node_type "VEHICLE" --prediction_horizon 2 4 6 8 > baseline_out.txt -python model_to_metric_nuScenes.py --model "../../data/nuScenes/models/full" --checkpoint 1 --data "../../data/processed/nuScenes_test.pkl" --output "./csv/full_ped" --node_type "PEDESTRIAN" --prediction_horizon 2 4 6 8 > full_out_ped.txt \ No newline at end of file diff --git a/code/train.py b/code/train.py deleted file mode 100644 index 4f85def..0000000 --- a/code/train.py +++ /dev/null @@ -1,476 +0,0 @@ -import torch -from torch import nn, optim -import numpy as np -import os -import time -import psutil -import pickle -import json -import random -import argparse -import pathlib -import visualization -import evaluation -import matplotlib.pyplot as plt -from model.dyn_stg import SpatioTemporalGraphCVAEModel -from model.model_registrar import ModelRegistrar -from model.model_utils import cyclical_lr -from tensorboardX import SummaryWriter -#torch.autograd.set_detect_anomaly(True) # TODO Remove for speed - - -parser = argparse.ArgumentParser() -parser.add_argument("--conf", help="path to json config file for hyperparameters", - type=str, default='config.json') -parser.add_argument("--offline_scene_graph", help="whether to precompute the scene graphs offline, options are 'no' and 'yes'", - type=str, default='yes') -parser.add_argument("--dynamic_edges", help="whether to use dynamic edges or not, options are 'no' and 'yes'", - type=str, default='yes') -parser.add_argument("--edge_radius", help="the radius (in meters) within which two nodes will be connected by an edge", - type=float, default=3.0) -parser.add_argument("--edge_state_combine_method", help="the method to use for combining edges of the same type", - type=str, default='sum') -parser.add_argument("--edge_influence_combine_method", help="the method to use for combining edge influences", - type=str, default='attention') -parser.add_argument('--edge_addition_filter', nargs='+', help="what scaling to use for edges as they're created", - type=float, default=[0.25, 0.5, 0.75, 1.0]) # We automatically pad left with 0.0 -parser.add_argument('--edge_removal_filter', nargs='+', help="what scaling to use for edges as they're removed", - type=float, default=[1.0, 0.0]) # We automatically pad right with 0.0 -parser.add_argument('--incl_robot_node', help="whether to include a robot node in the graph or simply model all agents", - action='store_true') -parser.add_argument('--use_map_encoding', help="Whether to use map encoding or not", - action='store_true') - -parser.add_argument("--data_dir", help="what dir to look in for data", - type=str, default='../data/processed') -parser.add_argument("--train_data_dict", help="what file to load for training data", - type=str, default='nuScenes_train.pkl') -parser.add_argument("--eval_data_dict", help="what file to load for evaluation data", - type=str, default='nuScenes_val.pkl') -parser.add_argument("--log_dir", help="what dir to save training information (i.e., saved models, logs, etc)", - type=str, default='../data/nuScenes/logs') -parser.add_argument("--log_tag", help="tag for the log folder", - type=str, default='') - -parser.add_argument('--device', help='what device to perform training on', - type=str, default='cuda:1') -parser.add_argument("--eval_device", help="what device to use during evaluation", - type=str, default=None) - -parser.add_argument("--num_iters", help="number of iterations to train for", - type=int, default=2000) -parser.add_argument('--batch_multiplier', help='how many minibatches to run per iteration of training', - type=int, default=1) -parser.add_argument('--batch_size', help='training batch size', - type=int, default=256) -parser.add_argument('--eval_batch_size', help='evaluation batch size', - type=int, default=256) -parser.add_argument('--k_eval', help='how many samples to take during evaluation', - type=int, default=50) - -parser.add_argument('--seed', help='manual seed to use, default is 123', - type=int, default=123) -parser.add_argument('--eval_every', help='how often to evaluate during training, never if None', - type=int, default=50) -parser.add_argument('--vis_every', help='how often to visualize during training, never if None', - type=int, default=50) -parser.add_argument('--save_every', help='how often to save during training, never if None', - type=int, default=100) -args = parser.parse_args() - -if not torch.cuda.is_available() or args.device == 'cpu': - args.device = torch.device('cpu') -else: - if torch.cuda.device_count() == 1: - # If you have CUDA_VISIBLE_DEVICES set, which you should, - # then this will prevent leftover flag arguments from - # messing with the device allocation. - args.device = 'cuda:0' - - args.device = torch.device(args.device) - -if args.eval_device is None: - args.eval_device = 'cpu' - -if args.seed is not None: - random.seed(args.seed) - np.random.seed(args.seed) - torch.manual_seed(args.seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(args.seed) - -def main(): - # Load hyperparameters from json - if not os.path.exists(args.conf): - print('Config json not found!') - with open(args.conf, 'r') as conf_json: - hyperparams = json.load(conf_json) - - # Add hyperparams from arguments - hyperparams['dynamic_edges'] = args.dynamic_edges - hyperparams['edge_state_combine_method'] = args.edge_state_combine_method - hyperparams['edge_influence_combine_method'] = args.edge_influence_combine_method - hyperparams['edge_radius'] = args.edge_radius - hyperparams['use_map_encoding'] = args.use_map_encoding - hyperparams['edge_addition_filter'] = args.edge_addition_filter - hyperparams['edge_removal_filter'] = args.edge_removal_filter - hyperparams['batch_size'] = args.batch_size - hyperparams['k_eval'] = args.k_eval - hyperparams['offline_scene_graph'] = args.offline_scene_graph - hyperparams['incl_robot_node'] = args.incl_robot_node - - print('-----------------------') - print('| TRAINING PARAMETERS |') - print('-----------------------') - print('| iterations: %d' % args.num_iters) - print('| batch_size: %d' % args.batch_size) - print('| batch_multiplier: %d' % args.batch_multiplier) - print('| effective batch size: %d (= %d * %d)' % (args.batch_size * args.batch_multiplier, args.batch_size, args.batch_multiplier)) - print('| device: %s' % args.device) - print('| eval_device: %s' % args.eval_device) - print('| Offline Scene Graph Calculation: %s' % args.offline_scene_graph) - print('| edge_radius: %s' % args.edge_radius) - print('| EE state_combine_method: %s' % args.edge_state_combine_method) - print('| EIE scheme: %s' % args.edge_influence_combine_method) - print('| dynamic_edges: %s' % args.dynamic_edges) - print('| robot node: %s' % args.incl_robot_node) - print('| map encoding: %s' % args.use_map_encoding) - print('| edge_addition_filter: %s' % args.edge_addition_filter) - print('| edge_removal_filter: %s' % args.edge_removal_filter) - print('| MHL: %s' % hyperparams['minimum_history_length']) - print('| PH: %s' % hyperparams['prediction_horizon']) - print('-----------------------') - - # Create the log and model directiory if they're not present. - model_dir = os.path.join(args.log_dir, 'models_' + time.strftime('%d_%b_%Y_%H_%M_%S', time.localtime()) + args.log_tag) - pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True) - - # Save config to model directory - with open(os.path.join(model_dir, 'config.json'), 'w') as conf_json: - json.dump(hyperparams, conf_json) - - log_writer = SummaryWriter(log_dir=model_dir) - - train_scenes = [] - train_data_path = os.path.join(args.data_dir, args.train_data_dict) - with open(train_data_path, 'rb') as f: - train_env = pickle.load(f, encoding='latin1') - train_scenes = train_env.scenes - print('Loaded training data from %s' % (train_data_path,)) - - eval_scenes = [] - if args.eval_every is not None: - eval_data_path = os.path.join(args.data_dir, args.eval_data_dict) - with open(eval_data_path, 'rb') as f: - eval_env = pickle.load(f, encoding='latin1') - eval_scenes = eval_env.scenes - print('Loaded evaluation data from %s' % (eval_data_path, )) - - # Calculate Scene Graph - if hyperparams['offline_scene_graph'] == 'yes': - print(f"Offline calculating scene graphs") - for i, scene in enumerate(train_scenes): - scene.calculate_scene_graph(train_env.attention_radius, - hyperparams['state'], - hyperparams['edge_addition_filter'], - hyperparams['edge_removal_filter']) - print(f"Created Scene Graph for Scene {i}") - - for i, scene in enumerate(eval_scenes): - scene.calculate_scene_graph(eval_env.attention_radius, - hyperparams['state'], - hyperparams['edge_addition_filter'], - hyperparams['edge_removal_filter']) - print(f"Created Scene Graph for Scene {i}") - - model_registrar = ModelRegistrar(model_dir, args.device) - - # We use pre trained weights for the map CNN - if args.use_map_encoding: - inf_encoder_registrar = os.path.join(args.log_dir, 'weight_trans/model_registrar-1499.pt') - model_dict = torch.load(inf_encoder_registrar, map_location=args.device) - - for key in model_dict.keys(): - if 'map_encoder' in key: - model_registrar.model_dict[key] = model_dict[key] - assert model_registrar.get_model(key) is model_dict[key] - - stg = SpatioTemporalGraphCVAEModel(model_registrar, - hyperparams, - log_writer, args.device) - stg.set_scene_graph(train_env) - stg.set_annealing_params() - print('Created training STG model.') - - eval_stg = None - if args.eval_every is not None or args.vis_ervery is not None: - eval_stg = SpatioTemporalGraphCVAEModel(model_registrar, - hyperparams, - log_writer, args.device) - eval_stg.set_scene_graph(eval_env) - eval_stg.set_annealing_params() # TODO Check if necessary - if hyperparams['learning_rate_style'] == 'const': - optimizer = optim.Adam(model_registrar.parameters(), lr=hyperparams['learning_rate']) - lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=1.0) - elif hyperparams['learning_rate_style'] == 'exp': - optimizer = optim.Adam(model_registrar.parameters(), lr=hyperparams['learning_rate']) - lr_scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=hyperparams['learning_decay_rate']) - elif hyperparams['learning_rate_style'] == 'triangle': - optimizer = optim.Adam(model_registrar.parameters(), lr=1.0) - clr = cyclical_lr(100, min_lr=hyperparams['min_learning_rate'], max_lr=hyperparams['learning_rate'], decay=hyperparams['learning_decay_rate']) - lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer, [clr]) - - print_training_header(newline_start=True) - for curr_iter in range(args.num_iters): - # Necessary because we flip the weights contained between GPU and CPU sometimes. - model_registrar.to(args.device) - - # Setting the current iterator value for internal logging. - stg.set_curr_iter(curr_iter) - if args.vis_every is not None: - eval_stg.set_curr_iter(curr_iter) - - # Stepping forward the learning rate scheduler and annealers. - lr_scheduler.step() - log_writer.add_scalar('train/learning_rate', - lr_scheduler.get_lr()[0], - curr_iter) - stg.step_annealers() - - # Zeroing gradients for the upcoming iteration. - optimizer.zero_grad() - train_losses = dict() - for node_type in train_env.NodeType: - train_losses[node_type] = [] - for scene in np.random.choice(train_scenes, 10): - for mb_num in range(args.batch_multiplier): - # Obtaining the batch's training loss. - timesteps = scene.sample_timesteps(hyperparams['batch_size']) - - # Compute the training loss. - train_loss_by_type = stg.train_loss(scene, timesteps, max_nodes=hyperparams['batch_size']) - for node_type, train_loss in train_loss_by_type.items(): - if train_loss is not None: - train_loss = train_loss / (args.batch_multiplier * 10) - train_losses[node_type].append(train_loss.item()) - - # Calculating gradients. - train_loss.backward() - - # Print training information. Also, no newline here. It's added in at a later line. - print('{:9} | '.format(curr_iter), end='', flush=True) - for node_type in train_env.NodeType: - print('{}:{:10} | '.format(node_type.name[0], '%.2f' % sum(train_losses[node_type])), end='', flush=True) - - for node_type in train_env.NodeType: - if len(train_losses[node_type]) > 0: - log_writer.add_histogram(f"{node_type.name}/train/minibatch_losses", np.asarray(train_losses[node_type]), curr_iter) - log_writer.add_scalar(f"{node_type.name}/train/loss", sum(train_losses[node_type]), curr_iter) - - # Clipping gradients. - if hyperparams['grad_clip'] is not None: - nn.utils.clip_grad_value_(model_registrar.parameters(), hyperparams['grad_clip']) - - # Performing a gradient step. - optimizer.step() - - del train_loss # TODO Necessary? - - if args.vis_every is not None and (curr_iter + 1) % args.vis_every == 0: - max_hl = hyperparams['maximum_history_length'] - ph = hyperparams['prediction_horizon'] - with torch.no_grad(): - # Predict random timestep to plot for train data set - scene = np.random.choice(train_scenes) - timestep = scene.sample_timesteps(1, min_future_timesteps=ph) - predictions = stg.predict(scene, - timestep, - ph, - num_samples_z=100, - most_likely_z=False, - all_z=False) - - # Plot predicted timestep for random scene - fig, ax = plt.subplots(figsize=(5, 5)) - visualization.visualize_prediction(ax, - predictions, - scene.dt, - max_hl=max_hl, - ph=ph) - ax.set_title(f"{scene.name}-t: {timestep}") - log_writer.add_figure('train/prediction', fig, curr_iter) - - # Predict random timestep to plot for eval data set - scene = np.random.choice(eval_scenes) - timestep = scene.sample_timesteps(1, min_future_timesteps=ph) - predictions = eval_stg.predict(scene, - timestep, - ph, - num_samples_z=100, - most_likely_z=False, - all_z=False, - max_nodes=4 * args.eval_batch_size) - - # Plot predicted timestep for random scene - fig, ax = plt.subplots(figsize=(5, 5)) - visualization.visualize_prediction(ax, - predictions, - scene.dt, - max_hl=max_hl, - ph=ph) - ax.set_title(f"{scene.name}-t: {timestep}") - log_writer.add_figure('eval/prediction', fig, curr_iter) - - # Plot predicted timestep for random scene in map - fig, ax = plt.subplots(figsize=(15, 15)) - visualization.visualize_prediction(ax, - predictions, - scene.dt, - max_hl=max_hl, - ph=ph, - map=scene.map['PLOT']) - ax.set_title(f"{scene.name}-t: {timestep}") - log_writer.add_figure('eval/prediction_map', fig, curr_iter) - - # Predict random timestep to plot for eval data set - predictions = eval_stg.predict(scene, - timestep, - ph, - num_samples_gmm=50, - most_likely_z=False, - all_z=True, - max_nodes=4 * args.eval_batch_size) - - # Plot predicted timestep for random scene - fig, ax = plt.subplots(figsize=(5, 5)) - visualization.visualize_prediction(ax, - predictions, - scene.dt, - max_hl=max_hl, - ph=ph) - ax.set_title(f"{scene.name}-t: {timestep}") - log_writer.add_figure('eval/prediction_all_z', fig, curr_iter) - - if args.eval_every is not None and (curr_iter + 1) % args.eval_every == 0: - max_hl = hyperparams['maximum_history_length'] - ph = hyperparams['prediction_horizon'] - with torch.no_grad(): - # Predict batch timesteps for training dataset evaluation - train_batch_errors = [] - max_scenes = np.min([len(train_scenes), 5]) - for scene in np.random.choice(train_scenes, max_scenes): - timesteps = scene.sample_timesteps(args.eval_batch_size) - predictions = stg.predict(scene, - timesteps, - ph, - num_samples_z=100, - min_future_timesteps=ph, - max_nodes=4*args.eval_batch_size) - - train_batch_errors.append(evaluation.compute_batch_statistics(predictions, - scene.dt, - max_hl=max_hl, - ph=ph, - node_type_enum=train_env.NodeType, - map=scene.map)) - - evaluation.log_batch_errors(train_batch_errors, - log_writer, - 'train', - curr_iter, - bar_plot=['kde'], - box_plot=['ade', 'fde']) - - # Predict batch timesteps for evaluation dataset evaluation - eval_batch_errors = [] - for scene in eval_scenes: - timesteps = scene.sample_timesteps(args.eval_batch_size) - - predictions = eval_stg.predict(scene, - timesteps, - ph, - num_samples_z=100, - min_future_timesteps=ph, - max_nodes=4 * args.eval_batch_size) - - eval_batch_errors.append(evaluation.compute_batch_statistics(predictions, - scene.dt, - max_hl=max_hl, - ph=ph, - node_type_enum=eval_env.NodeType, - map=scene.map)) - - evaluation.log_batch_errors(eval_batch_errors, - log_writer, - 'eval', - curr_iter, - bar_plot=['kde'], - box_plot=['ade', 'fde']) - - - # Predict maximum likelihood batch timesteps for evaluation dataset evaluation - eval_batch_errors_ml = [] - for scene in eval_scenes: - timesteps = scene.sample_timesteps(scene.timesteps) - - predictions = eval_stg.predict(scene, - timesteps, - ph, - num_samples_z=1, - min_future_timesteps=ph, - most_likely_z=True, - most_likely_gmm=True) - - eval_batch_errors_ml.append(evaluation.compute_batch_statistics(predictions, - scene.dt, - max_hl=max_hl, - ph=ph, - map=scene.map, - node_type_enum=eval_env.NodeType, - kde=False)) - - evaluation.log_batch_errors(eval_batch_errors_ml, - log_writer, - 'eval/ml', - curr_iter) - - eval_loss = [] - max_scenes = np.min([len(eval_scenes), 25]) - for scene in np.random.choice(eval_scenes, max_scenes): - eval_loss.append(eval_stg.eval_loss(scene, timesteps)) - - evaluation.log_batch_errors(eval_loss, - log_writer, - 'eval/loss', - curr_iter) - - - else: - print('{:15} | {:10} | {:14}'.format('', '', ''), - end='', flush=True) - - # Here's the newline that ends the current training information printing. - print('') - - if args.save_every is not None and (curr_iter + 1) % args.save_every == 0: - model_registrar.save_models(curr_iter) - print_training_header() - - -def print_training_header(newline_start=False): - if newline_start: - print('') - - print('Iteration | Train Loss | Eval NLL Q (IS) | Eval NLL P | Eval NLL Exact') - print('----------------------------------------------------------------------') - - -def memInUse(): - pid = os.getpid() - py = psutil.Process(pid) - memoryUse = py.memory_info()[0] / 2. ** 30 # memory use in GB...I think - print('memory GB:', memoryUse) - - -if __name__ == '__main__': - main() diff --git a/code/utils/__init__.py b/code/utils/__init__.py deleted file mode 100644 index a24ea74..0000000 --- a/code/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .trajectory_utils import integrate_trajectory, prediction_output_to_trajectories diff --git a/config/config.json b/config/config.json new file mode 100644 index 0000000..578529a --- /dev/null +++ b/config/config.json @@ -0,0 +1,90 @@ +{ + + "batch_size": 256, + "grad_clip": 1.0, + + "learning_rate_style": "exp", + "learning_rate": 0.001, + "min_learning_rate": 0.00001, + "learning_decay_rate": 0.9999, + + "prediction_horizon": 12, + "minimum_history_length": 1, + "maximum_history_length": 8, + + "map_encoder": { + "PEDESTRIAN": { + "heading_state_index": 6, + "patch_size": [50, 10, 50, 90], + "map_channels": 3, + "hidden_channels": [10, 20, 10, 1], + "output_size": 32, + "masks": [5, 5, 5, 5], + "strides": [1, 1, 1, 1], + "dropout": 0.5 + } + }, + + "k": 1, + "k_eval": 1, + + "kl_min": 0.07, + "kl_weight": 100.0, + "kl_weight_start": 0, + "kl_decay_rate": 0.99995, + "kl_crossover": 400, + "kl_sigmoid_divisor": 4, + + "rnn_kwargs": { + "dropout_keep_prob": 0.75 + }, + "MLP_dropout_keep_prob": 0.9, + "enc_rnn_dim_edge": 32, + "enc_rnn_dim_edge_influence": 32, + "enc_rnn_dim_history": 32, + "enc_rnn_dim_future": 32, + "dec_rnn_dim": 128, + + "q_z_xy_MLP_dims": null, + "p_z_x_MLP_dims": 32, + "GMM_components": 1, + + "log_p_yt_xz_max": 6, + + "N": 1, + "K": 25, + + "tau_init": 2.0, + "tau_final": 0.05, + "tau_decay_rate": 0.997, + + "use_z_logit_clipping": true, + "z_logit_clip_start": 0.05, + "z_logit_clip_final": 5.0, + "z_logit_clip_crossover": 300, + "z_logit_clip_divisor": 5, + + "dynamic": { + "PEDESTRIAN": { + "name": "SingleIntegrator", + "distribution": true, + "limits": {} + } + }, + + "state": { + "PEDESTRIAN": { + "position": ["x", "y"], + "velocity": ["x", "y"], + "acceleration": ["x", "y"] + } + }, + + "pred_state": { + "PEDESTRIAN": { + "position": ["x", "y"] + } + }, + + "log_histograms": false +} \ No newline at end of file diff --git a/config/nuScenes.json b/config/nuScenes.json new file mode 100644 index 0000000..919ea6b --- /dev/null +++ b/config/nuScenes.json @@ -0,0 +1,109 @@ +{ + + "batch_size": 256, + "grad_clip": 1.0, + + "learning_rate_style": "exp", + "learning_rate": 0.003, + "min_learning_rate": 0.00001, + "learning_decay_rate": 0.9999, + + "prediction_horizon": 6, + "minimum_history_length": 1, + "maximum_history_length": 8, + + "map_encoder": { + "VEHICLE": { + "heading_state_index": 6, + "patch_size": [50, 10, 50, 90], + "map_channels": 3, + "hidden_channels": [10, 20, 10, 1], + "output_size": 32, + "masks": [5, 5, 5, 3], + "strides": [2, 2, 1, 1], + "dropout": 0.5 + } + }, + + "k": 1, + "k_eval": 1, + + "kl_min": 0.07, + "kl_weight": 100.0, + "kl_weight_start": 0, + "kl_decay_rate": 0.99995, + "kl_crossover": 400, + "kl_sigmoid_divisor": 4, + + "rnn_kwargs": { + "dropout_keep_prob": 0.75 + }, + "MLP_dropout_keep_prob": 0.9, + "enc_rnn_dim_edge": 32, + "enc_rnn_dim_edge_influence": 32, + "enc_rnn_dim_history": 32, + "enc_rnn_dim_future": 32, + "dec_rnn_dim": 128, + + "q_z_xy_MLP_dims": null, + "p_z_x_MLP_dims": 32, + "GMM_components": 1, + + "log_p_yt_xz_max": 6, + + "N": 1, + "K": 25, + + "tau_init": 2.0, + "tau_final": 0.05, + "tau_decay_rate": 0.997, + + "use_z_logit_clipping": true, + "z_logit_clip_start": 0.05, + "z_logit_clip_final": 5.0, + "z_logit_clip_crossover": 300, + "z_logit_clip_divisor": 5, + + "dynamic": { + "PEDESTRIAN": { + "name": "SingleIntegrator", + "distribution": true, + "limits": {} + }, + "VEHICLE": { + "name": "Unicycle", + "distribution": true, + "limits": { + "max_a": 4, + "min_a": -5, + "max_heading_change": 0.7, + "min_heading_change": -0.7 + } + } + }, + + "state": { + "PEDESTRIAN": { + "position": ["x", "y"], + "velocity": ["x", "y"], + "acceleration": ["x", "y"] + }, + "VEHICLE": { + "position": ["x", "y"], + "velocity": ["x", "y"], + "acceleration": ["x", "y"], + "heading": ["°", "d°"] + } + }, + + "pred_state": { + "VEHICLE": { + "position": ["x", "y"] + }, + "PEDESTRIAN": { + "position": ["x", "y"] + } + }, + + "log_histograms": false +} \ No newline at end of file diff --git a/data/nuScenes/download_file.py b/data/nuScenes/download_file.py deleted file mode 100644 index e386434..0000000 --- a/data/nuScenes/download_file.py +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/python - -import sys -import urllib.request - -def main(): - # print command line arguments - urllib.request.urlretrieve(sys.argv[1], sys.argv[2]) - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/data/nuScenes/kalman_filter.py b/data/nuScenes/kalman_filter.py deleted file mode 100644 index fb39533..0000000 --- a/data/nuScenes/kalman_filter.py +++ /dev/null @@ -1,191 +0,0 @@ -import numpy as np - - -class LinearPointMass: - """Linear Kalman Filter for an autonomous point mass system, assuming constant velocity""" - - def __init__(self, dt, sPos=None, sVel=None, sMeasurement=None): - """ - input matrices must be numpy arrays - :param A: state transition matrix - :param B: state control matrix - :param C: measurement matrix - :param Q: covariance of the Gaussian error in state transition - :param R: covariance of the Gaussain error in measurement - """ - self.dt = dt - - # matrices of state transition and measurement - self.A = np.array([[1, dt, 0, 0], [0, 1, 0, 0], [0, 0, 1, dt], [0, 0, 0, 1]]) - self.B = np.array([[0, 0], [dt, 0], [0, 0], [0, dt]]) - self.C = np.array([[1, 0, 0, 0], [0, 0, 1, 0]]) - - # default noise covariance - if (sPos is None) and (sVel is None) and (sMeasurement is None): - # sPos = 0.5 * 5 * dt ** 2 # assume 5m/s2 as maximum acceleration - # sVel = 5.0 * dt # assume 8.8m/s2 as maximum acceleration - sPos = 1.3*self.dt # assume 5m/s2 as maximum acceleration - sVel = 4*self.dt # assume 8.8m/s2 as maximum acceleration - sMeasurement = 0.2 # 68% of the measurement is within [-sMeasurement, sMeasurement] - - # state transition noise - self.Q = np.diag([sPos ** 2, sVel ** 2, sPos ** 2, sVel ** 2]) - # measurement noise - self.R = np.diag([sMeasurement ** 2, sMeasurement ** 2]) - - def predict_and_update(self, x_vec_est, u_vec, P_matrix, z_new): - """ - for background please refer to wikipedia: https://en.wikipedia.org/wiki/Kalman_filter - :param x_vec_est: - :param u_vec: - :param P_matrix: - :param z_new: - :return: - """ - - ## Prediction Step - # predicted state estimate - x_pred = self.A.dot(x_vec_est) + self.B.dot(u_vec) - # predicted error covariance - P_pred = self.A.dot(P_matrix.dot(self.A.transpose())) + self.Q - - ## Update Step - # innovation or measurement pre-fit residual - y_telda = z_new - self.C.dot(x_pred) - # innovation covariance - S = self.C.dot(P_pred.dot(self.C.transpose())) + self.R - # optimal Kalman gain - K = P_pred.dot(self.C.transpose().dot(np.linalg.inv(S))) - # updated (a posteriori) state estimate - x_vec_est_new = x_pred + K.dot(y_telda) - # updated (a posteriori) estimate covariance - P_matrix_new = np.dot((np.identity(4) - K.dot(self.C)), P_pred) - - return x_vec_est_new, P_matrix_new - - -class NonlinearKinematicBicycle: - """ - Nonlinear Kalman Filter for a kinematic bicycle model, assuming constant longitudinal speed - and constant heading angle - """ - - def __init__(self, lf, lr, dt, sPos=None, sHeading=None, sVel=None, sMeasurement=None): - self.dt = dt - - # params for state transition - self.lf = lf - self.lr = lr - # measurement matrix - self.C = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) - - # default noise covariance - if (sPos is None) and (sHeading is None) and (sVel is None) and (sMeasurement is None): - # TODO need to further check - # sPos = 0.5 * 8.8 * dt ** 2 # assume 8.8m/s2 as maximum acceleration - # sHeading = 0.5 * dt # assume 0.5rad/s as maximum turn rate - # sVel = 8.8 * dt # assume 8.8m/s2 as maximum acceleration - # sMeasurement = 1.0 - sPos = 16 * self.dt # assume 8.8m/s2 as maximum acceleration - sHeading = np.pi/2 * self.dt # assume 0.5rad/s as maximum turn rate - sVel = 8 * self.dt # assume 8.8m/s2 as maximum acceleration - sMeasurement = 0.8 - # state transition noise - self.Q = np.diag([sPos ** 2, sPos ** 2, sHeading ** 2, sVel ** 2]) - # measurement noise - self.R = np.diag([sMeasurement ** 2, sMeasurement ** 2, sMeasurement ** 2, sMeasurement ** 2]) - - def predict_and_update(self, x_vec_est, u_vec, P_matrix, z_new): - """ - for background please refer to wikipedia: https://en.wikipedia.org/wiki/Extended_Kalman_filter - :param x_vec_est: - :param u_vec: - :param P_matrix: - :param z_new: - :return: - """ - - ## Prediction Step - # predicted state estimate - x_pred = self._kinematic_bicycle_model_rearCG(x_vec_est, u_vec) - # Compute Jacobian to obtain the state transition matrix - A = self._cal_state_Jacobian(x_vec_est, u_vec) - # predicted error covariance - P_pred = A.dot(P_matrix.dot(A.transpose())) + self.Q - - ## Update Step - # innovation or measurement pre-fit residual - y_telda = z_new - self.C.dot(x_pred) - # innovation covariance - S = self.C.dot(P_pred.dot(self.C.transpose())) + self.R - # near-optimal Kalman gain - K = P_pred.dot(self.C.transpose().dot(np.linalg.inv(S))) - # updated (a posteriori) state estimate - x_vec_est_new = x_pred + K.dot(y_telda) - # updated (a posteriori) estimate covariance - P_matrix_new = np.dot((np.identity(4) - K.dot(self.C)), P_pred) - - return x_vec_est_new, P_matrix_new - - def _kinematic_bicycle_model_rearCG(self, x_old, u): - """ - :param x: vehicle state vector = [x position, y position, heading, velocity] - :param u: control vector = [acceleration, steering angle] - :param dt: - :return: - """ - - acc = u[0] - delta = u[1] - - x = x_old[0] - y = x_old[1] - psi = x_old[2] - vel = x_old[3] - - x_new = np.array([[0.], [0.], [0.], [0.]]) - - beta = np.arctan(self.lr * np.tan(delta) / (self.lf + self.lr)) - - x_new[0] = x + self.dt * vel * np.cos(psi + beta) - x_new[1] = y + self.dt * vel * np.sin(psi + beta) - x_new[2] = psi + self.dt * vel * np.cos(beta) / (self.lf + self.lr) * np.tan(delta) - #x_new[2] = _heading_angle_correction(x_new[2]) - x_new[3] = vel + self.dt * acc - - return x_new - - def _cal_state_Jacobian(self, x_vec, u_vec): - acc = u_vec[0] - delta = u_vec[1] - - x = x_vec[0] - y = x_vec[1] - psi = x_vec[2] - vel = x_vec[3] - - beta = np.arctan(self.lr * np.tan(delta) / (self.lf + self.lr)) - - a13 = -self.dt * vel * np.sin(psi + beta) - a14 = self.dt * np.cos(psi + beta) - a23 = self.dt * vel * np.cos(psi + beta) - a24 = self.dt * np.sin(psi + beta) - a34 = self.dt * np.cos(beta) / (self.lf + self.lr) * np.tan(delta) - - JA = np.array([[1.0, 0.0, a13[0], a14[0]], - [0.0, 1.0, a23[0], a24[0]], - [0.0, 0.0, 1.0, a34[0]], - [0.0, 0.0, 0.0, 1.0]]) - - return JA - - -def _heading_angle_correction(theta): - """ - correct heading angle so that it always remains in [-pi, pi] - :param theta: - :return: - """ - theta_corrected = (theta + np.pi) % (2.0 * np.pi) - np.pi - return theta_corrected - diff --git a/data/nuScenes/models/baseline/config.json b/data/nuScenes/models/baseline/config.json deleted file mode 100644 index f708c39..0000000 --- a/data/nuScenes/models/baseline/config.json +++ /dev/null @@ -1 +0,0 @@ -{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 0.0005, "learning_decay_rate": 0.9995, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_context": 120, "map_enc_num_layers": 4, "map_enc_hidden_size": 512, "map_enc_output_size": 512, "map_enc_dropout": 0.3, "alpha": 1, "k": 30, "k_eval": 50, "use_iwae": false, "kl_exact": true, "kl_min": 0.07, "kl_weight": 5.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 500, "kl_sigmoid_divisor": 4, "inf_warmup": 1.0, "inf_warmup_start": 1.0, "inf_warmup_crossover": 1500, "inf_warmup_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.7}, "MLP_dropout_keep_prob": 0.9, "rnn_io_dropout_keep_prob": 1.0, "enc_rnn_dim_multiple_inputs": 8, "enc_rnn_dim_edge": 8, "enc_rnn_dim_edge_influence": 8, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 512, "dec_GMM_proj_MLP_dims": null, "sample_model_during_dec": true, "dec_sample_model_prob_start": 1.0, "dec_sample_model_prob_final": 1.0, "dec_sample_model_prob_crossover": 200, "dec_sample_model_prob_divisor": 4, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "fuzz_factor": 0.05, "GMM_components": 12, "log_sigma_min": -10, "log_sigma_max": 10, "log_p_yt_xz_max": 50, "N": 2, "K": 5, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 500, "z_logit_clip_divisor": 5, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["value"]}, "BICYCLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}, "BICYCLE": {"velocity": ["x", "y"]}, "VEHICLE": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_radius": 0.0, "use_map_encoding": false, "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes"} \ No newline at end of file diff --git a/data/nuScenes/models/baseline/model_registrar-1.pt b/data/nuScenes/models/baseline/model_registrar-1.pt deleted file mode 100644 index 8ced20e..0000000 Binary files a/data/nuScenes/models/baseline/model_registrar-1.pt and /dev/null differ diff --git a/data/nuScenes/models/edge/config.json b/data/nuScenes/models/edge/config.json deleted file mode 100644 index ef403f3..0000000 --- a/data/nuScenes/models/edge/config.json +++ /dev/null @@ -1 +0,0 @@ -{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 0.0005, "learning_decay_rate": 0.9995, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_context": 120, "map_enc_num_layers": 4, "map_enc_hidden_size": 512, "map_enc_output_size": 512, "map_enc_dropout": 0.15, "alpha": 1, "k": 30, "k_eval": 50, "use_iwae": false, "kl_exact": true, "kl_min": 0.07, "kl_weight": 5.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 500, "kl_sigmoid_divisor": 4, "inf_warmup": 1.0, "inf_warmup_start": 1.0, "inf_warmup_crossover": 1500, "inf_warmup_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.7}, "MLP_dropout_keep_prob": 0.9, "rnn_io_dropout_keep_prob": 1.0, "enc_rnn_dim_multiple_inputs": 8, "enc_rnn_dim_edge": 8, "enc_rnn_dim_edge_influence": 8, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 512, "dec_GMM_proj_MLP_dims": null, "sample_model_during_dec": true, "dec_sample_model_prob_start": 1.0, "dec_sample_model_prob_final": 1.0, "dec_sample_model_prob_crossover": 200, "dec_sample_model_prob_divisor": 4, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "fuzz_factor": 0.05, "GMM_components": 12, "log_sigma_min": -10, "log_sigma_max": 10, "log_p_yt_xz_max": 50, "N": 2, "K": 5, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 500, "z_logit_clip_divisor": 5, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["value"]}, "BICYCLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}, "BICYCLE": {"velocity": ["x", "y"]}, "VEHICLE": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_radius": 20.0, "use_map_encoding": false, "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes"} \ No newline at end of file diff --git a/data/nuScenes/models/edge/model_registrar-1.pt b/data/nuScenes/models/edge/model_registrar-1.pt deleted file mode 100644 index 5e2d713..0000000 Binary files a/data/nuScenes/models/edge/model_registrar-1.pt and /dev/null differ diff --git a/data/nuScenes/models/full/config.json b/data/nuScenes/models/full/config.json deleted file mode 100644 index eef2c13..0000000 --- a/data/nuScenes/models/full/config.json +++ /dev/null @@ -1 +0,0 @@ -{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "const", "learning_rate": 0.002, "min_learning_rate": 0.0005, "learning_decay_rate": 0.9995, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_context": 120, "map_enc_num_layers": 4, "map_enc_hidden_size": 512, "map_enc_output_size": 512, "map_enc_dropout": 0.5, "alpha": 1, "k": 30, "k_eval": 50, "use_iwae": false, "kl_exact": true, "kl_min": 0.07, "kl_weight": 5.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 500, "kl_sigmoid_divisor": 4, "inf_warmup": 1.0, "inf_warmup_start": 1.0, "inf_warmup_crossover": 1500, "inf_warmup_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.5}, "MLP_dropout_keep_prob": 0.9, "rnn_io_dropout_keep_prob": 1.0, "enc_rnn_dim_multiple_inputs": 8, "enc_rnn_dim_edge": 8, "enc_rnn_dim_edge_influence": 8, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 512, "dec_GMM_proj_MLP_dims": null, "sample_model_during_dec": true, "dec_sample_model_prob_start": 1.0, "dec_sample_model_prob_final": 1.0, "dec_sample_model_prob_crossover": 200, "dec_sample_model_prob_divisor": 4, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "fuzz_factor": 0.05, "GMM_components": 12, "log_sigma_min": -10, "log_sigma_max": 10, "log_p_yt_xz_max": 50, "N": 2, "K": 5, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 500, "z_logit_clip_divisor": 5, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["value"]}, "BICYCLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}, "BICYCLE": {"velocity": ["x", "y"]}, "VEHICLE": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_radius": 20.0, "use_map_encoding": true, "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false} \ No newline at end of file diff --git a/data/nuScenes/models/full/model_registrar-1.pt b/data/nuScenes/models/full/model_registrar-1.pt deleted file mode 100644 index 1183cea..0000000 Binary files a/data/nuScenes/models/full/model_registrar-1.pt and /dev/null differ diff --git a/data/nuScenes/models/me_demo/config.json b/data/nuScenes/models/me_demo/config.json deleted file mode 100644 index beea3c7..0000000 --- a/data/nuScenes/models/me_demo/config.json +++ /dev/null @@ -1 +0,0 @@ -{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.002, "min_learning_rate": 0.0005, "learning_decay_rate": 0.9995, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_context": 120, "map_enc_num_layers": 4, "map_enc_hidden_size": 512, "map_enc_output_size": 512, "map_enc_dropout": 0.5, "alpha": 1, "k": 30, "k_eval": 50, "use_iwae": false, "kl_exact": true, "kl_min": 0.07, "kl_weight": 5.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 500, "kl_sigmoid_divisor": 4, "inf_warmup": 1.0, "inf_warmup_start": 1.0, "inf_warmup_crossover": 1500, "inf_warmup_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.5}, "MLP_dropout_keep_prob": 0.9, "rnn_io_dropout_keep_prob": 1.0, "enc_rnn_dim_multiple_inputs": 8, "enc_rnn_dim_edge": 8, "enc_rnn_dim_edge_influence": 8, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 512, "dec_GMM_proj_MLP_dims": null, "sample_model_during_dec": true, "dec_sample_model_prob_start": 1.0, "dec_sample_model_prob_final": 1.0, "dec_sample_model_prob_crossover": 200, "dec_sample_model_prob_divisor": 4, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "fuzz_factor": 0.05, "GMM_components": 12, "log_sigma_min": -10, "log_sigma_max": 10, "log_p_yt_xz_max": 50, "N": 2, "K": 5, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 500, "z_logit_clip_divisor": 5, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["value"]}, "BICYCLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}, "BICYCLE": {"velocity": ["x", "y"]}, "VEHICLE": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_radius": 0.0, "use_map_encoding": true, "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes"} \ No newline at end of file diff --git a/data/nuScenes/models/me_demo/model_registrar-1.pt b/data/nuScenes/models/me_demo/model_registrar-1.pt deleted file mode 100644 index 4cf73d5..0000000 Binary files a/data/nuScenes/models/me_demo/model_registrar-1.pt and /dev/null differ diff --git a/data/nuScenes/models/robot_demo/config.json b/data/nuScenes/models/robot_demo/config.json deleted file mode 100644 index 17f22af..0000000 --- a/data/nuScenes/models/robot_demo/config.json +++ /dev/null @@ -1 +0,0 @@ -{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 0.0005, "learning_decay_rate": 0.9995, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_context": 120, "map_enc_num_layers": 4, "map_enc_hidden_size": 512, "map_enc_output_size": 512, "map_enc_dropout": 0.5, "alpha": 1, "k": 30, "k_eval": 50, "use_iwae": false, "kl_exact": true, "kl_min": 0.07, "kl_weight": 5.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 500, "kl_sigmoid_divisor": 4, "inf_warmup": 1.0, "inf_warmup_start": 1.0, "inf_warmup_crossover": 1500, "inf_warmup_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.5}, "MLP_dropout_keep_prob": 0.9, "rnn_io_dropout_keep_prob": 1.0, "enc_rnn_dim_multiple_inputs": 8, "enc_rnn_dim_edge": 8, "enc_rnn_dim_edge_influence": 8, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 512, "dec_GMM_proj_MLP_dims": null, "sample_model_during_dec": true, "dec_sample_model_prob_start": 1.0, "dec_sample_model_prob_final": 1.0, "dec_sample_model_prob_crossover": 200, "dec_sample_model_prob_divisor": 4, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "fuzz_factor": 0.05, "GMM_components": 12, "log_sigma_min": -10, "log_sigma_max": 10, "log_p_yt_xz_max": 50, "N": 2, "K": 5, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 500, "z_logit_clip_divisor": 5, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["value"]}, "BICYCLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y", "m"], "acceleration": ["x", "y", "m"], "heading": ["value"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}, "BICYCLE": {"velocity": ["x", "y"]}, "VEHICLE": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_radius": 20.0, "use_map_encoding": false, "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": true} \ No newline at end of file diff --git a/data/nuScenes/models/robot_demo/model_registrar-1.pt b/data/nuScenes/models/robot_demo/model_registrar-1.pt deleted file mode 100644 index 40f3703..0000000 Binary files a/data/nuScenes/models/robot_demo/model_registrar-1.pt and /dev/null differ diff --git a/data/nuScenes/nuscenes-devkit b/data/nuScenes/nuscenes-devkit deleted file mode 160000 index f3594b9..0000000 --- a/data/nuScenes/nuscenes-devkit +++ /dev/null @@ -1 +0,0 @@ -Subproject commit f3594b967cbf42396da5c6cb08bd714437b53111 diff --git a/data/nuScenes/process_nuScenes.py b/data/nuScenes/process_nuScenes.py deleted file mode 100644 index dceb052..0000000 --- a/data/nuScenes/process_nuScenes.py +++ /dev/null @@ -1,491 +0,0 @@ -import sys -import os -import numpy as np -import pandas as pd -import pickle -import json -from tqdm import tqdm -from pyquaternion import Quaternion -from kalman_filter import LinearPointMass, NonlinearKinematicBicycle -from scipy.integrate import cumtrapz -from scipy.ndimage.morphology import binary_dilation, generate_binary_structure - -nu_path = './nuscenes-devkit/python-sdk/' -#op_path = './pytorch-openpose/python/' -sys.path.append(nu_path) -sys.path.append("../../code") -#sys.path.append(op_path) -from nuscenes.nuscenes import NuScenes -from nuscenes.map_expansion.map_api import NuScenesMap -from data import Environment, Scene, Node, BicycleNode, Position, Velocity, Acceleration, ActuatorAngle, Map, Scalar - -scene_blacklist = [3, 12, 18, 19, 33, 35, 36, 41, 45, 50, 54, 55, 61, 120, 121, 123, 126, 132, 133, 134, 149, - 154, 159, 196, 268, 278, 351, 365, 367, 368, 369, 372, 376, 377, 382, 385, 499, 515, 517, - 945, 947, 952, 955, 962, 963, 968] + [969] - - -types = ['PEDESTRIAN', - 'BICYCLE', - 'VEHICLE'] - -standardization = { - 'PEDESTRIAN': { - 'position': { - 'x': {'mean': 0, 'std': 25}, - 'y': {'mean': 0, 'std': 25} - }, - 'velocity': { - 'x': {'mean': 0, 'std': 2}, - 'y': {'mean': 0, 'std': 2} - }, - 'acceleration': { - 'x': {'mean': 0, 'std': 1}, - 'y': {'mean': 0, 'std': 1} - }, - 'heading': { - 'value': {'mean': 0, 'std': np.pi}, - 'derivative': {'mean': 0, 'std': np.pi / 4} - } - }, - 'BICYCLE': { - 'position': { - 'x': {'mean': 0, 'std': 50}, - 'y': {'mean': 0, 'std': 50} - }, - 'velocity': { - 'x': {'mean': 0, 'std': 6}, - 'y': {'mean': 0, 'std': 6}, - 'm': {'mean': 0, 'std': 6} - }, - 'acceleration': { - 'x': {'mean': 0, 'std': 4}, - 'y': {'mean': 0, 'std': 4}, - 'm': {'mean': 0, 'std': 4} - }, - 'actuator_angle': { - 'steering_angle': {'mean': 0, 'std': np.pi/2} - }, - 'heading': { - 'value': {'mean': 0, 'std': np.pi}, - 'derivative': {'mean': 0, 'std': np.pi / 4} - } - }, - 'VEHICLE': { - 'position': { - 'x': {'mean': 0, 'std': 100}, - 'y': {'mean': 0, 'std': 100} - }, - 'velocity': { - 'x': {'mean': 0, 'std': 20}, - 'y': {'mean': 0, 'std': 20}, - 'm': {'mean': 0, 'std': 20} - }, - 'acceleration': { - 'x': {'mean': 0, 'std': 4}, - 'y': {'mean': 0, 'std': 4}, - 'm': {'mean': 0, 'std': 4} - }, - 'actuator_angle': { - 'steering_angle': {'mean': 0, 'std': np.pi/2} - }, - 'heading': { - 'value': {'mean': 0, 'std': np.pi}, - 'derivative': {'mean': 0, 'std': np.pi / 4} - } - } -} - -def inverse_np_gradient(f, dx, F0=0.): - N = f.shape[0] - return F0 + np.hstack((np.zeros((N, 1)), cumtrapz(f, axis=1, dx=dx))) - -def integrate_trajectory(v, x0, dt): - xd_ = inverse_np_gradient(v[..., 0], dx=dt, F0=x0[0]) - yd_ = inverse_np_gradient(v[..., 1], dx=dt, F0=x0[1]) - integrated = np.stack([xd_, yd_], axis=2) - return integrated - -def integrate_heading_model(a, dh, h0, x0, v0, dt): - h = inverse_np_gradient(dh, dx=dt, F0=h0) - v_m = inverse_np_gradient(a, dx=dt, F0=v0) - - vx = np.cos(h) * v_m - vy = np.sin(h) * v_m - - v = np.stack((vx, vy), axis=2) - return integrate_trajectory(v, x0, dt) - -if __name__ == "__main__": - num_global_straight = 0 - num_global_curve = 0 - - test = False - if sys.argv[1] == 'mini': - data_path = './raw_data/mini' - nusc = NuScenes(version='v1.0-mini', dataroot=data_path, verbose=True) - add = "_mini" - train_scenes = nusc.scene[0:7] - val_scenes = nusc.scene[7:] - test_scenes = [] - elif sys.argv[1] == 'test': - test = True - data_path = './raw_data' - nusc = NuScenes(version='v1.0-test', dataroot=data_path, verbose=True) - train_scenes = [] - val_scenes = [] - test_scenes = nusc.scene - with open(os.path.join('./raw_data/results_test_megvii.json'), 'r') as test_json: - test_annotations = json.load(test_json) - else: - data_path = '/home/timsal/Documents/code/GenTrajectron_nuScenes_ssh/data/nuScenes/raw_data' - nusc = NuScenes(version='v1.0-trainval', dataroot=data_path, verbose=True) - add = "" - train_scenes = nusc.scene[0:] - val_scenes = nusc.scene[700:] - test_scenes = [] - - for data_class, nuscenes in [('train', train_scenes), ('val', val_scenes), ('test', test_scenes)]: - print(f"Processing data class {data_class}") - data_dict_path = os.path.join('../processed', '_'.join(['nuScenes', data_class])+ 'samp.pkl') - env = Environment(node_type_list=types, standardization=standardization) - attention_radius = dict() - attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.PEDESTRIAN)] = 3.0 - attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.VEHICLE)] = 20.0 - attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.BICYCLE)] = 10.0 - attention_radius[(env.NodeType.VEHICLE, env.NodeType.PEDESTRIAN)] = 20.0 - attention_radius[(env.NodeType.VEHICLE, env.NodeType.VEHICLE)] = 20.0 - attention_radius[(env.NodeType.VEHICLE, env.NodeType.BICYCLE)] = 20.0 - attention_radius[(env.NodeType.BICYCLE, env.NodeType.PEDESTRIAN)] = 10.0 - attention_radius[(env.NodeType.BICYCLE, env.NodeType.VEHICLE)] = 20.0 - attention_radius[(env.NodeType.BICYCLE, env.NodeType.BICYCLE)] = 10.0 - env.attention_radius = attention_radius - scenes = [] - pbar = tqdm(nuscenes, ncols=100) - for nuscene in pbar: - scene_id = int(nuscene['name'].replace('scene-', '')) - if scene_id in scene_blacklist: # Some scenes have bad localization - continue - - if not (scene_id == 1002 or scene_id == 234): - continue - - data = pd.DataFrame(columns=['frame_id', - 'type', - 'node_id', - 'robot', - 'x', 'y', 'z', - 'length', - 'width', - 'height', - 'heading', - 'orientation']) - - sample_token = nuscene['first_sample_token'] - sample = nusc.get('sample', sample_token) - frame_id = 0 - while sample['next']: - if not test: - annotation_tokens = sample['anns'] - else: - annotation_tokens = test_annotations['results'][sample['token']] - for annotation_token in annotation_tokens: - if not test: - annotation = nusc.get('sample_annotation', annotation_token) - category = annotation['category_name'] - if len(annotation['attribute_tokens']): - attribute = nusc.get('attribute', annotation['attribute_tokens'][0])['name'] - - if 'pedestrian' in category and not 'stroller' in category and not 'wheelchair' in category: - our_category = env.NodeType.PEDESTRIAN - elif ('vehicle.bicycle' in category) and 'with_rider' in attribute: - continue - our_category = env.NodeType.BICYCLE - elif 'vehicle' in category and 'bicycle' not in category and 'motorcycle' not in category and 'parked' not in attribute: - our_category = env.NodeType.VEHICLE - # elif ('vehicle.motorcycle' in category) and 'with_rider' in attribute: - # our_category = env.NodeType.VEHICLE - else: - continue - else: - annotation = annotation_token - category = annotation['tracking_name'] - attribute = ""#annotation['attribute_name'] - - if 'pedestrian' in category : - our_category = env.NodeType.PEDESTRIAN - elif (('car' in category or 'bus' in category or 'construction_vehicle' in category) and 'parked' not in attribute): - our_category = env.NodeType.VEHICLE - # elif ('vehicle.motorcycle' in category) and 'with_rider' in attribute: - # our_category = env.NodeType.VEHICLE - else: - continue - - - data_point = pd.Series({'frame_id': frame_id, - 'type': our_category, - 'node_id': annotation['instance_token'] if not test else annotation['tracking_id'], - 'robot': False, - 'x': annotation['translation'][0], - 'y': annotation['translation'][1], - 'z': annotation['translation'][2], - 'length': annotation['size'][0], - 'width': annotation['size'][1], - 'height': annotation['size'][2], - 'heading': Quaternion(annotation['rotation']).yaw_pitch_roll[0], - 'orientation': None}) - data = data.append(data_point, ignore_index=True) - - # Ego Vehicle - our_category = env.NodeType.VEHICLE - sample_data = nusc.get('sample_data', sample['data']['CAM_FRONT']) - annotation = nusc.get('ego_pose', sample_data['ego_pose_token']) - data_point = pd.Series({'frame_id': frame_id, - 'type': our_category, - 'node_id': 'ego', - 'robot': True, - 'x': annotation['translation'][0], - 'y': annotation['translation'][1], - 'z': annotation['translation'][2], - 'length': 4, - 'width': 1.7, - 'height': 1.5, - 'heading': Quaternion(annotation['rotation']).yaw_pitch_roll[0], - 'orientation': None}) - data = data.append(data_point, ignore_index=True) - - sample = nusc.get('sample', sample['next']) - frame_id += 1 - - if len(data.index) == 0: - continue - - data.sort_values('frame_id', inplace=True) - max_timesteps = data['frame_id'].max() - - x_min = np.round(data['x'].min() - 50) - x_max = np.round(data['x'].max() + 50) - y_min = np.round(data['y'].min() - 50) - y_max = np.round(data['y'].max() + 50) - - data['x'] = data['x'] - x_min - data['y'] = data['y'] - y_min - - scene = Scene(timesteps=max_timesteps + 1, dt=0.5, name=str(scene_id)) - - # Generate Maps - map_name = nusc.get('log', nuscene['log_token'])['location'] - nusc_map = NuScenesMap(dataroot=data_path, map_name=map_name) - - type_map = dict() - x_size = x_max - x_min - y_size = y_max - y_min - patch_box = (x_min + 0.5 * (x_max - x_min), y_min + 0.5 * (y_max - y_min), y_size, x_size) - patch_angle = 0 # Default orientation where North is up - canvas_size = (np.round(3 * y_size).astype(int), np.round(3 * x_size).astype(int)) - homography = np.array([[3., 0., 0.], [0., 3., 0.], [0., 0., 3.]]) - layer_names = ['lane', 'road_segment', 'drivable_area', 'road_divider', 'lane_divider', 'stop_line', - 'ped_crossing', 'stop_line', 'ped_crossing', 'walkway'] - map_mask = (nusc_map.get_map_mask(patch_box, patch_angle, layer_names, canvas_size) * 255.0).astype( - np.uint8) - map_mask = np.swapaxes(map_mask, 1, 2) # x axis comes first - # PEDESTRIANS - map_mask_pedestrian = np.stack((map_mask[9], map_mask[8], np.max(map_mask[:3], axis=0)), axis=2) - type_map['PEDESTRIAN'] = Map(data=map_mask_pedestrian, homography=homography, - description=', '.join(layer_names)) - # Bicycles - map_mask_bicycles = np.stack((map_mask[9], map_mask[8], np.max(map_mask[:3], axis=0)), axis=2) - type_map['BICYCLE'] = Map(data=map_mask_bicycles, homography=homography, description=', '.join(layer_names)) - # VEHICLES - map_mask_vehicle = np.stack((np.max(map_mask[:3], axis=0), map_mask[3], map_mask[4]), axis=2) - type_map['VEHICLE'] = Map(data=map_mask_vehicle, homography=homography, description=', '.join(layer_names)) - - map_mask_plot = np.stack(((np.max(map_mask[:3], axis=0) - (map_mask[3] + 0.5 * map_mask[4]).clip( - max=255)).clip(min=0).astype(np.uint8), map_mask[8], map_mask[9]), axis=2) - type_map['PLOT'] = Map(data=map_mask_plot, homography=homography, description=', '.join(layer_names)) - - scene.map = type_map - del map_mask - del map_mask_pedestrian - del map_mask_vehicle - del map_mask_bicycles - del map_mask_plot - - for node_id in pd.unique(data['node_id']): - node_df = data[data['node_id'] == node_id] - - if node_df['x'].shape[0] < 2: - continue - - if not np.all(np.diff(node_df['frame_id']) == 1): - #print('Occlusion') - continue # TODO Make better - - node_values = node_df['x'].values - if node_df.iloc[0]['type'] == env.NodeType.PEDESTRIAN: - node = Node(type=node_df.iloc[0]['type']) - else: - node = BicycleNode(type=node_df.iloc[0]['type']) - node.first_timestep = node_df['frame_id'].iloc[0] - node.position = Position(node_df['x'].values, node_df['y'].values) - node.velocity = Velocity.from_position(node.position, scene.dt) - node.velocity.m = np.linalg.norm(np.vstack((node.velocity.x, node.velocity.y)), axis=0) - node.acceleration = Acceleration.from_velocity(node.velocity, scene.dt) - node.heading = Scalar(node_df['heading'].values) - heading_t = node_df['heading'].values.copy() - shifted_heading = np.zeros_like(node.heading.value) - shifted_heading[0] = node.heading.value[0] - for i in range(1, len(node.heading.value)): - if not (np.sign(node.heading.value[i]) == np.sign(node.heading.value[i - 1])) and np.abs( - node.heading.value[i]) > np.pi / 2: - shifted_heading[i] = shifted_heading[i - 1] + ( - node.heading.value[i] - node.heading.value[i - 1]) - np.sign( - (node.heading.value[i] - node.heading.value[i - 1])) * 2 * np.pi - else: - shifted_heading[i] = shifted_heading[i - 1] + ( - node.heading.value[i] - node.heading.value[i - 1]) - node.heading.value = shifted_heading - node.length = node_df.iloc[0]['length'] - node.width = node_df.iloc[0]['width'] - - if node_df.iloc[0]['robot'] == True: - node.is_robot = True - - if node_df.iloc[0]['type'] == env.NodeType.PEDESTRIAN: - filter_ped = LinearPointMass(dt=scene.dt) - for i in range(len(node.position.x)): - if i == 0: # initalize KF - P_matrix = np.identity(4) - elif i < len(node.position.x): - # assign new est values - node.position.x[i] = x_vec_est_new[0][0] - node.velocity.x[i] = x_vec_est_new[1][0] - node.position.y[i] = x_vec_est_new[2][0] - node.velocity.y[i] = x_vec_est_new[3][0] - - if i < len(node.position.x) - 1: # no action on last data - # filtering - x_vec_est = np.array([[node.position.x[i]], - [node.velocity.x[i]], - [node.position.y[i]], - [node.velocity.y[i]]]) - z_new = np.array([[node.position.x[i+1]], - [node.position.y[i+1]]]) - x_vec_est_new, P_matrix_new = filter_ped.predict_and_update( - x_vec_est=x_vec_est, - u_vec=np.array([[0.], [0.]]), - P_matrix=P_matrix, - z_new=z_new - ) - P_matrix = P_matrix_new - else: - filter_veh = NonlinearKinematicBicycle(lf=node.length*0.6, lr=node.length*0.4, dt=scene.dt) - for i in range(len(node.position.x)): - if i == 0: # initalize KF - # initial P_matrix - P_matrix = np.identity(4) - elif i < len(node.position.x): - # assign new est values - node.position.x[i] = x_vec_est_new[0][0] - node.position.y[i] = x_vec_est_new[1][0] - node.heading.value[i] = x_vec_est_new[2][0] - node.velocity.m[i] = x_vec_est_new[3][0] - - if i < len(node.position.x) - 1: # no action on last data - # filtering - x_vec_est = np.array([[node.position.x[i]], - [node.position.y[i]], - [node.heading.value[i]], - [node.velocity.m[i]]]) - z_new = np.array([[node.position.x[i+1]], - [node.position.y[i+1]], - [node.heading.value[i+1]], - [node.velocity.m[i+1]]]) - x_vec_est_new, P_matrix_new = filter_veh.predict_and_update( - x_vec_est=x_vec_est, - u_vec=np.array([[0.], [0.]]), - P_matrix=P_matrix, - z_new=z_new - ) - P_matrix = P_matrix_new - - v_tmp = node.velocity.m - node.velocity = Velocity.from_position(node.position, scene.dt) - node.velocity.m = v_tmp - #if (np.abs(np.linalg.norm(np.vstack((node.velocity.x, node.velocity.y)), axis=0) - v_tmp) > 0.4).any(): - # print(np.abs(np.linalg.norm(np.vstack((node.velocity.x, node.velocity.y)), axis=0) - v_tmp)) - - node.acceleration = Acceleration.from_velocity(node.velocity, scene.dt) - node.acceleration.m = np.gradient(v_tmp, scene.dt) - node.heading.derivative = np.gradient(node.heading.value, scene.dt) - node.heading.value = (node.heading.value + np.pi) % (2.0 * np.pi) - np.pi - - if node_df.iloc[0]['type'] == env.NodeType.VEHICLE: - node_pos = np.stack((node.position.x, node.position.y), axis=1) - node_pos_map = scene.map[env.NodeType.VEHICLE.name].to_map_points(node_pos) - node_pos_int = np.round(node_pos_map).astype(int) - dilated_map = binary_dilation(scene.map[env.NodeType.VEHICLE.name].data[..., 0], generate_binary_structure(2, 2)) - if np.sum((dilated_map[node_pos_int[:, 0], node_pos_int[:, 1]] == 0))/node_pos_int.shape[0] > 0.1: - del node - continue # Out of map - - - - if not node_df.iloc[0]['type'] == env.NodeType.PEDESTRIAN: - # Re Integrate: - i_pos = integrate_heading_model(np.array([node.acceleration.m[1:]]), - np.array([node.heading.derivative[1:]]), - node.heading.value[0], - np.vstack((node.position.x[0], node.position.y[0])), - node.velocity.m[0], 0.5) - - - #if (np.abs(node.heading.derivative) > np.pi/8).any(): - # print(np.abs(node.heading.derivative).max()) - scene.nodes.append(node) - if node.is_robot is True: - scene.robot = node - - robot = False - num_heading_changed = 0 - num_moving_vehicles = 0 - for node in scene.nodes: - node.description = "straight" - num_global_straight += 1 - if node.type == env.NodeType.VEHICLE: - if np.linalg.norm((node.position.x[0] - node.position.x[-1], node.position.y[0] - node.position.y[-1])) > 10: - num_moving_vehicles += 1 - if np.abs(node.heading.value[0] - node.heading.value[-1]) > np.pi / 6: - if not np.sign(node.heading.value[0]) == np.sign(node.heading.value[-1]) and np.abs(node.heading.value[0] > 1/2 * np.pi): - if (node.heading.value[0] - node.heading.value[-1]) - np.sign((node.heading.value[0] - node.heading.value[-1])) * 2 * np.pi > np.pi / 6: - node.description = "curve" - num_global_curve += 1 - num_global_straight -= 1 - num_heading_changed += 1 - else: - node.description = "curve" - num_global_curve += 1 - num_global_straight -= 1 - num_heading_changed += 1 - - if node.is_robot: - robot = True - - if num_moving_vehicles > 0 and num_heading_changed / num_moving_vehicles > 0.4: - scene.description = "curvy" - else: - scene.description = "straight" - - if robot: # If we dont have a ego vehicle there was bad localization - pbar.set_description(str(scene)) - scenes.append(scene) - - del data - - env.scenes = scenes - - if len(scenes) > 0: - with open(data_dict_path, 'wb') as f: - pickle.dump(env, f, protocol=pickle.HIGHEST_PROTOCOL) - - print(num_global_straight) - print(num_global_curve) \ No newline at end of file diff --git a/data/processed/nuScenes_samples.pkl b/data/processed/nuScenes_samples.pkl deleted file mode 100644 index 9e398d4..0000000 Binary files a/data/processed/nuScenes_samples.pkl and /dev/null differ diff --git a/experiments/nuScenes/NuScenes Qualitative.ipynb b/experiments/nuScenes/NuScenes Qualitative.ipynb new file mode 100644 index 0000000..f0a230c --- /dev/null +++ b/experiments/nuScenes/NuScenes Qualitative.ipynb @@ -0,0 +1,645 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import sys\n", + "sys.path.append('../../trajectron')\n", + "import os\n", + "import numpy as np\n", + "import torch\n", + "import dill\n", + "import json\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker\n", + "import matplotlib.patheffects as pe\n", + "from helper import *\n", + "import visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load nuScenes SDK and data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "nuScenes_data_path = # Data Path to nuScenes data set \n", + "nuScenes_devkit_path = './devkit/python-sdk/'\n", + "sys.path.append(nuScenes_devkit_path)\n", + "from nuscenes.map_expansion.map_api import NuScenesMap\n", + "nusc_map = NuScenesMap(dataroot=nuScenes_data_path, map_name='boston-seaport')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "line_colors = ['#375397','#80CBE5','#ABCB51','#F05F78', '#C8B0B0']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Map Encoding Demo" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "with open('../processed/nuScenes_test_full.pkl', 'rb') as f:\n", + " eval_env = dill.load(f, encoding='latin1')\n", + "eval_scenes = eval_env.scenes" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "ph = 6\n", + "log_dir = './models'" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading from ./models/int_ee_me/model_registrar-12.pt\n", + "Loaded!\n", + "\n" + ] + } + ], + "source": [ + "model_dir = os.path.join(log_dir, 'int_ee_me') \n", + "eval_stg, hyp = load_model(model_dir, eval_env, ts=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'105'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scene = eval_scenes[25]\n", + "scene.name" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Define ROI in nuScenes Map\n", + "x_min = 773.0\n", + "x_max = 1100.0\n", + "y_min = 1231.0\n", + "y_max = 1510.0" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "layers = ['drivable_area',\n", + " 'road_segment',\n", + " 'lane',\n", + " 'ped_crossing',\n", + " 'walkway',\n", + " 'stop_line',\n", + " 'road_divider',\n", + " 'lane_divider']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction including Map Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ph = 6\n", + "with torch.no_grad():\n", + " timestep = np.array([2])\n", + " predictions = eval_stg.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=500)\n", + "\n", + " predictions_mm = eval_stg.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=1,\n", + " z_mode=True,\n", + " gmm_mode=True)\n", + "\n", + " # Plot predicted timestep for random scene in map\n", + " my_patch = (x_min, y_min, x_max, y_max)\n", + " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10, 10), alpha=0.1, render_egoposes_range=False)\n", + "\n", + " ax.plot([], [], 'ko-',\n", + " zorder=620,\n", + " markersize=4,\n", + " linewidth=2, alpha=0.7, label='Ours (MM)')\n", + "\n", + " ax.plot([],\n", + " [],\n", + " 'w--o', label='Ground Truth',\n", + " linewidth=3,\n", + " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", + "\n", + " plot_vehicle_nice(ax,\n", + " predictions,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " plot_vehicle_mm(ax,\n", + " predictions_mm,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " ax.set_ylim((1385, 1435))\n", + " ax.set_xlim((850, 900))\n", + " leg = ax.legend(loc='upper right', fontsize=20, frameon=True)\n", + " ax.axis('off')\n", + " for lh in leg.legendHandles:\n", + " lh.set_alpha(.5)\n", + " ax.get_legend().remove()\n", + " fig.show()\n", + " fig.savefig('plots/qual_nuScenes_map_pos.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction without Map Encoding" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading from ./models/int_ee/model_registrar-12.pt\n", + "Loaded!\n", + "\n" + ] + } + ], + "source": [ + "model_dir = os.path.join(log_dir, 'int_ee') \n", + "eval_stg_nm, hyp = load_model(model_dir, eval_env, ts=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ph = 6\n", + "with torch.no_grad():\n", + " timestep = np.array([2])\n", + " predictions = eval_stg_nm.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=500)\n", + "\n", + " predictions_mm = eval_stg_nm.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=1,\n", + " z_mode=True,\n", + " gmm_mode=True)\n", + "\n", + " # Plot predicted timestep for random scene in map\n", + " my_patch = (x_min, y_min, x_max, y_max)\n", + " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10, 10), alpha=0.1, render_egoposes_range=False)\n", + "\n", + " ax.plot([], [], 'ko-',\n", + " zorder=620,\n", + " markersize=4,\n", + " linewidth=2, alpha=0.7, label='Ours (MM)')\n", + "\n", + " ax.plot([],\n", + " [],\n", + " 'w--o', label='Ground Truth',\n", + " linewidth=3,\n", + " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", + "\n", + " plot_vehicle_nice(ax,\n", + " predictions,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " plot_vehicle_mm(ax,\n", + " predictions_mm,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " ax.set_ylim((1385, 1435))\n", + " ax.set_xlim((850, 900))\n", + " leg = ax.legend(loc='upper right', fontsize=20, frameon=True)\n", + " ax.axis('off')\n", + " for lh in leg.legendHandles:\n", + " lh.set_alpha(.5)\n", + " ax.get_legend().remove()\n", + " fig.show()\n", + " fig.savefig('plots/qual_nuScenes_no_map_pos.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction using velocity output" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading from ./models/ee_vel/model_registrar-12.pt\n", + "Loaded!\n", + "\n" + ] + } + ], + "source": [ + "model_dir = os.path.join(log_dir, 'ee_vel') \n", + "eval_stg_vel, hyp = load_model(model_dir, eval_env, ts=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ph = 6\n", + "with torch.no_grad():\n", + " timestep = np.array([2])\n", + " predictions = eval_stg_vel.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=500)\n", + "\n", + " predictions_mm = eval_stg_vel.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=1,\n", + " z_mode=True,\n", + " gmm_mode=True)\n", + "\n", + " # Plot predicted timestep for random scene in map\n", + " my_patch = (x_min, y_min, x_max, y_max)\n", + " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10, 10), alpha=0.1, render_egoposes_range=False)\n", + "\n", + " ax.plot([], [], 'ko-',\n", + " zorder=620,\n", + " markersize=4,\n", + " linewidth=2, alpha=0.7, label='Ours (MM)')\n", + "\n", + " ax.plot([],\n", + " [],\n", + " 'w--o', label='Ground Truth',\n", + " linewidth=3,\n", + " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", + "\n", + " plot_vehicle_nice(ax,\n", + " predictions,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " plot_vehicle_mm(ax,\n", + " predictions_mm,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " ax.set_ylim((1385, 1435))\n", + " ax.set_xlim((850, 900))\n", + " leg = ax.legend(loc='upper right', fontsize=20, frameon=True)\n", + " ax.axis('off')\n", + " for lh in leg.legendHandles:\n", + " lh.set_alpha(.5)\n", + " ax.get_legend().remove()\n", + " fig.show()\n", + " fig.savefig('plots/qual_nuScenes_no_map_vel.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prediction using velocity output and map" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Loading from ./models/me_vel/model_registrar-12.pt\n", + "Loaded!\n", + "\n" + ] + } + ], + "source": [ + "model_dir = os.path.join(log_dir, 'me_vel') \n", + "eval_stg_vel_map, hyp = load_model(model_dir, eval_env, ts=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ph = 6\n", + "with torch.no_grad():\n", + " timestep = np.array([2])\n", + " predictions = eval_stg_vel_map.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=500)\n", + "\n", + " predictions_mm = eval_stg_vel_map.predict(scene,\n", + " timestep,\n", + " ph,\n", + " num_samples=1,\n", + " z_mode=True,\n", + " gmm_mode=True)\n", + "\n", + " # Plot predicted timestep for random scene in map\n", + " my_patch = (x_min, y_min, x_max, y_max)\n", + " fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(10, 10), alpha=0.1, render_egoposes_range=False)\n", + "\n", + " ax.plot([], [], 'ko-',\n", + " zorder=620,\n", + " markersize=4,\n", + " linewidth=2, alpha=0.7, label='Ours (MM)')\n", + "\n", + " ax.plot([],\n", + " [],\n", + " 'w--o', label='Ground Truth',\n", + " linewidth=3,\n", + " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", + "\n", + " plot_vehicle_nice(ax,\n", + " predictions,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " plot_vehicle_mm(ax,\n", + " predictions_mm,\n", + " scene.dt,\n", + " max_hl=10,\n", + " ph=ph,\n", + " map=None, x_min=x_min, y_min=y_min)\n", + "\n", + " ax.set_ylim((1385, 1435))\n", + " ax.set_xlim((850, 900))\n", + " leg = ax.legend(loc='upper right', fontsize=20, frameon=True)\n", + " ax.axis('off')\n", + " for lh in leg.legendHandles:\n", + " lh.set_alpha(.5)\n", + " ax.get_legend().remove()\n", + " fig.show()\n", + " fig.savefig('plots/qual_nuScenes_map_vel.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_patch = (0, 0, 1, 1)\n", + "fig, ax = nusc_map.render_map_patch(my_patch, layers, figsize=(1, 1), alpha=0.1, render_egoposes_range=False)\n", + "ax.plot([], [], 'ko',\n", + " zorder=620,\n", + " markersize=4,\n", + " linewidth=2, alpha=0.7, label='Ours (ML)')\n", + "\n", + "ax.plot([],\n", + " [],\n", + " 'w--o', label='Ground Truth',\n", + " linewidth=3,\n", + " path_effects=[pe.Stroke(linewidth=4, foreground='k'), pe.Normal()])\n", + "leg = ax.legend(loc='upper left', fontsize=30, frameon=True)\n", + "for lh in leg.legendHandles:\n", + " lh.set_alpha(.5)\n", + "ax.axis('off')\n", + "ax.grid('off')\n", + "fig.savefig('plots/qual_nuScenes_legend.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6 (GenTrajectron)", + "language": "python", + "name": "gentraj" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/nuScenes/NuScenes Quantitative.ipynb b/experiments/nuScenes/NuScenes Quantitative.ipynb new file mode 100644 index 0000000..ff147b6 --- /dev/null +++ b/experiments/nuScenes/NuScenes Quantitative.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.ticker as ticker" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vehicles" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Results for: int_ee\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.15211091398370916\n", + "RB Viols @1.0s: 0.0024947383125349227\n", + "FDE @1.0s: 0.06731242196606527\n", + "KDE @1.0s: -4.282745726122304\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.6665500898672814\n", + "RB Viols @2.0s: 0.006711305643509033\n", + "FDE @2.0s: 0.4448624482250238\n", + "KDE @2.0s: -2.817182897766484\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.6002716865301074\n", + "RB Viols @3.0s: 0.03183521139877072\n", + "FDE @3.0s: 1.1269273173570473\n", + "KDE @3.0s: -1.672070802164807\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 2.9889876939803504\n", + "RB Viols @4.0s: 0.08143642515676414\n", + "FDE @4.0s: 2.1726876209006143\n", + "KDE @4.0s: -0.7623941477599536\n", + "----------------------------------------------\n", + "\n", + "FDE Results for: int_ee_me\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.17146557916606217\n", + "RB Viols @1.0s: 0.002893866020984665\n", + "FDE @1.0s: 0.06825468723974978\n", + "KDE @1.0s: -4.174627329473856\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.6874934647937203\n", + "RB Viols @2.0s: 0.006347814614763767\n", + "FDE @2.0s: 0.4473287549407142\n", + "KDE @2.0s: -2.7424043655184898\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.6150508554078604\n", + "RB Viols @3.0s: 0.027944558266592166\n", + "FDE @3.0s: 1.1370181075818808\n", + "KDE @3.0s: -1.616241617749356\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 2.9834139311814645\n", + "RB Viols @4.0s: 0.07611557086980816\n", + "FDE @4.0s: 2.2067347028461923\n", + "KDE @4.0s: -0.7050671606779637\n", + "----------------------------------------------\n", + "\n", + "FDE Results for: vel_ee_me\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.21398885662219846\n", + "RB Viols @1.0s: 0.0024283075681380767\n", + "FDE @1.0s: 0.1792272294232774\n", + "KDE @1.0s: 0.8111385940397233\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.715463329547642\n", + "RB Viols @2.0s: 0.006407897187558204\n", + "FDE @2.0s: 0.5706283482566946\n", + "KDE @2.0s: 0.051893685490453464\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.5440473025828012\n", + "RB Viols @3.0s: 0.02805111131806047\n", + "FDE @3.0s: 1.2515989489585615\n", + "KDE @3.0s: 0.371638561867866\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 2.714255228812044\n", + "RB Viols @4.0s: 0.06920216365555348\n", + "FDE @4.0s: 2.2400267464847876\n", + "KDE @4.0s: 0.8726346089263975\n", + "----------------------------------------------\n", + "\n", + "FDE Results for: robot\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.1295215269389519\n", + "RB Viols @1.0s: 0.0026757717999638924\n", + "FDE @1.0s: 0.07820393052295552\n", + "KDE @1.0s: -3.906838146881899\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.45962341869964574\n", + "RB Viols @2.0s: 0.0053363964614551365\n", + "FDE @2.0s: 0.3403511030418785\n", + "KDE @2.0s: -2.7593676749477294\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.02267032097404\n", + "RB Viols @3.0s: 0.016484509839321176\n", + "FDE @3.0s: 0.805915047871091\n", + "KDE @3.0s: -1.7502450775203158\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 1.8380306576706953\n", + "RB Viols @4.0s: 0.042144791478606246\n", + "FDE @4.0s: 1.4979755853506684\n", + "KDE @4.0s: -0.9291549495198915\n", + "----------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "for model in ['int_ee', 'int_ee_me', 'vel_ee', 'robot']:\n", + " print(f\"FDE Results for: {model}\")\n", + " for ph in [2, 4, 6, 8]:\n", + " print(f\"-----------------PH: {ph} -------------------\")\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}_{ph}_fde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " \n", + " print(f\"FDE Mean @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].mean()}\")\n", + " del perf_df \n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}_{ph}_rv_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"RB Viols @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].sum() / (len(perf_df['value'][perf_df['type'] == 'full'].index)*2000)}\")\n", + " del perf_df\n", + "\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_fde_most_likely_z.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"FDE @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'ml'].mean()}\") \n", + " del perf_df\n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_kde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"KDE @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].mean()}\") \n", + " print(\"----------------------------------------------\")\n", + " del perf_df\n", + " print(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Results for: int_ee_me_no_ego\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.16815279412540554\n", + "RB Viols @1.0s: 0.002895014589929844\n", + "FDE @1.0s: 0.06937045846177256\n", + "KDE @1.0s: -4.262019931215572\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.6655379721067188\n", + "RB Viols @2.0s: 0.006153364996585336\n", + "FDE @2.0s: 0.4359008486971371\n", + "KDE @2.0s: -2.856656149202157\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.546091556287448\n", + "RB Viols @3.0s: 0.027780530204259017\n", + "FDE @3.0s: 1.0896218245514429\n", + "KDE @3.0s: -1.7563896369106704\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 2.8358865412257397\n", + "RB Viols @4.0s: 0.07581256596510834\n", + "FDE @4.0s: 2.0939721352439022\n", + "KDE @4.0s: -0.8690706892091696\n", + "----------------------------------------------\n", + "\n", + "FDE Results for: robot\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.1295215269389519\n", + "RB Viols @1.0s: 0.0026757717999638924\n", + "FDE @1.0s: 0.07820393052295552\n", + "KDE @1.0s: -3.906838146881899\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.45962341869964574\n", + "RB Viols @2.0s: 0.0053363964614551365\n", + "FDE @2.0s: 0.3403511030418785\n", + "KDE @2.0s: -2.7593676749477294\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 1.02267032097404\n", + "RB Viols @3.0s: 0.016484509839321176\n", + "FDE @3.0s: 0.805915047871091\n", + "KDE @3.0s: -1.7502450775203158\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 1.8380306576706953\n", + "RB Viols @4.0s: 0.042144791478606246\n", + "FDE @4.0s: 1.4979755853506684\n", + "KDE @4.0s: -0.9291549495198915\n", + "----------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "for model in ['int_ee_me_no_ego', 'robot']:\n", + " print(f\"FDE Results for: {model}\")\n", + " for ph in [2, 4, 6, 8]:\n", + " print(f\"-----------------PH: {ph} -------------------\")\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}_{ph}_fde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " \n", + " print(f\"FDE Mean @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].mean()}\")\n", + " del perf_df \n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}_{ph}_rv_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"RB Viols @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].sum() / (len(perf_df['value'][perf_df['type'] == 'full'].index)*2000)}\")\n", + " del perf_df\n", + "\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_fde_most_likely_z.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"FDE @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'ml'].mean()}\") \n", + " del perf_df\n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_kde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"KDE @{ph*0.5}s: {perf_df['value'][perf_df['type'] == 'full'].mean()}\") \n", + " print(\"----------------------------------------------\")\n", + " del perf_df\n", + " print(\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pedestrians" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Results for: int_ee_me_ped\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.03182535279935429\n", + "ADE Mean @1.0s: 0.034975306849922005\n", + "KDE Mean @1.0s: -5.577685316351455\n", + "FDE @1.0s: 0.014470668260911932\n", + "ADE @1.0s: 0.021401672730783382\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.21879313416975887\n", + "ADE Mean @2.0s: 0.10080166010252017\n", + "KDE Mean @2.0s: -3.9582677566570568\n", + "FDE @2.0s: 0.1656927524369561\n", + "ADE @2.0s: 0.07265244240382243\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 0.48124106327369537\n", + "ADE Mean @3.0s: 0.20455084715465008\n", + "KDE Mean @3.0s: -2.768212012793919\n", + "FDE @3.0s: 0.36991744855974507\n", + "ADE @3.0s: 0.1538591151610063\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 0.7897925016736143\n", + "ADE Mean @4.0s: 0.3309282373807616\n", + "KDE Mean @4.0s: -1.891451489507079\n", + "FDE @4.0s: 0.61780508431085\n", + "ADE @4.0s: 0.2535511093237994\n", + "----------------------------------------------\n", + "\n", + "FDE Results for: vel_ee_ped\n", + "-----------------PH: 2 -------------------\n", + "FDE Mean @1.0s: 0.05470159146400349\n", + "ADE Mean @1.0s: 0.04723856023122099\n", + "KDE Mean @1.0s: -2.693286369409014\n", + "FDE @1.0s: 0.03272132837594798\n", + "ADE @1.0s: 0.03440844320849249\n", + "----------------------------------------------\n", + "-----------------PH: 4 -------------------\n", + "FDE Mean @2.0s: 0.235549582909888\n", + "ADE Mean @2.0s: 0.11606559815399368\n", + "KDE Mean @2.0s: -2.4601640447400186\n", + "FDE @2.0s: 0.17398568920641183\n", + "ADE @2.0s: 0.08409326559182477\n", + "----------------------------------------------\n", + "-----------------PH: 6 -------------------\n", + "FDE Mean @3.0s: 0.4833427705400407\n", + "ADE Mean @3.0s: 0.21676831990727596\n", + "KDE Mean @3.0s: -1.7550238928047612\n", + "FDE @3.0s: 0.3705610422470493\n", + "ADE @3.0s: 0.16234687699669642\n", + "----------------------------------------------\n", + "-----------------PH: 8 -------------------\n", + "FDE Mean @4.0s: 0.7761647665317681\n", + "ADE Mean @4.0s: 0.3376368652760976\n", + "KDE Mean @4.0s: -1.0900967343150951\n", + "FDE @4.0s: 0.6033992852865975\n", + "ADE @4.0s: 0.25754615271005243\n", + "----------------------------------------------\n", + "\n" + ] + } + ], + "source": [ + "for model in ['int_ee_me_ped', 'vel_ee_ped']:\n", + " print(f\"FDE Results for: {model}\")\n", + " for ph in [2, 4, 6, 8]:\n", + " print(f\"-----------------PH: {ph} -------------------\")\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_fde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"FDE Mean @{ph*0.5}s: {perf_df['value'][perf_df['metric'] == 'fde'].mean()}\")\n", + " del perf_df \n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_ade_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"ADE Mean @{ph*0.5}s: {perf_df['value'][perf_df['metric'] == 'ade'].mean()}\")\n", + " del perf_df\n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_kde_full.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"KDE Mean @{ph*0.5}s: {perf_df['value'][perf_df['metric'] == 'kde'].mean()}\")\n", + " del perf_df \n", + "\n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_fde_most_likely_z.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"FDE @{ph*0.5}s: {perf_df['value'][perf_df['metric'] == 'fde'].mean()}\") \n", + " del perf_df\n", + " \n", + " perf_df = pd.DataFrame()\n", + " for f in glob.glob(f\"results/{model}*_{ph}_ade_most_likely_z.csv\"):\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['model'] = model\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + " print(f\"ADE @{ph*0.5}s: {perf_df['value'][perf_df['metric'] == 'ade'].mean()}\") \n", + " del perf_df\n", + " print(\"----------------------------------------------\")\n", + " print(\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:trajectron] *", + "language": "python", + "name": "conda-env-trajectron-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/nuScenes/devkit b/experiments/nuScenes/devkit new file mode 160000 index 0000000..1050c3d --- /dev/null +++ b/experiments/nuScenes/devkit @@ -0,0 +1 @@ +Subproject commit 1050c3d11b5413a1fc5ca4e73a9e426747263297 diff --git a/experiments/nuScenes/evaluate.py b/experiments/nuScenes/evaluate.py new file mode 100644 index 0000000..8ce852a --- /dev/null +++ b/experiments/nuScenes/evaluate.py @@ -0,0 +1,185 @@ +import sys +import os +import dill +import json +import argparse +import torch +import numpy as np +import pandas as pd + +sys.path.append("../../trajectron") +from tqdm import tqdm +from model.model_registrar import ModelRegistrar +from model.trajectron import Trajectron +import evaluation +import utils +from scipy.interpolate import RectBivariateSpline + +seed = 0 +np.random.seed(seed) +torch.manual_seed(seed) +if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + +parser = argparse.ArgumentParser() +parser.add_argument("--model", help="model full path", type=str) +parser.add_argument("--checkpoint", help="model checkpoint to evaluate", type=int) +parser.add_argument("--data", help="full path to data file", type=str) +parser.add_argument("--output_path", help="path to output csv file", type=str) +parser.add_argument("--output_tag", help="name tag for output file", type=str) +parser.add_argument("--node_type", help="node type to evaluate", type=str) +parser.add_argument("--prediction_horizon", nargs='+', help="prediction horizon", type=int, default=None) +args = parser.parse_args() + + +def compute_road_violations(predicted_trajs, map, channel): + obs_map = 1 - map.data[..., channel, :, :] / 255 + + interp_obs_map = RectBivariateSpline(range(obs_map.shape[0]), + range(obs_map.shape[1]), + obs_map, + kx=1, ky=1) + + old_shape = predicted_trajs.shape + pred_trajs_map = map.to_map_points(predicted_trajs.reshape((-1, 2))) + + traj_obs_values = interp_obs_map(pred_trajs_map[:, 0], pred_trajs_map[:, 1], grid=False) + traj_obs_values = traj_obs_values.reshape((old_shape[0], old_shape[1], old_shape[2])) + num_viol_trajs = np.sum(traj_obs_values.max(axis=2) > 0, dtype=float) + + return num_viol_trajs + + +def load_model(model_dir, env, ts=100): + model_registrar = ModelRegistrar(model_dir, 'cpu') + model_registrar.load_models(ts) + with open(os.path.join(model_dir, 'config.json'), 'r') as config_json: + hyperparams = json.load(config_json) + + trajectron = Trajectron(model_registrar, hyperparams, None, 'cpu') + + trajectron.set_environment(env) + trajectron.set_annealing_params() + return trajectron, hyperparams + + +if __name__ == "__main__": + with open(args.data, 'rb') as f: + env = dill.load(f, encoding='latin1') + + eval_stg, hyperparams = load_model(args.model, env, ts=args.checkpoint) + + if 'override_attention_radius' in hyperparams: + for attention_radius_override in hyperparams['override_attention_radius']: + node_type1, node_type2, attention_radius = attention_radius_override.split(' ') + env.attention_radius[(node_type1, node_type2)] = float(attention_radius) + + scenes = env.scenes + + print("-- Preparing Node Graph") + for scene in tqdm(scenes): + scene.calculate_scene_graph(env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) + + for ph in args.prediction_horizon: + print(f"Prediction Horizon: {ph}") + max_hl = hyperparams['maximum_history_length'] + + with torch.no_grad(): + ############### MOST LIKELY Z ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + + print("-- Evaluating GMM Z Mode (Most Likely)") + for scene in tqdm(scenes): + timesteps = np.arange(scene.timesteps) + + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=1, + min_future_timesteps=8, + z_mode=True, + gmm_mode=True, + full_dist=False) # This will trigger grid sampling + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + prune_ph_to_future=False, + kde=False) + + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + + print(np.mean(eval_fde_batch_errors)) + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'ml'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_ade_most_likely_z.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'ml'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_fde_most_likely_z.csv')) + + + ############### FULL ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + eval_kde_nll = np.array([]) + eval_road_viols = np.array([]) + print("-- Evaluating Full") + for scene in tqdm(scenes): + timesteps = np.arange(scene.timesteps) + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=2000, + min_future_timesteps=8, + z_mode=False, + gmm_mode=False, + full_dist=False) + + if not predictions: + continue + + prediction_dict, _, _ = utils.prediction_output_to_trajectories(predictions, + scene.dt, + max_hl, + ph, + prune_ph_to_future=False) + + eval_road_viols_batch = [] + for t in prediction_dict.keys(): + for node in prediction_dict[t].keys(): + if node.type == args.node_type: + viols = compute_road_violations(prediction_dict[t][node], + scene.map[args.node_type], + channel=0) + if viols == 2000: + viols = 0 + + eval_road_viols_batch.append(viols) + + eval_road_viols = np.hstack((eval_road_viols, eval_road_viols_batch)) + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + prune_ph_to_future=False) + + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + eval_kde_nll = np.hstack((eval_kde_nll, batch_error_dict[args.node_type]['kde'])) + + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_ade_full.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_fde_full.csv')) + pd.DataFrame({'value': eval_kde_nll, 'metric': 'kde', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_kde_full.csv')) + pd.DataFrame({'value': eval_road_viols, 'metric': 'road_viols', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + "_" + str(ph) + '_rv_full.csv')) diff --git a/code/notebooks/helper.py b/experiments/nuScenes/helper.py similarity index 81% rename from code/notebooks/helper.py rename to experiments/nuScenes/helper.py index 1909007..5e2f1e6 100644 --- a/code/notebooks/helper.py +++ b/experiments/nuScenes/helper.py @@ -8,20 +8,20 @@ from scipy.ndimage import rotate import seaborn as sns from model.model_registrar import ModelRegistrar -from model.dyn_stg import SpatioTemporalGraphCVAEModel +from model import Trajectron from utils import prediction_output_to_trajectories from scipy.integrate import cumtrapz -line_colors = ['#80CBE5', '#375397', '#F05F78', '#ABCB51', '#C8B0B0'] +line_colors = ['#375397', '#F05F78', '#80CBE5', '#ABCB51', '#C8B0B0'] -cars = [plt.imread('Car TOP_VIEW 80CBE5.png'), - plt.imread('Car TOP_VIEW 375397.png'), - plt.imread('Car TOP_VIEW F05F78.png'), - plt.imread('Car TOP_VIEW ABCB51.png'), - plt.imread('Car TOP_VIEW C8B0B0.png')] +cars = [plt.imread('icons/Car TOP_VIEW 375397.png'), + plt.imread('icons/Car TOP_VIEW F05F78.png'), + plt.imread('icons/Car TOP_VIEW 80CBE5.png'), + plt.imread('icons/Car TOP_VIEW ABCB51.png'), + plt.imread('icons/Car TOP_VIEW C8B0B0.png')] -robot = plt.imread('Car TOP_VIEW ROBOT.png') +robot = plt.imread('icons/Car TOP_VIEW ROBOT.png') def load_model(model_dir, env, ts=3999): @@ -34,11 +34,9 @@ def load_model(model_dir, env, ts=3999): if 'incl_robot_node' not in hyperparams: hyperparams['incl_robot_node'] = False - stg = SpatioTemporalGraphCVAEModel(model_registrar, - hyperparams, - None, 'cpu') + stg = Trajectron(model_registrar, hyperparams, None, 'cpu') - stg.set_scene_graph(env) + stg.set_environment(env) stg.set_annealing_params() @@ -71,7 +69,7 @@ def plot_vehicle_nice(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y node_circle_size = 0.3 a = [] i = 0 - node_list = sorted(histories_dict.keys(), key=lambda x: x.length) + node_list = sorted(histories_dict.keys(), key=lambda x: x.id) for node in node_list: history = histories_dict[node] + np.array([x_min, y_min]) future = futures_dict[node] + np.array([x_min, y_min]) @@ -87,16 +85,16 @@ def plot_vehicle_nice(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y zorder=650, path_effects=[pe.Stroke(linewidth=5, foreground='k'), pe.Normal()]) - for t in range(predictions.shape[1]): - sns.kdeplot(predictions[:, t, 0], predictions[:, t, 1], + for t in range(predictions.shape[2]): + sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1], ax=ax, shade=True, shade_lowest=False, color=line_colors[i % len(line_colors)], zorder=600, alpha=0.8) - vel = node.get(ts_key, {'velocity': ['x', 'y']}) + vel = node.get(np.array([ts_key]), {'velocity': ['x', 'y']}) h = np.arctan2(vel[0, 1], vel[0, 0]) - r_img = rotate(cars[i % len(cars)], node.get(ts_key, {'heading': ['value']})[0, 0] * 180 / np.pi, + r_img = rotate(cars[i % len(cars)], node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi, reshape=True) - oi = OffsetImage(r_img, zoom=0.035, zorder=700) + oi = OffsetImage(r_img, zoom=0.025, zorder=700) veh_box = AnnotationBbox(oi, (history[-1, 0], history[-1, 1]), frameon=False) veh_box.zorder = 700 ax.add_artist(veh_box) @@ -104,8 +102,8 @@ def plot_vehicle_nice(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y else: # ax.plot(history[:, 0], history[:, 1], 'k--') - for t in range(predictions.shape[1]): - sns.kdeplot(predictions[:, t, 0], predictions[:, t, 1], + for t in range(predictions.shape[2]): + sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1], ax=ax, shade=True, shade_lowest=False, color='b', zorder=600, alpha=0.8) @@ -151,21 +149,21 @@ def plot_vehicle_mm(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0, y_m node_circle_size = 0.5 a = [] i = 0 - node_list = sorted(histories_dict.keys(), key=lambda x: x.length) + node_list = sorted(histories_dict.keys(), key=lambda x: x.id) for node in node_list: history = histories_dict[node] + np.array([x_min, y_min]) future = futures_dict[node] + np.array([x_min, y_min]) predictions = prediction_dict[node] + np.array([x_min, y_min]) if node.type.name == 'VEHICLE': - for sample_num in range(prediction_dict[node].shape[0]): - ax.plot(predictions[sample_num, :, 0], predictions[sample_num, :, 1], 'ko-', + for sample_num in range(prediction_dict[node].shape[1]): + ax.plot(predictions[:, sample_num, :, 0], predictions[:, sample_num, :, 1], 'ko-', zorder=620, markersize=5, linewidth=3, alpha=0.7) else: - for sample_num in range(prediction_dict[node].shape[0]): - ax.plot(predictions[sample_num, :, 0], predictions[sample_num, :, 1], 'ko-', + for sample_num in range(prediction_dict[node].shape[1]): + ax.plot(predictions[:, sample_num, :, 0], predictions[:, sample_num, :, 1], 'ko-', zorder=620, markersize=2, linewidth=1, alpha=0.7) @@ -197,20 +195,20 @@ def plot_vehicle_nice_mv(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0 node_circle_size = 0.3 a = [] i = 0 - node_list = sorted(histories_dict.keys(), key=lambda x: x.length) + node_list = sorted(histories_dict.keys(), key=lambda x: x.id) for node in node_list: - h = node.get(ts_key, {'heading': ['value']})[0, 0] + h = node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] history_org = histories_dict[node] + np.array([x_min, y_min]) - history = histories_dict[node] + np.array([x_min, y_min]) + node.length * np.array([np.cos(h), np.sin(h)]) - future = futures_dict[node] + np.array([x_min, y_min]) + node.length * np.array([np.cos(h), np.sin(h)]) - predictions = prediction_dict[node] + np.array([x_min, y_min]) + node.length * np.array([np.cos(h), np.sin(h)]) + history = histories_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)]) + future = futures_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)]) + predictions = prediction_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)]) if node.type.name == 'VEHICLE': - for t in range(predictions.shape[1]): - sns.kdeplot(predictions[:, t, 0], predictions[:, t, 1], + for t in range(predictions.shape[2]): + sns.kdeplot(predictions[0, :, t, 0], predictions[0, :, t, 1], ax=ax, shade=True, shade_lowest=False, color=line_colors[i % len(line_colors)], zorder=600, alpha=1.0) - r_img = rotate(cars[i % len(cars)], node.get(ts_key, {'heading': ['value']})[0, 0] * 180 / np.pi, + r_img = rotate(cars[i % len(cars)], node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi, reshape=True) oi = OffsetImage(r_img, zoom=0.08, zorder=700) veh_box = AnnotationBbox(oi, (history_org[-1, 0], history_org[-1, 1]), frameon=False) @@ -219,7 +217,7 @@ def plot_vehicle_nice_mv(ax, predictions, dt, max_hl=10, ph=6, map=None, x_min=0 i += 1 else: - for t in range(predictions.shape[1]): + for t in range(predictions.shape[2]): sns.kdeplot(predictions[:, t, 0], predictions[:, t, 1], ax=ax, shade=True, shade_lowest=False, color='b', zorder=600, alpha=0.8) @@ -260,12 +258,12 @@ def plot_vehicle_nice_mv_robot(ax, predictions, dt, max_hl=10, ph=6, map=None, x circle_edge_width = 0.5 node_circle_size = 0.3 - node_list = sorted(histories_dict.keys(), key=lambda x: x.length) + node_list = sorted(histories_dict.keys(), key=lambda x: x.id) for node in node_list: - h = node.get(ts_key, {'heading': ['value']})[0, 0] - history_org = histories_dict[node] + np.array([x_min, y_min]) + node.length / 2 * np.array( + h = node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] + history_org = histories_dict[node] + np.array([x_min, y_min]) + 5 / 2 * np.array( [np.cos(h), np.sin(h)]) - future = futures_dict[node] + np.array([x_min, y_min]) + node.length * np.array([np.cos(h), np.sin(h)]) + future = futures_dict[node] + np.array([x_min, y_min]) + 5 * np.array([np.cos(h), np.sin(h)]) ax.plot(future[:, 0], future[:, 1], @@ -276,7 +274,7 @@ def plot_vehicle_nice_mv_robot(ax, predictions, dt, max_hl=10, ph=6, map=None, x zorder=650, path_effects=[pe.Stroke(linewidth=5, foreground='k'), pe.Normal()]) - r_img = rotate(robot, node.get(ts_key, {'heading': ['value']})[0, 0] * 180 / np.pi, reshape=True) + r_img = rotate(robot, node.get(np.array([ts_key]), {'heading': ['°']})[0, 0] * 180 / np.pi, reshape=True) oi = OffsetImage(r_img, zoom=0.08, zorder=700) veh_box = AnnotationBbox(oi, (history_org[-1, 0], history_org[-1, 1]), frameon=False) veh_box.zorder = 700 @@ -285,4 +283,4 @@ def plot_vehicle_nice_mv_robot(ax, predictions, dt, max_hl=10, ph=6, map=None, x def integrate(f, dx, F0=0.): N = f.shape[0] - return F0 + np.hstack((np.zeros((N, 1)), cumtrapz(f, axis=1, dx=dx))) + return F0 + np.hstack((np.zeros((N, 1)), cumtrapz(f, axis=1, dx=dx))) \ No newline at end of file diff --git a/code/notebooks/Car TOP_VIEW 375397.png b/experiments/nuScenes/icons/Car TOP_VIEW 375397.png similarity index 100% rename from code/notebooks/Car TOP_VIEW 375397.png rename to experiments/nuScenes/icons/Car TOP_VIEW 375397.png diff --git a/code/notebooks/Car TOP_VIEW 80CBE5.png b/experiments/nuScenes/icons/Car TOP_VIEW 80CBE5.png similarity index 100% rename from code/notebooks/Car TOP_VIEW 80CBE5.png rename to experiments/nuScenes/icons/Car TOP_VIEW 80CBE5.png diff --git a/code/notebooks/Car TOP_VIEW ABCB51.png b/experiments/nuScenes/icons/Car TOP_VIEW ABCB51.png similarity index 100% rename from code/notebooks/Car TOP_VIEW ABCB51.png rename to experiments/nuScenes/icons/Car TOP_VIEW ABCB51.png diff --git a/code/notebooks/Car TOP_VIEW C8B0B0.png b/experiments/nuScenes/icons/Car TOP_VIEW C8B0B0.png similarity index 100% rename from code/notebooks/Car TOP_VIEW C8B0B0.png rename to experiments/nuScenes/icons/Car TOP_VIEW C8B0B0.png diff --git a/code/notebooks/Car TOP_VIEW F05F78.png b/experiments/nuScenes/icons/Car TOP_VIEW F05F78.png similarity index 100% rename from code/notebooks/Car TOP_VIEW F05F78.png rename to experiments/nuScenes/icons/Car TOP_VIEW F05F78.png diff --git a/code/notebooks/Car TOP_VIEW ROBOT.png b/experiments/nuScenes/icons/Car TOP_VIEW ROBOT.png similarity index 100% rename from code/notebooks/Car TOP_VIEW ROBOT.png rename to experiments/nuScenes/icons/Car TOP_VIEW ROBOT.png diff --git a/experiments/nuScenes/kalman_filter.py b/experiments/nuScenes/kalman_filter.py new file mode 100644 index 0000000..ebcb572 --- /dev/null +++ b/experiments/nuScenes/kalman_filter.py @@ -0,0 +1,124 @@ +import numpy as np + + +import numpy as np + + +class NonlinearKinematicBicycle: + """ + Nonlinear Kalman Filter for a kinematic bicycle model, assuming constant longitudinal speed + and constant heading array + """ + + def __init__(self, dt, sPos=None, sHeading=None, sVel=None, sMeasurement=None): + self.dt = dt + + # measurement matrix + self.C = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) + + # default noise covariance + if (sPos is None) and (sHeading is None) and (sVel is None): + # TODO need to further check + # sPos = 0.5 * 8.8 * dt ** 2 # assume 8.8m/s2 as maximum acceleration + # sHeading = 0.5 * dt # assume 0.5rad/s as maximum turn rate + # sVel = 8.8 * dt # assume 8.8m/s2 as maximum acceleration + # sMeasurement = 1.0 + sPos = 12 * self.dt # assume 6m/s2 as maximum acceleration + sHeading = 0.5 * self.dt # assume 0.5rad/s as maximum turn rate + sVel = 6 * self.dt # assume 6m/s2 as maximum acceleration + if sMeasurement is None: + sMeasurement = 5.0 + # state transition noise + self.Q = np.diag([sPos ** 2, sPos ** 2, sHeading ** 2, sVel ** 2]) + # measurement noise + self.R = np.diag([sMeasurement ** 2, sMeasurement ** 2, sMeasurement ** 2, sMeasurement ** 2]) + + def predict_and_update(self, x_vec_est, u_vec, P_matrix, z_new): + """ + for background please refer to wikipedia: https://en.wikipedia.org/wiki/Extended_Kalman_filter + :param x_vec_est: + :param u_vec: + :param P_matrix: + :param z_new: + :return: + """ + + ## Prediction Step + # predicted state estimate + x_pred = self._kinematic_bicycle_model_rearCG(x_vec_est, u_vec) + # Compute Jacobian to obtain the state transition matrix + A = self._cal_state_Jacobian(x_vec_est, u_vec) + # predicted error covariance + P_pred = A.dot(P_matrix.dot(A.transpose())) + self.Q + + ## Update Step + # innovation or measurement pre-fit residual + y_telda = z_new - self.C.dot(x_pred) + # innovation covariance + S = self.C.dot(P_pred.dot(self.C.transpose())) + self.R + # near-optimal Kalman gain + K = P_pred.dot(self.C.transpose().dot(np.linalg.inv(S))) + # updated (a posteriori) state estimate + x_vec_est_new = x_pred + K.dot(y_telda) + # updated (a posteriori) estimate covariance + P_matrix_new = np.dot((np.identity(4) - K.dot(self.C)), P_pred) + + return x_vec_est_new, P_matrix_new + + def _kinematic_bicycle_model_rearCG(self, x_old, u): + """ + :param x: vehicle state vector = [x position, y position, heading, velocity] + :param u: control vector = [acceleration, steering array] + :param dt: + :return: + """ + + acc = u[0] + delta = u[1] + + x = x_old[0] + y = x_old[1] + psi = x_old[2] + vel = x_old[3] + + x_new = np.array([[0.], [0.], [0.], [0.]]) + + x_new[0] = x + self.dt * vel * np.cos(psi + delta) + x_new[1] = y + self.dt * vel * np.sin(psi + delta) + x_new[2] = psi + self.dt * delta + #x_new[2] = _heading_angle_correction(x_new[2]) + x_new[3] = vel + self.dt * acc + + return x_new + + def _cal_state_Jacobian(self, x_vec, u_vec): + acc = u_vec[0] + delta = u_vec[1] + + x = x_vec[0] + y = x_vec[1] + psi = x_vec[2] + vel = x_vec[3] + + a13 = -self.dt * vel * np.sin(psi + delta) + a14 = self.dt * np.cos(psi + delta) + a23 = self.dt * vel * np.cos(psi + delta) + a24 = self.dt * np.sin(psi + delta) + a34 = self.dt * delta + + JA = np.array([[1.0, 0.0, a13[0], a14[0]], + [0.0, 1.0, a23[0], a24[0]], + [0.0, 0.0, 1.0, a34[0]], + [0.0, 0.0, 0.0, 1.0]]) + + return JA + + +def _heading_angle_correction(theta): + """ + correct heading array so that it always remains in [-pi, pi] + :param theta: + :return: + """ + theta_corrected = (theta + np.pi) % (2.0 * np.pi) - np.pi + return theta_corrected \ No newline at end of file diff --git a/experiments/nuScenes/models/int_ee/config.json b/experiments/nuScenes/models/int_ee/config.json new file mode 100644 index 0000000..7b2702b --- /dev/null +++ b/experiments/nuScenes/models/int_ee/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"VEHICLE": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 3], "strides": [2, 2, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}, "VEHICLE": {"name": "Unicycle", "distribution": true, "limits": {"max_a": 4, "min_a": -5, "max_heading_change": 0.7, "min_heading_change": -0.7}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["\u00b0", "d\u00b0"]}}, "pred_state": {"VEHICLE": {"position": ["x", "y"]}, "PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": true, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/nuScenes/models/int_ee/model_registrar-12.pt b/experiments/nuScenes/models/int_ee/model_registrar-12.pt new file mode 100644 index 0000000..53c415a Binary files /dev/null and b/experiments/nuScenes/models/int_ee/model_registrar-12.pt differ diff --git a/experiments/nuScenes/models/int_ee_me/config.json b/experiments/nuScenes/models/int_ee_me/config.json new file mode 100644 index 0000000..df709e7 --- /dev/null +++ b/experiments/nuScenes/models/int_ee_me/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"VEHICLE": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 3], "strides": [2, 2, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}, "VEHICLE": {"name": "Unicycle", "distribution": true, "limits": {"max_a": 4, "min_a": -5, "max_heading_change": 0.7, "min_heading_change": -0.7}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["\u00b0", "d\u00b0"]}}, "pred_state": {"VEHICLE": {"position": ["x", "y"]}, "PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": true, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": true, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/nuScenes/models/int_ee_me/model_registrar-12.pt b/experiments/nuScenes/models/int_ee_me/model_registrar-12.pt new file mode 100644 index 0000000..a0b75b0 Binary files /dev/null and b/experiments/nuScenes/models/int_ee_me/model_registrar-12.pt differ diff --git a/experiments/nuScenes/models/robot/config.json b/experiments/nuScenes/models/robot/config.json new file mode 100644 index 0000000..ff13bc3 --- /dev/null +++ b/experiments/nuScenes/models/robot/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"VEHICLE": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 3], "strides": [2, 2, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}, "VEHICLE": {"name": "Unicycle", "distribution": true, "limits": {"max_a": 4, "min_a": -5, "max_heading_change": 0.7, "min_heading_change": -0.7}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["\u00b0", "d\u00b0"]}}, "pred_state": {"VEHICLE": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": true, "node_freq_mult_train": true, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": true, "augment": false, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/nuScenes/models/robot/model_registrar-12.pt b/experiments/nuScenes/models/robot/model_registrar-12.pt new file mode 100644 index 0000000..cbb906a Binary files /dev/null and b/experiments/nuScenes/models/robot/model_registrar-12.pt differ diff --git a/experiments/nuScenes/models/vel_ee/config.json b/experiments/nuScenes/models/vel_ee/config.json new file mode 100644 index 0000000..dabd7c5 --- /dev/null +++ b/experiments/nuScenes/models/vel_ee/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.003, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 6, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"VEHICLE": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 3], "strides": [2, 2, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": false, "limits": {}}, "VEHICLE": {"name": "SingleIntegrator", "distribution": false, "limits": {"max_a": 4, "min_a": -5, "max_heading_change": 0.7, "min_heading_change": -0.7}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}, "VEHICLE": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"], "heading": ["\u00b0", "d\u00b0"]}}, "pred_state": {"VEHICLE": {"velocity": ["x", "y"]}, "PEDESTRIAN": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": true, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/nuScenes/models/vel_ee/model_registrar-12.pt b/experiments/nuScenes/models/vel_ee/model_registrar-12.pt new file mode 100644 index 0000000..181ed52 Binary files /dev/null and b/experiments/nuScenes/models/vel_ee/model_registrar-12.pt differ diff --git a/experiments/nuScenes/process_data.py b/experiments/nuScenes/process_data.py new file mode 100644 index 0000000..cda5663 --- /dev/null +++ b/experiments/nuScenes/process_data.py @@ -0,0 +1,472 @@ +import sys +import os +import numpy as np +import pandas as pd +import dill +import argparse +from tqdm import tqdm +from pyquaternion import Quaternion +from kalman_filter import NonlinearKinematicBicycle +from sklearn.model_selection import train_test_split + +nu_path = './devkit/python-sdk/' +sys.path.append(nu_path) +sys.path.append("../../trajectron") +from nuscenes.nuscenes import NuScenes +from nuscenes.map_expansion.map_api import NuScenesMap +from nuscenes.utils.splits import create_splits_scenes +from environment import Environment, Scene, Node, GeometricMap, derivative_of + +scene_blacklist = [499, 515, 517] + +FREQUENCY = 2 +dt = 1 / FREQUENCY +data_columns_vehicle = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration', 'heading'], ['x', 'y']]) +data_columns_vehicle = data_columns_vehicle.append(pd.MultiIndex.from_tuples([('heading', '°'), ('heading', 'd°')])) +data_columns_vehicle = data_columns_vehicle.append(pd.MultiIndex.from_product([['velocity', 'acceleration'], ['norm']])) + +data_columns_pedestrian = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']]) + +curv_0_2 = 0 +curv_0_1 = 0 +total = 0 + +standardization = { + 'PEDESTRIAN': { + 'position': { + 'x': {'mean': 0, 'std': 1}, + 'y': {'mean': 0, 'std': 1} + }, + 'velocity': { + 'x': {'mean': 0, 'std': 2}, + 'y': {'mean': 0, 'std': 2} + }, + 'acceleration': { + 'x': {'mean': 0, 'std': 1}, + 'y': {'mean': 0, 'std': 1} + } + }, + 'VEHICLE': { + 'position': { + 'x': {'mean': 0, 'std': 80}, + 'y': {'mean': 0, 'std': 80} + }, + 'velocity': { + 'x': {'mean': 0, 'std': 15}, + 'y': {'mean': 0, 'std': 15}, + 'norm': {'mean': 0, 'std': 15} + }, + 'acceleration': { + 'x': {'mean': 0, 'std': 4}, + 'y': {'mean': 0, 'std': 4}, + 'norm': {'mean': 0, 'std': 4} + }, + 'heading': { + 'x': {'mean': 0, 'std': 1}, + 'y': {'mean': 0, 'std': 1}, + '°': {'mean': 0, 'std': np.pi}, + 'd°': {'mean': 0, 'std': 1} + } + } +} + + +def augment_scene(scene, angle): + def rotate_pc(pc, alpha): + M = np.array([[np.cos(alpha), -np.sin(alpha)], + [np.sin(alpha), np.cos(alpha)]]) + return M @ pc + + data_columns_vehicle = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration', 'heading'], ['x', 'y']]) + data_columns_vehicle = data_columns_vehicle.append(pd.MultiIndex.from_tuples([('heading', '°'), ('heading', 'd°')])) + data_columns_vehicle = data_columns_vehicle.append(pd.MultiIndex.from_product([['velocity', 'acceleration'], ['norm']])) + + data_columns_pedestrian = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']]) + + scene_aug = Scene(timesteps=scene.timesteps, dt=scene.dt, name=scene.name, non_aug_scene=scene) + + alpha = angle * np.pi / 180 + + for node in scene.nodes: + if node.type == 'PEDESTRIAN': + x = node.data.position.x.copy() + y = node.data.position.y.copy() + + x, y = rotate_pc(np.array([x, y]), alpha) + + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + ax = derivative_of(vx, scene.dt) + ay = derivative_of(vy, scene.dt) + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay} + + node_data = pd.DataFrame(data_dict, columns=data_columns_pedestrian) + + node = Node(node_type=node.type, node_id=node.id, data=node_data, first_timestep=node.first_timestep) + elif node.type == 'VEHICLE': + x = node.data.position.x.copy() + y = node.data.position.y.copy() + + heading = getattr(node.data.heading, '°').copy() + heading += alpha + heading = (heading + np.pi) % (2.0 * np.pi) - np.pi + + x, y = rotate_pc(np.array([x, y]), alpha) + + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + ax = derivative_of(vx, scene.dt) + ay = derivative_of(vy, scene.dt) + + v = np.stack((vx, vy), axis=-1) + v_norm = np.linalg.norm(np.stack((vx, vy), axis=-1), axis=-1, keepdims=True) + heading_v = np.divide(v, v_norm, out=np.zeros_like(v), where=(v_norm > 1.)) + heading_x = heading_v[:, 0] + heading_y = heading_v[:, 1] + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('velocity', 'norm'): np.linalg.norm(np.stack((vx, vy), axis=-1), axis=-1), + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay, + ('acceleration', 'norm'): np.linalg.norm(np.stack((ax, ay), axis=-1), axis=-1), + ('heading', 'x'): heading_x, + ('heading', 'y'): heading_y, + ('heading', '°'): heading, + ('heading', 'd°'): derivative_of(heading, dt, radian=True)} + + node_data = pd.DataFrame(data_dict, columns=data_columns_vehicle) + + node = Node(node_type=node.type, node_id=node.id, data=node_data, first_timestep=node.first_timestep, + non_aug_node=node) + + scene_aug.nodes.append(node) + return scene_aug + + +def augment(scene): + scene_aug = np.random.choice(scene.augmented) + scene_aug.temporal_scene_graph = scene.temporal_scene_graph + scene_aug.map = scene.map + return scene_aug + + +def trajectory_curvature(t): + path_distance = np.linalg.norm(t[-1] - t[0]) + + lengths = np.sqrt(np.sum(np.diff(t, axis=0) ** 2, axis=1)) # Length between points + path_length = np.sum(lengths) + if np.isclose(path_distance, 0.): + return 0, 0, 0 + return (path_length / path_distance) - 1, path_length, path_distance + + +def process_scene(ns_scene, env, nusc, data_path): + scene_id = int(ns_scene['name'].replace('scene-', '')) + data = pd.DataFrame(columns=['frame_id', + 'type', + 'node_id', + 'robot', + 'x', 'y', 'z', + 'length', + 'width', + 'height', + 'heading']) + + sample_token = ns_scene['first_sample_token'] + sample = nusc.get('sample', sample_token) + frame_id = 0 + while sample['next']: + annotation_tokens = sample['anns'] + for annotation_token in annotation_tokens: + annotation = nusc.get('sample_annotation', annotation_token) + category = annotation['category_name'] + if len(annotation['attribute_tokens']): + attribute = nusc.get('attribute', annotation['attribute_tokens'][0])['name'] + else: + continue + + if 'pedestrian' in category and not 'stroller' in category and not 'wheelchair' in category: + our_category = env.NodeType.PEDESTRIAN + elif 'vehicle' in category and 'bicycle' not in category and 'motorcycle' not in category and 'parked' not in attribute: + our_category = env.NodeType.VEHICLE + else: + continue + + data_point = pd.Series({'frame_id': frame_id, + 'type': our_category, + 'node_id': annotation['instance_token'], + 'robot': False, + 'x': annotation['translation'][0], + 'y': annotation['translation'][1], + 'z': annotation['translation'][2], + 'length': annotation['size'][0], + 'width': annotation['size'][1], + 'height': annotation['size'][2], + 'heading': Quaternion(annotation['rotation']).yaw_pitch_roll[0]}) + data = data.append(data_point, ignore_index=True) + + # Ego Vehicle + our_category = env.NodeType.VEHICLE + sample_data = nusc.get('sample_data', sample['data']['CAM_FRONT']) + annotation = nusc.get('ego_pose', sample_data['ego_pose_token']) + data_point = pd.Series({'frame_id': frame_id, + 'type': our_category, + 'node_id': 'ego', + 'robot': True, + 'x': annotation['translation'][0], + 'y': annotation['translation'][1], + 'z': annotation['translation'][2], + 'length': 4, + 'width': 1.7, + 'height': 1.5, + 'heading': Quaternion(annotation['rotation']).yaw_pitch_roll[0], + 'orientation': None}) + data = data.append(data_point, ignore_index=True) + + sample = nusc.get('sample', sample['next']) + frame_id += 1 + + if len(data.index) == 0: + return None + + data.sort_values('frame_id', inplace=True) + max_timesteps = data['frame_id'].max() + + x_min = np.round(data['x'].min() - 50) + x_max = np.round(data['x'].max() + 50) + y_min = np.round(data['y'].min() - 50) + y_max = np.round(data['y'].max() + 50) + + data['x'] = data['x'] - x_min + data['y'] = data['y'] - y_min + + scene = Scene(timesteps=max_timesteps + 1, dt=dt, name=str(scene_id), aug_func=augment) + + # Generate Maps + map_name = nusc.get('log', ns_scene['log_token'])['location'] + nusc_map = NuScenesMap(dataroot=data_path, map_name=map_name) + + type_map = dict() + x_size = x_max - x_min + y_size = y_max - y_min + patch_box = (x_min + 0.5 * (x_max - x_min), y_min + 0.5 * (y_max - y_min), y_size, x_size) + patch_angle = 0 # Default orientation where North is up + canvas_size = (np.round(3 * y_size).astype(int), np.round(3 * x_size).astype(int)) + homography = np.array([[3., 0., 0.], [0., 3., 0.], [0., 0., 3.]]) + layer_names = ['lane', 'road_segment', 'drivable_area', 'road_divider', 'lane_divider', 'stop_line', + 'ped_crossing', 'stop_line', 'ped_crossing', 'walkway'] + map_mask = (nusc_map.get_map_mask(patch_box, patch_angle, layer_names, canvas_size) * 255.0).astype( + np.uint8) + map_mask = np.swapaxes(map_mask, 1, 2) # x axis comes first + # PEDESTRIANS + map_mask_pedestrian = np.stack((map_mask[9], map_mask[8], np.max(map_mask[:3], axis=0)), axis=0) + type_map['PEDESTRIAN'] = GeometricMap(data=map_mask_pedestrian, homography=homography, description=', '.join(layer_names)) + # VEHICLES + map_mask_vehicle = np.stack((np.max(map_mask[:3], axis=0), map_mask[3], map_mask[4]), axis=0) + type_map['VEHICLE'] = GeometricMap(data=map_mask_vehicle, homography=homography, description=', '.join(layer_names)) + + map_mask_plot = np.stack(((np.max(map_mask[:3], axis=0) - (map_mask[3] + 0.5 * map_mask[4]).clip( + max=255)).clip(min=0).astype(np.uint8), map_mask[8], map_mask[9]), axis=0) + type_map['VISUALIZATION'] = GeometricMap(data=map_mask_plot, homography=homography, description=', '.join(layer_names)) + + scene.map = type_map + del map_mask + del map_mask_pedestrian + del map_mask_vehicle + del map_mask_plot + + for node_id in pd.unique(data['node_id']): + node_frequency_multiplier = 1 + node_df = data[data['node_id'] == node_id] + + if node_df['x'].shape[0] < 2: + continue + + if not np.all(np.diff(node_df['frame_id']) == 1): + # print('Occlusion') + continue # TODO Make better + + node_values = node_df[['x', 'y']].values + x = node_values[:, 0] + y = node_values[:, 1] + heading = node_df['heading'].values + if node_df.iloc[0]['type'] == env.NodeType.VEHICLE and not node_id == 'ego': + # Kalman filter Agent + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + velocity = np.linalg.norm(np.stack((vx, vy), axis=-1), axis=-1) + + filter_veh = NonlinearKinematicBicycle(dt=scene.dt, sMeasurement=1.0) + P_matrix = None + for i in range(len(x)): + if i == 0: # initalize KF + # initial P_matrix + P_matrix = np.identity(4) + elif i < len(x): + # assign new est values + x[i] = x_vec_est_new[0][0] + y[i] = x_vec_est_new[1][0] + heading[i] = x_vec_est_new[2][0] + velocity[i] = x_vec_est_new[3][0] + + if i < len(x) - 1: # no action on last data + # filtering + x_vec_est = np.array([[x[i]], + [y[i]], + [heading[i]], + [velocity[i]]]) + z_new = np.array([[x[i + 1]], + [y[i + 1]], + [heading[i + 1]], + [velocity[i + 1]]]) + x_vec_est_new, P_matrix_new = filter_veh.predict_and_update( + x_vec_est=x_vec_est, + u_vec=np.array([[0.], [0.]]), + P_matrix=P_matrix, + z_new=z_new + ) + P_matrix = P_matrix_new + + curvature, pl, _ = trajectory_curvature(np.stack((x, y), axis=-1)) + if pl < 1.0: # vehicle is "not" moving + x = x[0].repeat(max_timesteps + 1) + y = y[0].repeat(max_timesteps + 1) + heading = heading[0].repeat(max_timesteps + 1) + global total + global curv_0_2 + global curv_0_1 + total += 1 + if pl > 1.0: + if curvature > .2: + curv_0_2 += 1 + node_frequency_multiplier = 3*int(np.floor(total/curv_0_2)) + elif curvature > .1: + curv_0_1 += 1 + node_frequency_multiplier = 3*int(np.floor(total/curv_0_1)) + + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + ax = derivative_of(vx, scene.dt) + ay = derivative_of(vy, scene.dt) + + if node_df.iloc[0]['type'] == env.NodeType.VEHICLE: + v = np.stack((vx, vy), axis=-1) + v_norm = np.linalg.norm(np.stack((vx, vy), axis=-1), axis=-1, keepdims=True) + heading_v = np.divide(v, v_norm, out=np.zeros_like(v), where=(v_norm > 1.)) + heading_x = heading_v[:, 0] + heading_y = heading_v[:, 1] + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('velocity', 'norm'): np.linalg.norm(np.stack((vx, vy), axis=-1), axis=-1), + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay, + ('acceleration', 'norm'): np.linalg.norm(np.stack((ax, ay), axis=-1), axis=-1), + ('heading', 'x'): heading_x, + ('heading', 'y'): heading_y, + ('heading', '°'): heading, + ('heading', 'd°'): derivative_of(heading, dt, radian=True)} + node_data = pd.DataFrame(data_dict, columns=data_columns_vehicle) + else: + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay} + node_data = pd.DataFrame(data_dict, columns=data_columns_pedestrian) + + node = Node(node_type=node_df.iloc[0]['type'], node_id=node_id, data=node_data, frequency_multiplier=node_frequency_multiplier) + node.first_timestep = node_df['frame_id'].iloc[0] + if node_df.iloc[0]['robot'] == True: + node.is_robot = True + scene.robot = node + + scene.nodes.append(node) + + return scene + + +def process_data(data_path, version, output_path, val_split): + nusc = NuScenes(version=version, dataroot=data_path, verbose=True) + splits = create_splits_scenes() + train_scenes, val_scenes = train_test_split(splits['train' if 'mini' not in version else 'mini_train'], test_size=val_split) + train_scene_names = splits['train' if 'mini' not in version else 'mini_train'] + val_scene_names = []#val_scenes + test_scene_names = splits['val' if 'mini' not in version else 'mini_val'] + + ns_scene_names = dict() + ns_scene_names['train'] = train_scene_names + ns_scene_names['val'] = val_scene_names + ns_scene_names['test'] = test_scene_names + + for data_class in ['train', 'val', 'test']: + env = Environment(node_type_list=['VEHICLE', 'PEDESTRIAN'], standardization=standardization) + attention_radius = dict() + attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.PEDESTRIAN)] = 10.0 + attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.VEHICLE)] = 20.0 + attention_radius[(env.NodeType.VEHICLE, env.NodeType.PEDESTRIAN)] = 20.0 + attention_radius[(env.NodeType.VEHICLE, env.NodeType.VEHICLE)] = 30.0 + + env.attention_radius = attention_radius + env.robot_type = env.NodeType.VEHICLE + scenes = [] + + for ns_scene_name in tqdm(ns_scene_names[data_class]): + ns_scene = nusc.get('scene', nusc.field2token('scene', 'name', ns_scene_name)[0]) + scene_id = int(ns_scene['name'].replace('scene-', '')) + if scene_id in scene_blacklist: # Some scenes have bad localization + continue + + scene = process_scene(ns_scene, env, nusc, data_path) + if scene is not None: + if data_class == 'train': + scene.augmented = list() + angles = np.arange(0, 360, 15) + for angle in angles: + scene.augmented.append(augment_scene(scene, angle)) + scenes.append(scene) + + print(f'Processed {len(scenes):.2f} scenes') + + env.scenes = scenes + + if len(scenes) > 0: + mini_string = '' + if 'mini' in version: + mini_string = '_mini' + data_dict_path = os.path.join(output_path, 'nuScenes_' + data_class + mini_string + '_full.pkl') + with open(data_dict_path, 'wb') as f: + dill.dump(env, f, protocol=dill.HIGHEST_PROTOCOL) + print('Saved Environment!') + + global total + global curv_0_2 + global curv_0_1 + print(f"Total Nodes: {total}") + print(f"Curvature > 0.1 Nodes: {curv_0_1}") + print(f"Curvature > 0.2 Nodes: {curv_0_2}") + total = 0 + curv_0_1 = 0 + curv_0_2 = 0 + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, required=True) + parser.add_argument('--version', type=str, required=True) + parser.add_argument('--output_path', type=str, required=True) + parser.add_argument('--val_split', type=int, default=0.15) + args = parser.parse_args() + process_data(args.data, args.version, args.output_path, args.val_split) \ No newline at end of file diff --git a/code/model/__init__.py b/experiments/nuScenes/results/.gitkeep similarity index 100% rename from code/model/__init__.py rename to experiments/nuScenes/results/.gitkeep diff --git a/experiments/pedestrians/Result Analysis.ipynb b/experiments/pedestrians/Result Analysis.ipynb new file mode 100644 index 0000000..607a9c5 --- /dev/null +++ b/experiments/pedestrians/Result Analysis.ipynb @@ -0,0 +1,1607 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import glob\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "from collections import OrderedDict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def pretty_dataset_name(dataset_name):\n", + " if dataset_name == 'eth':\n", + " return 'ETH - Univ'\n", + " elif dataset_name == 'hotel':\n", + " return 'ETH - Hotel'\n", + " elif dataset_name == 'univ':\n", + " return 'UCY - Univ'\n", + " elif dataset_name == 'zara1':\n", + " return 'UCY - Zara 1'\n", + " elif dataset_name == 'zara2':\n", + " return 'UCY - Zara 2'\n", + " else:\n", + " return dataset_name" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_names = ['eth', 'hotel', 'univ', 'zara1', 'zara2', 'Average']\n", + "alg_name = \"Ours\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Displacement Error Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "prior_work_fse_results = {\n", + " 'ETH - Univ': OrderedDict([('Linear', 2.94), ('Vanilla LSTM', 2.41), ('Social LSTM', 2.35), ('Social Attention', 3.74)]),\n", + " 'ETH - Hotel': OrderedDict([('Linear', 0.72), ('Vanilla LSTM', 1.91), ('Social LSTM', 1.76), ('Social Attention', 2.64)]),\n", + " 'UCY - Univ': OrderedDict([('Linear', 1.59), ('Vanilla LSTM', 1.31), ('Social LSTM', 1.40), ('Social Attention', 0.52)]),\n", + " 'UCY - Zara 1': OrderedDict([('Linear', 1.21), ('Vanilla LSTM', 0.88), ('Social LSTM', 1.00), ('Social Attention', 2.13)]),\n", + " 'UCY - Zara 2': OrderedDict([('Linear', 1.48), ('Vanilla LSTM', 1.11), ('Social LSTM', 1.17), ('Social Attention', 3.92)]),\n", + " 'Average': OrderedDict([('Linear', 1.59), ('Vanilla LSTM', 1.52), ('Social LSTM', 1.54), ('Social Attention', 2.59)])\n", + "}\n", + "\n", + "\n", + "# These are for a prediction horizon of 12 timesteps.\n", + "prior_work_ade_results = {\n", + " 'ETH - Univ': OrderedDict([('Linear', 1.33), ('Vanilla LSTM', 1.09), ('Social LSTM', 1.09), ('Social Attention', 0.39)]),\n", + " 'ETH - Hotel': OrderedDict([('Linear', 0.39), ('Vanilla LSTM', 0.86), ('Social LSTM', 0.79), ('Social Attention', 0.29)]),\n", + " 'UCY - Univ': OrderedDict([('Linear', 0.82), ('Vanilla LSTM', 0.61), ('Social LSTM', 0.67), ('Social Attention', 0.20)]),\n", + " 'UCY - Zara 1': OrderedDict([('Linear', 0.62), ('Vanilla LSTM', 0.41), ('Social LSTM', 0.47), ('Social Attention', 0.30)]),\n", + " 'UCY - Zara 2': OrderedDict([('Linear', 0.77), ('Vanilla LSTM', 0.52), ('Social LSTM', 0.56), ('Social Attention', 0.33)]),\n", + " 'Average': OrderedDict([('Linear', 0.79), ('Vanilla LSTM', 0.70), ('Social LSTM', 0.72), ('Social Attention', 0.30)])\n", + "}\n", + "\n", + "linestyles = ['--', '-.', '-', ':']" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "mean_markers = 'X'\n", + "marker_size = 7\n", + "line_colors = ['#1f78b4','#33a02c','#fb9a99','#e31a1c']\n", + "area_colors = ['#80CBE5','#ABCB51', '#F05F78']\n", + "area_rgbs = list()\n", + "for c in area_colors:\n", + " area_rgbs.append([int(c[i:i+2], 16) for i in (1, 3, 5)])" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_attention_radius_3_fde_most_likely.csv\n", + "results/hotel_attention_radius_3_fde_most_likely.csv\n", + "results/univ_attention_radius_3_fde_most_likely.csv\n", + "results/zara1_attention_radius_3_fde_most_likely.csv\n", + "results/zara2_attention_radius_3_fde_most_likely.csv\n" + ] + }, + { + "data": { + "text/html": [ + "
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error_valueerror_typetypedatasetmethod
00.242668fdemlethOurs
10.158331fdemlethOurs
20.095482fdemlethOurs
31.069288fdemlethOurs
41.734359fdemlethOurs
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" + ], + "text/plain": [ + " error_value error_type type dataset method\n", + "0 0.242668 fde ml eth Ours\n", + "1 0.158331 fde ml eth Ours\n", + "2 0.095482 fde ml eth Ours\n", + "3 1.069288 fde ml eth Ours\n", + "4 1.734359 fde ml eth Ours" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load Ours\n", + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}*attention_radius_3*fde_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']\n", + "perf_df = perf_df.rename(columns={\"metric\": \"error_type\", \"value\": \"error_value\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Trajectron and GAN\n", + "errors_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_errors.csv')], ignore_index=True)\n", + "del errors_df['data_precondition']\n", + "errors_df = errors_df[~(errors_df['method'] == 'our_full')]\n", + "errors_df = errors_df[~(errors_df['error_type'] == 'mse')]\n", + "errors_df.loc[errors_df['error_type'] =='fse', 'error_type'] = 'fde'\n", + "#errors_df.loc[errors_df['error_type'] =='mse', 'error_type'] = 'ade'\n", + "errors_df.loc[errors_df['method'] == 'our_most_likely', 'method'] = 'Trajectron'" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=False'.\n", + "\n", + "To retain the current behavior and silence the warning, pass 'sort=True'.\n", + "\n", + " sort=sort,\n" + ] + }, + { + "data": { + "text/html": [ + "
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datasetmethodrunnodesampleerror_typeerror_value
2186000hotelsgan0Pedestrian/00fde4.045972
2186001hotelsgan0Pedestrian/01fde3.717624
2186002hotelsgan0Pedestrian/02fde5.378286
2186003hotelsgan0Pedestrian/03fde4.215567
2186004hotelsgan0Pedestrian/04fde4.663851
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77099995zara2sgan99Pedestrian/351995fde0.620136
77099996zara2sgan99Pedestrian/351996fde0.681608
77099997zara2sgan99Pedestrian/351997fde0.860765
77099998zara2sgan99Pedestrian/351998fde0.545317
77099999zara2sgan99Pedestrian/351999fde1.027843
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25700000 rows × 7 columns

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" + ], + "text/plain": [ + " dataset method run node sample error_type error_value\n", + "2186000 hotel sgan 0 Pedestrian/0 0 fde 4.045972\n", + "2186001 hotel sgan 0 Pedestrian/0 1 fde 3.717624\n", + "2186002 hotel sgan 0 Pedestrian/0 2 fde 5.378286\n", + "2186003 hotel sgan 0 Pedestrian/0 3 fde 4.215567\n", + "2186004 hotel sgan 0 Pedestrian/0 4 fde 4.663851\n", + "... ... ... ... ... ... ... ...\n", + "77099995 zara2 sgan 99 Pedestrian/35 1995 fde 0.620136\n", + "77099996 zara2 sgan 99 Pedestrian/35 1996 fde 0.681608\n", + "77099997 zara2 sgan 99 Pedestrian/35 1997 fde 0.860765\n", + "77099998 zara2 sgan 99 Pedestrian/35 1998 fde 0.545317\n", + "77099999 zara2 sgan 99 Pedestrian/35 1999 fde 1.027843\n", + "\n", + "[25700000 rows x 7 columns]" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "perf_df = perf_df.append(errors_df)\n", + "errors_df" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with sns.color_palette(\"muted\"):\n", + " fig_fse, ax_fses = plt.subplots(nrows=1, ncols=6, figsize=(8, 4), dpi=300, sharey=True)\n", + " for idx, ax_fse in enumerate(ax_fses):\n", + " dataset_name = dataset_names[idx]\n", + " if dataset_name != 'Average':\n", + " specific_df = perf_df[(perf_df['dataset'] == dataset_name) & (perf_df['error_type'] == 'fde')]\n", + " specific_df['dataset'] = pretty_dataset_name(dataset_name)\n", + " else:\n", + " specific_df = perf_df[(perf_df['error_type'] == 'fde')].copy()\n", + " specific_df['dataset'] = 'Average'\n", + "\n", + " sns.boxplot(x='dataset', y='error_value', hue='method',\n", + " data=specific_df, ax=ax_fse, showfliers=False,\n", + " palette=area_colors, hue_order=['sgan', 'Trajectron', alg_name], width=2.)\n", + " \n", + " ax_fse.get_legend().remove()\n", + " ax_fse.set_xlabel('')\n", + " ax_fse.set_ylabel('' if idx > 0 else 'Final Displacement Error (m)')\n", + "\n", + " ax_fse.scatter([-0.665, 0, 0.665],\n", + " [np.mean(specific_df[specific_df['method'] == 'sgan']['error_value']),\n", + " np.mean(specific_df[specific_df['method'] == 'Trajectron']['error_value']),\n", + " np.mean(specific_df[specific_df['method'] == alg_name]['error_value'])],\n", + " s=marker_size*marker_size, c=np.asarray(area_rgbs)/255.0, marker=mean_markers,\n", + " edgecolors='#545454', zorder=10)\n", + " \n", + " for baseline_idx, (baseline, fse_val) in enumerate(prior_work_fse_results[pretty_dataset_name(dataset_name)].items()):\n", + " ax_fse.axhline(y=fse_val, label=baseline, color=line_colors[baseline_idx], linestyle=linestyles[baseline_idx])\n", + " \n", + " if idx == 0:\n", + " handles, labels = ax_fse.get_legend_handles_labels()\n", + "\n", + "\n", + " handles = [handles[0], handles[4], handles[1], handles[5], handles[2], handles[6], handles[3]]\n", + " labels = [labels[0], 'Social GAN', labels[1], 'Trajectron', labels[2], alg_name, labels[3]]\n", + "\n", + " ax_fse.legend(handles, labels, \n", + " loc='lower center', bbox_to_anchor=(0.5, 0.9),\n", + " ncol=4, borderaxespad=0, frameon=False,\n", + " bbox_transform=fig_fse.transFigure)\n", + "\n", + "\n", + "# fig_fse.text(0.51, 0.03, 'Dataset', ha='center')\n", + "\n", + "plt.savefig('plots/fde_boxplots.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df\n", + "del errors_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Average Displacement Error" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_attention_radius_3_ade_most_likely.csv\n", + "results/hotel_attention_radius_3_ade_most_likely.csv\n", + "results/univ_attention_radius_3_ade_most_likely.csv\n", + "results/zara1_attention_radius_3_ade_most_likely.csv\n", + "results/zara2_attention_radius_3_ade_most_likely.csv\n" + ] + } + ], + "source": [ + "# Load Ours\n", + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}*attention_radius_3*ade_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']\n", + "perf_df = perf_df.rename(columns={\"metric\": \"error_type\", \"value\": \"error_value\"})\n", + "#perf_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Trajectron and GAN\n", + "errors_df = pd.concat([pd.read_csv(f) for f in glob.glob('old/curr_*_errors.csv')], ignore_index=True)\n", + "del errors_df['data_precondition']\n", + "errors_df = errors_df[~(errors_df['method'] == 'our_full')]\n", + "errors_df = errors_df[~(errors_df['error_type'] == 'fse')]\n", + "#errors_df.loc[errors_df['error_type'] =='fse', 'error_type'] = 'fde'\n", + "errors_df.loc[errors_df['error_type'] =='mse', 'error_type'] = 'ade'\n", + "errors_df.loc[errors_df['method'] == 'our_most_likely', 'method'] = 'Trajectron'" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=False'.\n", + "\n", + "To retain the current behavior and silence the warning, pass 'sort=True'.\n", + "\n", + " sort=sort,\n" + ] + } + ], + "source": [ + "perf_df = perf_df.append(errors_df)\n", + "del errors_df" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n", + "/home/timsal/anaconda3/envs/trajectron/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " import sys\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "with sns.color_palette(\"muted\"):\n", + " fig_fse, ax_fses = plt.subplots(nrows=1, ncols=6, figsize=(8, 4), dpi=300, sharey=True)\n", + " for idx, ax_fse in enumerate(ax_fses):\n", + " dataset_name = dataset_names[idx]\n", + " if dataset_name != 'Average':\n", + " specific_df = perf_df[(perf_df['dataset'] == dataset_name) & (perf_df['error_type'] == 'ade')]\n", + " specific_df['dataset'] = pretty_dataset_name(dataset_name)\n", + " else:\n", + " specific_df = perf_df[(perf_df['error_type'] == 'ade')].copy()\n", + " specific_df['dataset'] = 'Average'\n", + "\n", + " sns.boxplot(x='dataset', y='error_value', hue='method',\n", + " data=specific_df, ax=ax_fse, showfliers=False,\n", + " palette=area_colors, hue_order=['sgan', 'Trajectron', alg_name], width=2.)\n", + "\n", + " ax_fse.get_legend().remove()\n", + " ax_fse.set_xlabel('')\n", + " ax_fse.set_ylabel('' if idx > 0 else 'Average Displacement Error (m)')\n", + "\n", + " ax_fse.scatter([-0.665, 0, 0.665],\n", + " [np.mean(specific_df[specific_df['method'] == 'sgan']['error_value']),\n", + " np.mean(specific_df[specific_df['method'] == 'Trajectron']['error_value']),\n", + " np.mean(specific_df[specific_df['method'] == alg_name]['error_value'])],\n", + " s=marker_size*marker_size, c=np.asarray(area_rgbs)/255.0, marker=mean_markers,\n", + " edgecolors='#545454', zorder=10)\n", + " \n", + " for baseline_idx, (baseline, fse_val) in enumerate(prior_work_ade_results[pretty_dataset_name(dataset_name)].items()):\n", + " ax_fse.axhline(y=fse_val, label=baseline, color=line_colors[baseline_idx], linestyle=linestyles[baseline_idx])\n", + " \n", + " if idx == 0:\n", + " handles, labels = ax_fse.get_legend_handles_labels()\n", + "\n", + "\n", + " handles = [handles[0], handles[4], handles[1], handles[5], handles[2], handles[6], handles[3]]\n", + " labels = [labels[0], 'Social GAN', labels[1], 'Trajectron', labels[2], alg_name, labels[3]]\n", + "\n", + " ax_fse.legend(handles, labels, \n", + " loc='lower center', bbox_to_anchor=(0.5, 0.9),\n", + " ncol=4, borderaxespad=0, frameon=False,\n", + " bbox_transform=fig_fse.transFigure)\n", + "\n", + "# fig_fse.text(0.51, 0.03, 'Dataset', ha='center')\n", + "\n", + "plt.savefig('plots/ade_boxplots.pdf', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KDE Negative Log Likelihood Attention Radius 3m" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_12_kde_full.csv\n", + "results/hotel_12_kde_full.csv\n", + "results/univ_12_kde_full.csv\n", + "results/zara1_12_kde_full.csv\n", + "results/zara2_12_kde_full.csv\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/borisi/anaconda3/envs/gentraj/lib/python3.6/site-packages/pandas/core/frame.py:7123: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=False'.\n", + "\n", + "To retain the current behavior and silence the warning, pass 'sort=True'.\n", + "\n", + " sort=sort,\n" + ] + } + ], + "source": [ + "# Load Ours\n", + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_12*kde_full.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + "#perf_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# # Load Trajectron and SGAN\n", + "# lls_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_lls.csv')], ignore_index=True)\n", + "# lls_df.loc[lls_df['method'] == 'our_full', 'method'] = 'Trajectron'\n", + "# lls_df['error_type'] = 'KDE'\n", + "# #lls_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KDE NLL for ETH - Univ\n", + "Ours: 2.922618333137345\n", + "KDE NLL for ETH - Hotel\n", + "Ours: -1.4172003110091507\n", + "KDE NLL for UCY - Univ\n", + "Ours: -1.0312765478327917\n", + "KDE NLL for UCY - Zara 1\n", + "Ours: -1.320554662223125\n", + "KDE NLL for UCY - Zara 2\n", + "Ours: -2.265794496180711\n", + "KDE NLL for Average\n", + "Ours: -1.1628978588526018\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " if dataset != 'Average':\n", + " print('KDE NLL for ' + pretty_dataset_name(dataset))\n", + " #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n", + " #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print('KDE NLL for ' + pretty_dataset_name(dataset))\n", + " #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan')]['log-likelihood'].mean()}\")\n", + " #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')]['log-likelihood'].mean()}\")\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Most Likely FDE Attention Radius 3m" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_12_fde_most_likely.csv\n", + "results/hotel_12_fde_most_likely.csv\n", + "results/univ_12_fde_most_likely.csv\n", + "results/zara1_12_fde_most_likely.csv\n", + "results/zara2_12_fde_most_likely.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_12*fde_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = 'Trajectron++'\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Most Likely for ETH - Univ\n", + "Ours: 1.8771982385234873\n", + "FDE Most Likely for ETH - Hotel\n", + "Ours: 0.617328603355503\n", + "FDE Most Likely for UCY - Univ\n", + "Ours: 1.1245144943461012\n", + "FDE Most Likely for UCY - Zara 1\n", + "Ours: 0.7944446572286218\n", + "FDE Most Likely for UCY - Zara 2\n", + "Ours: 0.660121689972624\n", + "FDE Most Likely for Average\n", + "Ours: 1.0173143626632004\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Most Likely for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Most Likely Evaluation ADE Attention Radius 3m" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_12_ade_most_likely.csv\n", + "results/hotel_12_ade_most_likely.csv\n", + "results/univ_12_ade_most_likely.csv\n", + "results/zara1_12_ade_most_likely.csv\n", + "results/zara2_12_ade_most_likely.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_12*ade_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = 'Trajectron++'\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADE Most Likely for ETH - Univ\n", + "Ours: 0.7914500193014897\n", + "ADE Most Likely for ETH - Hotel\n", + "Ours: 0.27776006532871594\n", + "ADE Most Likely for UCY - Univ\n", + "Ours: 0.4266315625403817\n", + "ADE Most Likely for UCY - Zara 1\n", + "Ours: 0.3099620513771519\n", + "ADE Most Likely for UCY - Zara 2\n", + "Ours: 0.25468354869237164\n", + "ADE Most Likely for Average\n", + "Ours: 0.3919718759746782\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('ADE Most Likely for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Best of 20 Evaluation FDE Attention Radius 3m" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_12_fde_best_of.csv\n", + "results/hotel_12_fde_best_of.csv\n", + "results/univ_12_fde_best_of.csv\n", + "results/zara1_12_fde_best_of.csv\n", + "results/zara2_12_fde_best_of.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_12*fde_best_of.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Best of 20 for ETH - Univ\n", + "Trajectron++: 0.941833113317412\n", + "FDE Best of 20 for ETH - Hotel\n", + "Trajectron++: 0.25054278903414373\n", + "FDE Best of 20 for UCY - Univ\n", + "Trajectron++: 0.44114303527050003\n", + "FDE Best of 20 for UCY - Zara 1\n", + "Trajectron++: 0.3371937683488606\n", + "FDE Best of 20 for UCY - Zara 2\n", + "Trajectron++: 0.27854739859128275\n", + "FDE Best of 20 for Average\n", + "Trajectron++: 0.4106989096039851\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Best of 20 Evaluation ADE Attention Radius 3m" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_12_ade_best_of.csv\n", + "results/hotel_12_ade_best_of.csv\n", + "results/univ_12_ade_best_of.csv\n", + "results/zara1_12_ade_best_of.csv\n", + "results/zara2_12_ade_best_of.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_12*ade_best_of.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Best of 20 for ETH - Univ\n", + "Trajectron++: 0.4934326048690871\n", + "FDE Best of 20 for ETH - Hotel\n", + "Trajectron++: 0.15716624347036173\n", + "FDE Best of 20 for UCY - Univ\n", + "Trajectron++: 0.2269283937135511\n", + "FDE Best of 20 for UCY - Zara 1\n", + "Trajectron++: 0.1771332219260789\n", + "FDE Best of 20 for UCY - Zara 2\n", + "Trajectron++: 0.13879135677920432\n", + "FDE Best of 20 for Average\n", + "Trajectron++: 0.21259031594000022\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# KDE Negative Log Likelihood Attention Radius 3m Velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_vel_12_kde_full.csv\n", + "results/hotel_vel_12_kde_full.csv\n", + "results/univ_vel_12_kde_full.csv\n", + "results/zara1_vel_12_kde_full.csv\n", + "results/zara2_vel_12_kde_full.csv\n" + ] + } + ], + "source": [ + "# Load Ours\n", + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_vel_12*kde_full.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True)\n", + " del perf_df['Unnamed: 0']\n", + "#perf_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# # Load Trajectron and SGAN\n", + "# lls_df = pd.concat([pd.read_csv(f) for f in glob.glob('csv/old/curr_*_lls.csv')], ignore_index=True)\n", + "# lls_df.loc[lls_df['method'] == 'our_full', 'method'] = 'Trajectron'\n", + "# lls_df['error_type'] = 'KDE'\n", + "# #lls_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KDE NLL for ETH - Univ\n", + "Ours: 3.927605528941531\n", + "KDE NLL for ETH - Hotel\n", + "Ours: -0.6236621180717697\n", + "KDE NLL for UCY - Univ\n", + "Ours: -0.7887800746973215\n", + "KDE NLL for UCY - Zara 1\n", + "Ours: -1.0283870848449408\n", + "KDE NLL for UCY - Zara 2\n", + "Ours: -1.9260379497727524\n", + "KDE NLL for Average\n", + "Ours: -0.8485584209581651\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " if dataset != 'Average':\n", + " print('KDE NLL for ' + pretty_dataset_name(dataset))\n", + " #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n", + " #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron') & (lls_df['dataset'] == dataset)]['log-likelihood'].mean()}\")\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print('KDE NLL for ' + pretty_dataset_name(dataset))\n", + " #print(f\"SGAN: {-lls_df[(lls_df['method'] == 'sgan')]['log-likelihood'].mean()}\")\n", + " #print(f\"Trajectron: {-lls_df[(lls_df['method'] == 'Trajectron')]['log-likelihood'].mean()}\")\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Most Likely FDE Attention Radius 3m Velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_vel_12_fde_most_likely.csv\n", + "results/hotel_vel_12_fde_most_likely.csv\n", + "results/univ_vel_12_fde_most_likely.csv\n", + "results/zara1_vel_12_fde_most_likely.csv\n", + "results/zara2_vel_12_fde_most_likely.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_vel_12*fde_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = 'Trajectron++'\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Most Likely for ETH - Univ\n", + "Ours: 1.8566004278950379\n", + "FDE Most Likely for ETH - Hotel\n", + "Ours: 0.6021892704369326\n", + "FDE Most Likely for UCY - Univ\n", + "Ours: 1.2171244891615123\n", + "FDE Most Likely for UCY - Zara 1\n", + "Ours: 0.8159963144196981\n", + "FDE Most Likely for UCY - Zara 2\n", + "Ours: 0.6650947374043866\n", + "FDE Most Likely for Average\n", + "Ours: 1.080938659044269\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Most Likely for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Most Likely Evaluation ADE Attention Radius 3m Velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_vel_12_ade_most_likely.csv\n", + "results/hotel_vel_12_ade_most_likely.csv\n", + "results/univ_vel_12_ade_most_likely.csv\n", + "results/zara1_vel_12_ade_most_likely.csv\n", + "results/zara2_vel_12_ade_most_likely.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_vel_12*ade_most_likely.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = 'Trajectron++'\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ADE Most Likely for ETH - Univ\n", + "Ours: 0.7925476110083421\n", + "ADE Most Likely for ETH - Hotel\n", + "Ours: 0.27995871067462225\n", + "ADE Most Likely for UCY - Univ\n", + "Ours: 0.46060802655655825\n", + "ADE Most Likely for UCY - Zara 1\n", + "Ours: 0.3145776262029875\n", + "ADE Most Likely for UCY - Zara 2\n", + "Ours: 0.25594473472397583\n", + "ADE Most Likely for Average\n", + "Ours: 0.41564361286805335\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('ADE Most Likely for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++') & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"{alg_name}: {perf_df[(perf_df['method'] == 'Trajectron++')]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Best of 20 Evaluation FDE Attention Radius 3m Velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_vel_12_fde_best_of.csv\n", + "results/hotel_vel_12_fde_best_of.csv\n", + "results/univ_vel_12_fde_best_of.csv\n", + "results/zara1_vel_12_fde_best_of.csv\n", + "results/zara2_vel_12_fde_best_of.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_vel_12*fde_best_of.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Best of 20 for ETH - Univ\n", + "Trajectron++: 0.9436506373753673\n", + "FDE Best of 20 for ETH - Hotel\n", + "Trajectron++: 0.26871119754090905\n", + "FDE Best of 20 for UCY - Univ\n", + "Trajectron++: 0.45632249026126165\n", + "FDE Best of 20 for UCY - Zara 1\n", + "Trajectron++: 0.33819336402725053\n", + "FDE Best of 20 for UCY - Zara 2\n", + "Trajectron++: 0.278890779270787\n", + "FDE Best of 20 for Average\n", + "Trajectron++: 0.42212505975114584\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Best of 20 Evaluation ADE Attention Radius 3m Velocity" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "results/eth_vel_12_ade_best_of.csv\n", + "results/hotel_vel_12_ade_best_of.csv\n", + "results/univ_vel_12_ade_best_of.csv\n", + "results/zara1_vel_12_ade_best_of.csv\n", + "results/zara2_vel_12_ade_best_of.csv\n" + ] + } + ], + "source": [ + "perf_df = pd.DataFrame()\n", + "for dataset in dataset_names:\n", + " for f in glob.glob(f\"results/{dataset}_vel_12*ade_best_of.csv\"):\n", + " print(f)\n", + " dataset_df = pd.read_csv(f)\n", + " dataset_df['dataset'] = dataset\n", + " dataset_df['method'] = alg_name\n", + " perf_df = perf_df.append(dataset_df, ignore_index=True, sort=False)\n", + " del perf_df['Unnamed: 0']" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FDE Best of 20 for ETH - Univ\n", + "Trajectron++: 0.46389447635823006\n", + "FDE Best of 20 for ETH - Hotel\n", + "Trajectron++: 0.14917384309686665\n", + "FDE Best of 20 for UCY - Univ\n", + "Trajectron++: 0.20508554382763963\n", + "FDE Best of 20 for UCY - Zara 1\n", + "Trajectron++: 0.15763210664999946\n", + "FDE Best of 20 for UCY - Zara 2\n", + "Trajectron++: 0.12714387128413132\n", + "FDE Best of 20 for Average\n", + "Trajectron++: 0.19313473176031068\n" + ] + } + ], + "source": [ + "for dataset in dataset_names:\n", + " print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", + " if dataset != 'Average':\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", + " else:\n", + " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name)]['value'].mean()}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "del perf_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6 (GenTrajectron)", + "language": "python", + "name": "gentraj" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/experiments/pedestrians/eval_vel_cmds.txt b/experiments/pedestrians/eval_vel_cmds.txt new file mode 100644 index 0000000..868b00e --- /dev/null +++ b/experiments/pedestrians/eval_vel_cmds.txt @@ -0,0 +1,6 @@ +python evaluate.py --model ./models/eth_vel --checkpoint 100 --data ../processed/eth_test.pkl --output_path ./results/ --output_tag eth_vel_12 --node_type PEDESTRIAN +python evaluate.py --model ./models/hotel_vel --checkpoint 100 --data ../processed/hotel_test.pkl --output_path ./results/ --output_tag hotel_vel_12 --node_type PEDESTRIAN +python evaluate.py --model ./models/zara1_vel --checkpoint 100 --data ../processed/zara1_test.pkl --output_path ./results/ --output_tag zara1_vel_12 --node_type PEDESTRIAN +python evaluate.py --model ./models/zara2_vel --checkpoint 100 --data ../processed/zara2_test.pkl --output_path ./results/ --output_tag zara2_vel_12 --node_type PEDESTRIAN +python evaluate.py --model ./models/univ_vel --checkpoint 100 --data ../processed/univ_test.pkl --output_path ./results/ --output_tag univ_vel_12 --node_type PEDESTRIAN + diff --git a/experiments/pedestrians/evaluate.py b/experiments/pedestrians/evaluate.py new file mode 100644 index 0000000..ba2ea71 --- /dev/null +++ b/experiments/pedestrians/evaluate.py @@ -0,0 +1,222 @@ +import sys +import os +import dill +import json +import argparse +import torch +import numpy as np +import pandas as pd + +sys.path.append("../../trajectron") +from tqdm import tqdm +from model.model_registrar import ModelRegistrar +from model.trajectron import Trajectron +import evaluation + +seed = 0 +np.random.seed(seed) +torch.manual_seed(seed) +if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + +parser = argparse.ArgumentParser() +parser.add_argument("--model", help="model full path", type=str) +parser.add_argument("--checkpoint", help="model checkpoint to evaluate", type=int) +parser.add_argument("--data", help="full path to data file", type=str) +parser.add_argument("--output_path", help="path to output csv file", type=str) +parser.add_argument("--output_tag", help="name tag for output file", type=str) +parser.add_argument("--node_type", help="node type to evaluate", type=str) +args = parser.parse_args() + + +def load_model(model_dir, env, ts=100): + model_registrar = ModelRegistrar(model_dir, 'cpu') + model_registrar.load_models(ts) + with open(os.path.join(model_dir, 'config.json'), 'r') as config_json: + hyperparams = json.load(config_json) + + trajectron = Trajectron(model_registrar, hyperparams, None, 'cpu') + + trajectron.set_environment(env) + trajectron.set_annealing_params() + return trajectron, hyperparams + + +if __name__ == "__main__": + with open(args.data, 'rb') as f: + env = dill.load(f, encoding='latin1') + + eval_stg, hyperparams = load_model(args.model, env, ts=args.checkpoint) + + if 'override_attention_radius' in hyperparams: + for attention_radius_override in hyperparams['override_attention_radius']: + node_type1, node_type2, attention_radius = attention_radius_override.split(' ') + env.attention_radius[(node_type1, node_type2)] = float(attention_radius) + + scenes = env.scenes + + print("-- Preparing Node Graph") + for scene in tqdm(scenes): + scene.calculate_scene_graph(env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) + + ph = hyperparams['prediction_horizon'] + max_hl = hyperparams['maximum_history_length'] + + with torch.no_grad(): + ############### MOST LIKELY ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + print("-- Evaluating GMM Grid Sampled (Most Likely)") + for i, scene in enumerate(scenes): + print(f"---- Evaluating Scene {i + 1}/{len(scenes)}") + timesteps = np.arange(scene.timesteps) + + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=1, + min_future_timesteps=12, + z_mode=False, + gmm_mode=True, + full_dist=True) # This will trigger grid sampling + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + prune_ph_to_future=True, + kde=False) + + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + + print(np.mean(eval_fde_batch_errors)) + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'ml'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_ade_most_likely.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'ml'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_fde_most_likely.csv')) + + + ############### MODE Z ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + eval_kde_nll = np.array([]) + print("-- Evaluating Mode Z") + for i, scene in enumerate(scenes): + print(f"---- Evaluating Scene {i+1}/{len(scenes)}") + for t in tqdm(range(0, scene.timesteps, 10)): + timesteps = np.arange(t, t + 10) + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=2000, + min_future_timesteps=12, + z_mode=True, + full_dist=False) + + if not predictions: + continue + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + prune_ph_to_future=True) + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + eval_kde_nll = np.hstack((eval_kde_nll, batch_error_dict[args.node_type]['kde'])) + + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'z_mode'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_ade_z_mode.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'z_mode'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_fde_z_mode.csv')) + pd.DataFrame({'value': eval_kde_nll, 'metric': 'kde', 'type': 'z_mode'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_kde_z_mode.csv')) + + + ############### BEST OF 20 ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + eval_kde_nll = np.array([]) + print("-- Evaluating best of 20") + for i, scene in enumerate(scenes): + print(f"---- Evaluating Scene {i + 1}/{len(scenes)}") + for t in tqdm(range(0, scene.timesteps, 10)): + timesteps = np.arange(t, t + 10) + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=20, + min_future_timesteps=12, + z_mode=False, + gmm_mode=False, + full_dist=False) + + if not predictions: + continue + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + best_of=True, + prune_ph_to_future=True) + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + eval_kde_nll = np.hstack((eval_kde_nll, batch_error_dict[args.node_type]['kde'])) + + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'best_of'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_ade_best_of.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'best_of'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_fde_best_of.csv')) + pd.DataFrame({'value': eval_kde_nll, 'metric': 'kde', 'type': 'best_of'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_kde_best_of.csv')) + + + ############### FULL ############### + eval_ade_batch_errors = np.array([]) + eval_fde_batch_errors = np.array([]) + eval_kde_nll = np.array([]) + print("-- Evaluating Full") + for i, scene in enumerate(scenes): + print(f"---- Evaluating Scene {i + 1}/{len(scenes)}") + for t in tqdm(range(0, scene.timesteps, 10)): + timesteps = np.arange(t, t + 10) + predictions = eval_stg.predict(scene, + timesteps, + ph, + num_samples=2000, + min_future_timesteps=12, + z_mode=False, + gmm_mode=False, + full_dist=False) + + if not predictions: + continue + + batch_error_dict = evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=env.NodeType, + map=None, + prune_ph_to_future=True) + + eval_ade_batch_errors = np.hstack((eval_ade_batch_errors, batch_error_dict[args.node_type]['ade'])) + eval_fde_batch_errors = np.hstack((eval_fde_batch_errors, batch_error_dict[args.node_type]['fde'])) + eval_kde_nll = np.hstack((eval_kde_nll, batch_error_dict[args.node_type]['kde'])) + + pd.DataFrame({'value': eval_ade_batch_errors, 'metric': 'ade', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_ade_full.csv')) + pd.DataFrame({'value': eval_fde_batch_errors, 'metric': 'fde', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_fde_full.csv')) + pd.DataFrame({'value': eval_kde_nll, 'metric': 'kde', 'type': 'full'} + ).to_csv(os.path.join(args.output_path, args.output_tag + '_kde_full.csv')) diff --git a/experiments/pedestrians/models/eth_attention_radius_3/config.json b/experiments/pedestrians/models/eth_attention_radius_3/config.json new file mode 100644 index 0000000..8167b02 --- /dev/null +++ b/experiments/pedestrians/models/eth_attention_radius_3/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": false, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/pedestrians/models/eth_attention_radius_3/model_registrar-100.pt b/experiments/pedestrians/models/eth_attention_radius_3/model_registrar-100.pt new file mode 100644 index 0000000..04ae983 Binary files /dev/null and b/experiments/pedestrians/models/eth_attention_radius_3/model_registrar-100.pt differ diff --git a/experiments/pedestrians/models/eth_vel/config.json b/experiments/pedestrians/models/eth_vel/config.json new file mode 100644 index 0000000..c034d2b --- /dev/null +++ b/experiments/pedestrians/models/eth_vel/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": false, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": false, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/pedestrians/models/eth_vel/model_registrar-100.pt b/experiments/pedestrians/models/eth_vel/model_registrar-100.pt new file mode 100644 index 0000000..ccc35b4 Binary files /dev/null and b/experiments/pedestrians/models/eth_vel/model_registrar-100.pt differ diff --git a/experiments/pedestrians/models/hotel_attention_radius_3/config.json b/experiments/pedestrians/models/hotel_attention_radius_3/config.json new file mode 100644 index 0000000..8167b02 --- /dev/null +++ b/experiments/pedestrians/models/hotel_attention_radius_3/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", 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"override_attention_radius": []} \ No newline at end of file diff --git a/experiments/pedestrians/models/zara1_vel/model_registrar-100.pt b/experiments/pedestrians/models/zara1_vel/model_registrar-100.pt new file mode 100644 index 0000000..2188dd7 Binary files /dev/null and b/experiments/pedestrians/models/zara1_vel/model_registrar-100.pt differ diff --git a/experiments/pedestrians/models/zara2_attention_radius_3/config.json b/experiments/pedestrians/models/zara2_attention_radius_3/config.json new file mode 100644 index 0000000..8167b02 --- /dev/null +++ b/experiments/pedestrians/models/zara2_attention_radius_3/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, 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b/experiments/pedestrians/models/zara2_vel/config.json new file mode 100644 index 0000000..c034d2b --- /dev/null +++ b/experiments/pedestrians/models/zara2_vel/config.json @@ -0,0 +1 @@ +{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": false, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"velocity": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": false, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} \ No newline at end of file diff --git a/experiments/pedestrians/models/zara2_vel/model_registrar-100.pt b/experiments/pedestrians/models/zara2_vel/model_registrar-100.pt new file mode 100644 index 0000000..c1eb0bf Binary files /dev/null and b/experiments/pedestrians/models/zara2_vel/model_registrar-100.pt differ diff --git a/experiments/pedestrians/process_data.py b/experiments/pedestrians/process_data.py new file mode 100644 index 0000000..369fcd0 --- /dev/null +++ b/experiments/pedestrians/process_data.py @@ -0,0 +1,171 @@ +import sys +import os +import numpy as np +import pandas as pd +import dill + +sys.path.append("../../trajectron") +from environment import Environment, Scene, Node +from utils import maybe_makedirs +from environment import derivative_of + +desired_max_time = 100 +pred_indices = [2, 3] +state_dim = 6 +frame_diff = 10 +desired_frame_diff = 1 +dt = 0.4 + +standardization = { + 'PEDESTRIAN': { + 'position': { + 'x': {'mean': 0, 'std': 1}, + 'y': {'mean': 0, 'std': 1} + }, + 'velocity': { + 'x': {'mean': 0, 'std': 2}, + 'y': {'mean': 0, 'std': 2} + }, + 'acceleration': { + 'x': {'mean': 0, 'std': 1}, + 'y': {'mean': 0, 'std': 1} + } + } +} + + +def augment_scene(scene, angle): + def rotate_pc(pc, alpha): + M = np.array([[np.cos(alpha), -np.sin(alpha)], + [np.sin(alpha), np.cos(alpha)]]) + return M @ pc + + data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']]) + + scene_aug = Scene(timesteps=scene.timesteps, dt=scene.dt, name=scene.name) + + alpha = angle * np.pi / 180 + + for node in scene.nodes: + x = node.data.position.x.copy() + y = node.data.position.y.copy() + + x, y = rotate_pc(np.array([x, y]), alpha) + + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + ax = derivative_of(vx, scene.dt) + ay = derivative_of(vy, scene.dt) + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay} + + node_data = pd.DataFrame(data_dict, columns=data_columns) + + node = Node(node_type=node.type, node_id=node.id, data=node_data, first_timestep=node.first_timestep) + + scene_aug.nodes.append(node) + return scene_aug + + +def augment(scene): + scene_aug = np.random.choice(scene.augmented) + scene_aug.temporal_scene_graph = scene.temporal_scene_graph + return scene_aug + + +nl = 0 +l = 0 +maybe_makedirs('../processed') +data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']]) +for desired_source in ['eth', 'hotel', 'univ', 'zara1', 'zara2']: + for data_class in ['train', 'val', 'test']: + env = Environment(node_type_list=['PEDESTRIAN'], standardization=standardization) + attention_radius = dict() + attention_radius[(env.NodeType.PEDESTRIAN, env.NodeType.PEDESTRIAN)] = 3.0 + env.attention_radius = attention_radius + + scenes = [] + data_dict_path = os.path.join('../processed', '_'.join([desired_source, data_class]) + '.pkl') + + for subdir, dirs, files in os.walk(os.path.join('raw', desired_source, data_class)): + for file in files: + if file.endswith('.txt'): + input_data_dict = dict() + full_data_path = os.path.join(subdir, file) + print('At', full_data_path) + + data = pd.read_csv(full_data_path, sep='\t', index_col=False, header=None) + data.columns = ['frame_id', 'track_id', 'pos_x', 'pos_y'] + data['frame_id'] = pd.to_numeric(data['frame_id'], downcast='integer') + data['track_id'] = pd.to_numeric(data['track_id'], downcast='integer') + + data['frame_id'] = data['frame_id'] // 10 + + data['frame_id'] -= data['frame_id'].min() + + data['node_type'] = 'PEDESTRIAN' + data['node_id'] = data['track_id'].astype(str) + data.sort_values('frame_id', inplace=True) + + # Mean Position + data['pos_x'] = data['pos_x'] - data['pos_x'].mean() + data['pos_y'] = data['pos_y'] - data['pos_y'].mean() + + max_timesteps = data['frame_id'].max() + + scene = Scene(timesteps=max_timesteps+1, dt=dt, name=desired_source + "_" + data_class, aug_func=augment if data_class == 'train' else None) + + for node_id in pd.unique(data['node_id']): + + node_df = data[data['node_id'] == node_id] + assert np.all(np.diff(node_df['frame_id']) == 1) + + node_values = node_df[['pos_x', 'pos_y']].values + + if node_values.shape[0] < 2: + continue + + new_first_idx = node_df['frame_id'].iloc[0] + + x = node_values[:, 0] + y = node_values[:, 1] + vx = derivative_of(x, scene.dt) + vy = derivative_of(y, scene.dt) + ax = derivative_of(vx, scene.dt) + ay = derivative_of(vy, scene.dt) + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy, + ('acceleration', 'x'): ax, + ('acceleration', 'y'): ay} + + node_data = pd.DataFrame(data_dict, columns=data_columns) + node = Node(node_type=env.NodeType.PEDESTRIAN, node_id=node_id, data=node_data) + node.first_timestep = new_first_idx + + scene.nodes.append(node) + if data_class == 'train': + scene.augmented = list() + angles = np.arange(0, 360, 15) if data_class == 'train' else [0] + for angle in angles: + scene.augmented.append(augment_scene(scene, angle)) + + print(scene) + scenes.append(scene) + print(f'Processed {len(scenes):.2f} scene for data class {data_class}') + + env.scenes = scenes + + if len(scenes) > 0: + with open(data_dict_path, 'wb') as f: + dill.dump(env, f, protocol=dill.HIGHEST_PROTOCOL) + +print(f"Linear: {l}") +print(f"Non-Linear: {nl}") \ No newline at end of file diff --git a/experiments/pedestrians/raw/eth/test/biwi_eth.txt b/experiments/pedestrians/raw/eth/test/biwi_eth.txt new file mode 100644 index 0000000..7a8cad3 --- /dev/null +++ b/experiments/pedestrians/raw/eth/test/biwi_eth.txt @@ -0,0 +1,5492 @@ +780 1.0 8.46 3.59 +790 1.0 9.57 3.79 +800 1.0 10.67 3.99 +800 2.0 13.64 5.8 +810 1.0 11.73 4.32 +810 2.0 12.09 5.75 +820 1.0 12.81 4.61 +820 2.0 11.37 5.8 +830 2.0 10.31 5.97 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a/experiments/pedestrians/raw/zara2/train/uni_examples_train.txt b/experiments/pedestrians/raw/zara2/train/uni_examples_train.txt new file mode 100644 index 0000000..74a02a2 --- /dev/null +++ b/experiments/pedestrians/raw/zara2/train/uni_examples_train.txt @@ -0,0 +1,2266 @@ +0.0 1.0 0.294651153476 8.09056721029 +10.0 1.0 1.03127903717 7.85190741057 +10.0 2.0 0.279287200473 9.98910591701 +20.0 1.0 1.76790692085 7.61324761086 +20.0 2.0 1.06390112916 9.85879766636 +30.0 1.0 2.51547899024 7.36408677996 +30.0 2.0 1.84830459273 9.72872807552 +40.0 1.0 3.27925687308 7.09869708268 +40.0 2.0 2.6106092198 9.56739405091 +50.0 1.0 4.04324522102 6.8335460452 +50.0 2.0 3.36344291693 9.39317239712 +60.0 1.0 4.85079984665 6.61803624605 +60.0 2.0 4.11185684675 9.20176723775 +70.0 1.0 5.67708586704 6.42352850929 +70.0 2.0 4.82112426619 8.85690382717 +80.0 1.0 6.50337188743 6.22902077252 +80.0 2.0 5.53060215074 8.51227907638 +80.0 3.0 0.0782930207807 8.75213217509 +90.0 1.0 7.32355441964 6.04787798454 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b/requirements.txt index ae748ae..7c6002b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,6 +8,15 @@ tensorboard==1.14.0 tensorboardX==1.8 tensorflow==1.14.0 tensorflow-estimator==1.14.0 -torch==1.2.0 +torch==1.4.0 Pillow==6.1.0 pyquaternion==0.9.5 +pytest==5.3.0 +orjson==2.1.4 +ncls==0.0.51 +dill==0.3.1.1 +tqdm==4.45.0 +notebook==6.0.3 +scikit-learn==0.22.1 +opencv-python==4.1.1.26 +nuscenes-devkit==1.0.6 diff --git a/trajectron/__init__.py b/trajectron/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/trajectron/argument_parser.py b/trajectron/argument_parser.py new file mode 100644 index 0000000..3eb5f04 --- /dev/null +++ b/trajectron/argument_parser.py @@ -0,0 +1,172 @@ +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--conf", + help="path to json config file for hyperparameters", + type=str, + default='../config/config.json') + +parser.add_argument("--debug", + help="disable all disk writing processes.", + action='store_true') + +parser.add_argument("--preprocess_workers", + help="number of processes to spawn for preprocessing", + type=int, + default=0) + + +# Model Parameters +parser.add_argument("--offline_scene_graph", + help="whether to precompute the scene graphs offline, options are 'no' and 'yes'", + type=str, + default='yes') + +parser.add_argument("--dynamic_edges", + help="whether to use dynamic edges or not, options are 'no' and 'yes'", + type=str, + default='yes') + +parser.add_argument("--edge_state_combine_method", + help="the method to use for combining edges of the same type", + type=str, + default='sum') + +parser.add_argument("--edge_influence_combine_method", + help="the method to use for combining edge influences", + type=str, + default='attention') + +parser.add_argument('--edge_addition_filter', + nargs='+', + help="what scaling to use for edges as they're created", + type=float, + default=[0.25, 0.5, 0.75, 1.0]) # We don't automatically pad left with 0.0, if you want a sharp + # and short edge addition, then you need to have a 0.0 at the + # beginning, e.g. [0.0, 1.0]. + +parser.add_argument('--edge_removal_filter', + nargs='+', + help="what scaling to use for edges as they're removed", + type=float, + default=[1.0, 0.0]) # We don't automatically pad right with 0.0, if you want a sharp drop off like + # the default, then you need to have a 0.0 at the end. + +parser.add_argument('--override_attention_radius', + action='append', + help='Specify one attention radius to override. E.g. "PEDESTRIAN VEHICLE 10.0"', + default=[]) + +parser.add_argument('--incl_robot_node', + help="whether to include a robot node in the graph or simply model all agents", + action='store_true') + +parser.add_argument('--map_encoding', + help="Whether to use map encoding or not", + action='store_true') + +parser.add_argument('--augment', + help="Whether to augment the scene during training", + action='store_true') + +parser.add_argument('--node_freq_mult_train', + help="Whether to use frequency multiplying of nodes during training", + action='store_true') + +parser.add_argument('--node_freq_mult_eval', + help="Whether to use frequency multiplying of nodes during evaluation", + action='store_true') + +parser.add_argument('--scene_freq_mult_train', + help="Whether to use frequency multiplying of nodes during training", + action='store_true') + +parser.add_argument('--scene_freq_mult_eval', + help="Whether to use frequency multiplying of nodes during evaluation", + action='store_true') + +parser.add_argument('--scene_freq_mult_viz', + help="Whether to use frequency multiplying of nodes during evaluation", + action='store_true') + +parser.add_argument('--no_edge_encoding', + help="Whether to use neighbors edge encoding", + action='store_true') + +# Data Parameters +parser.add_argument("--data_dir", + help="what dir to look in for data", + type=str, + default='../experiments/processed') + +parser.add_argument("--train_data_dict", + help="what file to load for training data", + type=str, + default='train.pkl') + +parser.add_argument("--eval_data_dict", + help="what file to load for evaluation data", + type=str, + default='val.pkl') + +parser.add_argument("--log_dir", + help="what dir to save training information (i.e., saved models, logs, etc)", + type=str, + default='../experiments/logs') + +parser.add_argument("--log_tag", + help="tag for the log folder", + type=str, + default='') + +parser.add_argument('--device', + help='what device to perform training on', + type=str, + default='cuda:0') + +parser.add_argument("--eval_device", + help="what device to use during evaluation", + type=str, + default=None) + +# Training Parameters +parser.add_argument("--train_epochs", + help="number of iterations to train for", + type=int, + default=1) + +parser.add_argument('--batch_size', + help='training batch size', + type=int, + default=256) + +parser.add_argument('--eval_batch_size', + help='evaluation batch size', + type=int, + default=256) + +parser.add_argument('--k_eval', + help='how many samples to take during evaluation', + type=int, + default=25) + +parser.add_argument('--seed', + help='manual seed to use, default is 123', + type=int, + default=123) + +parser.add_argument('--eval_every', + help='how often to evaluate during training, never if None', + type=int, + default=1) + +parser.add_argument('--vis_every', + help='how often to visualize during training, never if None', + type=int, + default=1) + +parser.add_argument('--save_every', + help='how often to save during training, never if None', + type=int, + default=1) +args = parser.parse_args() diff --git a/trajectron/environment/__init__.py b/trajectron/environment/__init__.py new file mode 100644 index 0000000..9ad0681 --- /dev/null +++ b/trajectron/environment/__init__.py @@ -0,0 +1,8 @@ +from .data_structures import RingBuffer, SingleHeaderNumpyArray, DoubleHeaderNumpyArray +from .scene import Scene +from .node import Node +from .scene_graph import TemporalSceneGraph, SceneGraph +from .environment import Environment +from .node_type import NodeTypeEnum +from .data_utils import derivative_of +from .map import GeometricMap diff --git a/trajectron/environment/data_structures.py b/trajectron/environment/data_structures.py new file mode 100644 index 0000000..5ffdc6f --- /dev/null +++ b/trajectron/environment/data_structures.py @@ -0,0 +1,276 @@ +import numpy as np +import pandas as pd +from collections import Sequence, OrderedDict + + +class RingBuffer(Sequence): + def __init__(self, capacity, dtype=float, allow_overwrite=True): + """ + Create a new ring buffer with the given capacity and element type. + Code copy-pasted from: https://github.com/eric-wieser/numpy_ringbuffer + + Parameters + ---------- + capacity: int + The maximum capacity of the ring buffer + dtype: data-type, optional + Desired type of buffer elements. Use a type like (float, 2) to + produce a buffer with shape (N, 2) + allow_overwrite: bool + If false, throw an IndexError when trying to append to an already + full buffer + """ + self._arr = np.full(capacity, np.nan, dtype) + self._left_index = 0 + self._right_index = 0 + self._capacity = capacity + self._allow_overwrite = allow_overwrite + + def _unwrap(self): + """ Copy the data from this buffer into unwrapped form """ + return np.concatenate(( + self._arr[self._left_index:min(self._right_index, self._capacity)], + self._arr[:max(self._right_index - self._capacity, 0)] + )) + + def _fix_indices(self): + """ + Enforce our invariant that 0 <= self._left_index < self._capacity + """ + if self._left_index >= self._capacity: + self._left_index -= self._capacity + self._right_index -= self._capacity + elif self._left_index < 0: + self._left_index += self._capacity + self._right_index += self._capacity + + @property + def is_full(self): + """ True if there is no more space in the buffer """ + return len(self) == self._capacity + + # numpy compatibility + def __array__(self): + return self._unwrap() + + @property + def dtype(self): + return self._arr.dtype + + @property + def shape(self): + return (len(self),) + self._arr.shape[1:] + + # these mirror methods from deque + @property + def maxlen(self): + return self._capacity + + def append(self, value): + if self.is_full: + if not self._allow_overwrite: + raise IndexError('append to a full RingBuffer with overwrite disabled') + elif not len(self): + return + else: + self._left_index += 1 + + self._arr[self._right_index % self._capacity] = value + self._right_index += 1 + self._fix_indices() + + def appendleft(self, value): + if self.is_full: + if not self._allow_overwrite: + raise IndexError('append to a full RingBuffer with overwrite disabled') + elif not len(self): + return + else: + self._right_index -= 1 + + self._left_index -= 1 + self._fix_indices() + self._arr[self._left_index] = value + + def pop(self): + if len(self) == 0: + raise IndexError("pop from an empty RingBuffer") + self._right_index -= 1 + self._fix_indices() + res = self._arr[self._right_index % self._capacity] + return res + + def popleft(self): + if len(self) == 0: + raise IndexError("pop from an empty RingBuffer") + res = self._arr[self._left_index] + self._left_index += 1 + self._fix_indices() + return res + + def extend(self, values): + lv = len(values) + if len(self) + lv > self._capacity: + if not self._allow_overwrite: + raise IndexError('extend a RingBuffer such that it would overflow, with overwrite disabled') + elif not len(self): + return + if lv >= self._capacity: + # wipe the entire array! - this may not be threadsafe + self._arr[...] = values[-self._capacity:] + self._right_index = self._capacity + self._left_index = 0 + return + + ri = self._right_index % self._capacity + sl1 = np.s_[ri:min(ri + lv, self._capacity)] + sl2 = np.s_[:max(ri + lv - self._capacity, 0)] + self._arr[sl1] = values[:sl1.stop - sl1.start] + self._arr[sl2] = values[sl1.stop - sl1.start:] + self._right_index += lv + + self._left_index = max(self._left_index, self._right_index - self._capacity) + self._fix_indices() + + def extendleft(self, values): + lv = len(values) + if len(self) + lv > self._capacity: + if not self._allow_overwrite: + raise IndexError('extend a RingBuffer such that it would overflow, with overwrite disabled') + elif not len(self): + return + if lv >= self._capacity: + # wipe the entire array! - this may not be threadsafe + self._arr[...] = values[:self._capacity] + self._right_index = self._capacity + self._left_index = 0 + return + + self._left_index -= lv + self._fix_indices() + li = self._left_index + sl1 = np.s_[li:min(li + lv, self._capacity)] + sl2 = np.s_[:max(li + lv - self._capacity, 0)] + self._arr[sl1] = values[:sl1.stop - sl1.start] + self._arr[sl2] = values[sl1.stop - sl1.start:] + + self._right_index = min(self._right_index, self._left_index + self._capacity) + + # implement Sequence methods + def __len__(self): + return self._right_index - self._left_index + + def __getitem__(self, item): + # handle simple (b[1]) and basic (b[np.array([1, 2, 3])]) fancy indexing specially + if not isinstance(item, tuple): + item_arr = np.asarray(item) + if issubclass(item_arr.dtype.type, np.integer): + item_arr = (item_arr + self._left_index) % self._capacity + return self._arr[item_arr] + + # for everything else, get it right at the expense of efficiency + return self._unwrap()[item] + + def __iter__(self): + # alarmingly, this is comparable in speed to using itertools.chain + return iter(self._unwrap()) + + # Everything else + def __repr__(self): + return ''.format(np.asarray(self)) + + +class DoubleHeaderNumpyArray(object): + def __init__(self, data: np.ndarray, header: list): + """ + Data Structure mirroring some functionality of double indexed pandas DataFrames. + Indexing options are: + [:, (header1, header2)] + [:, [(header1, header2), (header1, header2)]] + [:, {header1: [header21, header22]}] + + A SingleHeaderNumpyArray can is returned if an element of the first header is querried as attribut: + doubleHeaderNumpyArray.position -> SingleHeaderNumpyArray + + :param data: The numpy array. + :param header: The double header structure as list of tuples [(header11, header21), (header11, header22) ...] + """ + self.data = data + self.header = header + self.double_header_lookup = OrderedDict() + self.tree_header_lookup = OrderedDict() + for i, header_item in enumerate(header): + self.double_header_lookup[header_item] = i + if header_item[0] not in self.tree_header_lookup: + self.tree_header_lookup[header_item[0]] = dict() + self.tree_header_lookup[header_item[0]][header_item[1]] = i + + def __mul__(self, other): + return DoubleHeaderNumpyArray(self.data * other, self.header) + + def get_single_header_array(self, h1: str, rows=slice(None, None, None)): + data_integer_indices = list() + h2_list = list() + for h2 in self.tree_header_lookup[h1]: + data_integer_indices.append(self.tree_header_lookup[h1][h2]) + h2_list.append(h2) + return SingleHeaderNumpyArray(self.data[rows, data_integer_indices], h2_list) + + def __getitem__(self, item): + rows, columns = item + data_integer_indices = list() + if type(columns) is dict: + for h1, h2s in columns.items(): + for h2 in h2s: + data_integer_indices.append(self.double_header_lookup[(h1, h2)]) + return self.data[rows, data_integer_indices] + elif type(columns) is list: + for column in columns: + assert type(column) is tuple, "If Index is list it hast to be list of double header tuples." + data_integer_indices.append(self.double_header_lookup[column]) + return self.data[rows, data_integer_indices] + elif type(columns) is tuple: + return self.data[rows, self.double_header_lookup[columns]] + else: + assert type(item) is str, "Index must be str, list of tuples or dict of tree structure." + return self.get_single_header_array(item, rows=rows) + + def __getattr__(self, item): + if not item.startswith('_'): + if item in self.tree_header_lookup.keys(): + return self.get_single_header_array(item) + else: + try: + return self.data.__getattribute__(item) + except AttributeError: + return super().__getattribute__(item) + else: + return super().__getattribute__(item) + + +class SingleHeaderNumpyArray(object): + def __init__(self, data: np.ndarray, header: list): + self.data = data + self.header_lookup = OrderedDict({h: i for i, h in enumerate(header)}) + + def __getitem__(self, item): + rows, columns = item + data_integer_indices = list() + if type(columns) is list or type(columns) is tuple: + for column in columns: + data_integer_indices.append(self.header_lookup[column]) + else: + data_integer_indices = self.header_lookup[columns] + return self.data[rows, data_integer_indices] + + def __getattr__(self, item): + if not item.startswith('_'): + if item in self.header_lookup.keys(): + return self[:, item] + else: + try: + return self.data.__getattribute__(item) + except AttributeError: + return super().__getattribute__(item) + else: + return super().__getattribute__(item) diff --git a/trajectron/environment/data_utils.py b/trajectron/environment/data_utils.py new file mode 100644 index 0000000..675f198 --- /dev/null +++ b/trajectron/environment/data_utils.py @@ -0,0 +1,29 @@ +import numpy as np + + +def make_continuous_copy(alpha): + alpha = (alpha + np.pi) % (2.0 * np.pi) - np.pi + continuous_x = np.zeros_like(alpha) + continuous_x[0] = alpha[0] + for i in range(1, len(alpha)): + if not (np.sign(alpha[i]) == np.sign(alpha[i - 1])) and np.abs(alpha[i]) > np.pi / 2: + continuous_x[i] = continuous_x[i - 1] + ( + alpha[i] - alpha[i - 1]) - np.sign( + (alpha[i] - alpha[i - 1])) * 2 * np.pi + else: + continuous_x[i] = continuous_x[i - 1] + (alpha[i] - alpha[i - 1]) + + return continuous_x + + +def derivative_of(x, dt=1, radian=False): + if radian: + x = make_continuous_copy(x) + + if x[~np.isnan(x)].shape[-1] < 2: + return np.zeros_like(x) + + dx = np.full_like(x, np.nan) + dx[~np.isnan(x)] = np.gradient(x[~np.isnan(x)], dt) + + return dx \ No newline at end of file diff --git a/code/data/environment.py b/trajectron/environment/environment.py similarity index 55% rename from code/data/environment.py rename to trajectron/environment/environment.py index 9a3ef21..22ddd14 100644 --- a/code/data/environment.py +++ b/trajectron/environment/environment.py @@ -1,33 +1,40 @@ +import orjson import numpy as np -from enum import Enum from itertools import product +from .node_type import NodeTypeEnum class Environment(object): - def __init__(self, node_type_list, standardization, scenes=None, attention_radius=None): + def __init__(self, node_type_list, standardization, scenes=None, attention_radius=None, robot_type=None): self.scenes = scenes self.node_type_list = node_type_list self.attention_radius = attention_radius - self.NodeType = Enum('NodeType', node_type_list) + self.NodeType = NodeTypeEnum(node_type_list) + self.robot_type = robot_type self.standardization = standardization + self.standardize_param_memo = dict() + + self._scenes_resample_prop = None def get_edge_types(self): - return [e for e in product([node_type for node_type in self.NodeType], repeat=2)] - - def edge_type_str(self, edge_type): - return edge_type[0].name + '-' + edge_type[1].name + return list(product(self.NodeType, repeat=2)) def get_standardize_params(self, state, node_type): + memo_key = (orjson.dumps(state), node_type) + if memo_key in self.standardize_param_memo: + return self.standardize_param_memo[memo_key] + standardize_mean_list = list() standardize_std_list = list() for entity, dims in state.items(): for dim in dims: - standardize_mean_list.append(self.standardization[node_type.name][entity][dim]['mean']) - standardize_std_list.append(self.standardization[node_type.name][entity][dim]['std']) + standardize_mean_list.append(self.standardization[node_type][entity][dim]['mean']) + standardize_std_list.append(self.standardization[node_type][entity][dim]['std']) standardize_mean = np.stack(standardize_mean_list) standardize_std = np.stack(standardize_std_list) + self.standardize_param_memo[memo_key] = (standardize_mean, standardize_std) return standardize_mean, standardize_std def standardize(self, array, state, node_type, mean=None, std=None): @@ -48,27 +55,10 @@ class Environment(object): _, std = self.get_standardize_params(state, node_type) return array * std + mean - # These two functions have to be implemented as pickle can not handle dynamic enums - def __getstate__(self): - for scene in self.scenes: - for node in scene.nodes: - node.type = node.type.name - attention_radius_no_enum = dict() - for key, value in self.attention_radius.items(): - attention_radius_no_enum[(key[0].name, key[1].name)] = value - self.attention_radius = attention_radius_no_enum - self.NodeType = None - state = self.__dict__.copy() - return state - - def __setstate__(self, state): - self.__dict__.update(state) - self.NodeType = Enum('NodeType', self.node_type_list) - for scene in self.scenes: - for node in scene.nodes: - node.type = getattr(self.NodeType, node.type) - attention_radius_enum = dict() - for key, value in self.attention_radius.items(): - attention_radius_enum[(getattr(self.NodeType, key[0]), getattr(self.NodeType, key[1]))] = value - self.attention_radius = attention_radius_enum + @property + def scenes_resample_prop(self): + if self._scenes_resample_prop is None: + self._scenes_resample_prop = np.array([scene.resample_prob for scene in self.scenes]) + self._scenes_resample_prop = self._scenes_resample_prop / np.sum(self._scenes_resample_prop) + return self._scenes_resample_prop diff --git a/trajectron/environment/map.py b/trajectron/environment/map.py new file mode 100644 index 0000000..d1b527d --- /dev/null +++ b/trajectron/environment/map.py @@ -0,0 +1,185 @@ +import torch +import numpy as np +from model.dataset.homography_warper import get_rotation_matrix2d, warp_affine_crop + + +class Map(object): + def __init__(self, data, homography, description=None): + self.data = data + self.homography = homography + self.description = description + + def as_image(self): + raise NotImplementedError + + def get_cropped_maps(self, world_pts, patch_size, rotation=None, device='cpu'): + raise NotImplementedError + + def to_map_points(self, scene_pts): + raise NotImplementedError + + +class GeometricMap(Map): + """ + A Geometric Map is a int tensor of shape [layers, x, y]. The homography must transform a point in scene + coordinates to the respective point in map coordinates. + + :param data: Numpy array of shape [layers, x, y] + :param homography: Numpy array of shape [3, 3] + """ + def __init__(self, data, homography, description=None): + #assert isinstance(data.dtype, np.floating), "Geometric Maps must be float values." + super(GeometricMap, self).__init__(data, homography, description=description) + + self._last_padding = None + self._last_padded_map = None + self._torch_map = None + + def torch_map(self, device): + if self._torch_map is not None: + return self._torch_map + self._torch_map = torch.tensor(self.data, dtype=torch.uint8, device=device) + return self._torch_map + + def as_image(self): + # We have to transpose x and y to rows and columns. Assumes origin is lower left for image + # Also we move the channels to the last dimension + return (np.transpose(self.data, (2, 1, 0))).astype(np.uint) + + def get_padded_map(self, padding_x, padding_y, device): + if self._last_padding == (padding_x, padding_y): + return self._last_padded_map + else: + self._last_padding = (padding_x, padding_y) + self._last_padded_map = torch.full((self.data.shape[0], + self.data.shape[1] + 2 * padding_x, + self.data.shape[2] + 2 * padding_y), + False, dtype=torch.uint8) + self._last_padded_map[..., padding_x:-padding_x, padding_y:-padding_y] = self.torch_map(device) + return self._last_padded_map + + @staticmethod + def batch_rotate(map_batched, centers, angles, out_height, out_width): + """ + As the input is a map and the warp_affine works on an image coordinate system we would have to + flip the y axis updown, negate the angles, and flip it back after transformation. + This, however, is the same as not flipping at and not negating the radian. + + :param map_batched: + :param centers: + :param angles: + :param out_height: + :param out_width: + :return: + """ + M = get_rotation_matrix2d(centers, angles, torch.ones_like(angles)) + rotated_map_batched = warp_affine_crop(map_batched, centers, M, + dsize=(out_height, out_width), padding_mode='zeros') + + return rotated_map_batched + + @classmethod + def get_cropped_maps_from_scene_map_batch(cls, maps, scene_pts, patch_size, rotation=None, device='cpu'): + """ + Returns rotated patches of each map around the transformed scene points. + ___________________ + | | | + | |ps[3] | + | | | + | | | + | o|__________| + | | ps[2] | + | | | + |_______|__________| + ps = patch_size + + :param maps: List of GeometricMap objects [bs] + :param scene_pts: Scene points: [bs, 2] + :param patch_size: Extracted Patch size after rotation: [-x, -y, +x, +y] + :param rotation: Rotations in degrees: [bs] + :param device: Device on which the rotated tensors should be returned. + :return: Rotated and cropped tensor patches. + """ + batch_size = scene_pts.shape[0] + lat_size = 2 * np.max((patch_size[0], patch_size[2])) + long_size = 2 * np.max((patch_size[1], patch_size[3])) + assert lat_size % 2 == 0, "Patch width must be divisible by 2" + assert long_size % 2 == 0, "Patch length must be divisible by 2" + lat_size_half = lat_size // 2 + long_size_half = long_size // 2 + + context_padding_x = int(np.ceil(np.sqrt(2) * lat_size)) + context_padding_y = int(np.ceil(np.sqrt(2) * long_size)) + + centers = torch.tensor([s_map.to_map_points(scene_pts[np.newaxis, i]) for i, s_map in enumerate(maps)], + dtype=torch.long, device=device).squeeze(dim=1) \ + + torch.tensor([context_padding_x, context_padding_y], device=device, dtype=torch.long) + + padded_map = [s_map.get_padded_map(context_padding_x, context_padding_y, device=device) for s_map in maps] + + padded_map_batched = torch.stack([padded_map[i][..., + centers[i, 0] - context_padding_x: centers[i, 0] + context_padding_x, + centers[i, 1] - context_padding_y: centers[i, 1] + context_padding_y] + for i in range(centers.shape[0])], dim=0) + + center_patches = torch.tensor([[context_padding_y, context_padding_x]], + dtype=torch.int, + device=device).repeat(batch_size, 1) + + if rotation is not None: + angles = torch.Tensor(rotation) + else: + angles = torch.zeros(batch_size) + + rotated_map_batched = cls.batch_rotate(padded_map_batched/255., + center_patches.float(), + angles, + long_size, + lat_size) + + del padded_map_batched + + return rotated_map_batched[..., + long_size_half - patch_size[1]:(long_size_half + patch_size[3]), + lat_size_half - patch_size[0]:(lat_size_half + patch_size[2])] + + def get_cropped_maps(self, scene_pts, patch_size, rotation=None, device='cpu'): + """ + Returns rotated patches of the map around the transformed scene points. + ___________________ + | | | + | |ps[3] | + | | | + | | | + | o|__________| + | | ps[2] | + | | | + |_______|__________| + ps = patch_size + + :param scene_pts: Scene points: [bs, 2] + :param patch_size: Extracted Patch size after rotation: [-lat, -long, +lat, +long] + :param rotation: Rotations in degrees: [bs] + :param device: Device on which the rotated tensors should be returned. + :return: Rotated and cropped tensor patches. + """ + return self.get_cropped_maps_from_scene_map_batch([self]*scene_pts.shape[0], scene_pts, + patch_size, rotation=rotation, device=device) + + def to_map_points(self, scene_pts): + org_shape = None + if len(scene_pts.shape) > 2: + org_shape = scene_pts.shape + scene_pts = scene_pts.reshape((-1, 2)) + N, dims = scene_pts.shape + points_with_one = np.ones((dims + 1, N)) + points_with_one[:dims] = scene_pts.T + map_points = (self.homography @ points_with_one).T[..., :dims] + if org_shape is not None: + map_points = map_points.reshape(org_shape) + return map_points + + +class ImageMap(Map): # TODO Implement for image maps -> watch flipped coordinate system + def __init__(self): + raise NotImplementedError \ No newline at end of file diff --git a/trajectron/environment/node.py b/trajectron/environment/node.py new file mode 100644 index 0000000..bd128af --- /dev/null +++ b/trajectron/environment/node.py @@ -0,0 +1,236 @@ +import random +import numpy as np +import pandas as pd +from environment import DoubleHeaderNumpyArray +from ncls import NCLS + + +class Node(object): + def __init__(self, node_type, node_id, data, length=None, width=None, height=None, first_timestep=0, + is_robot=False, description="", frequency_multiplier=1, non_aug_node=None): + self.type = node_type + self.id = node_id + self.length = length + self.width = width + self.height = height + self.first_timestep = first_timestep + self.non_aug_node = non_aug_node + + if data is not None: + if isinstance(data, pd.DataFrame): + self.data = DoubleHeaderNumpyArray(data.values, list(data.columns)) + elif isinstance(data, DoubleHeaderNumpyArray): + self.data = data + else: + self.data = None + + self.is_robot = is_robot + self._last_timestep = None + self.description = description + self.frequency_multiplier = frequency_multiplier + + self.forward_in_time_on_next_override = False + + def __eq__(self, other): + return ((isinstance(other, self.__class__) + or isinstance(self, other.__class__)) + and self.id == other.id + and self.type == other.type) + + def __ne__(self, other): + return not self.__eq__(other) + + def __hash__(self): + return hash((self.type, self.id)) + + def __repr__(self): + return '/'.join([self.type.name, self.id]) + + def overwrite_data(self, data, forward_in_time_on_next_overwrite=False): + """ + This function hard overwrites the data matrix. When using it you have to make sure that the columns + in the new data matrix correspond to the old structure. As well as setting first_timestep. + + :param data: New data matrix + :param forward_in_time_on_next_overwrite: On the !!NEXT!! call of overwrite_data first_timestep will be increased. + :return: None + """ + self.data.data = data + self._last_timestep = None + if self.forward_in_time_on_next_override: + self.first_timestep += 1 + self.forward_in_time_on_next_override = forward_in_time_on_next_overwrite + + def scene_ts_to_node_ts(self, scene_ts) -> (np.ndarray, int, int): + """ + Transforms timestamp from scene into timeframe of node data. + + :param scene_ts: Scene timesteps + :return: ts: Transformed timesteps, paddingl: Number of timesteps in scene range which are not available in + node data before data is available. paddingu: Number of timesteps in scene range which are not + available in node data after data is available. + """ + paddingl = (self.first_timestep - scene_ts[0]).clip(0) + paddingu = (scene_ts[1] - self.last_timestep).clip(0) + ts = np.array(scene_ts).clip(min=self.first_timestep, max=self.last_timestep) - self.first_timestep + return ts, paddingl, paddingu + + def history_points_at(self, ts) -> int: + """ + Number of history points in trajectory. Timestep is exclusive. + + :param ts: Scene timestep where the number of history points are queried. + :return: Number of history timesteps. + """ + return ts - self.first_timestep + + def get(self, tr_scene, state, padding=np.nan) -> np.ndarray: + """ + Returns a time range of multiple properties of the node. + + :param tr_scene: The timestep range (inklusive). + :param state: The state description for which the properties are returned. + :param padding: The value which should be used for padding if not enough information is available. + :return: Array of node property values. + """ + if tr_scene.size == 1: + tr_scene = np.array([tr_scene[0], tr_scene[0]]) + length = tr_scene[1] - tr_scene[0] + 1 # tr is inclusive + tr, paddingl, paddingu = self.scene_ts_to_node_ts(tr_scene) + data_array = self.data[tr[0]:tr[1] + 1, state] + padded_data_array = np.full((length, data_array.shape[1]), fill_value=padding) + padded_data_array[paddingl:length - paddingu] = data_array + return padded_data_array + + @property + def timesteps(self) -> int: + """ + Number of available timesteps for node. + + :return: Number of available timesteps. + """ + return self.data.shape[0] + + @property + def last_timestep(self) -> int: + """ + Nodes last timestep in the Scene. + + :return: Nodes last timestep. + """ + if self._last_timestep is None: + self._last_timestep = self.first_timestep + self.timesteps - 1 + return self._last_timestep + + +class MultiNode(Node): + def __init__(self, node_type, node_id, nodes_list, is_robot=False): + super(MultiNode, self).__init__(node_type, node_id, data=None, is_robot=is_robot) + self.nodes_list = nodes_list + for node in self.nodes_list: + node.is_robot = is_robot + + self.first_timestep = min(node.first_timestep for node in self.nodes_list) + self._last_timestep = max(node.last_timestep for node in self.nodes_list) + + starts = np.array([node.first_timestep for node in self.nodes_list], dtype=np.int64) + ends = np.array([node.last_timestep for node in self.nodes_list], dtype=np.int64) + ids = np.arange(len(self.nodes_list), dtype=np.int64) + self.interval_tree = NCLS(starts, ends, ids) + + @staticmethod + def find_non_overlapping_nodes(nodes_list, min_timesteps=1) -> list: + """ + Greedily finds a set of non-overlapping nodes in the provided scene. + + :return: A list of non-overlapping nodes. + """ + non_overlapping_nodes = list() + nodes = sorted(nodes_list, key=lambda n: n.last_timestep) + current_time = 0 + for node in nodes: + if node.first_timestep >= current_time and node.timesteps >= min_timesteps: + # Include the node + non_overlapping_nodes.append(node) + current_time = node.last_timestep + + return non_overlapping_nodes + + def get_node_at_timesteps(self, scene_ts) -> Node: + possible_node_ranges = list(self.interval_tree.find_overlap(scene_ts[0], scene_ts[1] + 1)) + if not possible_node_ranges: + return Node(node_type=self.type, + node_id='EMPTY', + data=self.nodes_list[0].data * np.nan, + is_robot=self.is_robot) + + node_idx = random.choice(possible_node_ranges)[2] + return self.nodes_list[node_idx] + + def scene_ts_to_node_ts(self, scene_ts) -> (Node, np.ndarray, int, int): + """ + Transforms timestamp from scene into timeframe of node data. + + :param scene_ts: Scene timesteps + :return: ts: Transformed timesteps, paddingl: Number of timesteps in scene range which are not available in + node data before data is available. paddingu: Number of timesteps in scene range which are not + available in node data after data is available. + """ + possible_node_ranges = list(self.interval_tree.find_overlap(scene_ts[0], scene_ts[1] + 1)) + if not possible_node_ranges: + return None, None, None, None + + node_idx = random.choice(possible_node_ranges)[2] + node = self.nodes_list[node_idx] + + paddingl = (node.first_timestep - scene_ts[0]).clip(0) + paddingu = (scene_ts[1] - node.last_timestep).clip(0) + ts = np.array(scene_ts).clip(min=node.first_timestep, max=node.last_timestep) - node.first_timestep + return node, ts, paddingl, paddingu + + def get(self, tr_scene, state, padding=np.nan) -> np.ndarray: + if tr_scene.size == 1: + tr_scene = np.array([tr_scene, tr_scene]) + length = tr_scene[1] - tr_scene[0] + 1 # tr is inclusive + + node, tr, paddingl, paddingu = self.scene_ts_to_node_ts(tr_scene) + if node is None: + state_length = sum([len(entity_dims) for entity_dims in state.values()]) + return np.full((length, state_length), fill_value=padding) + + data_array = node.data[tr[0]:tr[1] + 1, state] + padded_data_array = np.full((length, data_array.shape[1]), fill_value=padding) + padded_data_array[paddingl:length - paddingu] = data_array + return padded_data_array + + def get_all(self, tr_scene, state, padding=np.nan) -> np.ndarray: + # Assumption here is that the user is asking for all of the data in this MultiNode and to return it within a + # full scene-sized output array. + assert tr_scene.size == 2 and tr_scene[0] == 0 and self.last_timestep <= tr_scene[1] + length = tr_scene[1] - tr_scene[0] + 1 # tr is inclusive + state_length = sum([len(entity_dims) for entity_dims in state.values()]) + padded_data_array = np.full((length, state_length), fill_value=padding) + for node in self.nodes_list: + padded_data_array[node.first_timestep:node.last_timestep + 1] = node.data[:, state] + + return padded_data_array + + def history_points_at(self, ts) -> int: + """ + Number of history points in trajectory. Timestep is exclusive. + + :param ts: Scene timestep where the number of history points are queried. + :return: Number of history timesteps. + """ + node_idx = next(self.interval_tree.find_overlap(ts, ts + 1))[2] + node = self.nodes_list[node_idx] + return ts - node.first_timestep + + @property + def timesteps(self) -> int: + """ + Number of available timesteps for node. + + :return: Number of available timesteps. + """ + return self._last_timestep - self.first_timestep + 1 diff --git a/trajectron/environment/node_type.py b/trajectron/environment/node_type.py new file mode 100644 index 0000000..a44917a --- /dev/null +++ b/trajectron/environment/node_type.py @@ -0,0 +1,35 @@ +class NodeType(object): + def __init__(self, name, value): + self.name = name + self.value = value + + def __repr__(self): + return self.name + + def __eq__(self, other): + if type(other) == str and self.name == other: + return True + else: + return isinstance(other, self.__class__) and self.name == other.name + + def __ne__(self, other): + return not self.__eq__(other) + + def __hash__(self): + return hash(self.name) + + def __add__(self, other): + return self.name + other + + +class NodeTypeEnum(list): + def __init__(self, node_type_list): + self.node_type_list = node_type_list + node_types = [NodeType(name, node_type_list.index(name) + 1) for name in node_type_list] + super().__init__(node_types) + + def __getattr__(self, name): + if not name.startswith('_') and name in object.__getattribute__(self, "node_type_list"): + return self[object.__getattribute__(self, "node_type_list").index(name)] + else: + return object.__getattribute__(self, name) diff --git a/trajectron/environment/scene.py b/trajectron/environment/scene.py new file mode 100644 index 0000000..7a5c60b --- /dev/null +++ b/trajectron/environment/scene.py @@ -0,0 +1,204 @@ +import copy +import numpy as np +from .scene_graph import TemporalSceneGraph, SceneGraph +from .node import MultiNode + + +class Scene(object): + def __init__(self, timesteps, map=None, dt=1, name="", frequency_multiplier=1, aug_func=None, non_aug_scene=None): + self.map = map + self.timesteps = timesteps + self.dt = dt + self.name = name + + self.nodes = [] + + self.robot = None + + self.temporal_scene_graph = None + + self.frequency_multiplier = frequency_multiplier + + self.description = "" + + self.aug_func = aug_func + self.non_aug_scene = non_aug_scene + + def add_robot_from_nodes(self, robot_type): + nodes_list = [node for node in self.nodes if node.type == robot_type] + non_overlapping_nodes = MultiNode.find_non_overlapping_nodes(nodes_list, min_timesteps=3) + self.robot = MultiNode(robot_type, 'ROBOT', non_overlapping_nodes, is_robot=True) + + for node in non_overlapping_nodes: + self.nodes.remove(node) + self.nodes.append(self.robot) + + def get_clipped_pos_dict(self, timestep, state): + pos_dict = dict() + existing_nodes = self.get_nodes_clipped_at_time(timesteps=np.array([timestep]), + state=state) + tr_scene = np.array([timestep, timestep]) + for node in existing_nodes: + pos_dict[node] = node.get(tr_scene, {'position': ['x', 'y']}) + + return pos_dict + + def get_scene_graph(self, + timestep, + attention_radius=None, + edge_addition_filter=None, + edge_removal_filter=None) -> SceneGraph: + """ + Returns the Scene Graph for a given timestep. If the Temporal Scene Graph was pre calculated, + the temporal scene graph is sliced. Otherwise the scene graph is calculated on the spot. + + :param timestep: Timestep for which the scene graph is returned. + :param attention_radius: Attention radius for each node type permutation. (Only online) + :param edge_addition_filter: Filter for adding edges (Only online) + :param edge_removal_filter: Filter for removing edges (Only online) + :return: Scene Graph for given timestep. + """ + if self.temporal_scene_graph is None: + timestep_range = np.array([timestep - len(edge_removal_filter), timestep]) + node_pos_dict = dict() + present_nodes = self.present_nodes(np.array([timestep])) + + for node in present_nodes[timestep]: + node_pos_dict[node] = np.squeeze(node.get(timestep_range, {'position': ['x', 'y']})) + tsg = TemporalSceneGraph.create_from_temp_scene_dict(node_pos_dict, + attention_radius, + duration=(len(edge_removal_filter) + 1), + edge_addition_filter=edge_addition_filter, + edge_removal_filter=edge_removal_filter + ) + + return tsg.to_scene_graph(t=len(edge_removal_filter), + t_hist=len(edge_removal_filter), + t_fut=len(edge_addition_filter)) + else: + return self.temporal_scene_graph.to_scene_graph(timestep, + len(edge_removal_filter), + len(edge_addition_filter)) + + def calculate_scene_graph(self, + attention_radius, + edge_addition_filter=None, + edge_removal_filter=None) -> None: + """ + Calculate the Temporal Scene Graph for the entire Scene. + + :param attention_radius: Attention radius for each node type permutation. + :param edge_addition_filter: Filter for adding edges. + :param edge_removal_filter: Filter for removing edges. + :return: None + """ + timestep_range = np.array([0, self.timesteps-1]) + node_pos_dict = dict() + + for node in self.nodes: + if type(node) is MultiNode: + node_pos_dict[node] = np.squeeze(node.get_all(timestep_range, {'position': ['x', 'y']})) + else: + node_pos_dict[node] = np.squeeze(node.get(timestep_range, {'position': ['x', 'y']})) + + self.temporal_scene_graph = TemporalSceneGraph.create_from_temp_scene_dict(node_pos_dict, + attention_radius, + duration=self.timesteps, + edge_addition_filter=edge_addition_filter, + edge_removal_filter=edge_removal_filter) + + def duration(self): + """ + Calculates the duration of the scene. + + :return: Duration of the scene in s. + """ + return self.timesteps * self.dt + + def present_nodes(self, + timesteps, + type=None, + min_history_timesteps=0, + min_future_timesteps=0, + return_robot=True) -> dict: + """ + Finds all present nodes in the scene at a given timestemp + + :param timesteps: Timestep(s) for which all present nodes should be returned + :param type: Node type which should be returned. If None all node types are returned. + :param min_history_timesteps: Minimum history timesteps of a node to be returned. + :param min_future_timesteps: Minimum future timesteps of a node to be returned. + :param return_robot: Return a node if it is the robot. + :return: Dictionary with timesteps as keys and list of nodes as value. + """ + + present_nodes = {} + + for node in self.nodes: + if node.is_robot and not return_robot: + continue + if type is None or node.type == type: + lower_bound = timesteps - min_history_timesteps + upper_bound = timesteps + min_future_timesteps + mask = (node.first_timestep <= lower_bound) & (upper_bound <= node.last_timestep) + if mask.any(): + timestep_indices_present = np.nonzero(mask)[0] + for timestep_index_present in timestep_indices_present: + if timesteps[timestep_index_present] in present_nodes.keys(): + present_nodes[timesteps[timestep_index_present]].append(node) + else: + present_nodes[timesteps[timestep_index_present]] = [node] + + return present_nodes + + def get_nodes_clipped_at_time(self, timesteps, state): + clipped_nodes = list() + + existing_nodes = self.present_nodes(timesteps) + all_nodes = set().union(*existing_nodes.values()) + if not all_nodes: + return clipped_nodes + + tr_scene = np.array([timesteps.min(), timesteps.max()]) + for node in all_nodes: + if isinstance(node, MultiNode): + copied_node = copy.deepcopy(node.get_node_at_timesteps(tr_scene)) + copied_node.id = self.robot.id + else: + copied_node = copy.deepcopy(node) + + clipped_value = node.get(tr_scene, state[node.type]) + copied_node.overwrite_data(clipped_value) + copied_node.first_timestep = tr_scene[0] + + clipped_nodes.append(copied_node) + + return clipped_nodes + + def sample_timesteps(self, batch_size, min_future_timesteps=0) -> np.ndarray: + """ + Sample a batch size of possible timesteps for the scene. + + :param batch_size: Number of timesteps to sample. + :param min_future_timesteps: Minimum future timesteps in the scene for a timestep to be returned. + :return: Numpy Array of sampled timesteps. + """ + if batch_size > self.timesteps: + batch_size = self.timesteps + return np.random.choice(np.arange(0, self.timesteps-min_future_timesteps), size=batch_size, replace=False) + + def augment(self): + if self.aug_func is not None: + return self.aug_func(self) + else: + return self + + def get_node_by_id(self, id): + for node in self.nodes: + if node.id == id: + return node + + def __repr__(self): + return f"Scene: Duration: {self.duration()}s," \ + f" Nodes: {len(self.nodes)}," \ + f" Map: {'Yes' if self.map is not None else 'No'}." diff --git a/trajectron/environment/scene_graph.py b/trajectron/environment/scene_graph.py new file mode 100644 index 0000000..3806d4d --- /dev/null +++ b/trajectron/environment/scene_graph.py @@ -0,0 +1,493 @@ +import numpy as np +from scipy.spatial.distance import pdist, squareform +import scipy.signal as ss +from collections import defaultdict +import warnings +from .node import Node + + +class Edge(object): + def __init__(self, curr_node, other_node): + self.id = self.get_edge_id(curr_node, other_node) + self.type = self.get_edge_type(curr_node, other_node) + self.curr_node = curr_node + self.other_node = other_node + + @staticmethod + def get_edge_id(n1, n2): + raise NotImplementedError("Use one of the Edge subclasses!") + + @staticmethod + def get_str_from_types(nt1, nt2): + raise NotImplementedError("Use one of the Edge subclasses!") + + @staticmethod + def get_edge_type(n1, n2): + raise NotImplementedError("Use one of the Edge subclasses!") + + def __eq__(self, other): + return (isinstance(other, self.__class__) + and self.id == other.id) + + def __ne__(self, other): + return not self.__eq__(other) + + def __hash__(self): + return hash(self.id) + + def __repr__(self): + return self.id + + +class UndirectedEdge(Edge): + def __init__(self, curr_node, other_node): + super(UndirectedEdge, self).__init__(curr_node, other_node) + + @staticmethod + def get_edge_id(n1, n2): + return '-'.join(sorted([str(n1), str(n2)])) + + @staticmethod + def get_str_from_types(nt1, nt2): + return '-'.join(sorted([nt1.name, nt2.name])) + + @staticmethod + def get_edge_type(n1, n2): + return '-'.join(sorted([n1.type.name, n2.type.name])) + + +class DirectedEdge(Edge): + def __init__(self, curr_node, other_node): + super(DirectedEdge, self).__init__(curr_node, other_node) + + @staticmethod + def get_edge_id(n1, n2): + return '->'.join([str(n1), str(n2)]) + + @staticmethod + def get_str_from_types(nt1, nt2): + return '->'.join([nt1.name, nt2.name]) + + @staticmethod + def get_edge_type(n1, n2): + return '->'.join([n1.type.name, n2.type.name]) + + +class TemporalSceneGraph(object): + def __init__(self, + edge_radius, + nodes=None, + adj_cube=np.zeros((1, 0, 0)), + weight_cube=np.zeros((1, 0, 0)), + node_type_mat=np.zeros((0, 0)), + edge_scaling=None): + self.edge_radius = edge_radius + self.nodes = nodes + if nodes is None: + self.nodes = np.array([]) + self.adj_cube = adj_cube + self.weight_cube = weight_cube + self.node_type_mat = node_type_mat + self.adj_mat = np.max(self.adj_cube, axis=0).clip(max=1.0) + self.edge_scaling = edge_scaling + self.node_index_lookup = None + self.calculate_node_index_lookup() + + def calculate_node_index_lookup(self): + node_index_lookup = dict() + for i, node in enumerate(self.nodes): + node_index_lookup[node] = i + + self.node_index_lookup = node_index_lookup + + def get_num_edges(self, t=0): + return np.sum(self.adj_cube[t]) // 2 + + def get_index(self, node): + return self.node_index_lookup[node] + + @classmethod + def create_from_temp_scene_dict(cls, + scene_temp_dict, + attention_radius, + duration=1, + edge_addition_filter=None, + edge_removal_filter=None, + online=False): + """ + Construct a spatiotemporal graph from node positions in a dataset. + + :param scene_temp_dict: Dict with all nodes in scene as keys and np.ndarray with positions as value + :param attention_radius: Attention radius dict. + :param duration: Temporal duration of the graph. + :param edge_addition_filter: - + :param edge_removal_filter: - + :return: TemporalSceneGraph + """ + + nodes = scene_temp_dict.keys() + N = len(nodes) + total_timesteps = duration + + if N == 0: + return TemporalSceneGraph(attention_radius) + + position_cube = np.full((total_timesteps, N, 2), np.nan) + + adj_cube = np.zeros((total_timesteps, N, N), dtype=np.int8) + dist_cube = np.zeros((total_timesteps, N, N), dtype=np.float) + + node_type_mat = np.zeros((N, N), dtype=np.int8) + node_attention_mat = np.zeros((N, N), dtype=np.float) + + for node_idx, node in enumerate(nodes): + if online: + # RingBuffers do not have a fixed constant size. Instead, they grow up to their capacity. Thus, + # we need to fill the values preceding the RingBuffer values with NaNs to make them fill the + # position_cube. + position_cube[-scene_temp_dict[node].shape[0]:, node_idx] = scene_temp_dict[node] + else: + position_cube[:, node_idx] = scene_temp_dict[node] + + node_type_mat[:, node_idx] = node.type.value + for node_idx_from, node_from in enumerate(nodes): + node_attention_mat[node_idx_from, node_idx] = attention_radius[(node_from.type, node.type)] + + np.fill_diagonal(node_type_mat, 0) + + for timestep in range(position_cube.shape[0]): + dists = squareform(pdist(position_cube[timestep], metric='euclidean')) + + # Put a 1 for all agent pairs which are closer than the edge_radius. + # Can produce a warning as dists can be nan if no data for node is available. + # This is accepted as nan <= x evaluates to False + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + adj_matrix = (dists <= node_attention_mat).astype(np.int8) * node_type_mat + + # Remove self-loops. + np.fill_diagonal(adj_matrix, 0) + + adj_cube[timestep] = adj_matrix + dist_cube[timestep] = dists + + dist_cube[np.isnan(dist_cube)] = 0. + weight_cube = np.divide(1., + dist_cube, + out=np.zeros_like(dist_cube), + where=(dist_cube > 0.)) + edge_scaling = None + if edge_addition_filter is not None and edge_removal_filter is not None: + edge_scaling = cls.calculate_edge_scaling(adj_cube, edge_addition_filter, edge_removal_filter) + tsg = cls(attention_radius, + np.array(list(nodes)), + adj_cube, weight_cube, + node_type_mat, + edge_scaling=edge_scaling) + return tsg + + @staticmethod + def calculate_edge_scaling(adj_cube, edge_addition_filter, edge_removal_filter): + shifted_right = np.pad(adj_cube, ((len(edge_addition_filter) - 1, 0), (0, 0), (0, 0)), 'constant', constant_values=0) + + new_edges = np.minimum( + ss.convolve(shifted_right, np.reshape(edge_addition_filter, (-1, 1, 1)), 'full'), 1. + )[(len(edge_addition_filter) - 1):-(len(edge_addition_filter) - 1)] + + new_edges[adj_cube == 0] = 0 + + result = np.minimum( + ss.convolve(new_edges, np.reshape(edge_removal_filter, (-1, 1, 1)), 'full'), 1. + )[:-(len(edge_removal_filter) - 1)] + + return result + + def to_scene_graph(self, t, t_hist=0, t_fut=0): + """ + Creates a Scene Graph from a Temporal Scene Graph + + :param t: Time in Temporal Scene Graph for which Scene Graph is created. + :param t_hist: Number of history timesteps which are considered to form edges in Scene Graph. + :param t_fut: Number of future timesteps which are considered to form edges in Scene Graph. + :return: SceneGraph + """ + lower_t = np.clip(t-t_hist, a_min=0, a_max=None) + higher_t = np.clip(t + t_fut + 1, a_min=None, a_max=self.adj_cube.shape[0] + 1) + adj_mat = np.max(self.adj_cube[lower_t:higher_t], axis=0) + weight_mat = np.max(self.weight_cube[lower_t:higher_t], axis=0) + return SceneGraph(self.edge_radius, + self.nodes, + adj_mat, + weight_mat, + self.node_type_mat, + self.node_index_lookup, + edge_scaling=self.edge_scaling[t] if self.edge_scaling is not None else None) + + +class SceneGraph(object): + def __init__(self, + edge_radius, + nodes=None, + adj_mat=np.zeros((0, 0)), + weight_mat=np.zeros((0, 0)), + node_type_mat=np.zeros((0, 0)), + node_index_lookup=None, + edge_scaling=None): + self.edge_radius = edge_radius + self.nodes = nodes + if nodes is None: + self.nodes = np.array([]) + self.node_type_mat = node_type_mat + self.adj_mat = adj_mat + self.weight_mat = weight_mat + self.edge_scaling = edge_scaling + self.node_index_lookup = node_index_lookup + + def get_index(self, node): + return self.node_index_lookup[node] + + def get_num_edges(self): + return np.sum(self.adj_mat) // 2 + + def get_neighbors(self, node, node_type): + """ + Get all neighbors of a node. + + :param node: Node for which all neighbors are returned. + :param node_type: Specifies node types which are returned. + :return: List of all neighbors. + """ + node_index = self.get_index(node) + connection_mask = self.get_connection_mask(node_index) + mask = ((self.node_type_mat[node_index] == node_type.value) * connection_mask) + return self.nodes[mask] + + def get_edge_scaling(self, node=None): + if node is None: + return self.edge_scaling + else: + node_index = self.get_index(node) + connection_mask = self.get_connection_mask(node_index) + return self.edge_scaling[node_index, connection_mask] + + def get_edge_weight(self, node=None): + if node is None: + return self.weight_mat + else: + node_index = self.get_index(node) + connection_mask = self.get_connection_mask(node_index) + return self.weight_mat[node_index, connection_mask] + + def get_connection_mask(self, node_index): + if self.edge_scaling is None: # We do not use edge scaling + return self.adj_mat[node_index] > 0. + else: + return self.edge_scaling[node_index] > 1e-2 + + def __sub__(self, other): + new_nodes = [node for node in self.nodes if node not in other.nodes] + removed_nodes = [node for node in other.nodes if node not in self.nodes] + + our_types = set(node.type for node in self.nodes) + other_types = set(node.type for node in other.nodes) + all_node_types = our_types | other_types + + new_neighbors = defaultdict(dict) + for node in self.nodes: + if node in removed_nodes: + continue + + if node in other.nodes: + for node_type in all_node_types: + new_items = set(self.get_neighbors(node, node_type)) - set(other.get_neighbors(node, node_type)) + if len(new_items) > 0: + new_neighbors[node][DirectedEdge.get_edge_type(node, Node(node_type, None, None))] = new_items + else: + for node_type in our_types: + neighbors = self.get_neighbors(node, node_type) + if len(neighbors) > 0: + new_neighbors[node] = {DirectedEdge.get_edge_type(node, Node(node_type, None, None)): set(neighbors)} + + removed_neighbors = defaultdict(dict) + for node in other.nodes: + if node in removed_nodes: + continue + + if node in self.nodes: + for node_type in all_node_types: + removed_items = set(other.get_neighbors(node, node_type)) - set(self.get_neighbors(node, node_type)) + if len(removed_items) > 0: + removed_neighbors[node][DirectedEdge.get_edge_type(node, Node(node_type, None, None))] = removed_items + else: + for node_type in other_types: + neighbors = other.get_neighbors(node, node_type) + if len(neighbors) > 0: + removed_neighbors[node] = {DirectedEdge.get_edge_type(node, Node(node_type, None, None)): set(neighbors)} + + return new_nodes, removed_nodes, new_neighbors, removed_neighbors + + +if __name__ == '__main__': + from data import NodeTypeEnum + import time + + # # # # # # # # # # # # # # # # # + # Testing edge mask calculation # + # # # # # # # # # # # # # # # # # + B = np.array([[0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0], + [1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0]])[:, :, np.newaxis, np.newaxis] + print(B.shape) + + edge_addition_filter = [0.25, 0.5, 0.75, 1.0] + edge_removal_filter = [1.0, 0.5, 0.0] + for i in range(B.shape[0]): + A = B[i] # (time, N, N) + + print(A[:, 0, 0]) + + start = time.time() + new_edges = np.minimum(ss.convolve(A, np.reshape(edge_addition_filter, (-1, 1, 1)), 'full'), 1.)[(len(edge_addition_filter) - 1):] + old_edges = np.minimum(ss.convolve(A, np.reshape(edge_removal_filter, (-1, 1, 1)), 'full'), 1.)[:-(len(edge_removal_filter) - 1)] + res = np.minimum(new_edges + old_edges, 1.)[:, 0, 0] + end = time.time() + print(end - start) + print(res) + + start = time.time() + res = TemporalSceneGraph.calculate_edge_scaling(A, edge_addition_filter, edge_removal_filter)[:, 0, 0] + end = time.time() + print(end - start) + print(res) + + print('-'*40) + + # # # # # # # # # # # # # # # + # Testing graph subtraction # + # # # # # # # # # # # # # # # + print('\n' + '-' * 40 + '\n') + + node_type_list = ['PEDESTRIAN', + 'BICYCLE', + 'VEHICLE'] + nte = NodeTypeEnum(node_type_list) + + attention_radius = dict() + attention_radius[(nte.PEDESTRIAN, nte.PEDESTRIAN)] = 5.0 + attention_radius[(nte.PEDESTRIAN, nte.VEHICLE)] = 20.0 + attention_radius[(nte.PEDESTRIAN, nte.BICYCLE)] = 10.0 + attention_radius[(nte.VEHICLE, nte.PEDESTRIAN)] = 20.0 + attention_radius[(nte.VEHICLE, nte.VEHICLE)] = 20.0 + attention_radius[(nte.VEHICLE, nte.BICYCLE)] = 20.0 + attention_radius[(nte.BICYCLE, nte.PEDESTRIAN)] = 10.0 + attention_radius[(nte.BICYCLE, nte.VEHICLE)] = 20.0 + attention_radius[(nte.BICYCLE, nte.BICYCLE)] = 10.0 + + scene_dict1 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([0, 1])} + sg1 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict1, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + scene_dict2 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([1, 1])} + sg2 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict2, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + new_nodes, removed_nodes, new_neighbors, removed_neighbors = sg2 - sg1 + print('New Nodes:', new_nodes) + print('Removed Nodes:', removed_nodes) + print('New Neighbors:', new_neighbors) + print('Removed Neighbors:', removed_neighbors) + + # # # # # # # # # # # # # # # + print('\n' + '-' * 40 + '\n') + + scene_dict1 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([0, 1])} + sg1 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict1, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + scene_dict2 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([1, 1]), + Node(nte.PEDESTRIAN, node_id='3'): np.array([20, 1])} + sg2 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict2, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + new_nodes, removed_nodes, new_neighbors, removed_neighbors = sg2 - sg1 + print('New Nodes:', new_nodes) + print('Removed Nodes:', removed_nodes) + print('New Neighbors:', new_neighbors) + print('Removed Neighbors:', removed_neighbors) + + # # # # # # # # # # # # # # # + print('\n' + '-' * 40 + '\n') + + scene_dict1 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([0, 1])} + sg1 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict1, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + scene_dict2 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([1, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([10, 1]), + Node(nte.PEDESTRIAN, node_id='3'): np.array([20, 1])} + sg2 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict2, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + new_nodes, removed_nodes, new_neighbors, removed_neighbors = sg2 - sg1 + print('New Nodes:', new_nodes) + print('Removed Nodes:', removed_nodes) + print('New Neighbors:', new_neighbors) + print('Removed Neighbors:', removed_neighbors) + + # # # # # # # # # # # # # # # + print('\n' + '-' * 40 + '\n') + + scene_dict1 = {Node(nte.PEDESTRIAN, node_id='1'): np.array([0, 0]), + Node(nte.PEDESTRIAN, node_id='2'): np.array([0, 1])} + sg1 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict1, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + scene_dict2 = {Node(nte.PEDESTRIAN, node_id='2'): np.array([10, 1]), + Node(nte.PEDESTRIAN, node_id='3'): np.array([12, 1]), + Node(nte.PEDESTRIAN, node_id='4'): np.array([13, 1])} + sg2 = TemporalSceneGraph.create_from_temp_scene_dict( + scene_dict2, + attention_radius=attention_radius, + duration=1, + edge_addition_filter=[0.25, 0.5, 0.75, 1.0], + edge_removal_filter=[1.0, 0.0]).to_scene_graph(t=0) + + new_nodes, removed_nodes, new_neighbors, removed_neighbors = sg2 - sg1 + print('New Nodes:', new_nodes) + print('Removed Nodes:', removed_nodes) + print('New Neighbors:', new_neighbors) + print('Removed Neighbors:', removed_neighbors) diff --git a/code/evaluation/__init__.py b/trajectron/evaluation/__init__.py similarity index 74% rename from code/evaluation/__init__.py rename to trajectron/evaluation/__init__.py index 808fd76..6674ebb 100644 --- a/code/evaluation/__init__.py +++ b/trajectron/evaluation/__init__.py @@ -1 +1 @@ -from .evaluation import compute_batch_statistics, log_batch_errors \ No newline at end of file +from .evaluation import compute_batch_statistics, log_batch_errors, print_batch_errors \ No newline at end of file diff --git a/code/evaluation/evaluation.py b/trajectron/evaluation/evaluation.py similarity index 62% rename from code/evaluation/evaluation.py rename to trajectron/evaluation/evaluation.py index 9780080..8e5a643 100644 --- a/code/evaluation/evaluation.py +++ b/trajectron/evaluation/evaluation.py @@ -10,33 +10,38 @@ from matplotlib import pyplot as plt def compute_ade(predicted_trajs, gt_traj): error = np.linalg.norm(predicted_trajs - gt_traj, axis=-1) ade = np.mean(error, axis=-1) - return ade + return ade.flatten() def compute_fde(predicted_trajs, gt_traj): - final_error = np.linalg.norm(predicted_trajs[:, -1] - gt_traj[-1], axis=-1) - return final_error + final_error = np.linalg.norm(predicted_trajs[:, :, -1] - gt_traj[-1], axis=-1) + return final_error.flatten() def compute_kde_nll(predicted_trajs, gt_traj): kde_ll = 0. log_pdf_lower_bound = -20 num_timesteps = gt_traj.shape[0] + num_batches = predicted_trajs.shape[0] - for timestep in range(num_timesteps): # TODO First timestep - kde = gaussian_kde(predicted_trajs[:, timestep].T) - pdf = np.clip(kde.logpdf(gt_traj[timestep].T), a_min=log_pdf_lower_bound, a_max=None)[0] - kde_ll += pdf / num_timesteps + for batch_num in range(num_batches): + for timestep in range(num_timesteps): + try: + kde = gaussian_kde(predicted_trajs[batch_num, :, timestep].T) + pdf = np.clip(kde.logpdf(gt_traj[timestep].T), a_min=log_pdf_lower_bound, a_max=None)[0] + kde_ll += pdf / (num_timesteps * num_batches) + except np.linalg.LinAlgError: + kde_ll = np.nan return -kde_ll def compute_obs_violations(predicted_trajs, map): - obs_map = 1 - map.fdata[..., 0] + obs_map = map.data - interp_obs_map = RectBivariateSpline(range(obs_map.shape[0]), - range(obs_map.shape[1]), - obs_map, + interp_obs_map = RectBivariateSpline(range(obs_map.shape[1]), + range(obs_map.shape[0]), + binary_dilation(obs_map.T, iterations=4), kx=1, ky=1) old_shape = predicted_trajs.shape @@ -49,11 +54,24 @@ def compute_obs_violations(predicted_trajs, map): return num_viol_trajs -def compute_batch_statistics(prediction_output_dict, dt, max_hl, ph, node_type_enum, kde=True, obs=False, map=None): +def compute_batch_statistics(prediction_output_dict, + dt, + max_hl, + ph, + node_type_enum, + kde=True, + obs=False, + map=None, + prune_ph_to_future=False, + best_of=False): (prediction_dict, _, - futures_dict) = prediction_output_to_trajectories(prediction_output_dict, dt, max_hl, ph) + futures_dict) = prediction_output_to_trajectories(prediction_output_dict, + dt, + max_hl, + ph, + prune_ph_to_future=prune_ph_to_future) batch_error_dict = dict() for node_type in node_type_enum: @@ -71,6 +89,10 @@ def compute_batch_statistics(prediction_output_dict, dt, max_hl, ph, node_type_e obs_viols = compute_obs_violations(prediction_dict[t][node], map) else: obs_viols = 0 + if best_of: + ade_errors = np.min(ade_errors, keepdims=True) + fde_errors = np.min(fde_errors, keepdims=True) + kde_ll = np.min(kde_ll) batch_error_dict[node.type]['ade'].extend(list(ade_errors)) batch_error_dict[node.type]['fde'].extend(list(fde_errors)) batch_error_dict[node.type]['kde'].extend([kde_ll]) @@ -104,3 +126,15 @@ def log_batch_errors(batch_errors_list, log_writer, namespace, curr_iter, bar_pl fig, ax = plt.subplots(figsize=(5, 5)) visualization.visualization_utils.plot_boxplots(ax, mse_fde_pd, 'dataset', metric) log_writer.add_figure(f"{node_type.name}/{namespace}/{metric}_box_plot", fig, curr_iter) + + +def print_batch_errors(batch_errors_list, namespace, curr_iter): + for node_type in batch_errors_list[0].keys(): + for metric in batch_errors_list[0][node_type].keys(): + metric_batch_error = [] + for batch_errors in batch_errors_list: + metric_batch_error.extend(batch_errors[node_type][metric]) + + if len(metric_batch_error) > 0: + print(f"{curr_iter}: {node_type.name}/{namespace}/{metric}_mean", np.mean(metric_batch_error)) + print(f"{curr_iter}: {node_type.name}/{namespace}/{metric}_median", np.median(metric_batch_error)) diff --git a/trajectron/model/__init__.py b/trajectron/model/__init__.py new file mode 100644 index 0000000..910c40a --- /dev/null +++ b/trajectron/model/__init__.py @@ -0,0 +1,2 @@ +from .trajectron import Trajectron +from .mgcvae import MultimodalGenerativeCVAE diff --git a/code/model/components/__init__.py b/trajectron/model/components/__init__.py similarity index 100% rename from code/model/components/__init__.py rename to trajectron/model/components/__init__.py diff --git a/code/model/components/additive_attention.py b/trajectron/model/components/additive_attention.py similarity index 100% rename from code/model/components/additive_attention.py rename to trajectron/model/components/additive_attention.py diff --git a/code/model/components/discrete_latent.py b/trajectron/model/components/discrete_latent.py similarity index 78% rename from code/model/components/discrete_latent.py rename to trajectron/model/components/discrete_latent.py index f0fcee5..b3f8688 100644 --- a/code/model/components/discrete_latent.py +++ b/trajectron/model/components/discrete_latent.py @@ -1,7 +1,7 @@ import torch import torch.distributions as td import numpy as np -from model.model_utils import ModeKeys, tile +from model.model_utils import ModeKeys class DiscreteLatent(object): @@ -28,34 +28,36 @@ class DiscreteLatent(object): return td.OneHotCategorical(logits=logits) - def sample_q(self, k, mode): - if mode == ModeKeys.TRAIN: - z_dist = td.RelaxedOneHotCategorical(self.temp, logits=self.q_dist.logits) - z_NK = z_dist.rsample((k,)) - elif mode == ModeKeys.EVAL: - z_NK = self.q_dist.sample((k,)) - return torch.reshape(z_NK, (k, -1, self.z_dim)) + def sample_q(self, num_samples, mode): + bs = self.p_dist.probs.size()[0] + num_components = self.N * self.K + z_NK = torch.from_numpy(self.all_one_hot_combinations(self.N, self.K)).float().to(self.device).repeat(num_samples, bs) + return torch.reshape(z_NK, (num_samples * num_components, -1, self.z_dim)) - def sample_p(self, num_samples_z, mode, num_samples_gmm=1, most_likely=False, all_z=False): - if all_z: + def sample_p(self, num_samples, mode, most_likely_z=False, full_dist=True, all_z_sep=False): + num_components = 1 + if full_dist: + bs = self.p_dist.probs.size()[0] + z_NK = torch.from_numpy(self.all_one_hot_combinations(self.N, self.K)).float().to(self.device).repeat(num_samples, bs) + num_components = self.K ** self.N + k = num_samples * num_components + elif all_z_sep: bs = self.p_dist.probs.size()[0] z_NK = torch.from_numpy(self.all_one_hot_combinations(self.N, self.K)).float().to(self.device).repeat(1, bs) - num_samples_z = self.K ** self.N - - elif most_likely: + k = self.K ** self.N + num_samples = k + elif most_likely_z: # Sampling the most likely z from p(z|x). eye_mat = torch.eye(self.p_dist.event_shape[-1], device=self.device) argmax_idxs = torch.argmax(self.p_dist.probs, dim=2) - z_NK = torch.unsqueeze(eye_mat[argmax_idxs], dim=0).expand(num_samples_z, -1, -1, -1) - + z_NK = torch.unsqueeze(eye_mat[argmax_idxs], dim=0).expand(num_samples, -1, -1, -1) + k = num_samples else: - z_NK = self.p_dist.sample((num_samples_z,)) - - z_NK = tile(z_NK, 0, num_samples_gmm, device=self.device) - k = num_samples_z * num_samples_gmm + z_NK = self.p_dist.sample((num_samples,)) + k = num_samples if mode == ModeKeys.PREDICT: - return torch.reshape(z_NK, (k, -1, self.N * self.K)), num_samples_z + return torch.reshape(z_NK, (k, -1, self.N * self.K)), num_samples, num_components else: return torch.reshape(z_NK, (k, -1, self.N * self.K)) diff --git a/trajectron/model/components/gmm2d.py b/trajectron/model/components/gmm2d.py new file mode 100644 index 0000000..6fb4836 --- /dev/null +++ b/trajectron/model/components/gmm2d.py @@ -0,0 +1,158 @@ +import torch +import torch.distributions as td +import numpy as np +from model.model_utils import to_one_hot + + +class GMM2D(td.Distribution): + r""" + Gaussian Mixture Model using 2D Multivariate Gaussians each of as N components: + Cholesky decompesition and affine transformation for sampling: + + .. math:: Z \sim N(0, I) + + .. math:: S = \mu + LZ + + .. math:: S \sim N(\mu, \Sigma) \rightarrow N(\mu, LL^T) + + where :math:`L = chol(\Sigma)` and + + .. math:: \Sigma = \left[ {\begin{array}{cc} \sigma^2_x & \rho \sigma_x \sigma_y \\ \rho \sigma_x \sigma_y & \sigma^2_y \\ \end{array} } \right] + + such that + + .. math:: L = chol(\Sigma) = \left[ {\begin{array}{cc} \sigma_x & 0 \\ \rho \sigma_y & \sigma_y \sqrt{1-\rho^2} \\ \end{array} } \right] + + :param log_pis: Log Mixing Proportions :math:`log(\pi)`. [..., N] + :param mus: Mixture Components mean :math:`\mu`. [..., N * 2] + :param log_sigmas: Log Standard Deviations :math:`log(\sigma_d)`. [..., N * 2] + :param corrs: Cholesky factor of correlation :math:`\rho`. [..., N] + :param clip_lo: Clips the lower end of the standard deviation. + :param clip_hi: Clips the upper end of the standard deviation. + """ + def __init__(self, log_pis, mus, log_sigmas, corrs): + super(GMM2D, self).__init__(batch_shape=log_pis.shape[0], event_shape=log_pis.shape[1:]) + self.components = log_pis.shape[-1] + self.dimensions = 2 + self.device = log_pis.device + + log_pis = torch.clamp(log_pis, min=-1e5) + self.log_pis = log_pis - torch.logsumexp(log_pis, dim=-1, keepdim=True) # [..., N] + self.mus = self.reshape_to_components(mus) # [..., N, 2] + self.log_sigmas = self.reshape_to_components(log_sigmas) # [..., N, 2] + self.sigmas = torch.exp(self.log_sigmas) # [..., N, 2] + self.one_minus_rho2 = 1 - corrs**2 # [..., N] + self.one_minus_rho2 = torch.clamp(self.one_minus_rho2, min=1e-5, max=1) # otherwise log can be nan + self.corrs = corrs # [..., N] + + self.L = torch.stack([torch.stack([self.sigmas[..., 0], torch.zeros_like(self.log_pis)], dim=-1), + torch.stack([self.sigmas[..., 1] * self.corrs, + self.sigmas[..., 1] * torch.sqrt(self.one_minus_rho2)], + dim=-1)], + dim=-2) + + self.pis_cat_dist = td.Categorical(logits=log_pis) + + @classmethod + def from_log_pis_mus_cov_mats(cls, log_pis, mus, cov_mats): + corrs_sigma12 = cov_mats[..., 0, 1] + sigma_1 = torch.clamp(cov_mats[..., 0, 0], min=1e-8) + sigma_2 = torch.clamp(cov_mats[..., 1, 1], min=1e-8) + sigmas = torch.stack([torch.sqrt(sigma_1), torch.sqrt(sigma_2)], dim=-1) + log_sigmas = torch.log(sigmas) + corrs = corrs_sigma12 / (torch.prod(sigmas, dim=-1)) + return cls(log_pis, mus, log_sigmas, corrs) + + def rsample(self, sample_shape=torch.Size()): + """ + Generates a sample_shape shaped reparameterized sample or sample_shape + shaped batch of reparameterized samples if the distribution parameters + are batched. + + :param sample_shape: Shape of the samples + :return: Samples from the GMM. + """ + mvn_samples = (self.mus + + torch.squeeze( + torch.matmul(self.L, + torch.unsqueeze( + torch.randn(size=sample_shape + self.mus.shape, device=self.device), + dim=-1) + ), + dim=-1)) + component_cat_samples = self.pis_cat_dist.sample(sample_shape) + selector = torch.unsqueeze(to_one_hot(component_cat_samples, self.components), dim=-1) + return torch.sum(mvn_samples*selector, dim=-2) + + def log_prob(self, value): + r""" + Calculates the log probability of a value using the PDF for bivariate normal distributions: + + .. math:: + f(x | \mu, \sigma, \rho)={\frac {1}{2\pi \sigma _{x}\sigma _{y}{\sqrt {1-\rho ^{2}}}}}\exp + \left(-{\frac {1}{2(1-\rho ^{2})}}\left[{\frac {(x-\mu _{x})^{2}}{\sigma _{x}^{2}}}+ + {\frac {(y-\mu _{y})^{2}}{\sigma _{y}^{2}}}-{\frac {2\rho (x-\mu _{x})(y-\mu _{y})} + {\sigma _{x}\sigma _{y}}}\right]\right) + + :param value: The log probability density function is evaluated at those values. + :return: Log probability + """ + # x: [..., 2] + value = torch.unsqueeze(value, dim=-2) # [..., 1, 2] + dx = value - self.mus # [..., N, 2] + + exp_nominator = ((torch.sum((dx/self.sigmas)**2, dim=-1) # first and second term of exp nominator + - 2*self.corrs*torch.prod(dx, dim=-1)/torch.prod(self.sigmas, dim=-1))) # [..., N] + + component_log_p = -(2*np.log(2*np.pi) + + torch.log(self.one_minus_rho2) + + 2*torch.sum(self.log_sigmas, dim=-1) + + exp_nominator/self.one_minus_rho2) / 2 + + return torch.logsumexp(self.log_pis + component_log_p, dim=-1) + + def get_for_node_at_time(self, n, t): + return self.__class__(self.log_pis[:, n:n+1, t:t+1], self.mus[:, n:n+1, t:t+1], + self.log_sigmas[:, n:n+1, t:t+1], self.corrs[:, n:n+1, t:t+1]) + + def mode(self): + """ + Calculates the mode of the GMM by calculating probabilities of a 2D mesh grid + + :param required_accuracy: Accuracy of the meshgrid + :return: Mode of the GMM + """ + if self.mus.shape[-2] > 1: + samp, bs, time, comp, _ = self.mus.shape + assert samp == 1, "For taking the mode only one sample makes sense." + mode_node_list = [] + for n in range(bs): + mode_t_list = [] + for t in range(time): + nt_gmm = self.get_for_node_at_time(n, t) + x_min = self.mus[:, n, t, :, 0].min() + x_max = self.mus[:, n, t, :, 0].max() + y_min = self.mus[:, n, t, :, 1].min() + y_max = self.mus[:, n, t, :, 1].max() + search_grid = torch.stack(torch.meshgrid([torch.arange(x_min, x_max, 0.01), + torch.arange(y_min, y_max, 0.01)]), dim=2 + ).view(-1, 2).float().to(self.device) + + ll_score = nt_gmm.log_prob(search_grid) + argmax = torch.argmax(ll_score.squeeze(), dim=0) + mode_t_list.append(search_grid[argmax]) + mode_node_list.append(torch.stack(mode_t_list, dim=0)) + return torch.stack(mode_node_list, dim=0).unsqueeze(dim=0) + return torch.squeeze(self.mus, dim=-2) + + def reshape_to_components(self, tensor): + if len(tensor.shape) == 5: + return tensor + return torch.reshape(tensor, list(tensor.shape[:-1]) + [self.components, self.dimensions]) + + def get_covariance_matrix(self): + cov = self.corrs * torch.prod(self.sigmas, dim=-1) + E = torch.stack([torch.stack([self.sigmas[..., 0]**2, cov], dim=-1), + torch.stack([cov, self.sigmas[..., 1]**2], dim=-1)], + dim=-2) + return E diff --git a/trajectron/model/components/graph_attention.py b/trajectron/model/components/graph_attention.py new file mode 100644 index 0000000..fc8d89a --- /dev/null +++ b/trajectron/model/components/graph_attention.py @@ -0,0 +1,58 @@ +import warnings +import math +import numbers +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init, Parameter + + +class GraphMultiTypeAttention(nn.Module): + def __init__(self, in_features, hidden_features, out_features, bias=True, types=1): + super(GraphMultiTypeAttention, self).__init__() + self.types = types + self.in_features = in_features + self.out_features = out_features + self.node_self_loop_weight = Parameter(torch.Tensor(hidden_features, in_features[0])) + + self.weight_per_type = nn.ParameterList() + for i in range(types): + self.weight_per_type.append(Parameter(torch.Tensor(hidden_features, in_features[i]))) + if bias: + self.bias = Parameter(torch.Tensor(hidden_features)) + else: + self.register_parameter('bias', None) + + self.linear_to_out = nn.Linear(hidden_features, out_features, bias=bias) + + self.reset_parameters() + + def reset_parameters(self): + for weight in self.weight_per_type: + bound = 1 / math.sqrt(weight.size(1)) + init.uniform_(weight, -bound, bound) + bound = 1 / math.sqrt(self.node_self_loop_weight.size(1)) + init.uniform_(self.node_self_loop_weight, -bound, bound) + if self.bias is not None: + init.uniform_(self.bias, -bound, bound) + + def forward(self, inputs, types, edge_weights): + weight_list = list() + for i, type in enumerate(types): + weight_list.append((edge_weights[i] / len(edge_weights)) * self.weight_per_type[type].T) + weight_list.append(self.node_self_loop_weight.T) + weight = torch.cat(weight_list, dim=0) + stacked_input = torch.cat(inputs, dim=-1) + output = stacked_input.matmul(weight) + + output = output + + if self.bias is not None: + output += self.bias + + return torch.relu(self.linear_to_out(torch.relu(output))) + + def extra_repr(self): + return 'in_features={}, hidden_features={},, out_features={}, types={}, bias={}'.format( + self.in_features, self.hidden_features, self.out_features, self.types, self.bias is not None + ) \ No newline at end of file diff --git a/trajectron/model/components/map_encoder.py b/trajectron/model/components/map_encoder.py new file mode 100644 index 0000000..27d6e1d --- /dev/null +++ b/trajectron/model/components/map_encoder.py @@ -0,0 +1,28 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class CNNMapEncoder(nn.Module): + def __init__(self, map_channels, hidden_channels, output_size, masks, strides, patch_size): + super(CNNMapEncoder, self).__init__() + self.convs = nn.ModuleList() + patch_size_x = patch_size[0] + patch_size[2] + patch_size_y = patch_size[1] + patch_size[3] + input_size = (map_channels, patch_size_x, patch_size_y) + x_dummy = torch.ones(input_size).unsqueeze(0) * torch.tensor(float('nan')) + + for i, hidden_size in enumerate(hidden_channels): + self.convs.append(nn.Conv2d(map_channels if i == 0 else hidden_channels[i-1], + hidden_channels[i], masks[i], + stride=strides[i])) + x_dummy = self.convs[i](x_dummy) + + self.fc = nn.Linear(x_dummy.numel(), output_size) + + def forward(self, x, training): + for conv in self.convs: + x = F.leaky_relu(conv(x), 0.2) + x = torch.flatten(x, start_dim=1) + x = self.fc(x) + return x diff --git a/trajectron/model/dataset/__init__.py b/trajectron/model/dataset/__init__.py new file mode 100644 index 0000000..a18c16c --- /dev/null +++ b/trajectron/model/dataset/__init__.py @@ -0,0 +1,2 @@ +from .dataset import EnvironmentDataset, NodeTypeDataset +from .preprocessing import collate, get_node_timestep_data, get_timesteps_data, restore diff --git a/trajectron/model/dataset/dataset.py b/trajectron/model/dataset/dataset.py new file mode 100644 index 0000000..ef36182 --- /dev/null +++ b/trajectron/model/dataset/dataset.py @@ -0,0 +1,76 @@ +from torch.utils import data +import numpy as np +from .preprocessing import get_node_timestep_data + + +class EnvironmentDataset(object): + def __init__(self, env, state, pred_state, node_freq_mult, scene_freq_mult, hyperparams, **kwargs): + self.env = env + self.state = state + self.pred_state = pred_state + self.hyperparams = hyperparams + self.max_ht = self.hyperparams['maximum_history_length'] + self.max_ft = kwargs['min_future_timesteps'] + self.node_type_datasets = list() + self._augment = False + for node_type in env.NodeType: + if node_type not in hyperparams['pred_state']: + continue + self.node_type_datasets.append(NodeTypeDataset(env, node_type, state, pred_state, node_freq_mult, + scene_freq_mult, hyperparams, **kwargs)) + + @property + def augment(self): + return self._augment + + @augment.setter + def augment(self, value): + self._augment = value + for node_type_dataset in self.node_type_datasets: + node_type_dataset.augment = value + + def __iter__(self): + return iter(self.node_type_datasets) + + +class NodeTypeDataset(data.Dataset): + def __init__(self, env, node_type, state, pred_state, node_freq_mult, + scene_freq_mult, hyperparams, augment=False, **kwargs): + self.env = env + self.state = state + self.pred_state = pred_state + self.hyperparams = hyperparams + self.max_ht = self.hyperparams['maximum_history_length'] + self.max_ft = kwargs['min_future_timesteps'] + + self.augment = augment + + self.node_type = node_type + self.index = self.index_env(node_freq_mult, scene_freq_mult, **kwargs) + self.len = len(self.index) + self.edge_types = [edge_type for edge_type in env.get_edge_types() if edge_type[0] is node_type] + + def index_env(self, node_freq_mult, scene_freq_mult, **kwargs): + index = list() + for scene in self.env.scenes: + present_node_dict = scene.present_nodes(np.arange(0, scene.timesteps), type=self.node_type, **kwargs) + for t, nodes in present_node_dict.items(): + for node in nodes: + index += [(scene, t, node)] *\ + (scene.frequency_multiplier if scene_freq_mult else 1) *\ + (node.frequency_multiplier if node_freq_mult else 1) + + return index + + def __len__(self): + return self.len + + def __getitem__(self, i): + (scene, t, node) = self.index[i] + + if self.augment: + scene = scene.augment() + node = scene.get_node_by_id(node.id) + + return get_node_timestep_data(self.env, scene, t, node, self.state, self.pred_state, + self.edge_types, self.max_ht, self.max_ft, self.hyperparams) diff --git a/trajectron/model/dataset/homography_warper.py b/trajectron/model/dataset/homography_warper.py new file mode 100644 index 0000000..885ab5f --- /dev/null +++ b/trajectron/model/dataset/homography_warper.py @@ -0,0 +1,471 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Tuple, Optional + + +pi = torch.tensor(3.14159265358979323846) + + +def deg2rad(tensor: torch.Tensor) -> torch.Tensor: + r"""Function that converts angles from degrees to radians. + Args: + tensor (torch.Tensor): Tensor of arbitrary shape. + Returns: + torch.Tensor: tensor with same shape as input. + """ + if not isinstance(tensor, torch.Tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(tensor))) + + return tensor * pi.to(tensor.device).type(tensor.dtype) / 180. + + +def angle_to_rotation_matrix(angle: torch.Tensor) -> torch.Tensor: + """ + Creates a rotation matrix out of angles in degrees + Args: + angle: (torch.Tensor): tensor of angles in degrees, any shape. + Returns: + torch.Tensor: tensor of *x2x2 rotation matrices. + Shape: + - Input: :math:`(*)` + - Output: :math:`(*, 2, 2)` + Example: + >>> input = torch.rand(1, 3) # Nx3 + >>> output = kornia.angle_to_rotation_matrix(input) # Nx3x2x2 + """ + ang_rad = deg2rad(angle) + cos_a: torch.Tensor = torch.cos(ang_rad) + sin_a: torch.Tensor = torch.sin(ang_rad) + return torch.stack([cos_a, sin_a, -sin_a, cos_a], dim=-1).view(*angle.shape, 2, 2) + + +def get_rotation_matrix2d( + center: torch.Tensor, + angle: torch.Tensor, + scale: torch.Tensor) -> torch.Tensor: + r"""Calculates an affine matrix of 2D rotation. + The function calculates the following matrix: + .. math:: + \begin{bmatrix} + \alpha & \beta & (1 - \alpha) \cdot \text{x} + - \beta \cdot \text{y} \\ + -\beta & \alpha & \beta \cdot \text{x} + + (1 - \alpha) \cdot \text{y} + \end{bmatrix} + where + .. math:: + \alpha = \text{scale} \cdot cos(\text{radian}) \\ + \beta = \text{scale} \cdot sin(\text{radian}) + The transformation maps the rotation center to itself + If this is not the target, adjust the shift. + Args: + center (Tensor): center of the rotation in the source image. + angle (Tensor): rotation radian in degrees. Positive values mean + counter-clockwise rotation (the coordinate origin is assumed to + be the top-left corner). + scale (Tensor): isotropic scale factor. + Returns: + Tensor: the affine matrix of 2D rotation. + Shape: + - Input: :math:`(B, 2)`, :math:`(B)` and :math:`(B)` + - Output: :math:`(B, 2, 3)` + Example: + >>> center = torch.zeros(1, 2) + >>> scale = torch.ones(1) + >>> radian = 45. * torch.ones(1) + >>> M = kornia.get_rotation_matrix2d(center, radian, scale) + tensor([[[ 0.7071, 0.7071, 0.0000], + [-0.7071, 0.7071, 0.0000]]]) + """ + if not torch.is_tensor(center): + raise TypeError("Input center type is not a torch.Tensor. Got {}" + .format(type(center))) + if not torch.is_tensor(angle): + raise TypeError("Input radian type is not a torch.Tensor. Got {}" + .format(type(angle))) + if not torch.is_tensor(scale): + raise TypeError("Input scale type is not a torch.Tensor. Got {}" + .format(type(scale))) + if not (len(center.shape) == 2 and center.shape[1] == 2): + raise ValueError("Input center must be a Bx2 tensor. Got {}" + .format(center.shape)) + if not len(angle.shape) == 1: + raise ValueError("Input radian must be a B tensor. Got {}" + .format(angle.shape)) + if not len(scale.shape) == 1: + raise ValueError("Input scale must be a B tensor. Got {}" + .format(scale.shape)) + if not (center.shape[0] == angle.shape[0] == scale.shape[0]): + raise ValueError("Inputs must have same batch size dimension. Got {}" + .format(center.shape, angle.shape, scale.shape)) + # convert radian and apply scale + scaled_rotation: torch.Tensor = angle_to_rotation_matrix(angle) * scale.view(-1, 1, 1) + alpha: torch.Tensor = scaled_rotation[:, 0, 0] + beta: torch.Tensor = scaled_rotation[:, 0, 1] + + # unpack the center to x, y coordinates + x: torch.Tensor = center[..., 0] + y: torch.Tensor = center[..., 1] + + # create output tensor + batch_size: int = center.shape[0] + M: torch.Tensor = torch.zeros( + batch_size, 2, 3, device=center.device, dtype=center.dtype) + M[..., 0:2, 0:2] = scaled_rotation + M[..., 0, 2] = (torch.tensor(1.) - alpha) * x - beta * y + M[..., 1, 2] = beta * x + (torch.tensor(1.) - alpha) * y + return M + +def convert_points_to_homogeneous(points: torch.Tensor) -> torch.Tensor: + r"""Function that converts points from Euclidean to homogeneous space. + Examples:: + >>> input = torch.rand(2, 4, 3) # BxNx3 + >>> output = kornia.convert_points_to_homogeneous(input) # BxNx4 + """ + if not isinstance(points, torch.Tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(points))) + if len(points.shape) < 2: + raise ValueError("Input must be at least a 2D tensor. Got {}".format( + points.shape)) + + return torch.nn.functional.pad(points, [0, 1], "constant", 1.0) + + +def convert_points_from_homogeneous( + points: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + r"""Function that converts points from homogeneous to Euclidean space. + Examples:: + >>> input = torch.rand(2, 4, 3) # BxNx3 + >>> output = kornia.convert_points_from_homogeneous(input) # BxNx2 + """ + if not isinstance(points, torch.Tensor): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(points))) + + if len(points.shape) < 2: + raise ValueError("Input must be at least a 2D tensor. Got {}".format( + points.shape)) + + # we check for points at infinity + z_vec: torch.Tensor = points[..., -1:] + + # set the results of division by zeror/near-zero to 1.0 + # follow the convention of opencv: + # https://github.com/opencv/opencv/pull/14411/files + mask: torch.Tensor = torch.abs(z_vec) > eps + scale: torch.Tensor = torch.ones_like(z_vec).masked_scatter_( + mask, torch.tensor(1.0).to(points.device) / z_vec[mask]) + + return scale * points[..., :-1] + +def transform_points(trans_01: torch.Tensor, + points_1: torch.Tensor) -> torch.Tensor: + r"""Function that applies transformations to a set of points. + Args: + trans_01 (torch.Tensor): tensor for transformations of shape + :math:`(B, D+1, D+1)`. + points_1 (torch.Tensor): tensor of points of shape :math:`(B, N, D)`. + Returns: + torch.Tensor: tensor of N-dimensional points. + Shape: + - Output: :math:`(B, N, D)` + Examples: + >>> points_1 = torch.rand(2, 4, 3) # BxNx3 + >>> trans_01 = torch.eye(4).view(1, 4, 4) # Bx4x4 + >>> points_0 = kornia.transform_points(trans_01, points_1) # BxNx3 + """ + if not torch.is_tensor(trans_01) or not torch.is_tensor(points_1): + raise TypeError("Input type is not a torch.Tensor") + if not trans_01.device == points_1.device: + raise TypeError("Tensor must be in the same device") + if not trans_01.shape[0] == points_1.shape[0] and trans_01.shape[0] != 1: + raise ValueError("Input batch size must be the same for both tensors or 1") + if not trans_01.shape[-1] == (points_1.shape[-1] + 1): + raise ValueError("Last input dimensions must differe by one unit") + # to homogeneous + points_1_h = convert_points_to_homogeneous(points_1) # BxNxD+1 + # transform coordinates + points_0_h = torch.matmul( + trans_01.unsqueeze(1), points_1_h.unsqueeze(-1)) + points_0_h = torch.squeeze(points_0_h, dim=-1) + # to euclidean + points_0 = convert_points_from_homogeneous(points_0_h) # BxNxD + return points_0 + + +def multi_linspace(a, b, num, endpoint=True, device='cpu', dtype=torch.float): + """This function is just like np.linspace, but will create linearly + spaced vectors from a start to end vector. + Inputs: + a - Start vector. + b - End vector. + num - Number of samples to generate. Default is 50. Must be above 0. + endpoint - If True, b is the last sample. + Otherwise, it is not included. Default is True. + """ + + return a[..., None] + (b-a)[..., None]/(num-endpoint) * torch.arange(num, device=device, dtype=dtype) + + +def create_batched_meshgrid( + x_min: torch.Tensor, + y_min: torch.Tensor, + x_max: torch.Tensor, + y_max: torch.Tensor, + height: int, + width: int, + device: Optional[torch.device] = torch.device('cpu')) -> torch.Tensor: + """Generates a coordinate grid for an image. + When the flag `normalized_coordinates` is set to True, the grid is + normalized to be in the range [-1,1] to be consistent with the pytorch + function grid_sample. + http://pytorch.org/docs/master/nn.html#torch.nn.functional.grid_sample + Args: + height (int): the image height (rows). + width (int): the image width (cols). + normalized_coordinates (Optional[bool]): whether to normalize + coordinates in the range [-1, 1] in order to be consistent with the + PyTorch function grid_sample. + Return: + torch.Tensor: returns a grid tensor with shape :math:`(1, H, W, 2)`. + """ + # generate coordinates + xs = multi_linspace(x_min, x_max, width, device=device, dtype=torch.float) + ys = multi_linspace(y_min, y_max, height, device=device, dtype=torch.float) + + # generate grid by stacking coordinates + bs = x_min.shape[0] + batched_grid_i_list = list() + for i in range(bs): + batched_grid_i_list.append(torch.stack(torch.meshgrid([xs[i], ys[i]])).transpose(1, 2)) # 2xHxW + batched_grid: torch.Tensor = torch.stack(batched_grid_i_list, dim=0) + return batched_grid.permute(0, 2, 3, 1) # BxHxWx2 + + +def homography_warp(patch_src: torch.Tensor, + centers: torch.Tensor, + dst_homo_src: torch.Tensor, + dsize: Tuple[int, int], + mode: str = 'bilinear', + padding_mode: str = 'zeros') -> torch.Tensor: + r"""Function that warps image patchs or tensors by homographies. + See :class:`~kornia.geometry.warp.HomographyWarper` for details. + Args: + patch_src (torch.Tensor): The image or tensor to warp. Should be from + source of shape :math:`(N, C, H, W)`. + dst_homo_src (torch.Tensor): The homography or stack of homographies + from source to destination of shape + :math:`(N, 3, 3)`. + dsize (Tuple[int, int]): The height and width of the image to warp. + mode (str): interpolation mode to calculate output values + 'bilinear' | 'nearest'. Default: 'bilinear'. + padding_mode (str): padding mode for outside grid values + 'zeros' | 'border' | 'reflection'. Default: 'zeros'. + Return: + torch.Tensor: Patch sampled at locations from source to destination. + Example: + >>> input = torch.rand(1, 3, 32, 32) + >>> homography = torch.eye(3).view(1, 3, 3) + >>> output = kornia.homography_warp(input, homography, (32, 32)) + """ + + out_height, out_width = dsize + image_height, image_width = patch_src.shape[-2:] + x_min = 2. * (centers[..., 0] - out_width/2) / image_width - 1. + y_min = 2. * (centers[..., 1] - out_height/2) / image_height - 1. + x_max = 2. * (centers[..., 0] + out_width/2) / image_width - 1. + y_max = 2. * (centers[..., 1] + out_height/2) / image_height - 1. + warper = HomographyWarper(x_min, y_min, x_max, y_max, out_height, out_width, mode, padding_mode) + return warper(patch_src, dst_homo_src) + + +def normal_transform_pixel(height, width): + + tr_mat = torch.Tensor([[1.0, 0.0, -1.0], + [0.0, 1.0, -1.0], + [0.0, 0.0, 1.0]]) # 1x3x3 + + tr_mat[0, 0] = tr_mat[0, 0] * 2.0 / (width - 1.0) + tr_mat[1, 1] = tr_mat[1, 1] * 2.0 / (height - 1.0) + + tr_mat = tr_mat.unsqueeze(0) + + return tr_mat + + +def src_norm_to_dst_norm(dst_pix_trans_src_pix: torch.Tensor, + dsize_src: Tuple[int, int], dsize_dst: Tuple[int, int]) -> torch.Tensor: + # source and destination sizes + src_h, src_w = dsize_src + dst_h, dst_w = dsize_dst + # the devices and types + device: torch.device = dst_pix_trans_src_pix.device + dtype: torch.dtype = dst_pix_trans_src_pix.dtype + # compute the transformation pixel/norm for src/dst + src_norm_trans_src_pix: torch.Tensor = normal_transform_pixel( + src_h, src_w).to(device, dtype) + src_pix_trans_src_norm = torch.inverse(src_norm_trans_src_pix) + dst_norm_trans_dst_pix: torch.Tensor = normal_transform_pixel( + dst_h, dst_w).to(device, dtype) + # compute chain transformations + dst_norm_trans_src_norm: torch.Tensor = ( + dst_norm_trans_dst_pix @ (dst_pix_trans_src_pix @ src_pix_trans_src_norm) + ) + return dst_norm_trans_src_norm + + +def transform_warp_impl(src: torch.Tensor, centers: torch.Tensor, dst_pix_trans_src_pix: torch.Tensor, + dsize_src: Tuple[int, int], dsize_dst: Tuple[int, int], + grid_mode: str, padding_mode: str) -> torch.Tensor: + """Compute the transform in normalized cooridnates and perform the warping. + """ + dst_norm_trans_src_norm: torch.Tensor = src_norm_to_dst_norm( + dst_pix_trans_src_pix, dsize_src, dsize_src) + + src_norm_trans_dst_norm = torch.inverse(dst_norm_trans_src_norm) + return homography_warp(src, centers, src_norm_trans_dst_norm, dsize_dst, grid_mode, padding_mode) + + +class HomographyWarper(nn.Module): + r"""Warps image patches or tensors by homographies. + .. math:: + X_{dst} = H_{src}^{\{dst\}} * X_{src} + Args: + height (int): The height of the image to warp. + width (int): The width of the image to warp. + mode (str): interpolation mode to calculate output values + 'bilinear' | 'nearest'. Default: 'bilinear'. + padding_mode (str): padding mode for outside grid values + 'zeros' | 'border' | 'reflection'. Default: 'zeros'. + """ + + def __init__( + self, + x_min: torch.Tensor, + y_min: torch.Tensor, + x_max: torch.Tensor, + y_max: torch.Tensor, + height: int, + width: int, + mode: str = 'bilinear', + padding_mode: str = 'zeros') -> None: + super(HomographyWarper, self).__init__() + self.width: int = width + self.height: int = height + self.mode: str = mode + self.padding_mode: str = padding_mode + + # create base grid to compute the flow + self.grid: torch.Tensor = create_batched_meshgrid(x_min, y_min, x_max, y_max, height, width) + + def warp_grid(self, dst_homo_src: torch.Tensor) -> torch.Tensor: + r"""Computes the grid to warp the coordinates grid by an homography. + Args: + dst_homo_src (torch.Tensor): Homography or homographies (stacked) to + transform all points in the grid. Shape of the + homography has to be :math:`(N, 3, 3)`. + Returns: + torch.Tensor: the transformed grid of shape :math:`(N, H, W, 2)`. + """ + batch_size: int = dst_homo_src.shape[0] + device: torch.device = dst_homo_src.device + dtype: torch.dtype = dst_homo_src.dtype + # expand grid to match the input batch size + grid: torch.Tensor = self.grid + if len(dst_homo_src.shape) == 3: # local homography case + dst_homo_src = dst_homo_src.view(batch_size, 1, 3, 3) # NxHxWx3x3 + # perform the actual grid transformation, + # the grid is copied to input device and casted to the same type + flow: torch.Tensor = transform_points( + dst_homo_src, grid.to(device).to(dtype)) # NxHxWx2 + return flow.view(batch_size, self.height, self.width, 2) # NxHxWx2 + + def forward( # type: ignore + self, + patch_src: torch.Tensor, + dst_homo_src: torch.Tensor) -> torch.Tensor: + r"""Warps an image or tensor from source into reference frame. + Args: + patch_src (torch.Tensor): The image or tensor to warp. + Should be from source. + dst_homo_src (torch.Tensor): The homography or stack of homographies + from source to destination. The homography assumes normalized + coordinates [-1, 1]. + Return: + torch.Tensor: Patch sampled at locations from source to destination. + Shape: + - Input: :math:`(N, C, H, W)` and :math:`(N, 3, 3)` + - Output: :math:`(N, C, H, W)` + Example: + >>> input = torch.rand(1, 3, 32, 32) + >>> homography = torch.eye(3).view(1, 3, 3) + >>> warper = kornia.HomographyWarper(32, 32) + >>> output = warper(input, homography) # NxCxHxW + """ + if not dst_homo_src.device == patch_src.device: + raise TypeError("Patch and homography must be on the same device. \ + Got patch.device: {} dst_H_src.device: {}." + .format(patch_src.device, dst_homo_src.device)) + + return F.grid_sample(patch_src, self.warp_grid(dst_homo_src), # type: ignore + mode=self.mode, padding_mode=self.padding_mode, align_corners=True) + + +def warp_affine_crop(src: torch.Tensor, centers: torch.Tensor, M: torch.Tensor, + dsize: Tuple[int, int], flags: str = 'bilinear', + padding_mode: str = 'zeros') -> torch.Tensor: + r"""Applies an affine transformation to a tensor. + + The function warp_affine transforms the source tensor using + the specified matrix: + + .. math:: + \text{dst}(x, y) = \text{src} \left( M_{11} x + M_{12} y + M_{13} , + M_{21} x + M_{22} y + M_{23} \right ) + + Args: + src (torch.Tensor): input tensor of shape :math:`(B, C, H, W)`. + M (torch.Tensor): affine transformation of shape :math:`(B, 2, 3)`. + dsize (Tuple[int, int]): size of the output image (height, width). + mode (str): interpolation mode to calculate output values + 'bilinear' | 'nearest'. Default: 'bilinear'. + padding_mode (str): padding mode for outside grid values + 'zeros' | 'border' | 'reflection'. Default: 'zeros'. + + Returns: + torch.Tensor: the warped tensor. + + Shape: + - Output: :math:`(B, C, H, W)` + + .. note:: + See a working example `here `__. + """ + if not torch.is_tensor(src): + raise TypeError("Input src type is not a torch.Tensor. Got {}" + .format(type(src))) + + if not torch.is_tensor(M): + raise TypeError("Input M type is not a torch.Tensor. Got {}" + .format(type(M))) + + if not len(src.shape) == 4: + raise ValueError("Input src must be a BxCxHxW tensor. Got {}" + .format(src.shape)) + + if not (len(M.shape) == 3 or M.shape[-2:] == (2, 3)): + raise ValueError("Input M must be a Bx2x3 tensor. Got {}" + .format(src.shape)) + + # we generate a 3x3 transformation matrix from 2x3 affine + M_3x3: torch.Tensor = F.pad(M, [0, 0, 0, 1, 0, 0], + mode="constant", value=0) + M_3x3[:, 2, 2] += 1.0 + + # launches the warper + h, w = src.shape[-2:] + return transform_warp_impl(src, centers, M_3x3, (h, w), dsize, flags, padding_mode) \ No newline at end of file diff --git a/trajectron/model/dataset/preprocessing.py b/trajectron/model/dataset/preprocessing.py new file mode 100644 index 0000000..e36f37a --- /dev/null +++ b/trajectron/model/dataset/preprocessing.py @@ -0,0 +1,233 @@ +import torch +import numpy as np +import collections.abc +from torch.utils.data._utils.collate import default_collate +import dill +container_abcs = collections.abc + + +def restore(data): + """ + In case we dilled some structures to share between multiple process this function will restore them. + If the data input are not bytes we assume it was not dilled in the first place + + :param data: Possibly dilled data structure + :return: Un-dilled data structure + """ + if type(data) is bytes: + return dill.loads(data) + return data + + +def collate(batch): + if len(batch) == 0: + return batch + elem = batch[0] + if elem is None: + return None + elif isinstance(elem, container_abcs.Sequence): + if len(elem) == 4: # We assume those are the maps, map points, headings and patch_size + scene_map, scene_pts, heading_angle, patch_size = zip(*batch) + if heading_angle[0] is None: + heading_angle = None + else: + heading_angle = torch.Tensor(heading_angle) + map = scene_map[0].get_cropped_maps_from_scene_map_batch(scene_map, + scene_pts=torch.Tensor(scene_pts), + patch_size=patch_size[0], + rotation=heading_angle) + return map + transposed = zip(*batch) + return [collate(samples) for samples in transposed] + elif isinstance(elem, container_abcs.Mapping): + # We have to dill the neighbors structures. Otherwise each tensor is put into + # shared memory separately -> slow, file pointer overhead + # we only do this in multiprocessing + neighbor_dict = {key: [d[key] for d in batch] for key in elem} + return dill.dumps(neighbor_dict) if torch.utils.data.get_worker_info() else neighbor_dict + return default_collate(batch) + + +def get_relative_robot_traj(env, state, node_traj, robot_traj, node_type, robot_type): + # TODO: We will have to make this more generic if robot_type != node_type + # Make Robot State relative to node + _, std = env.get_standardize_params(state[robot_type], node_type=robot_type) + std[0:2] = env.attention_radius[(node_type, robot_type)] + robot_traj_st = env.standardize(robot_traj, + state[robot_type], + node_type=robot_type, + mean=node_traj, + std=std) + robot_traj_st_t = torch.tensor(robot_traj_st, dtype=torch.float) + + return robot_traj_st_t + + +def get_node_timestep_data(env, scene, t, node, state, pred_state, + edge_types, max_ht, max_ft, hyperparams, + scene_graph=None): + """ + Pre-processes the data for a single batch element: node state over time for a specific time in a specific scene + as well as the neighbour data for it. + + :param env: Environment + :param scene: Scene + :param t: Timestep in scene + :param node: Node + :param state: Specification of the node state + :param pred_state: Specification of the prediction state + :param edge_types: List of all Edge Types for which neighbours are pre-processed + :param max_ht: Maximum history timesteps + :param max_ft: Maximum future timesteps (prediction horizon) + :param hyperparams: Model hyperparameters + :param scene_graph: If scene graph was already computed for this scene and time you can pass it here + :return: Batch Element + """ + + # Node + timestep_range_x = np.array([t - max_ht, t]) + timestep_range_y = np.array([t + 1, t + max_ft]) + + x = node.get(timestep_range_x, state[node.type]) + y = node.get(timestep_range_y, pred_state[node.type]) + first_history_index = (max_ht - node.history_points_at(t)).clip(0) + + _, std = env.get_standardize_params(state[node.type], node.type) + std[0:2] = env.attention_radius[(node.type, node.type)] + rel_state = np.zeros_like(x[0]) + rel_state[0:2] = np.array(x)[-1, 0:2] + x_st = env.standardize(x, state[node.type], node.type, mean=rel_state, std=std) + if list(pred_state[node.type].keys())[0] == 'position': # If we predict position we do it relative to current pos + y_st = env.standardize(y, pred_state[node.type], node.type, mean=rel_state[0:2]) + else: + y_st = env.standardize(y, pred_state[node.type], node.type) + + x_t = torch.tensor(x, dtype=torch.float) + y_t = torch.tensor(y, dtype=torch.float) + x_st_t = torch.tensor(x_st, dtype=torch.float) + y_st_t = torch.tensor(y_st, dtype=torch.float) + + # Neighbors + neighbors_data_st = None + neighbors_edge_value = None + if hyperparams['edge_encoding']: + # Scene Graph + scene_graph = scene.get_scene_graph(t, + env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) if scene_graph is None else scene_graph + + neighbors_data_st = dict() + neighbors_edge_value = dict() + for edge_type in edge_types: + neighbors_data_st[edge_type] = list() + # We get all nodes which are connected to the current node for the current timestep + connected_nodes = scene_graph.get_neighbors(node, edge_type[1]) + + if hyperparams['dynamic_edges'] == 'yes': + # We get the edge masks for the current node at the current timestep + edge_masks = torch.tensor(scene_graph.get_edge_scaling(node), dtype=torch.float) + neighbors_edge_value[edge_type] = edge_masks + + for connected_node in connected_nodes: + neighbor_state_np = connected_node.get(np.array([t - max_ht, t]), + state[connected_node.type], + padding=0.0) + + # Make State relative to node where neighbor and node have same state + _, std = env.get_standardize_params(state[connected_node.type], node_type=connected_node.type) + std[0:2] = env.attention_radius[edge_type] + equal_dims = np.min((neighbor_state_np.shape[-1], x.shape[-1])) + rel_state = np.zeros_like(neighbor_state_np) + rel_state[:, ..., :equal_dims] = x[-1, ..., :equal_dims] + neighbor_state_np_st = env.standardize(neighbor_state_np, + state[connected_node.type], + node_type=connected_node.type, + mean=rel_state, + std=std) + + neighbor_state = torch.tensor(neighbor_state_np_st, dtype=torch.float) + neighbors_data_st[edge_type].append(neighbor_state) + + # Robot + robot_traj_st_t = None + timestep_range_r = np.array([t, t + max_ft]) + if hyperparams['incl_robot_node']: + x_node = node.get(timestep_range_r, state[node.type]) + if scene.non_aug_scene is not None: + robot = scene.get_node_by_id(scene.non_aug_scene.robot.id) + else: + robot = scene.robot + robot_type = robot.type + robot_traj = robot.get(timestep_range_r, state[robot_type], padding=0.0) + robot_traj_st_t = get_relative_robot_traj(env, state, x_node, robot_traj, node.type, robot_type) + + # Map + map_tuple = None + if hyperparams['use_map_encoding']: + if node.type in hyperparams['map_encoder']: + if node.non_aug_node is not None: + x = node.non_aug_node.get(np.array([t]), state[node.type]) + me_hyp = hyperparams['map_encoder'][node.type] + if 'heading_state_index' in me_hyp: + heading_state_index = me_hyp['heading_state_index'] + # We have to rotate the map in the opposit direction of the agent to match them + if type(heading_state_index) is list: # infer from velocity or heading vector + heading_angle = -np.arctan2(x[-1, heading_state_index[1]], + x[-1, heading_state_index[0]]) * 180 / np.pi + else: + heading_angle = -x[-1, heading_state_index] * 180 / np.pi + else: + heading_angle = None + + scene_map = scene.map[node.type] + map_point = x[-1, :2] + + + patch_size = hyperparams['map_encoder'][node.type]['patch_size'] + map_tuple = (scene_map, map_point, heading_angle, patch_size) + + return (first_history_index, x_t, y_t, x_st_t, y_st_t, neighbors_data_st, + neighbors_edge_value, robot_traj_st_t, map_tuple) + + +def get_timesteps_data(env, scene, t, node_type, state, pred_state, + edge_types, min_ht, max_ht, min_ft, max_ft, hyperparams): + """ + Puts together the inputs for ALL nodes in a given scene and timestep in it. + + :param env: Environment + :param scene: Scene + :param t: Timestep in scene + :param node_type: Node Type of nodes for which the data shall be pre-processed + :param state: Specification of the node state + :param pred_state: Specification of the prediction state + :param edge_types: List of all Edge Types for which neighbors are pre-processed + :param max_ht: Maximum history timesteps + :param max_ft: Maximum future timesteps (prediction horizon) + :param hyperparams: Model hyperparameters + :return: + """ + nodes_per_ts = scene.present_nodes(t, + type=node_type, + min_history_timesteps=min_ht, + min_future_timesteps=max_ft, + return_robot=not hyperparams['incl_robot_node']) + batch = list() + nodes = list() + out_timesteps = list() + for timestep in nodes_per_ts.keys(): + scene_graph = scene.get_scene_graph(timestep, + env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) + present_nodes = nodes_per_ts[timestep] + for node in present_nodes: + nodes.append(node) + out_timesteps.append(timestep) + batch.append(get_node_timestep_data(env, scene, timestep, node, state, pred_state, + edge_types, max_ht, max_ft, hyperparams, + scene_graph=scene_graph)) + if len(out_timesteps) == 0: + return None + return collate(batch), nodes, out_timesteps \ No newline at end of file diff --git a/trajectron/model/dynamics/__init__.py b/trajectron/model/dynamics/__init__.py new file mode 100644 index 0000000..8f506d4 --- /dev/null +++ b/trajectron/model/dynamics/__init__.py @@ -0,0 +1,4 @@ +from .dynamic import Dynamic +from .single_integrator import SingleIntegrator +from .unicycle import Unicycle +from .linear import Linear diff --git a/trajectron/model/dynamics/dynamic.py b/trajectron/model/dynamics/dynamic.py new file mode 100644 index 0000000..6b03e13 --- /dev/null +++ b/trajectron/model/dynamics/dynamic.py @@ -0,0 +1,30 @@ + + +class Dynamic(object): + def __init__(self, dt, dyn_limits, device, model_registrar, xz_size, node_type): + self.dt = dt + self.device = device + self.dyn_limits = dyn_limits + self.initial_conditions = None + self.model_registrar = model_registrar + self.node_type = node_type + self.init_constants() + self.create_graph(xz_size) + + def set_initial_condition(self, init_con): + self.initial_conditions = init_con + + def init_constants(self): + pass + + def create_graph(self, xz_size): + pass + + def integrate_samples(self, s, x): + raise NotImplementedError + + def integrate_distribution(self, dist, x): + raise NotImplementedError + + def create_graph(self, xz_size): + pass diff --git a/trajectron/model/dynamics/linear.py b/trajectron/model/dynamics/linear.py new file mode 100644 index 0000000..9b55e64 --- /dev/null +++ b/trajectron/model/dynamics/linear.py @@ -0,0 +1,12 @@ +from model.dynamics import Dynamic + + +class Linear(Dynamic): + def init_constants(self): + pass + + def integrate_samples(self, v, x): + return v + + def integrate_distribution(self, v_dist, x): + return v_dist \ No newline at end of file diff --git a/trajectron/model/dynamics/single_integrator.py b/trajectron/model/dynamics/single_integrator.py new file mode 100644 index 0000000..984f5fa --- /dev/null +++ b/trajectron/model/dynamics/single_integrator.py @@ -0,0 +1,64 @@ +import torch +from model.dynamics import Dynamic +from utils import block_diag +from model.components import GMM2D + + +class SingleIntegrator(Dynamic): + def init_constants(self): + self.F = torch.eye(4, device=self.device, dtype=torch.float32) + self.F[0:2, 2:] = torch.eye(2, device=self.device, dtype=torch.float32) * self.dt + self.F_t = self.F.transpose(-2, -1) + + def integrate_samples(self, v, x=None): + """ + Integrates deterministic samples of velocity. + + :param v: Velocity samples + :param x: Not used for SI. + :return: Position samples + """ + p_0 = self.initial_conditions['pos'].unsqueeze(1) + return torch.cumsum(v, dim=2) * self.dt + p_0 + + def integrate_distribution(self, v_dist, x=None): + r""" + Integrates the GMM velocity distribution to a distribution over position. + The Kalman Equations are used. + + .. math:: \mu_{t+1} =\textbf{F} \mu_{t} + + .. math:: \mathbf{\Sigma}_{t+1}={\textbf {F}} \mathbf{\Sigma}_{t} {\textbf {F}}^{T} + + .. math:: + \textbf{F} = \left[ + \begin{array}{cccc} + \sigma_x^2 & \rho_p \sigma_x \sigma_y & 0 & 0 \\ + \rho_p \sigma_x \sigma_y & \sigma_y^2 & 0 & 0 \\ + 0 & 0 & \sigma_{v_x}^2 & \rho_v \sigma_{v_x} \sigma_{v_y} \\ + 0 & 0 & \rho_v \sigma_{v_x} \sigma_{v_y} & \sigma_{v_y}^2 \\ + \end{array} + \right]_{t} + + :param v_dist: Joint GMM Distribution over velocity in x and y direction. + :param x: Not used for SI. + :return: Joint GMM Distribution over position in x and y direction. + """ + p_0 = self.initial_conditions['pos'].unsqueeze(1) + ph = v_dist.mus.shape[-3] + sample_batch_dim = list(v_dist.mus.shape[0:2]) + pos_dist_sigma_matrix_list = [] + + pos_mus = p_0[:, None] + torch.cumsum(v_dist.mus, dim=2) * self.dt + + vel_dist_sigma_matrix = v_dist.get_covariance_matrix() + pos_dist_sigma_matrix_t = torch.zeros(sample_batch_dim + [v_dist.components, 2, 2], device=self.device) + + for t in range(ph): + vel_sigma_matrix_t = vel_dist_sigma_matrix[:, :, t] + full_sigma_matrix_t = block_diag([pos_dist_sigma_matrix_t, vel_sigma_matrix_t]) + pos_dist_sigma_matrix_t = self.F[..., :2, :].matmul(full_sigma_matrix_t.matmul(self.F_t)[..., :2]) + pos_dist_sigma_matrix_list.append(pos_dist_sigma_matrix_t) + + pos_dist_sigma_matrix = torch.stack(pos_dist_sigma_matrix_list, dim=2) + return GMM2D.from_log_pis_mus_cov_mats(v_dist.log_pis, pos_mus, pos_dist_sigma_matrix) \ No newline at end of file diff --git a/trajectron/model/dynamics/unicycle.py b/trajectron/model/dynamics/unicycle.py new file mode 100644 index 0000000..2026a5e --- /dev/null +++ b/trajectron/model/dynamics/unicycle.py @@ -0,0 +1,222 @@ +import torch +import torch.nn as nn +from model.dynamics import Dynamic +from utils import block_diag +from model.components import GMM2D + + +class Unicycle(Dynamic): + def init_constants(self): + self.F_s = torch.eye(4, device=self.device, dtype=torch.float32) + self.F_s[0:2, 2:] = torch.eye(2, device=self.device, dtype=torch.float32) * self.dt + self.F_s_t = self.F_s.transpose(-2, -1) + + def create_graph(self, xz_size): + model_if_absent = nn.Linear(xz_size + 1, 1) + self.p0_model = self.model_registrar.get_model(f"{self.node_type}/unicycle_initializer", model_if_absent) + + def dynamic(self, x, u): + r""" + TODO: Boris: Add docstring + :param x: + :param u: + :return: + """ + x_p = x[0] + y_p = x[1] + phi = x[2] + v = x[3] + dphi = u[0] + a = u[1] + + mask = torch.abs(dphi) <= 1e-2 + dphi = ~mask * dphi + (mask) * 1 + + phi_p_omega_dt = phi + dphi * self.dt + dsin_domega = (torch.sin(phi_p_omega_dt) - torch.sin(phi)) / dphi + dcos_domega = (torch.cos(phi_p_omega_dt) - torch.cos(phi)) / dphi + + d1 = torch.stack([(x_p + + (a / dphi) * dcos_domega + + v * dsin_domega + + (a / dphi) * torch.sin(phi_p_omega_dt) * self.dt), + (y_p + - v * dcos_domega + + (a / dphi) * dsin_domega + - (a / dphi) * torch.cos(phi_p_omega_dt) * self.dt), + phi + dphi * self.dt, + v + a * self.dt], dim=0) + d2 = torch.stack([x_p + v * torch.cos(phi) * self.dt + (a / 2) * torch.cos(phi) * self.dt ** 2, + y_p + v * torch.sin(phi) * self.dt + (a / 2) * torch.sin(phi) * self.dt ** 2, + phi * torch.ones_like(a), + v + a * self.dt], dim=0) + return torch.where(~mask, d1, d2) + + def integrate_samples(self, control_samples, x=None): + r""" + TODO: Boris: Add docstring + :param x: + :param u: + :return: + """ + ph = control_samples.shape[-2] + p_0 = self.initial_conditions['pos'].unsqueeze(1) + v_0 = self.initial_conditions['vel'].unsqueeze(1) + phi_0 = torch.atan2(v_0[..., 1], v_0[..., 0]) + + phi_0 = phi_0 + torch.tanh(self.p0_model(torch.cat((x, phi_0), dim=-1))) + + u = torch.stack([control_samples[..., 0], control_samples[..., 1]], dim=0) + x = torch.stack([p_0[..., 0], p_0[..., 1], phi_0, torch.norm(v_0, dim=-1)], dim = 0).squeeze(dim=-1) + + mus_list = [] + for t in range(ph): + x = self.dynamic(x, u[..., t]) + mus_list.append(torch.stack((x[0], x[1]), dim=-1)) + + pos_mus = torch.stack(mus_list, dim=2) + return pos_mus + + def compute_control_jacobian(self, sample_batch_dim, components, x, u): + r""" + TODO: Boris: Add docstring + :param x: + :param u: + :return: + """ + F = torch.zeros(sample_batch_dim + [components, 4, 2], + device=self.device, + dtype=torch.float32) + + phi = x[2] + v = x[3] + dphi = u[0] + a = u[1] + + mask = torch.abs(dphi) <= 1e-2 + dphi = ~mask * dphi + (mask) * 1 + + phi_p_omega_dt = phi + dphi * self.dt + dsin_domega = (torch.sin(phi_p_omega_dt) - torch.sin(phi)) / dphi + dcos_domega = (torch.cos(phi_p_omega_dt) - torch.cos(phi)) / dphi + + F[..., 0, 0] = ((v / dphi) * torch.cos(phi_p_omega_dt) * self.dt + - (v / dphi) * dsin_domega + - (2 * a / dphi ** 2) * torch.sin(phi_p_omega_dt) * self.dt + - (2 * a / dphi ** 2) * dcos_domega + + (a / dphi) * torch.cos(phi_p_omega_dt) * self.dt ** 2) + F[..., 0, 1] = (1 / dphi) * dcos_domega + (1 / dphi) * torch.sin(phi_p_omega_dt) * self.dt + + F[..., 1, 0] = ((v / dphi) * dcos_domega + - (2 * a / dphi ** 2) * dsin_domega + + (2 * a / dphi ** 2) * torch.cos(phi_p_omega_dt) * self.dt + + (v / dphi) * torch.sin(phi_p_omega_dt) * self.dt + + (a / dphi) * torch.sin(phi_p_omega_dt) * self.dt ** 2) + F[..., 1, 1] = (1 / dphi) * dsin_domega - (1 / dphi) * torch.cos(phi_p_omega_dt) * self.dt + + F[..., 2, 0] = self.dt + + F[..., 3, 1] = self.dt + + F_sm = torch.zeros(sample_batch_dim + [components, 4, 2], + device=self.device, + dtype=torch.float32) + + F_sm[..., 0, 1] = (torch.cos(phi) * self.dt ** 2) / 2 + + F_sm[..., 1, 1] = (torch.sin(phi) * self.dt ** 2) / 2 + + F_sm[..., 3, 1] = self.dt + + return torch.where(~mask.unsqueeze(-1).unsqueeze(-1), F, F_sm) + + def compute_jacobian(self, sample_batch_dim, components, x, u): + r""" + TODO: Boris: Add docstring + :param x: + :param u: + :return: + """ + one = torch.tensor(1) + F = torch.zeros(sample_batch_dim + [components, 4, 4], + device=self.device, + dtype=torch.float32) + + phi = x[2] + v = x[3] + dphi = u[0] + a = u[1] + + mask = torch.abs(dphi) <= 1e-2 + dphi = ~mask * dphi + (mask) * 1 + + phi_p_omega_dt = phi + dphi * self.dt + dsin_domega = (torch.sin(phi_p_omega_dt) - torch.sin(phi)) / dphi + dcos_domega = (torch.cos(phi_p_omega_dt) - torch.cos(phi)) / dphi + + F[..., 0, 0] = one + F[..., 1, 1] = one + F[..., 2, 2] = one + F[..., 3, 3] = one + + F[..., 0, 2] = v * dcos_domega - (a / dphi) * dsin_domega + (a / dphi) * torch.cos(phi_p_omega_dt) * self.dt + F[..., 0, 3] = dsin_domega + + F[..., 1, 2] = v * dsin_domega + (a / dphi) * dcos_domega + (a / dphi) * torch.sin(phi_p_omega_dt) * self.dt + F[..., 1, 3] = -dcos_domega + + F_sm = torch.zeros(sample_batch_dim + [components, 4, 4], + device=self.device, + dtype=torch.float32) + + F_sm[..., 0, 0] = one + F_sm[..., 1, 1] = one + F_sm[..., 2, 2] = one + F_sm[..., 3, 3] = one + + F_sm[..., 0, 2] = -v * torch.sin(phi) * self.dt - (a * torch.sin(phi) * self.dt ** 2) / 2 + F_sm[..., 0, 3] = torch.cos(phi) * self.dt + + F_sm[..., 1, 2] = v * torch.cos(phi) * self.dt + (a * torch.cos(phi) * self.dt ** 2) / 2 + F_sm[..., 1, 3] = torch.sin(phi) * self.dt + + return torch.where(~mask.unsqueeze(-1).unsqueeze(-1), F, F_sm) + + def integrate_distribution(self, control_dist_dphi_a, x): + r""" + TODO: Boris: Add docstring + :param x: + :param u: + :return: + """ + sample_batch_dim = list(control_dist_dphi_a.mus.shape[0:2]) + ph = control_dist_dphi_a.mus.shape[-3] + p_0 = self.initial_conditions['pos'].unsqueeze(1) + v_0 = self.initial_conditions['vel'].unsqueeze(1) + phi_0 = torch.atan2(v_0[..., 1], v_0[..., 0]) + + phi_0 = phi_0 + torch.tanh(self.p0_model(torch.cat((x, phi_0), dim=-1))) + + dist_sigma_matrix = control_dist_dphi_a.get_covariance_matrix() + pos_dist_sigma_matrix_t = torch.zeros(sample_batch_dim + [control_dist_dphi_a.components, 4, 4], + device=self.device) + + u = torch.stack([control_dist_dphi_a.mus[..., 0], control_dist_dphi_a.mus[..., 1]], dim=0) + x = torch.stack([p_0[..., 0], p_0[..., 1], phi_0, torch.norm(v_0, dim=-1)], dim=0) + + pos_dist_sigma_matrix_list = [] + mus_list = [] + for t in range(ph): + F_t = self.compute_jacobian(sample_batch_dim, control_dist_dphi_a.components, x, u[:, :, :, t]) + G_t = self.compute_control_jacobian(sample_batch_dim, control_dist_dphi_a.components, x, u[:, :, :, t]) + dist_sigma_matrix_t = dist_sigma_matrix[:, :, t] + pos_dist_sigma_matrix_t = (F_t.matmul(pos_dist_sigma_matrix_t.matmul(F_t.transpose(-2, -1))) + + G_t.matmul(dist_sigma_matrix_t.matmul(G_t.transpose(-2, -1)))) + pos_dist_sigma_matrix_list.append(pos_dist_sigma_matrix_t[..., :2, :2]) + + x = self.dynamic(x, u[:, :, :, t]) + mus_list.append(torch.stack((x[0], x[1]), dim=-1)) + + pos_dist_sigma_matrix = torch.stack(pos_dist_sigma_matrix_list, dim=2) + pos_mus = torch.stack(mus_list, dim=2) + return GMM2D.from_log_pis_mus_cov_mats(control_dist_dphi_a.log_pis, pos_mus, pos_dist_sigma_matrix) diff --git a/trajectron/model/mgcvae.py b/trajectron/model/mgcvae.py new file mode 100644 index 0000000..47d09c2 --- /dev/null +++ b/trajectron/model/mgcvae.py @@ -0,0 +1,1142 @@ +import warnings +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from model.components import * +from model.model_utils import * +import model.dynamics as dynamic_module +from environment.scene_graph import DirectedEdge + + +class MultimodalGenerativeCVAE(object): + def __init__(self, + env, + node_type, + model_registrar, + hyperparams, + device, + edge_types, + log_writer=None): + self.hyperparams = hyperparams + self.env = env + self.node_type = node_type + self.model_registrar = model_registrar + self.log_writer = log_writer + self.device = device + self.edge_types = [edge_type for edge_type in edge_types if edge_type[0] is node_type] + self.curr_iter = 0 + + self.node_modules = dict() + + self.min_hl = self.hyperparams['minimum_history_length'] + self.max_hl = self.hyperparams['maximum_history_length'] + self.ph = self.hyperparams['prediction_horizon'] + self.state = self.hyperparams['state'] + self.pred_state = self.hyperparams['pred_state'][node_type] + self.state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[node_type].values()])) + if self.hyperparams['incl_robot_node']: + self.robot_state_length = int( + np.sum([len(entity_dims) for entity_dims in self.state[env.robot_type].values()]) + ) + self.pred_state_length = int(np.sum([len(entity_dims) for entity_dims in self.pred_state.values()])) + + edge_types_str = [DirectedEdge.get_str_from_types(*edge_type) for edge_type in self.edge_types] + self.create_graphical_model(edge_types_str) + + dynamic_class = getattr(dynamic_module, hyperparams['dynamic'][self.node_type]['name']) + dyn_limits = hyperparams['dynamic'][self.node_type]['limits'] + self.dynamic = dynamic_class(self.env.scenes[0].dt, dyn_limits, device, + self.model_registrar, self.x_size, self.node_type) + + def set_curr_iter(self, curr_iter): + self.curr_iter = curr_iter + + def add_submodule(self, name, model_if_absent): + self.node_modules[name] = self.model_registrar.get_model(name, model_if_absent) + + def clear_submodules(self): + self.node_modules.clear() + + def create_node_models(self): + ############################ + # Node History Encoder # + ############################ + self.add_submodule(self.node_type + '/node_history_encoder', + model_if_absent=nn.LSTM(input_size=self.state_length, + hidden_size=self.hyperparams['enc_rnn_dim_history'], + batch_first=True)) + + ########################### + # Node Future Encoder # + ########################### + # We'll create this here, but then later check if in training mode. + # Based on that, we'll factor this into the computation graph (or not). + self.add_submodule(self.node_type + '/node_future_encoder', + model_if_absent=nn.LSTM(input_size=self.pred_state_length, + hidden_size=self.hyperparams['enc_rnn_dim_future'], + bidirectional=True, + batch_first=True)) + # These are related to how you initialize states for the node future encoder. + self.add_submodule(self.node_type + '/node_future_encoder/initial_h', + model_if_absent=nn.Linear(self.state_length, + self.hyperparams['enc_rnn_dim_future'])) + self.add_submodule(self.node_type + '/node_future_encoder/initial_c', + model_if_absent=nn.Linear(self.state_length, + self.hyperparams['enc_rnn_dim_future'])) + + ############################ + # Robot Future Encoder # + ############################ + # We'll create this here, but then later check if we're next to the robot. + # Based on that, we'll factor this into the computation graph (or not). + if self.hyperparams['incl_robot_node']: + self.add_submodule('robot_future_encoder', + model_if_absent=nn.LSTM(input_size=self.robot_state_length, + hidden_size=self.hyperparams['enc_rnn_dim_future'], + bidirectional=True, + batch_first=True)) + # These are related to how you initialize states for the robot future encoder. + self.add_submodule('robot_future_encoder/initial_h', + model_if_absent=nn.Linear(self.robot_state_length, + self.hyperparams['enc_rnn_dim_future'])) + self.add_submodule('robot_future_encoder/initial_c', + model_if_absent=nn.Linear(self.robot_state_length, + self.hyperparams['enc_rnn_dim_future'])) + + if self.hyperparams['edge_encoding']: + ############################## + # Edge Influence Encoder # + ############################## + # NOTE: The edge influence encoding happens during calls + # to forward or incremental_forward, so we don't create + # a model for it here for the max and sum variants. + if self.hyperparams['edge_influence_combine_method'] == 'bi-rnn': + self.add_submodule(self.node_type + '/edge_influence_encoder', + model_if_absent=nn.LSTM(input_size=self.hyperparams['enc_rnn_dim_edge'], + hidden_size=self.hyperparams['enc_rnn_dim_edge_influence'], + bidirectional=True, + batch_first=True)) + + # Four times because we're trying to mimic a bi-directional + # LSTM's output (which, here, is c and h from both ends). + self.eie_output_dims = 4 * self.hyperparams['enc_rnn_dim_edge_influence'] + + elif self.hyperparams['edge_influence_combine_method'] == 'attention': + # Chose additive attention because of https://arxiv.org/pdf/1703.03906.pdf + # We calculate an attention context vector using the encoded edges as the "encoder" + # (that we attend _over_) + # and the node history encoder representation as the "decoder state" (that we attend _on_). + self.add_submodule(self.node_type + '/edge_influence_encoder', + model_if_absent=AdditiveAttention( + encoder_hidden_state_dim=self.hyperparams['enc_rnn_dim_edge_influence'], + decoder_hidden_state_dim=self.hyperparams['enc_rnn_dim_history'])) + + self.eie_output_dims = self.hyperparams['enc_rnn_dim_edge_influence'] + + ################### + # Map Encoder # + ################### + if self.hyperparams['use_map_encoding']: + if self.node_type in self.hyperparams['map_encoder']: + me_params = self.hyperparams['map_encoder'][self.node_type] + self.add_submodule(self.node_type + '/map_encoder', + model_if_absent=CNNMapEncoder(me_params['map_channels'], + me_params['hidden_channels'], + me_params['output_size'], + me_params['masks'], + me_params['strides'], + me_params['patch_size'])) + + ################################ + # Discrete Latent Variable # + ################################ + self.latent = DiscreteLatent(self.hyperparams, self.device) + + ###################################################################### + # Various Fully-Connected Layers from Encoder to Latent Variable # + ###################################################################### + # Node History Encoder + x_size = self.hyperparams['enc_rnn_dim_history'] + if self.hyperparams['edge_encoding']: + # Edge Encoder + x_size += self.eie_output_dims + if self.hyperparams['incl_robot_node']: + # Future Conditional Encoder + x_size += 4 * self.hyperparams['enc_rnn_dim_future'] + if self.hyperparams['use_map_encoding'] and self.node_type in self.hyperparams['map_encoder']: + # Map Encoder + x_size += self.hyperparams['map_encoder'][self.node_type]['output_size'] + + z_size = self.hyperparams['N'] * self.hyperparams['K'] + + if self.hyperparams['p_z_x_MLP_dims'] is not None: + self.add_submodule(self.node_type + '/p_z_x', + model_if_absent=nn.Linear(x_size, self.hyperparams['p_z_x_MLP_dims'])) + hx_size = self.hyperparams['p_z_x_MLP_dims'] + else: + hx_size = x_size + + self.add_submodule(self.node_type + '/hx_to_z', + model_if_absent=nn.Linear(hx_size, self.latent.z_dim)) + + if self.hyperparams['q_z_xy_MLP_dims'] is not None: + self.add_submodule(self.node_type + '/q_z_xy', + # Node Future Encoder + model_if_absent=nn.Linear(x_size + 4 * self.hyperparams['enc_rnn_dim_future'], + self.hyperparams['q_z_xy_MLP_dims'])) + hxy_size = self.hyperparams['q_z_xy_MLP_dims'] + else: + # Node Future Encoder + hxy_size = x_size + 4 * self.hyperparams['enc_rnn_dim_future'] + + self.add_submodule(self.node_type + '/hxy_to_z', + model_if_absent=nn.Linear(hxy_size, self.latent.z_dim)) + + #################### + # Decoder LSTM # + #################### + if self.hyperparams['incl_robot_node']: + decoder_input_dims = self.pred_state_length + self.robot_state_length + z_size + x_size + else: + decoder_input_dims = self.pred_state_length + z_size + x_size + + self.add_submodule(self.node_type + '/decoder/state_action', + model_if_absent=nn.Sequential( + nn.Linear(self.state_length, self.pred_state_length))) + + self.add_submodule(self.node_type + '/decoder/rnn_cell', + model_if_absent=nn.GRUCell(decoder_input_dims, self.hyperparams['dec_rnn_dim'])) + self.add_submodule(self.node_type + '/decoder/initial_h', + model_if_absent=nn.Linear(z_size + x_size, self.hyperparams['dec_rnn_dim'])) + + ################### + # Decoder GMM # + ################### + self.add_submodule(self.node_type + '/decoder/proj_to_GMM_log_pis', + model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], + self.hyperparams['GMM_components'])) + self.add_submodule(self.node_type + '/decoder/proj_to_GMM_mus', + model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], + self.hyperparams['GMM_components'] * self.pred_state_length)) + self.add_submodule(self.node_type + '/decoder/proj_to_GMM_log_sigmas', + model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], + self.hyperparams['GMM_components'] * self.pred_state_length)) + self.add_submodule(self.node_type + '/decoder/proj_to_GMM_corrs', + model_if_absent=nn.Linear(self.hyperparams['dec_rnn_dim'], + self.hyperparams['GMM_components'])) + + self.x_size = x_size + self.z_size = z_size + + def create_edge_models(self, edge_types): + for edge_type in edge_types: + neighbor_state_length = int( + np.sum([len(entity_dims) for entity_dims in self.state[edge_type.split('->')[1]].values()])) + if self.hyperparams['edge_state_combine_method'] == 'pointnet': + self.add_submodule(edge_type + '/pointnet_encoder', + model_if_absent=nn.Sequential( + nn.Linear(self.state_length, 2 * self.state_length), + nn.ReLU(), + nn.Linear(2 * self.state_length, 2 * self.state_length), + nn.ReLU())) + + edge_encoder_input_size = 2 * self.state_length + self.state_length + + elif self.hyperparams['edge_state_combine_method'] == 'attention': + self.add_submodule(self.node_type + '/edge_attention_combine', + model_if_absent=TemporallyBatchedAdditiveAttention( + encoder_hidden_state_dim=self.state_length, + decoder_hidden_state_dim=self.state_length)) + edge_encoder_input_size = self.state_length + neighbor_state_length + + else: + edge_encoder_input_size = self.state_length + neighbor_state_length + + self.add_submodule(edge_type + '/edge_encoder', + model_if_absent=nn.LSTM(input_size=edge_encoder_input_size, + hidden_size=self.hyperparams['enc_rnn_dim_edge'], + batch_first=True)) + + def create_graphical_model(self, edge_types): + """ + Creates or queries all trainable components. + + :param edge_types: List containing strings for all possible edge types for the node type. + :return: None + """ + self.clear_submodules() + + ############################ + # Everything but Edges # + ############################ + self.create_node_models() + + ##################### + # Edge Encoders # + ##################### + if self.hyperparams['edge_encoding']: + self.create_edge_models(edge_types) + + for name, module in self.node_modules.items(): + module.to(self.device) + + def create_new_scheduler(self, name, annealer, annealer_kws, creation_condition=True): + value_scheduler = None + rsetattr(self, name + '_scheduler', value_scheduler) + if creation_condition: + annealer_kws['device'] = self.device + value_annealer = annealer(annealer_kws) + rsetattr(self, name + '_annealer', value_annealer) + + # This is the value that we'll update on each call of + # step_annealers(). + rsetattr(self, name, value_annealer(0).clone().detach()) + dummy_optimizer = optim.Optimizer([rgetattr(self, name)], {'lr': value_annealer(0).clone().detach()}) + rsetattr(self, name + '_optimizer', dummy_optimizer) + + value_scheduler = CustomLR(dummy_optimizer, + value_annealer) + rsetattr(self, name + '_scheduler', value_scheduler) + + self.schedulers.append(value_scheduler) + self.annealed_vars.append(name) + + def set_annealing_params(self): + self.schedulers = list() + self.annealed_vars = list() + + self.create_new_scheduler(name='kl_weight', + annealer=sigmoid_anneal, + annealer_kws={ + 'start': self.hyperparams['kl_weight_start'], + 'finish': self.hyperparams['kl_weight'], + 'center_step': self.hyperparams['kl_crossover'], + 'steps_lo_to_hi': self.hyperparams['kl_crossover'] / self.hyperparams[ + 'kl_sigmoid_divisor'] + }) + + self.create_new_scheduler(name='latent.temp', + annealer=exp_anneal, + annealer_kws={ + 'start': self.hyperparams['tau_init'], + 'finish': self.hyperparams['tau_final'], + 'rate': self.hyperparams['tau_decay_rate'] + }) + + self.create_new_scheduler(name='latent.z_logit_clip', + annealer=sigmoid_anneal, + annealer_kws={ + 'start': self.hyperparams['z_logit_clip_start'], + 'finish': self.hyperparams['z_logit_clip_final'], + 'center_step': self.hyperparams['z_logit_clip_crossover'], + 'steps_lo_to_hi': self.hyperparams['z_logit_clip_crossover'] / self.hyperparams[ + 'z_logit_clip_divisor'] + }, + creation_condition=self.hyperparams['use_z_logit_clipping']) + + def step_annealers(self): + # This should manage all of the step-wise changed + # parameters automatically. + for idx, annealed_var in enumerate(self.annealed_vars): + if rgetattr(self, annealed_var + '_scheduler') is not None: + # First we step the scheduler. + with warnings.catch_warnings(): # We use a dummy optimizer: Warning because no .step() was called on it + warnings.simplefilter("ignore") + rgetattr(self, annealed_var + '_scheduler').step() + + # Then we set the annealed vars' value. + rsetattr(self, annealed_var, rgetattr(self, annealed_var + '_optimizer').param_groups[0]['lr']) + + self.summarize_annealers() + + def summarize_annealers(self): + if self.log_writer is not None: + for annealed_var in self.annealed_vars: + if rgetattr(self, annealed_var) is not None: + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), annealed_var.replace('.', '/')), + rgetattr(self, annealed_var), self.curr_iter) + + def obtain_encoded_tensors(self, + mode, + inputs, + inputs_st, + labels, + labels_st, + first_history_indices, + neighbors, + neighbors_edge_value, + robot, + map) -> (torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor): + """ + Encodes input and output tensors for node and robot. + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param inputs: Input tensor including the state for each agent over time [bs, t, state]. + :param inputs_st: Standardized input tensor. + :param labels: Label tensor including the label output for each agent over time [bs, t, pred_state]. + :param labels_st: Standardized label tensor. + :param first_history_indices: First timestep (index) in scene for which data is available for a node [bs] + :param neighbors: Preprocessed dict (indexed by edge type) of list of neighbor states over time. + [[bs, t, neighbor state]] + :param neighbors_edge_value: Preprocessed edge values for all neighbor nodes [[N]] + :param robot: Standardized robot state over time. [bs, t, robot_state] + :param map: Tensor of Map information. [bs, channels, x, y] + :return: tuple(x, x_nr_t, y_e, y_r, y, n_s_t0) + WHERE + - x: Encoded input / condition tensor to the CVAE x_e. + - x_r_t: Robot state (if robot is in scene). + - y_e: Encoded label / future of the node. + - y_r: Encoded future of the robot. + - y: Label / future of the node. + - n_s_t0: Standardized current state of the node. + """ + + x, x_r_t, y_e, y_r, y = None, None, None, None, None + initial_dynamics = dict() + + batch_size = inputs.shape[0] + + ######################################### + # Provide basic information to encoders # + ######################################### + node_history = inputs + node_present_state = inputs[:, -1] + node_pos = inputs[:, -1, 0:2] + node_vel = inputs[:, -1, 2:4] + + node_history_st = inputs_st + node_present_state_st = inputs_st[:, -1] + node_pos_st = inputs_st[:, -1, 0:2] + node_vel_st = inputs_st[:, -1, 2:4] + + n_s_t0 = node_present_state_st + + initial_dynamics['pos'] = node_pos + initial_dynamics['vel'] = node_vel + + self.dynamic.set_initial_condition(initial_dynamics) + + if self.hyperparams['incl_robot_node']: + x_r_t, y_r = robot[..., 0, :], robot[..., 1:, :] + + ################## + # Encode History # + ################## + node_history_encoded = self.encode_node_history(mode, + node_history_st, + first_history_indices) + + ################## + # Encode Present # + ################## + node_present = node_present_state_st # [bs, state_dim] + + ################## + # Encode Future # + ################## + if mode != ModeKeys.PREDICT: + y = labels_st + + ############################## + # Encode Node Edges per Type # + ############################## + if self.hyperparams['edge_encoding']: + node_edges_encoded = list() + for edge_type in self.edge_types: + # Encode edges for given edge type + encoded_edges_type = self.encode_edge(mode, + node_history, + node_history_st, + edge_type, + neighbors[edge_type], + neighbors_edge_value[edge_type], + first_history_indices) + node_edges_encoded.append(encoded_edges_type) # List of [bs/nbs, enc_rnn_dim] + ##################### + # Encode Node Edges # + ##################### + total_edge_influence = self.encode_total_edge_influence(mode, + node_edges_encoded, + node_history_encoded, + batch_size) + + ################ + # Map Encoding # + ################ + if self.hyperparams['use_map_encoding'] and self.node_type in self.hyperparams['map_encoder']: + if self.log_writer and (self.curr_iter + 1) % 500 == 0: + map_clone = map.clone() + map_patch = self.hyperparams['map_encoder'][self.node_type]['patch_size'] + map_clone[:, :, map_patch[1]-5:map_patch[1]+5, map_patch[0]-5:map_patch[0]+5] = 1. + self.log_writer.add_images(f"{self.node_type}/cropped_maps", map_clone, + self.curr_iter, dataformats='NCWH') + + encoded_map = self.node_modules[self.node_type + '/map_encoder'](map * 2. - 1., (mode == ModeKeys.TRAIN)) + do = self.hyperparams['map_encoder'][self.node_type]['dropout'] + encoded_map = F.dropout(encoded_map, do, training=(mode == ModeKeys.TRAIN)) + + ###################################### + # Concatenate Encoder Outputs into x # + ###################################### + x_concat_list = list() + + # Every node has an edge-influence encoder (which could just be zero). + if self.hyperparams['edge_encoding']: + x_concat_list.append(total_edge_influence) # [bs/nbs, 4*enc_rnn_dim] + + # Every node has a history encoder. + x_concat_list.append(node_history_encoded) # [bs/nbs, enc_rnn_dim_history] + + if self.hyperparams['incl_robot_node']: + robot_future_encoder = self.encode_robot_future(mode, x_r_t, y_r) + x_concat_list.append(robot_future_encoder) + + if self.hyperparams['use_map_encoding'] and self.node_type in self.hyperparams['map_encoder']: + if self.log_writer: + self.log_writer.add_scalar(f"{self.node_type}/encoded_map_max", + torch.max(torch.abs(encoded_map)), self.curr_iter) + x_concat_list.append(encoded_map) + + x = torch.cat(x_concat_list, dim=1) + + if mode == ModeKeys.TRAIN or mode == ModeKeys.EVAL: + y_e = self.encode_node_future(mode, node_present, y) + + return x, x_r_t, y_e, y_r, y, n_s_t0 + + def encode_node_history(self, mode, node_hist, first_history_indices): + """ + Encodes the nodes history. + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param node_hist: Historic and current state of the node. [bs, mhl, state] + :param first_history_indices: First timestep (index) in scene for which data is available for a node [bs] + :return: Encoded node history tensor. [bs, enc_rnn_dim] + """ + outputs, _ = run_lstm_on_variable_length_seqs(self.node_modules[self.node_type + '/node_history_encoder'], + original_seqs=node_hist, + lower_indices=first_history_indices) + + outputs = F.dropout(outputs, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) # [bs, max_time, enc_rnn_dim] + + last_index_per_sequence = -(first_history_indices + 1) + + return outputs[torch.arange(first_history_indices.shape[0]), last_index_per_sequence] + + def encode_edge(self, + mode, + node_history, + node_history_st, + edge_type, + neighbors, + neighbors_edge_value, + first_history_indices): + + max_hl = self.hyperparams['maximum_history_length'] + + edge_states_list = list() # list of [#of neighbors, max_ht, state_dim] + for i, neighbor_states in enumerate(neighbors): # Get neighbors for timestep in batch + if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? + neighbor_state_length = int( + np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1]].values()]) + ) + edge_states_list.append(torch.zeros((1, max_hl + 1, neighbor_state_length), device=self.device)) + else: + edge_states_list.append(torch.stack(neighbor_states, dim=0).to(self.device)) + + if self.hyperparams['edge_state_combine_method'] == 'sum': + # Used in Structural-RNN to combine edges as well. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_value in neighbors_edge_value: + op_applied_edge_mask_list.append(torch.clamp(torch.sum(edge_value.to(self.device), + dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + elif self.hyperparams['edge_state_combine_method'] == 'max': + # Used in NLP, e.g. max over word embeddings in a sentence. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.max(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_value in neighbors_edge_value: + op_applied_edge_mask_list.append(torch.clamp(torch.max(edge_value.to(self.device), + dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + elif self.hyperparams['edge_state_combine_method'] == 'mean': + # Used in NLP, e.g. mean over word embeddings in a sentence. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.mean(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_value in neighbors_edge_value: + op_applied_edge_mask_list.append(torch.clamp(torch.mean(edge_value.to(self.device), + dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + joint_history = torch.cat([combined_neighbors, node_history_st], dim=-1) + + outputs, _ = run_lstm_on_variable_length_seqs( + self.node_modules[DirectedEdge.get_str_from_types(*edge_type) + '/edge_encoder'], + original_seqs=joint_history, + lower_indices=first_history_indices + ) + + outputs = F.dropout(outputs, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) # [bs, max_time, enc_rnn_dim] + + last_index_per_sequence = -(first_history_indices + 1) + ret = outputs[torch.arange(last_index_per_sequence.shape[0]), last_index_per_sequence] + if self.hyperparams['dynamic_edges'] == 'yes': + return ret * combined_edge_masks + else: + return ret + + def encode_total_edge_influence(self, mode, encoded_edges, node_history_encoder, batch_size): + if self.hyperparams['edge_influence_combine_method'] == 'sum': + stacked_encoded_edges = torch.stack(encoded_edges, dim=0) + combined_edges = torch.sum(stacked_encoded_edges, dim=0) + + elif self.hyperparams['edge_influence_combine_method'] == 'mean': + stacked_encoded_edges = torch.stack(encoded_edges, dim=0) + combined_edges = torch.mean(stacked_encoded_edges, dim=0) + + elif self.hyperparams['edge_influence_combine_method'] == 'max': + stacked_encoded_edges = torch.stack(encoded_edges, dim=0) + combined_edges = torch.max(stacked_encoded_edges, dim=0) + + elif self.hyperparams['edge_influence_combine_method'] == 'bi-rnn': + if len(encoded_edges) == 0: + combined_edges = torch.zeros((batch_size, self.eie_output_dims), device=self.device) + + else: + # axis=1 because then we get size [batch_size, max_time, depth] + encoded_edges = torch.stack(encoded_edges, dim=1) + + _, state = self.node_modules[self.node_type + '/edge_influence_encoder'](encoded_edges) + combined_edges = unpack_RNN_state(state) + combined_edges = F.dropout(combined_edges, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + elif self.hyperparams['edge_influence_combine_method'] == 'attention': + # Used in Social Attention (https://arxiv.org/abs/1710.04689) + if len(encoded_edges) == 0: + combined_edges = torch.zeros((batch_size, self.eie_output_dims), device=self.device) + + else: + # axis=1 because then we get size [batch_size, max_time, depth] + encoded_edges = torch.stack(encoded_edges, dim=1) + combined_edges, _ = self.node_modules[self.node_type + '/edge_influence_encoder'](encoded_edges, + node_history_encoder) + combined_edges = F.dropout(combined_edges, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + return combined_edges + + def encode_node_future(self, mode, node_present, node_future) -> torch.Tensor: + """ + Encodes the node future (during training) using a bi-directional LSTM + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param node_present: Current state of the node. [bs, state] + :param node_future: Future states of the node. [bs, ph, state] + :return: Encoded future. + """ + initial_h_model = self.node_modules[self.node_type + '/node_future_encoder/initial_h'] + initial_c_model = self.node_modules[self.node_type + '/node_future_encoder/initial_c'] + + # Here we're initializing the forward hidden states, + # but zeroing the backward ones. + initial_h = initial_h_model(node_present) + initial_h = torch.stack([initial_h, torch.zeros_like(initial_h, device=self.device)], dim=0) + + initial_c = initial_c_model(node_present) + initial_c = torch.stack([initial_c, torch.zeros_like(initial_c, device=self.device)], dim=0) + + initial_state = (initial_h, initial_c) + + _, state = self.node_modules[self.node_type + '/node_future_encoder'](node_future, initial_state) + state = unpack_RNN_state(state) + state = F.dropout(state, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + return state + + def encode_robot_future(self, mode, robot_present, robot_future) -> torch.Tensor: + """ + Encodes the robot future (during training) using a bi-directional LSTM + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param robot_present: Current state of the robot. [bs, state] + :param robot_future: Future states of the robot. [bs, ph, state] + :return: Encoded future. + """ + initial_h_model = self.node_modules['robot_future_encoder/initial_h'] + initial_c_model = self.node_modules['robot_future_encoder/initial_c'] + + # Here we're initializing the forward hidden states, + # but zeroing the backward ones. + initial_h = initial_h_model(robot_present) + initial_h = torch.stack([initial_h, torch.zeros_like(initial_h, device=self.device)], dim=0) + + initial_c = initial_c_model(robot_present) + initial_c = torch.stack([initial_c, torch.zeros_like(initial_c, device=self.device)], dim=0) + + initial_state = (initial_h, initial_c) + + _, state = self.node_modules['robot_future_encoder'](robot_future, initial_state) + state = unpack_RNN_state(state) + state = F.dropout(state, + p=1. - self.hyperparams['rnn_kwargs']['dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + return state + + def q_z_xy(self, mode, x, y_e) -> torch.Tensor: + r""" + .. math:: q_\phi(z \mid \mathbf{x}_i, \mathbf{y}_i) + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param x: Input / Condition tensor. + :param y_e: Encoded future tensor. + :return: Latent distribution of the CVAE. + """ + xy = torch.cat([x, y_e], dim=1) + + if self.hyperparams['q_z_xy_MLP_dims'] is not None: + dense = self.node_modules[self.node_type + '/q_z_xy'] + h = F.dropout(F.relu(dense(xy)), + p=1. - self.hyperparams['MLP_dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + else: + h = xy + + to_latent = self.node_modules[self.node_type + '/hxy_to_z'] + return self.latent.dist_from_h(to_latent(h), mode) + + def p_z_x(self, mode, x): + r""" + .. math:: p_\theta(z \mid \mathbf{x}_i) + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param x: Input / Condition tensor. + :return: Latent distribution of the CVAE. + """ + if self.hyperparams['p_z_x_MLP_dims'] is not None: + dense = self.node_modules[self.node_type + '/p_z_x'] + h = F.dropout(F.relu(dense(x)), + p=1. - self.hyperparams['MLP_dropout_keep_prob'], + training=(mode == ModeKeys.TRAIN)) + + else: + h = x + + to_latent = self.node_modules[self.node_type + '/hx_to_z'] + return self.latent.dist_from_h(to_latent(h), mode) + + def project_to_GMM_params(self, tensor) -> (torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor): + """ + Projects tensor to parameters of a GMM with N components and D dimensions. + + :param tensor: Input tensor. + :return: tuple(log_pis, mus, log_sigmas, corrs) + WHERE + - log_pis: Weight (logarithm) of each GMM component. [N] + - mus: Mean of each GMM component. [N, D] + - log_sigmas: Standard Deviation (logarithm) of each GMM component. [N, D] + - corrs: Correlation between the GMM components. [N] + """ + log_pis = self.node_modules[self.node_type + '/decoder/proj_to_GMM_log_pis'](tensor) + mus = self.node_modules[self.node_type + '/decoder/proj_to_GMM_mus'](tensor) + log_sigmas = self.node_modules[self.node_type + '/decoder/proj_to_GMM_log_sigmas'](tensor) + corrs = torch.tanh(self.node_modules[self.node_type + '/decoder/proj_to_GMM_corrs'](tensor)) + return log_pis, mus, log_sigmas, corrs + + def p_y_xz(self, mode, x, x_nr_t, y_r, n_s_t0, z_stacked, prediction_horizon, + num_samples, num_components=1, gmm_mode=False): + r""" + .. math:: p_\psi(\mathbf{y}_i \mid \mathbf{x}_i, z) + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param x: Input / Condition tensor. + :param x_nr_t: Joint state of node and robot (if robot is in scene). + :param y: Future tensor. + :param y_r: Encoded future tensor. + :param n_s_t0: Standardized current state of the node. + :param z_stacked: Stacked latent state. [num_samples_z * num_samples_gmm, bs, latent_state] + :param prediction_horizon: Number of prediction timesteps. + :param num_samples: Number of samples from the latent space. + :param num_components: Number of GMM components. + :param gmm_mode: If True: The mode of the GMM is sampled. + :return: GMM2D. If mode is Predict, also samples from the GMM. + """ + ph = prediction_horizon + pred_dim = self.pred_state_length + + z = torch.reshape(z_stacked, (-1, self.latent.z_dim)) + zx = torch.cat([z, x.repeat(num_samples * num_components, 1)], dim=1) + + cell = self.node_modules[self.node_type + '/decoder/rnn_cell'] + initial_h_model = self.node_modules[self.node_type + '/decoder/initial_h'] + + initial_state = initial_h_model(zx) + + log_pis, mus, log_sigmas, corrs, a_sample = [], [], [], [], [] + + # Infer initial action state for node from current state + a_0 = self.node_modules[self.node_type + '/decoder/state_action'](n_s_t0) + + state = initial_state + if self.hyperparams['incl_robot_node']: + input_ = torch.cat([zx, + a_0.repeat(num_samples*num_components, 1), + x_nr_t.repeat(num_samples*num_components, 1)], dim=1) + else: + input_ = torch.cat([zx, a_0.repeat(num_samples*num_components, 1)], dim=1) + + for j in range(ph): + h_state = cell(input_, state) + log_pi_t, mu_t, log_sigma_t, corr_t = self.project_to_GMM_params(h_state) + + gmm = GMM2D(log_pi_t, mu_t, log_sigma_t, corr_t) # [k;bs, pred_dim] + + if mode == ModeKeys.PREDICT and gmm_mode: + a_t = gmm.mode() + else: + a_t = gmm.rsample() + + if num_components > 1: + if mode == ModeKeys.PREDICT: + log_pis.append(self.latent.p_dist.logits.repeat(num_samples, 1, 1)) + else: + log_pis.append(self.latent.q_dist.logits.repeat(num_samples, 1, 1)) + else: + log_pis.append( + torch.ones_like(corr_t.reshape(num_samples, num_components, -1).permute(0, 2, 1).reshape(-1, 1)) + ) + + mus.append( + mu_t.reshape( + num_samples, num_components, -1, 2 + ).permute(0, 2, 1, 3).reshape(-1, 2 * num_components) + ) + log_sigmas.append( + log_sigma_t.reshape( + num_samples, num_components, -1, 2 + ).permute(0, 2, 1, 3).reshape(-1, 2 * num_components)) + corrs.append( + corr_t.reshape( + num_samples, num_components, -1 + ).permute(0, 2, 1).reshape(-1, num_components)) + + if self.hyperparams['incl_robot_node']: + dec_inputs = [zx, a_t, y_r[:, j].repeat(num_samples * num_components, 1)] + else: + dec_inputs = [zx, a_t] + input_ = torch.cat(dec_inputs, dim=1) + state = h_state + + log_pis = torch.stack(log_pis, dim=1) + mus = torch.stack(mus, dim=1) + log_sigmas = torch.stack(log_sigmas, dim=1) + corrs = torch.stack(corrs, dim=1) + + a_dist = GMM2D(torch.reshape(log_pis, [num_samples, -1, ph, num_components]), + torch.reshape(mus, [num_samples, -1, ph, num_components * pred_dim]), + torch.reshape(log_sigmas, [num_samples, -1, ph, num_components * pred_dim]), + torch.reshape(corrs, [num_samples, -1, ph, num_components])) + + if self.hyperparams['dynamic'][self.node_type]['distribution']: + y_dist = self.dynamic.integrate_distribution(a_dist, x) + else: + y_dist = a_dist + + if mode == ModeKeys.PREDICT: + if gmm_mode: + a_sample = a_dist.mode() + else: + a_sample = a_dist.rsample() + sampled_future = self.dynamic.integrate_samples(a_sample, x) + return y_dist, sampled_future + else: + return y_dist + + def encoder(self, mode, x, y_e, num_samples=None): + """ + Encoder of the CVAE. + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param x: Input / Condition tensor. + :param y_e: Encoded future tensor. + :param num_samples: Number of samples from the latent space during Prediction. + :return: tuple(z, kl_obj) + WHERE + - z: Samples from the latent space. + - kl_obj: KL Divergenze between q and p + """ + if mode == ModeKeys.TRAIN: + sample_ct = self.hyperparams['k'] + elif mode == ModeKeys.EVAL: + sample_ct = self.hyperparams['k_eval'] + elif mode == ModeKeys.PREDICT: + sample_ct = num_samples + if num_samples is None: + raise ValueError("num_samples cannot be None with mode == PREDICT.") + + self.latent.q_dist = self.q_z_xy(mode, x, y_e) + self.latent.p_dist = self.p_z_x(mode, x) + + z = self.latent.sample_q(sample_ct, mode) + + if mode == ModeKeys.TRAIN: + kl_obj = self.latent.kl_q_p(self.log_writer, '%s' % str(self.node_type), self.curr_iter) + if self.log_writer is not None: + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'kl'), kl_obj, self.curr_iter) + else: + kl_obj = None + + return z, kl_obj + + def decoder(self, mode, x, x_nr_t, y, y_r, n_s_t0, z, labels, prediction_horizon, num_samples): + """ + Decoder of the CVAE. + + :param mode: Mode in which the model is operated. E.g. Train, Eval, Predict. + :param x: Input / Condition tensor. + :param x: Input / Condition tensor. + :param x_nr_t: Joint state of node and robot (if robot is in scene). + :param y: Future tensor. + :param y_r: Encoded future tensor. + :param n_s_t0: Standardized current state of the node. + :param z: Stacked latent state. + :param prediction_horizon: Number of prediction timesteps. + :param num_samples: Number of samples from the latent space. + :return: Log probability of y over p. + """ + + num_components = self.hyperparams['N'] * self.hyperparams['K'] + y_dist = self.p_y_xz(mode, x, x_nr_t, y_r, n_s_t0, z, + prediction_horizon, num_samples, num_components=num_components) + log_p_yt_xz = torch.clamp(y_dist.log_prob(labels), max=self.hyperparams['log_p_yt_xz_max']) + if self.hyperparams['log_histograms'] and self.log_writer is not None: + self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'log_p_yt_xz'), log_p_yt_xz, self.curr_iter) + + log_p_y_xz = torch.sum(log_p_yt_xz, dim=2) + return log_p_y_xz + + def train_loss(self, + inputs, + inputs_st, + first_history_indices, + labels, + labels_st, + neighbors, + neighbors_edge_value, + robot, + map, + prediction_horizon) -> torch.Tensor: + """ + Calculates the training loss for a batch. + + :param inputs: Input tensor including the state for each agent over time [bs, t, state]. + :param inputs_st: Standardized input tensor. + :param first_history_indices: First timestep (index) in scene for which data is available for a node [bs] + :param labels: Label tensor including the label output for each agent over time [bs, t, pred_state]. + :param labels_st: Standardized label tensor. + :param neighbors: Preprocessed dict (indexed by edge type) of list of neighbor states over time. + [[bs, t, neighbor state]] + :param neighbors_edge_value: Preprocessed edge values for all neighbor nodes [[N]] + :param robot: Standardized robot state over time. [bs, t, robot_state] + :param map: Tensor of Map information. [bs, channels, x, y] + :param prediction_horizon: Number of prediction timesteps. + :return: Scalar tensor -> nll loss + """ + mode = ModeKeys.TRAIN + + x, x_nr_t, y_e, y_r, y, n_s_t0 = self.obtain_encoded_tensors(mode=mode, + inputs=inputs, + inputs_st=inputs_st, + labels=labels, + labels_st=labels_st, + first_history_indices=first_history_indices, + neighbors=neighbors, + neighbors_edge_value=neighbors_edge_value, + robot=robot, + map=map) + + z, kl = self.encoder(mode, x, y_e) + log_p_y_xz = self.decoder(mode, x, x_nr_t, y, y_r, n_s_t0, z, + labels, # Loss is calculated on unstandardized label + prediction_horizon, + self.hyperparams['k']) + + log_p_y_xz_mean = torch.mean(log_p_y_xz, dim=0) # [nbs] + log_likelihood = torch.mean(log_p_y_xz_mean) + + mutual_inf_q = mutual_inf_mc(self.latent.q_dist) + mutual_inf_p = mutual_inf_mc(self.latent.p_dist) + + ELBO = log_likelihood - self.kl_weight * kl + 1. * mutual_inf_p + loss = -ELBO + + if self.hyperparams['log_histograms'] and self.log_writer is not None: + self.log_writer.add_histogram('%s/%s' % (str(self.node_type), 'log_p_y_xz'), + log_p_y_xz_mean, + self.curr_iter) + + if self.log_writer is not None: + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'mutual_information_q'), + mutual_inf_q, + self.curr_iter) + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'mutual_information_p'), + mutual_inf_p, + self.curr_iter) + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'log_likelihood'), + log_likelihood, + self.curr_iter) + self.log_writer.add_scalar('%s/%s' % (str(self.node_type), 'loss'), + loss, + self.curr_iter) + if self.hyperparams['log_histograms']: + self.latent.summarize_for_tensorboard(self.log_writer, str(self.node_type), self.curr_iter) + return loss + + def eval_loss(self, + inputs, + inputs_st, + first_history_indices, + labels, + labels_st, + neighbors, + neighbors_edge_value, + robot, + map, + prediction_horizon) -> torch.Tensor: + """ + Calculates the evaluation loss for a batch. + + :param inputs: Input tensor including the state for each agent over time [bs, t, state]. + :param inputs_st: Standardized input tensor. + :param first_history_indices: First timestep (index) in scene for which data is available for a node [bs] + :param labels: Label tensor including the label output for each agent over time [bs, t, pred_state]. + :param labels_st: Standardized label tensor. + :param neighbors: Preprocessed dict (indexed by edge type) of list of neighbor states over time. + [[bs, t, neighbor state]] + :param neighbors_edge_value: Preprocessed edge values for all neighbor nodes [[N]] + :param robot: Standardized robot state over time. [bs, t, robot_state] + :param map: Tensor of Map information. [bs, channels, x, y] + :param prediction_horizon: Number of prediction timesteps. + :return: tuple(nll_q_is, nll_p, nll_exact, nll_sampled) + """ + + mode = ModeKeys.EVAL + + x, x_nr_t, y_e, y_r, y, n_s_t0 = self.obtain_encoded_tensors(mode=mode, + inputs=inputs, + inputs_st=inputs_st, + labels=labels, + labels_st=labels_st, + first_history_indices=first_history_indices, + neighbors=neighbors, + neighbors_edge_value=neighbors_edge_value, + robot=robot, + map=map) + + num_components = self.hyperparams['N'] * self.hyperparams['K'] + ### Importance sampled NLL estimate + z, _ = self.encoder(mode, x, y_e) # [k_eval, nbs, N*K] + z = self.latent.sample_p(1, mode, full_dist=True) + y_dist, _ = self.p_y_xz(ModeKeys.PREDICT, x, x_nr_t, y_r, n_s_t0, z, + prediction_horizon, num_samples=1, num_components=num_components) + # We use unstandardized labels to compute the loss + log_p_yt_xz = torch.clamp(y_dist.log_prob(labels), max=self.hyperparams['log_p_yt_xz_max']) + log_p_y_xz = torch.sum(log_p_yt_xz, dim=2) + log_p_y_xz_mean = torch.mean(log_p_y_xz, dim=0) # [nbs] + log_likelihood = torch.mean(log_p_y_xz_mean) + nll = -log_likelihood + + return nll + + def predict(self, + inputs, + inputs_st, + first_history_indices, + neighbors, + neighbors_edge_value, + robot, + map, + prediction_horizon, + num_samples, + z_mode=False, + gmm_mode=False, + full_dist=True, + all_z_sep=False): + """ + Predicts the future of a batch of nodes. + + :param inputs: Input tensor including the state for each agent over time [bs, t, state]. + :param inputs_st: Standardized input tensor. + :param first_history_indices: First timestep (index) in scene for which data is available for a node [bs] + :param neighbors: Preprocessed dict (indexed by edge type) of list of neighbor states over time. + [[bs, t, neighbor state]] + :param neighbors_edge_value: Preprocessed edge values for all neighbor nodes [[N]] + :param robot: Standardized robot state over time. [bs, t, robot_state] + :param map: Tensor of Map information. [bs, channels, x, y] + :param prediction_horizon: Number of prediction timesteps. + :param num_samples: Number of samples from the latent space. + :param z_mode: If True: Select the most likely latent state. + :param gmm_mode: If True: The mode of the GMM is sampled. + :param all_z_sep: Samples each latent mode individually without merging them into a GMM. + :param full_dist: Samples all latent states and merges them into a GMM as output. + :return: + """ + mode = ModeKeys.PREDICT + + x, x_nr_t, _, y_r, _, n_s_t0 = self.obtain_encoded_tensors(mode=mode, + inputs=inputs, + inputs_st=inputs_st, + labels=None, + labels_st=None, + first_history_indices=first_history_indices, + neighbors=neighbors, + neighbors_edge_value=neighbors_edge_value, + robot=robot, + map=map) + + self.latent.p_dist = self.p_z_x(mode, x) + z, num_samples, num_components = self.latent.sample_p(num_samples, + mode, + most_likely_z=z_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) + + _, our_sampled_future = self.p_y_xz(mode, x, x_nr_t, y_r, n_s_t0, z, + prediction_horizon, + num_samples, + num_components, + gmm_mode) + + return our_sampled_future diff --git a/code/model/model_registrar.py b/trajectron/model/model_registrar.py similarity index 80% rename from code/model/model_registrar.py rename to trajectron/model/model_registrar.py index 8bfc302..111a8ab 100644 --- a/code/model/model_registrar.py +++ b/trajectron/model/model_registrar.py @@ -14,11 +14,9 @@ class ModelRegistrar(nn.Module): self.model_dir = model_dir self.device = device - def forward(self): raise NotImplementedError('Although ModelRegistrar is a nn.Module, it is only to store parameters.') - def get_model(self, name, model_if_absent=None): # 4 cases: name in self.model_dict and model_if_absent is None (OK) # name in self.model_dict and model_if_absent is not None (OK) @@ -35,21 +33,29 @@ class ModelRegistrar(nn.Module): else: raise ValueError(f'{name} was never initialized in this Registrar!') + def get_name_match(self, name): + ret_model_list = nn.ModuleList() + for key in self.model_dict.keys(): + if name in key: + ret_model_list.append(self.model_dict[key]) + return ret_model_list + + def get_all_but_name_match(self, name): + ret_model_list = nn.ModuleList() + for key in self.model_dict.keys(): + if name not in key: + ret_model_list.append(self.model_dict[key]) + return ret_model_list def print_model_names(self): print(self.model_dict.keys()) - def save_models(self, curr_iter): # Create the model directiory if it's not present. save_path = os.path.join(self.model_dir, 'model_registrar-%d.pt' % curr_iter) - print('') - print('Saving to ' + save_path) - torch.save(self.model_dict, save_path) - print('Saved!') - print('') + torch.save(self.model_dict, save_path) def load_models(self, iter_num): self.model_dict.clear() @@ -63,7 +69,6 @@ class ModelRegistrar(nn.Module): print('Loaded!') print('') - def to(self, device): for name, model in self.model_dict.items(): if get_model_device(model) != device: diff --git a/code/model/model_utils.py b/trajectron/model/model_utils.py similarity index 52% rename from code/model/model_utils.py rename to trajectron/model/model_utils.py index 82d8b86..0d516b3 100644 --- a/code/model/model_utils.py +++ b/trajectron/model/model_utils.py @@ -4,7 +4,6 @@ from enum import Enum import functools import numpy as np import math -from scipy.ndimage import interpolation class ModeKeys(Enum): @@ -12,8 +11,8 @@ class ModeKeys(Enum): EVAL = 2 PREDICT = 3 -def cyclical_lr(stepsize, min_lr=3e-4, max_lr=3e-3, decay=1.): +def cyclical_lr(stepsize, min_lr=3e-4, max_lr=3e-3, decay=1.): # Lambda function to calculate the LR lr_lambda = lambda it: min_lr + (max_lr - min_lr) * relative(it, stepsize) * decay**it @@ -26,17 +25,8 @@ def cyclical_lr(stepsize, min_lr=3e-4, max_lr=3e-3, decay=1.): return lr_lambda -def tile(a, dim, n_tile, device='cpu'): - init_dim = a.size(dim) - repeat_idx = [1] * a.dim() - repeat_idx[dim] = n_tile - a = a.repeat(*(repeat_idx)) - order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])).to(device) - return torch.index_select(a, dim, order_index) - - -def to_one_hot(labels, n_labels, device): - return torch.eye(n_labels, device=device)[labels] +def to_one_hot(labels, n_labels): + return torch.eye(n_labels, device=labels.device)[labels] def exp_anneal(anneal_kws): @@ -65,10 +55,23 @@ class CustomLR(torch.optim.lr_scheduler.LambdaLR): for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)] -def run_lstm_on_variable_length_seqs(lstm_module, original_seqs, break_indices, lower_indices, total_length): +def mutual_inf_mc(x_dist): + dist = x_dist.__class__ + H_y = dist(probs=x_dist.probs.mean(dim=0)).entropy() + return (H_y - x_dist.entropy().mean(dim=0)).sum() + + +def run_lstm_on_variable_length_seqs(lstm_module, original_seqs, lower_indices=None, upper_indices=None, total_length=None): + bs, tf = original_seqs.shape[:2] + if lower_indices is None: + lower_indices = torch.zeros(bs, dtype=torch.int) + if upper_indices is None: + upper_indices = torch.ones(bs, dtype=torch.int) * (tf - 1) + if total_length is None: + total_length = max(upper_indices) + 1 # This is done so that we can just pass in self.prediction_timesteps # (which we want to INCLUDE, so this will exclude the next timestep). - inclusive_break_indices = break_indices + 1 + inclusive_break_indices = upper_indices + 1 pad_list = list() for i, seq_len in enumerate(inclusive_break_indices): @@ -120,50 +123,3 @@ def rgetattr(obj, attr, *args): def _getattr(obj, attr): return getattr(obj, attr, *args) return functools.reduce(_getattr, [obj] + attr.split('.')) - - -def get_cropped_maps(world_pts, map, context_size=50): - """world_pts: N x 2 array of positions relative to the world.""" - expanded_obs_img = np.full((map.data.shape[0] + context_size, map.data.shape[1] + context_size, map.data.shape[2]), False, dtype=np.float32) - expanded_obs_img[context_size//2:-context_size//2, context_size//2:-context_size//2] = map.fdata.astype(np.float32) - img_pts = context_size//2 + np.round(map.to_map_points(world_pts)).astype(int) - return np.stack([expanded_obs_img[img_pts[i, 0] - context_size//2 : img_pts[i, 0] + context_size//2, - img_pts[i, 1] - context_size//2 : img_pts[i, 1] + context_size//2] - for i in range(world_pts.shape[0])], axis=0) - -def get_cropped_maps_heading(world_pts, map, context_size=50, heading=None): - """world_pts: N x 2 array of positions relative to the world.""" - rotations = np.round((heading) / (np.pi / 2)).astype(int) - - expanded_obs_img = np.full((map.data.shape[0] + context_size, map.data.shape[1] + context_size, map.data.shape[2]), - False, dtype=np.float32) - expanded_obs_img[context_size // 2:-context_size // 2, context_size // 2:-context_size // 2] = map.fdata.astype( - np.float32) - img_pts = context_size // 2 + np.round(map.to_map_points(world_pts)).astype(int) - map_h = np.stack([expanded_obs_img[img_pts[i, 0] - context_size // 2: img_pts[i, 0] + context_size // 2, - img_pts[i, 1] - context_size // 2: img_pts[i, 1] + context_size // 2] - for i in range(world_pts.shape[0])], axis=0) - - map_h[rotations == 1] = np.rot90(map_h[rotations == 1], -1, axes=(1, 2)) - map_h[rotations == 2] = np.rot90(map_h[rotations == 2], 2, axes=(1, 2)) - map_h[rotations == -1] = np.rot90(map_h[rotations == -1], 1, axes=(1, 2)) - map_h[rotations == -2] = np.rot90(map_h[rotations == -2], 2, axes=(1, 2)) - return map_h - -def get_cropped_maps_heading_exact(world_pts, map, context_size=50, heading=None): - """world_pts: N x 2 array of positions relative to the world.""" - angles = -heading * 180 / np.pi - - expanded_obs_img = np.full((map.data.shape[0] + context_size, map.data.shape[1] + context_size, map.data.shape[2]), - False, dtype=np.float32) - expanded_obs_img[context_size // 2:-context_size // 2, context_size // 2:-context_size // 2] = map.fdata.astype( - np.float32) - img_pts = context_size // 2 + np.round(map.to_map_points(world_pts)).astype(int) - map_h = np.stack([expanded_obs_img[img_pts[i, 0] - context_size // 2: img_pts[i, 0] + context_size // 2, - img_pts[i, 1] - context_size // 2: img_pts[i, 1] + context_size // 2] - for i in range(world_pts.shape[0])], axis=0) - - for i in range(angles.shape[0]): - map_h[i] = interpolation.rotate(map_h[i], reshape=False, angle=angles[i], prefilter=False) - - return map_h \ No newline at end of file diff --git a/trajectron/model/online/__init__.py b/trajectron/model/online/__init__.py new file mode 100644 index 0000000..a1c9070 --- /dev/null +++ b/trajectron/model/online/__init__.py @@ -0,0 +1,2 @@ +from .online_trajectron import OnlineTrajectron +from .online_mgcvae import OnlineMultimodalGenerativeCVAE diff --git a/trajectron/model/online/online_mgcvae.py b/trajectron/model/online/online_mgcvae.py new file mode 100644 index 0000000..fd61b5c --- /dev/null +++ b/trajectron/model/online/online_mgcvae.py @@ -0,0 +1,387 @@ +import warnings +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from collections import defaultdict, Counter +from model.components import * +from model.model_utils import * +import model.dynamics as dynamic_module +from model.mgcvae import MultimodalGenerativeCVAE +from environment.scene_graph import DirectedEdge +from environment.node_type import NodeType + + +class OnlineMultimodalGenerativeCVAE(MultimodalGenerativeCVAE): + def __init__(self, + env, + node, + model_registrar, + hyperparams, + device): + self.hyperparams = hyperparams + self.node = node + self.node_type = self.node.type + + if len(env.scenes) != 1: + raise ValueError("Passed in Environment has number of scenes != 1") + self.robot = env.scenes[0].robot + self.model_registrar = model_registrar + self.device = device + + self.node_modules = dict() + self.env = env + self.scene_graph = None + + self.state = self.hyperparams['state'] + self.pred_state = self.hyperparams['pred_state'][self.node.type] + self.state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[self.node.type].values()])) + if self.hyperparams['incl_robot_node']: + self.robot_state_length = int( + np.sum([len(entity_dims) for entity_dims in self.state[self.robot.type].values()])) + self.pred_state_length = int(np.sum([len(entity_dims) for entity_dims in self.pred_state.values()])) + + self.curr_hidden_states = dict() + self.edge_types = Counter() + + self.create_graphical_model() + + dynamic_class = getattr(dynamic_module, self.hyperparams['dynamic'][self.node_type]['name']) + dyn_limits = hyperparams['dynamic'][self.node_type]['limits'] + self.dynamic = dynamic_class(self.env.scenes[0].dt, dyn_limits, device, self.model_registrar, self.x_size) + + def create_graphical_model(self): + """ + Creates or queries all trainable components. + + :return: None + """ + self.clear_submodules() + + ############################ + # Everything but Edges # + ############################ + self.create_node_models() + + for name, module in self.node_modules.items(): + module.to(self.device) + + def update_graph(self, new_scene_graph, new_neighbors, removed_neighbors): + self.scene_graph = new_scene_graph + + if self.node in new_neighbors: + for edge_type, new_neighbor_nodes in new_neighbors[self.node].items(): + self.add_edge_model(edge_type) + self.edge_types += Counter({edge_type: len(new_neighbor_nodes)}) + + if self.node in removed_neighbors: + for edge_type, removed_neighbor_nodes in removed_neighbors[self.node].items(): + self.remove_edge_model(edge_type) + self.edge_types -= Counter({edge_type: len(removed_neighbor_nodes)}) + + def get_edge_to(self, other_node): + return DirectedEdge(self.node, other_node) + + def add_edge_model(self, edge_type): + if self.hyperparams['edge_encoding']: + if edge_type + '/edge_encoder' not in self.node_modules: + neighbor_state_length = int( + np.sum([len(entity_dims) for entity_dims in + self.state[self._get_other_node_type_from_edge(edge_type)].values()])) + if self.hyperparams['edge_state_combine_method'] == 'pointnet': + self.add_submodule(edge_type + '/pointnet_encoder', + model_if_absent=nn.Sequential( + nn.Linear(self.state_length, 2 * self.state_length), + nn.ReLU(), + nn.Linear(2 * self.state_length, 2 * self.state_length), + nn.ReLU())) + + edge_encoder_input_size = 2 * self.state_length + self.state_length + + elif self.hyperparams['edge_state_combine_method'] == 'attention': + self.add_submodule(self.node.type + '/edge_attention_combine', + model_if_absent=TemporallyBatchedAdditiveAttention( + encoder_hidden_state_dim=self.state_length, + decoder_hidden_state_dim=self.state_length)) + edge_encoder_input_size = self.state_length + neighbor_state_length + + else: + edge_encoder_input_size = self.state_length + neighbor_state_length + + self.add_submodule(edge_type + '/edge_encoder', + model_if_absent=nn.LSTM(input_size=edge_encoder_input_size, + hidden_size=self.hyperparams['enc_rnn_dim_edge'], + batch_first=True)) + + def _get_other_node_type_from_edge(self, edge_type_str): + n2_type_str = edge_type_str.split('->')[1] + return NodeType(n2_type_str, self.env.node_type_list.index(n2_type_str) + 1) + + def _get_edge_type_from_str(self, edge_type_str): + n1_type_str, n2_type_str = edge_type_str.split('->') + return (NodeType(n1_type_str, self.env.node_type_list.index(n1_type_str) + 1), + NodeType(n2_type_str, self.env.node_type_list.index(n2_type_str) + 1)) + + def remove_edge_model(self, edge_type): + if self.hyperparams['edge_encoding']: + if len(self.scene_graph.get_neighbors(self.node, self._get_other_node_type_from_edge(edge_type))) == 0: + del self.node_modules[edge_type + '/edge_encoder'] + + def obtain_encoded_tensors(self, mode, inputs, inputs_st, inputs_np, robot_present_and_future) -> (torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor): + x, x_r_t, y_r = None, None, None + batch_size = 1 + + our_inputs = inputs[self.node] + our_inputs_st = inputs_st[self.node] + + initial_dynamics = dict() + initial_dynamics['pos'] = our_inputs_st[:, 0:2] # TODO: Generalize + initial_dynamics['vel'] = our_inputs_st[:, 2:4] # TODO: Generalize + self.dynamic.set_initial_condition(initial_dynamics) + + ######################################### + # Provide basic information to encoders # + ######################################### + if self.hyperparams['incl_robot_node'] and self.robot is not None: + x_r_t, y_r = self.get_relative_robot_traj(our_inputs, robot_present_and_future, self.robot.type) + + ################## + # Encode History # + ################## + node_history_encoded = self.encode_node_history(our_inputs_st) + + ############################## + # Encode Node Edges per Type # + ############################## + total_edge_influence = None + if self.hyperparams['edge_encoding']: + node_edges_encoded = list() + for edge_type in self.edge_types: + connected_nodes_batched = list() + edge_masks_batched = list() + + # We get all nodes which are connected to the current node for the current timestep + connected_nodes_batched.append(self.scene_graph.get_neighbors(self.node, + self._get_other_node_type_from_edge( + edge_type))) + + if self.hyperparams['dynamic_edges'] == 'yes': + # We get the edge masks for the current node at the current timestep + edge_masks_for_node = self.scene_graph.get_edge_scaling(self.node) + edge_masks_batched.append(torch.tensor(edge_masks_for_node, dtype=torch.float, device=self.device)) + + # Encode edges for given edge type + encoded_edges_type = self.encode_edge(inputs, + inputs_st, + inputs_np, + edge_type, + connected_nodes_batched, + edge_masks_batched) + node_edges_encoded.append(encoded_edges_type) # List of [bs/nbs, enc_rnn_dim] + + ##################### + # Encode Node Edges # + ##################### + total_edge_influence = self.encode_total_edge_influence(mode, + node_edges_encoded, + node_history_encoded, + batch_size) + + self.TD = {'node_history_encoded': node_history_encoded, + 'total_edge_influence': total_edge_influence} + + ###################################### + # Concatenate Encoder Outputs into x # + ###################################### + return self.create_encoder_rep(mode, self.TD, x_r_t, y_r) + + def create_encoder_rep(self, mode, + TD, + robot_present_st, + robot_future_st): + # Unpacking TD + node_history_encoded = TD['node_history_encoded'] + if self.hyperparams['edge_encoding']: + total_edge_influence = TD['total_edge_influence'] + + if (self.hyperparams['incl_robot_node'] + and self.robot is not None + and robot_future_st is not None + and robot_present_st is not None): + robot_future_encoder = self.encode_robot_future(mode, robot_present_st, robot_future_st) + + # Tiling for multiple samples + # This tiling is done because: + # a) we must consider the prediction case where there are many candidate robot future actions, + # b) the edge and history encoders are all the same regardless of which candidate future robot action + # we're evaluating. + node_history_encoded = TD['node_history_encoded'].repeat(robot_future_st.size()[0], 1) + if self.hyperparams['edge_encoding']: + total_edge_influence = TD['total_edge_influence'].repeat(robot_future_st.size()[0], 1) + + elif self.hyperparams['incl_robot_node'] and self.robot is not None: + # Four times because we're trying to mimic a bi-directional RNN's output (which is c and h from both ends). + robot_future_encoder = torch.zeros([1, 4 * self.hyperparams['enc_rnn_dim_future']], device=self.device) + + x_concat_list = list() + + # Every node has an edge-influence encoder (which could just be zero). + if self.hyperparams['edge_encoding']: + x_concat_list.append(total_edge_influence) # [bs/nbs, 4*enc_rnn_dim] + + # Every node has a history encoder. + x_concat_list.append(node_history_encoded) # [bs/nbs, enc_rnn_dim_history] + + if self.hyperparams['incl_robot_node'] and self.robot is not None: + x_concat_list.append(robot_future_encoder) # [bs/nbs, 4*enc_rnn_dim_history] + + return torch.cat(x_concat_list, dim=1) + + def encode_node_history(self, inputs_st): + new_state = torch.unsqueeze(inputs_st, dim=1) # [bs, 1, state_dim] + if self.node.type + '/node_history_encoder' not in self.curr_hidden_states: + outputs, self.curr_hidden_states[self.node.type + '/node_history_encoder'] = self.node_modules[ + self.node.type + '/node_history_encoder'](new_state) + else: + outputs, self.curr_hidden_states[self.node.type + '/node_history_encoder'] = self.node_modules[ + self.node.type + '/node_history_encoder'](new_state, self.curr_hidden_states[ + self.node.type + '/node_history_encoder']) + + return outputs[:, 0, :] + + def encode_edge(self, inputs, inputs_st, inputs_np, edge_type, connected_nodes, edge_masks): + edge_type_tuple = self._get_edge_type_from_str(edge_type) + edge_states_list = list() # list of [#of neighbors, max_ht, state_dim] + neighbor_states = list() + + rel_state = inputs[self.node].cpu().numpy() + for node in connected_nodes[0]: + neighbor_state_np = inputs_np[node] + + # Make State relative to node + _, std = self.env.get_standardize_params(self.state[node.type], node_type=node.type) + std[0:2] = self.env.attention_radius[edge_type_tuple] + neighbor_state_np_st = self.env.standardize(neighbor_state_np, + self.state[node.type], + node_type=node.type, + mean=rel_state, + std=std) + + neighbor_state = torch.tensor(neighbor_state_np_st).float().to(self.device) + neighbor_states.append(neighbor_state) + + if len(neighbor_states) == 0: # There are no neighbors for edge type # TODO necessary? + neighbor_state_length = int(np.sum([len(entity_dims) for entity_dims in self.state[edge_type[1]].values()])) + edge_states_list.append(torch.zeros((1, 1, neighbor_state_length), device=self.device)) + else: + edge_states_list.append(torch.stack(neighbor_states, dim=0)) + + if self.hyperparams['edge_state_combine_method'] == 'sum': + # Used in Structural-RNN to combine edges as well. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.sum(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_mask in edge_masks: + op_applied_edge_mask_list.append(torch.clamp(torch.sum(edge_mask, dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + elif self.hyperparams['edge_state_combine_method'] == 'max': + # Used in NLP, e.g. max over word embeddings in a sentence. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.max(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_mask in edge_masks: + op_applied_edge_mask_list.append(torch.clamp(torch.max(edge_mask, dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + elif self.hyperparams['edge_state_combine_method'] == 'mean': + # Used in NLP, e.g. mean over word embeddings in a sentence. + op_applied_edge_states_list = list() + for neighbors_state in edge_states_list: + op_applied_edge_states_list.append(torch.mean(neighbors_state, dim=0)) + combined_neighbors = torch.stack(op_applied_edge_states_list, dim=0) + if self.hyperparams['dynamic_edges'] == 'yes': + # Should now be (bs, time, 1) + op_applied_edge_mask_list = list() + for edge_mask in edge_masks: + op_applied_edge_mask_list.append(torch.clamp(torch.mean(edge_mask, dim=0, keepdim=True), max=1.)) + combined_edge_masks = torch.stack(op_applied_edge_mask_list, dim=0) + + joint_history = torch.cat([combined_neighbors, torch.unsqueeze(inputs_st[self.node], dim=0)], dim=-1) + + if edge_type + '/edge_encoder' not in self.curr_hidden_states: + outputs, self.curr_hidden_states[edge_type + '/edge_encoder'] = self.node_modules[ + edge_type + '/edge_encoder'](joint_history) + else: + outputs, self.curr_hidden_states[edge_type + '/edge_encoder'] = self.node_modules[ + edge_type + '/edge_encoder'](joint_history, self.curr_hidden_states[edge_type + '/edge_encoder']) + + if self.hyperparams['dynamic_edges'] == 'yes': + return outputs[:, 0, :] * combined_edge_masks + else: + return outputs[:, 0, :] # [bs, enc_rnn_dim] + + def encoder_forward(self, inputs, inputs_st, inputs_np, robot_present_and_future=None): + # Always predicting with the online model. + mode = ModeKeys.PREDICT + + self.x = self.obtain_encoded_tensors(mode, + inputs, + inputs_st, + inputs_np, + robot_present_and_future) + + self.latent.p_dist = self.p_z_x(mode, self.x) + + # robot_future_st is optional here since you can use the same one from encoder_forward, + # but if it's given then we'll re-run that part of the model (if the node is adjacent to the robot). + def decoder_forward(self, prediction_horizon, + num_samples, + robot_present_and_future=None, + z_mode=False, + gmm_mode=False, + full_dist=False, + all_z_sep=False): + # Always predicting with the online model. + mode = ModeKeys.PREDICT + + x_nr_t, y_r = None, None + if (self.hyperparams['incl_robot_node'] + and self.robot is not None + and robot_present_and_future is not None): + x_nr_t, y_r = self.get_relative_robot_traj( + torch.tensor(self.node.get(np.array([self.node.last_timestep]), + self.state[self.node.type], + padding=0.0), + dtype=torch.float, + device=self.device), + robot_present_and_future, + self.robot.type) + self.x = self.create_encoder_rep(mode, self.TD, x_nr_t, y_r) + self.latent.p_dist = self.p_z_x(mode, self.x) + + z, num_samples, num_components = self.latent.sample_p(num_samples, + mode, + most_likely_z=z_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) + + _, our_sampled_future = self.p_y_xz(mode, self.x, x_nr_t, y_r, z, + prediction_horizon, + num_samples, + num_components, + gmm_mode) + + return our_sampled_future diff --git a/trajectron/model/online/online_trajectron.py b/trajectron/model/online/online_trajectron.py new file mode 100644 index 0000000..33c35dd --- /dev/null +++ b/trajectron/model/online/online_trajectron.py @@ -0,0 +1,306 @@ +import torch +import numpy as np +from collections import Counter +from model.trajectron import Trajectron +from model.online.online_mgcvae import OnlineMultimodalGenerativeCVAE +from model.model_utils import ModeKeys +from data import RingBuffer, TemporalSceneGraph, SceneGraph, derivative_of + + +class OnlineTrajectron(Trajectron): + def __init__(self, model_registrar, + hyperparams, device): + super(OnlineTrajectron, self).__init__(model_registrar=model_registrar, + hyperparams=hyperparams, + log_writer=False, + device=device) + self.node_data = dict() + self.scene_graph = None + self.RING_CAPACITY = max(len(self.hyperparams['edge_removal_filter']), + len(self.hyperparams['edge_addition_filter']), + self.hyperparams['maximum_history_length']) + 1 + self.rel_states = dict() + self.removed_nodes = Counter() + + def __repr__(self): + return f"OnlineTrajectron(# nodes: {len(self.nodes)}, device: {self.device}, hyperparameters: {str(self.hyperparams)}) " + + def _add_node_model(self, node): + if node in self.nodes: + raise ValueError('%s was already added to this graph!' % str(node)) + + self.nodes.add(node) + self.node_models_dict[node] = OnlineMultimodalGenerativeCVAE(self.env, + node, + self.model_registrar, + self.hyperparams, + self.device) + + def update_removed_nodes(self): + for node in list(self.removed_nodes.keys()): + if self.removed_nodes[node] >= len(self.hyperparams['edge_removal_filter']): + del self.node_data[node] + del self.removed_nodes[node] + + def _remove_node_model(self, node): + if node not in self.nodes: + raise ValueError('%s is not in this graph!' % str(node)) + + self.nodes.remove(node) + del self.node_models_dict[node] + + def set_environment(self, env, init_timestep=0): + self.env = env + self.scene_graph = SceneGraph(edge_radius=self.env.attention_radius) + self.nodes.clear() + self.node_data.clear() + self.node_models_dict.clear() + + # Fast-forwarding ourselves to the initial timestep, without running any of the underlying models. + for timestep in range(init_timestep + 1): + self.incremental_forward(self.env.scenes[0].get_clipped_pos_dict(timestep, self.hyperparams['state']), + run_models=False) + + def incremental_forward(self, new_inputs_dict, + prediction_horizon=0, + num_samples=0, + robot_present_and_future=None, + z_mode=False, + gmm_mode=False, + full_dist=False, + all_z_sep=False, + run_models=True): + # The way this function works is by appending the new datapoints to the + # ends of each of the LSTMs in the graph. Then, we recalculate the + # encoder's output vector h_x and feed that into the decoder to sample new outputs. + mode = ModeKeys.PREDICT + + # No grad since we're predicting always, as evidenced by the line above. + with torch.no_grad(): + for node, new_input in new_inputs_dict.items(): + if node not in self.node_data: + self.node_data[node] = RingBuffer(capacity=self.RING_CAPACITY, + dtype=(float, len(self.state[node.type]['position']))) + self.node_data[node].append(new_input) + + if node in self.removed_nodes: + del self.removed_nodes[node] + + # Nodes in self.node_data that aren't in new_inputs_dict were just removed. + newly_removed_nodes = (set(self.node_data.keys()) - set(self.removed_nodes.keys())) - set( + new_inputs_dict.keys()) + + # We update self.removed_nodes with the newly removed nodes as well as all existing removed nodes to get + # the time since their last removal increased by one. + self.removed_nodes.update(newly_removed_nodes | set(self.removed_nodes.keys())) + + # For any nodes that are older than the length of the edge_removal_filter, we can safely clear their data. + self.update_removed_nodes() + + # Any remaining removed nodes that aren't yet old enough for data clearing simply have NaNs appended so + # that when it's passed through the LSTMs, the hidden state keeps propagating but the input plays no role + # (the NaNs get converted to zeros later on). + for node in self.removed_nodes: + self.node_data[node].append(np.full((1, 2), np.nan)) + + for node in self.node_data: + x = self.node_data[node][:, 0] + y = self.node_data[node][:, 1] + vx = derivative_of(x, self.env.scenes[0].dt) + vy = derivative_of(y, self.env.scenes[0].dt) + ax = derivative_of(vx, self.env.scenes[0].dt) + ay = derivative_of(vy, self.env.scenes[0].dt) + new_node_data = np.stack([x, y, vx, vy, ax, ay], axis=-1) + node.overwrite_data(new_node_data, + forward_in_time_on_next_overwrite=(self.node_data[node].shape[0] + == self.RING_CAPACITY)) + + temp_scene_dict = {k: v[:, 0:2] for k, v in self.node_data.items()} + if not temp_scene_dict: + new_scene_graph = SceneGraph(edge_radius=self.env.attention_radius) + else: + new_scene_graph = TemporalSceneGraph.create_from_temp_scene_dict( + temp_scene_dict, + self.env.attention_radius, + duration=self.RING_CAPACITY, + edge_addition_filter=self.hyperparams['edge_addition_filter'], + edge_removal_filter=self.hyperparams['edge_removal_filter'], + online=True).to_scene_graph(t=self.RING_CAPACITY - 1) + + if self.hyperparams['dynamic_edges'] == 'yes': + new_nodes, removed_nodes, new_neighbors, removed_neighbors = new_scene_graph - self.scene_graph + + # Aside from updating the scene graph, this for loop updates the graph model + # structure of all affected nodes. + not_removed_nodes = [node for node in self.nodes if node not in removed_nodes] + self.scene_graph = new_scene_graph + for node in not_removed_nodes: + self.node_models_dict[node].update_graph(new_scene_graph, new_neighbors, removed_neighbors) + + # These next 2 for loops add or remove entire node models. + for node in new_nodes: + if node.is_robot: + continue + + self._add_node_model(node) + self.node_models_dict[node].update_graph(new_scene_graph, new_neighbors, removed_neighbors) + + for node in removed_nodes: + if node.is_robot: + continue + + self._remove_node_model(node) + + # This actually updates the node models with the newly observed data. + if run_models: + inputs = dict() + inputs_st = dict() + inputs_np = dict() + + iter_list = list(self.node_models_dict.keys()) + if self.env.scenes[0].robot is not None: + iter_list.append(self.env.scenes[0].robot) + + for node in iter_list: + input_np = node.get(np.array([node.last_timestep, node.last_timestep]), self.state[node.type]) + + _, std = self.env.get_standardize_params(self.state[node.type.name], node.type) + std[0:2] = self.env.attention_radius[(node.type, node.type)] + rel_state = np.zeros_like(input_np) + rel_state[:, 0:2] = input_np[:, 0:2] + input_st = self.env.standardize(input_np, + self.state[node.type.name], + node.type, + mean=rel_state) + self.rel_states[node] = rel_state + + # Converting NaNs to zeros. + input_np[np.isnan(input_np)] = 0 + input_st[np.isnan(input_st)] = 0 + + # Convert to torch tensors + inputs[node] = torch.tensor(input_np, dtype=torch.float, device=self.device) + inputs_st[node] = torch.tensor(input_st, dtype=torch.float, device=self.device) + inputs_np[node] = input_np + + # We want tensors of shape (1, ph + 1, state_dim) where the first 1 is the batch size. + if (self.hyperparams['incl_robot_node'] + and self.env.scenes[0].robot is not None + and robot_present_and_future is not None): + if len(robot_present_and_future.shape) == 2: + robot_present_and_future = robot_present_and_future[np.newaxis, :] + + assert robot_present_and_future.shape[1] == prediction_horizon + 1 + + for node in self.node_models_dict: + self.node_models_dict[node].encoder_forward(inputs, + inputs_st, + inputs_np, + robot_present_and_future) + + # If num_predicted_timesteps or num_samples == 0 then do not run the decoder at all, + # just update the encoder LSTMs. + if prediction_horizon == 0 or num_samples == 0: + return + + return self.sample_model(prediction_horizon, + num_samples, + robot_present_and_future=robot_present_and_future, + z_mode=z_mode, + gmm_mode=gmm_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) + + def _run_decoder(self, node, + num_predicted_timesteps, + num_samples, + robot_present_and_future=None, + z_mode=False, + gmm_mode=False, + full_dist=False, + all_z_sep=False): + model = self.node_models_dict[node] + predictions = model.decoder_forward(num_predicted_timesteps, + num_samples, + robot_present_and_future=robot_present_and_future, + z_mode=z_mode, + gmm_mode=gmm_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) + + predictions_uns = self.env.unstandardize(predictions.cpu().detach().numpy(), + self.pred_state[node.type.name], + node.type, + mean=self.rel_states[node][..., 0:2]) + + # Return will be of shape (batch_size, num_samples, num_predicted_timesteps, 2) + return np.transpose(predictions_uns, (1, 0, 2, 3)) + + def sample_model(self, num_predicted_timesteps, + num_samples, + robot_present_and_future=None, + z_mode=False, + gmm_mode=False, + full_dist=False, + all_z_sep=False): + # Just start from the encoder output (minus the + # robot future) and get num_samples of + # num_predicted_timesteps-length trajectories. + if num_predicted_timesteps == 0 or num_samples == 0: + return + + mode = ModeKeys.PREDICT + + # We want tensors of shape (1, ph + 1, state_dim) where the first 1 is the batch size. + if self.hyperparams['incl_robot_node'] and self.env.scenes[ + 0].robot is not None and robot_present_and_future is not None: + if len(robot_present_and_future.shape) == 2: + robot_present_and_future = robot_present_and_future[np.newaxis, :] + + assert robot_present_and_future.shape[1] == num_predicted_timesteps + 1 + + # No grad since we're predicting always, as evidenced by the line above. + with torch.no_grad(): + predictions_dict = dict() + for node in set(self.nodes) - set(self.removed_nodes.keys()): + if node.is_robot: + continue + + predictions_dict[node] = self._run_decoder(node, num_predicted_timesteps, + num_samples, + robot_present_and_future, + z_mode, + gmm_mode, + full_dist, + all_z_sep) + + return predictions_dict + + def forward(self, init_env, + init_timestep, + pos_dicts, # After the initial environment + num_predicted_timesteps, + num_samples, + robot_present_and_future=None, + z_mode=False, + gmm_mode=False, + full_dist=False, + all_z_sep=False): + # This is the standard forward prediction function, + # if you have some historical data and just want to + # predict forward some number of timesteps. + + # Setting us back to the initial scene graph we had. + self.set_environment(init_env, init_timestep) + + # Looping through and applying updates to the model. + for i in range(len(pos_dicts)): + self.incremental_forward(pos_dicts[i]) + + return self.sample_model(num_predicted_timesteps, + num_samples, + robot_present_and_future=robot_present_and_future, + z_mode=z_mode, + gmm_mode=gmm_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) diff --git a/trajectron/model/trajectron.py b/trajectron/model/trajectron.py new file mode 100644 index 0000000..1806702 --- /dev/null +++ b/trajectron/model/trajectron.py @@ -0,0 +1,195 @@ +import torch +import numpy as np +from model.mgcvae import MultimodalGenerativeCVAE +from model.dataset import get_timesteps_data, restore + + +class Trajectron(object): + def __init__(self, model_registrar, + hyperparams, log_writer, + device): + super(Trajectron, self).__init__() + self.hyperparams = hyperparams + self.log_writer = log_writer + self.device = device + self.curr_iter = 0 + + self.model_registrar = model_registrar + self.node_models_dict = dict() + self.nodes = set() + + self.env = None + + self.min_ht = self.hyperparams['minimum_history_length'] + self.max_ht = self.hyperparams['maximum_history_length'] + self.ph = self.hyperparams['prediction_horizon'] + self.state = self.hyperparams['state'] + self.state_length = dict() + for state_type in self.state.keys(): + self.state_length[state_type] = int( + np.sum([len(entity_dims) for entity_dims in self.state[state_type].values()]) + ) + self.pred_state = self.hyperparams['pred_state'] + + def set_environment(self, env): + self.env = env + + self.node_models_dict.clear() + edge_types = env.get_edge_types() + + for node_type in env.NodeType: + # Only add a Model for NodeTypes we want to predict + if node_type in self.pred_state.keys(): + self.node_models_dict[node_type] = MultimodalGenerativeCVAE(env, + node_type, + self.model_registrar, + self.hyperparams, + self.device, + edge_types, + log_writer=self.log_writer) + + def set_curr_iter(self, curr_iter): + self.curr_iter = curr_iter + for node_str, model in self.node_models_dict.items(): + model.set_curr_iter(curr_iter) + + def set_annealing_params(self): + for node_str, model in self.node_models_dict.items(): + model.set_annealing_params() + + def step_annealers(self, node_type=None): + if node_type is None: + for node_type in self.node_models_dict: + self.node_models_dict[node_type].step_annealers() + else: + self.node_models_dict[node_type].step_annealers() + + def train_loss(self, batch, node_type): + (first_history_index, + x_t, y_t, x_st_t, y_st_t, + neighbors_data_st, + neighbors_edge_value, + robot_traj_st_t, + map) = batch + + x = x_t.to(self.device) + y = y_t.to(self.device) + x_st_t = x_st_t.to(self.device) + y_st_t = y_st_t.to(self.device) + if robot_traj_st_t is not None: + robot_traj_st_t = robot_traj_st_t.to(self.device) + if type(map) == torch.Tensor: + map = map.to(self.device) + + # Run forward pass + model = self.node_models_dict[node_type] + loss = model.train_loss(inputs=x, + inputs_st=x_st_t, + first_history_indices=first_history_index, + labels=y, + labels_st=y_st_t, + neighbors=restore(neighbors_data_st), + neighbors_edge_value=restore(neighbors_edge_value), + robot=robot_traj_st_t, + map=map, + prediction_horizon=self.ph) + + return loss + + def eval_loss(self, batch, node_type): + (first_history_index, + x_t, y_t, x_st_t, y_st_t, + neighbors_data_st, + neighbors_edge_value, + robot_traj_st_t, + map) = batch + + x = x_t.to(self.device) + y = y_t.to(self.device) + x_st_t = x_st_t.to(self.device) + y_st_t = y_st_t.to(self.device) + if robot_traj_st_t is not None: + robot_traj_st_t = robot_traj_st_t.to(self.device) + if type(map) == torch.Tensor: + map = map.to(self.device) + + # Run forward pass + model = self.node_models_dict[node_type] + nll = model.eval_loss(inputs=x, + inputs_st=x_st_t, + first_history_indices=first_history_index, + labels=y, + labels_st=y_st_t, + neighbors=restore(neighbors_data_st), + neighbors_edge_value=restore(neighbors_edge_value), + robot=robot_traj_st_t, + map=map, + prediction_horizon=self.ph) + + return nll.cpu().detach().numpy() + + def predict(self, + scene, + timesteps, + ph, + num_samples=1, + min_future_timesteps=0, + min_history_timesteps=1, + z_mode=False, + gmm_mode=False, + full_dist=True, + all_z_sep=False): + + predictions_dict = {} + for node_type in self.env.NodeType: + if node_type not in self.pred_state: + continue + + model = self.node_models_dict[node_type] + + # Get Input data for node type and given timesteps + batch = get_timesteps_data(env=self.env, scene=scene, t=timesteps, node_type=node_type, state=self.state, + pred_state=self.pred_state, edge_types=model.edge_types, + min_ht=min_history_timesteps, max_ht=self.max_ht, min_ft=min_future_timesteps, + max_ft=min_future_timesteps, hyperparams=self.hyperparams) + # There are no nodes of type present for timestep + if batch is None: + continue + (first_history_index, + x_t, y_t, x_st_t, y_st_t, + neighbors_data_st, + neighbors_edge_value, + robot_traj_st_t, + map), nodes, timesteps_o = batch + + x = x_t.to(self.device) + x_st_t = x_st_t.to(self.device) + if robot_traj_st_t is not None: + robot_traj_st_t = robot_traj_st_t.to(self.device) + if type(map) == torch.Tensor: + map = map.to(self.device) + + # Run forward pass + predictions = model.predict(inputs=x, + inputs_st=x_st_t, + first_history_indices=first_history_index, + neighbors=neighbors_data_st, + neighbors_edge_value=neighbors_edge_value, + robot=robot_traj_st_t, + map=map, + prediction_horizon=ph, + num_samples=num_samples, + z_mode=z_mode, + gmm_mode=gmm_mode, + full_dist=full_dist, + all_z_sep=all_z_sep) + + predictions_np = predictions.cpu().detach().numpy() + + # Assign predictions to node + for i, ts in enumerate(timesteps_o): + if ts not in predictions_dict.keys(): + predictions_dict[ts] = dict() + predictions_dict[ts][nodes[i]] = np.transpose(predictions_np[:, [i]], (1, 0, 2, 3)) + + return predictions_dict diff --git a/trajectron/test/__init__.py b/trajectron/test/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/trajectron/test/test_data_structures.py b/trajectron/test/test_data_structures.py new file mode 100644 index 0000000..6da777c --- /dev/null +++ b/trajectron/test/test_data_structures.py @@ -0,0 +1,48 @@ +import numpy as np +import pandas as pd +from data import SingleHeaderNumpyArray, DoubleHeaderNumpyArray + + +def test_single_header_numpy_array(): + x = np.random.rand(10) + y = np.random.rand(10) + + array = SingleHeaderNumpyArray(np.stack((x, y), axis=-1), ['x', 'y']) + + assert (array[:, 'x'] == x).all() + assert (array[:, 'y'] == y).all() + assert (array[3:7, 'y'] == y[3:7]).all() + assert (array.x == x).all() + assert (array.y == y).all() + + +def test_double_header_numpy_array(): + x = np.random.rand(10) + y = np.random.rand(10) + vx = np.random.rand(10) + vy = np.random.rand(10) + + data_dict = {('position', 'x'): x, + ('position', 'y'): y, + ('velocity', 'x'): vx, + ('velocity', 'y'): vy} + + data_columns = pd.MultiIndex.from_product([['position', 'velocity'], ['x', 'y']]) + + node_data = pd.DataFrame(data_dict, columns=data_columns) + + array = DoubleHeaderNumpyArray(node_data.values, list(node_data.columns)) + + test_header_dict = {'position': ['x', 'y'], 'velocity': ['y']} + + assert (array[:, ('position', 'x')] == x).all() + assert (array[:, ('velocity', 'y')] == vy).all() + assert (array[4:7, ('velocity', 'y')] == vy[4:7]).all() + assert (array[:, [('position', 'x'), ('velocity', 'y')]] == np.stack((x, vy), axis=-1)).all() + assert (array[:, [('position', 'y'), ('velocity', 'x')]] == np.stack((y, vx), axis=-1)).all() + assert (array[2:6, [('position', 'y'), ('velocity', 'x')]] == np.stack((y, vx), axis=-1)[2:6]).all() + assert (array[:, test_header_dict] == np.stack((x, y, vy), axis=-1)).all() + assert (array[1:8, test_header_dict] == np.stack((x, y, vy), axis=-1)[1:8]).all() + assert (array.position.x == x).all() + + diff --git a/trajectron/test_online.py b/trajectron/test_online.py new file mode 100644 index 0000000..1539368 --- /dev/null +++ b/trajectron/test_online.py @@ -0,0 +1,196 @@ +import os +import time +import json +import torch +import pickle +import random +import pathlib +import evaluation +import numpy as np +import visualization as vis +from argument_parser import args +from model.online.online_trajectron import OnlineTrajectron +from model.model_registrar import ModelRegistrar +from data import Environment, Scene +import matplotlib.pyplot as plt + +if not torch.cuda.is_available() or args.device == 'cpu': + args.device = torch.device('cpu') +else: + if torch.cuda.device_count() == 1: + # If you have CUDA_VISIBLE_DEVICES set, which you should, + # then this will prevent leftover flag arguments from + # messing with the device allocation. + args.device = 'cuda:0' + + args.device = torch.device(args.device) + +if args.eval_device is None: + args.eval_device = 'cpu' + +if args.seed is not None: + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + +def create_online_env(env, hyperparams, scene_idx, init_timestep): + test_scene = env.scenes[scene_idx] + + online_scene = Scene(timesteps=init_timestep + 1, + map=test_scene.map, + dt=test_scene.dt) + online_scene.nodes = test_scene.get_nodes_clipped_at_time( + timesteps=np.arange(init_timestep - hyperparams['maximum_history_length'], + init_timestep + 1), + state=hyperparams['state']) + online_scene.robot = test_scene.robot + online_scene.calculate_scene_graph(attention_radius=env.attention_radius, + edge_addition_filter=hyperparams['edge_addition_filter'], + edge_removal_filter=hyperparams['edge_removal_filter']) + + return Environment(node_type_list=env.node_type_list, + standardization=env.standardization, + scenes=[online_scene], + attention_radius=env.attention_radius, + robot_type=env.robot_type) + + +def main(): + model_dir = os.path.join(args.log_dir, 'models_14_Jan_2020_00_24_21eth_no_rob') + + # Load hyperparameters from json + config_file = os.path.join(model_dir, args.conf) + if not os.path.exists(config_file): + raise ValueError('Config json not found!') + with open(config_file, 'r') as conf_json: + hyperparams = json.load(conf_json) + + # Add hyperparams from arguments + hyperparams['dynamic_edges'] = args.dynamic_edges + hyperparams['edge_state_combine_method'] = args.edge_state_combine_method + hyperparams['edge_influence_combine_method'] = args.edge_influence_combine_method + hyperparams['edge_addition_filter'] = args.edge_addition_filter + hyperparams['edge_removal_filter'] = args.edge_removal_filter + hyperparams['batch_size'] = args.batch_size + hyperparams['k_eval'] = args.k_eval + hyperparams['offline_scene_graph'] = args.offline_scene_graph + hyperparams['incl_robot_node'] = args.incl_robot_node + hyperparams['scene_batch_size'] = args.scene_batch_size + hyperparams['node_resample_train'] = args.node_resample_train + hyperparams['node_resample_eval'] = args.node_resample_eval + hyperparams['scene_resample_train'] = args.scene_resample_train + hyperparams['scene_resample_eval'] = args.scene_resample_eval + hyperparams['scene_resample_viz'] = args.scene_resample_viz + hyperparams['edge_encoding'] = not args.no_edge_encoding + + output_save_dir = os.path.join(model_dir, 'pred_figs') + pathlib.Path(output_save_dir).mkdir(parents=True, exist_ok=True) + + eval_data_path = os.path.join(args.data_dir, args.eval_data_dict) + with open(eval_data_path, 'rb') as f: + eval_env = pickle.load(f, encoding='latin1') + + if eval_env.robot_type is None and hyperparams['incl_robot_node']: + eval_env.robot_type = eval_env.NodeType[0] # TODO: Make more general, allow the user to specify? + for scene in eval_env.scenes: + scene.add_robot_from_nodes(eval_env.robot_type) + + print('Loaded evaluation data from %s' % (eval_data_path,)) + + # Creating a dummy environment with a single scene that contains information about the world. + # When using this code, feel free to use whichever scene index or initial timestep you wish. + scene_idx = 0 + + # You need to have at least acceleration, so you want 2 timesteps of prior data, e.g. [0, 1], + # so that you can immediately start incremental inference from the 3rd timestep onwards. + init_timestep = 1 + + eval_scene = eval_env.scenes[scene_idx] + online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep) + + model_registrar = ModelRegistrar(model_dir, args.eval_device) + model_registrar.load_models(iter_num=1999) + + trajectron = OnlineTrajectron(model_registrar, + hyperparams, + args.eval_device) + + # If you want to see what different robot futures do to the predictions, uncomment this line as well as + # related "... += adjustment" lines below. + # adjustment = np.stack([np.arange(13)/float(i*2.0) for i in range(6, 12)], axis=1) + + # Here's how you'd incrementally run the model, e.g. with streaming data. + trajectron.set_environment(online_env, init_timestep) + + for timestep in range(init_timestep + 1, eval_scene.timesteps): + pos_dict = eval_scene.get_clipped_pos_dict(timestep, hyperparams['state']) + + robot_present_and_future = None + if eval_scene.robot is not None: + robot_present_and_future = eval_scene.robot.get(np.array([timestep, + timestep + hyperparams['prediction_horizon']]), + hyperparams['state'][eval_scene.robot.type], + padding=0.0) + robot_present_and_future = np.stack([robot_present_and_future, robot_present_and_future], axis=0) + # robot_present_and_future += adjustment + + start = time.time() + preds = trajectron.incremental_forward(pos_dict, + prediction_horizon=12, + num_samples=25, + robot_present_and_future=robot_present_and_future) + end = time.time() + print("t=%d: took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (timestep, end - start, + 1. / (end - start), len(trajectron.nodes), + trajectron.scene_graph.get_num_edges())) + + detailed_preds_dict = dict() + for node in eval_scene.nodes: + if node in preds: + detailed_preds_dict[node] = preds[node] + + batch_stats = evaluation.compute_batch_statistics({timestep: detailed_preds_dict}, + eval_scene.dt, + max_hl=hyperparams['maximum_history_length'], + ph=hyperparams['prediction_horizon'], + node_type_enum=online_env.NodeType, + prune_ph_to_future=True) + + evaluation.print_batch_errors([batch_stats], 'eval', timestep) + + fig, ax = plt.subplots() + vis.visualize_prediction(ax, + {timestep: preds}, + eval_scene.dt, + hyperparams['maximum_history_length'], + hyperparams['prediction_horizon']) + + if eval_scene.robot is not None: + robot_for_plotting = eval_scene.robot.get(np.array([timestep, + timestep + hyperparams['prediction_horizon']]), + hyperparams['state'][eval_scene.robot.type]) + # robot_for_plotting += adjustment + + ax.plot(robot_for_plotting[1:, 1], robot_for_plotting[1:, 0], + color='r', + linewidth=1.0, alpha=1.0) + + # Current Node Position + circle = plt.Circle((robot_for_plotting[0, 1], + robot_for_plotting[0, 0]), + 0.3, + facecolor='r', + edgecolor='k', + lw=0.5, + zorder=3) + ax.add_artist(circle) + + fig.savefig(os.path.join(output_save_dir, f'pred_{timestep}.pdf'), dpi=300) + plt.close(fig) + + +if __name__ == '__main__': + main() diff --git a/trajectron/train.py b/trajectron/train.py new file mode 100644 index 0000000..7857f1a --- /dev/null +++ b/trajectron/train.py @@ -0,0 +1,433 @@ +import torch +from torch import nn, optim, utils +import numpy as np +import os +import time +import dill +import json +import random +import pathlib +import warnings +from tqdm import tqdm +import visualization +import evaluation +import matplotlib.pyplot as plt +from argument_parser import args +from model.trajectron import Trajectron +from model.model_registrar import ModelRegistrar +from model.model_utils import cyclical_lr +from model.dataset import EnvironmentDataset, collate +from tensorboardX import SummaryWriter +# torch.autograd.set_detect_anomaly(True) + +if not torch.cuda.is_available() or args.device == 'cpu': + args.device = torch.device('cpu') +else: + if torch.cuda.device_count() == 1: + # If you have CUDA_VISIBLE_DEVICES set, which you should, + # then this will prevent leftover flag arguments from + # messing with the device allocation. + args.device = 'cuda:0' + + args.device = torch.device(args.device) + +if args.eval_device is None: + args.eval_device = torch.device('cpu') + +# This is needed for memory pinning using a DataLoader (otherwise memory is pinned to cuda:0 by default) +torch.cuda.set_device(args.device) + +if args.seed is not None: + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + + +def main(): + # Load hyperparameters from json + if not os.path.exists(args.conf): + print('Config json not found!') + with open(args.conf, 'r', encoding='utf-8') as conf_json: + hyperparams = json.load(conf_json) + + # Add hyperparams from arguments + hyperparams['dynamic_edges'] = args.dynamic_edges + hyperparams['edge_state_combine_method'] = args.edge_state_combine_method + hyperparams['edge_influence_combine_method'] = args.edge_influence_combine_method + hyperparams['edge_addition_filter'] = args.edge_addition_filter + hyperparams['edge_removal_filter'] = args.edge_removal_filter + hyperparams['batch_size'] = args.batch_size + hyperparams['k_eval'] = args.k_eval + hyperparams['offline_scene_graph'] = args.offline_scene_graph + hyperparams['incl_robot_node'] = args.incl_robot_node + hyperparams['node_freq_mult_train'] = args.node_freq_mult_train + hyperparams['node_freq_mult_eval'] = args.node_freq_mult_eval + hyperparams['scene_freq_mult_train'] = args.scene_freq_mult_train + hyperparams['scene_freq_mult_eval'] = args.scene_freq_mult_eval + hyperparams['scene_freq_mult_viz'] = args.scene_freq_mult_viz + hyperparams['edge_encoding'] = not args.no_edge_encoding + hyperparams['use_map_encoding'] = args.map_encoding + hyperparams['augment'] = args.augment + hyperparams['override_attention_radius'] = args.override_attention_radius + + print('-----------------------') + print('| TRAINING PARAMETERS |') + print('-----------------------') + print('| batch_size: %d' % args.batch_size) + print('| device: %s' % args.device) + print('| eval_device: %s' % args.eval_device) + print('| Offline Scene Graph Calculation: %s' % args.offline_scene_graph) + print('| EE state_combine_method: %s' % args.edge_state_combine_method) + print('| EIE scheme: %s' % args.edge_influence_combine_method) + print('| dynamic_edges: %s' % args.dynamic_edges) + print('| robot node: %s' % args.incl_robot_node) + print('| edge_addition_filter: %s' % args.edge_addition_filter) + print('| edge_removal_filter: %s' % args.edge_removal_filter) + print('| MHL: %s' % hyperparams['minimum_history_length']) + print('| PH: %s' % hyperparams['prediction_horizon']) + print('-----------------------') + + log_writer = None + model_dir = None + if not args.debug: + # Create the log and model directiory if they're not present. + model_dir = os.path.join(args.log_dir, + 'models_' + time.strftime('%d_%b_%Y_%H_%M_%S', time.localtime()) + args.log_tag) + pathlib.Path(model_dir).mkdir(parents=True, exist_ok=True) + + # Save config to model directory + with open(os.path.join(model_dir, 'config.json'), 'w') as conf_json: + json.dump(hyperparams, conf_json) + + log_writer = SummaryWriter(log_dir=model_dir) + + # Load training and evaluation environments and scenes + train_scenes = [] + train_data_path = os.path.join(args.data_dir, args.train_data_dict) + with open(train_data_path, 'rb') as f: + train_env = dill.load(f, encoding='latin1') + + for attention_radius_override in args.override_attention_radius: + node_type1, node_type2, attention_radius = attention_radius_override.split(' ') + train_env.attention_radius[(node_type1, node_type2)] = float(attention_radius) + + if train_env.robot_type is None and hyperparams['incl_robot_node']: + train_env.robot_type = train_env.NodeType[0] # TODO: Make more general, allow the user to specify? + for scene in train_env.scenes: + scene.add_robot_from_nodes(train_env.robot_type) + + train_scenes = train_env.scenes + train_scenes_sample_probs = train_env.scenes_freq_mult_prop if args.scene_freq_mult_train else None + + train_dataset = EnvironmentDataset(train_env, + hyperparams['state'], + hyperparams['pred_state'], + scene_freq_mult=hyperparams['scene_freq_mult_train'], + node_freq_mult=hyperparams['node_freq_mult_train'], + hyperparams=hyperparams, + min_history_timesteps=hyperparams['minimum_history_length'], + min_future_timesteps=hyperparams['prediction_horizon'], + return_robot=not args.incl_robot_node) + train_data_loader = dict() + for node_type_data_set in train_dataset: + node_type_dataloader = utils.data.DataLoader(node_type_data_set, + collate_fn=collate, + pin_memory=False if args.device is 'cpu' else True, + batch_size=args.batch_size, + shuffle=True, + num_workers=args.preprocess_workers) + train_data_loader[node_type_data_set.node_type] = node_type_dataloader + + print(f"Loaded training data from {train_data_path}") + + eval_scenes = [] + eval_scenes_sample_probs = None + if args.eval_every is not None: + eval_data_path = os.path.join(args.data_dir, args.eval_data_dict) + with open(eval_data_path, 'rb') as f: + eval_env = dill.load(f, encoding='latin1') + + for attention_radius_override in args.override_attention_radius: + node_type1, node_type2, attention_radius = attention_radius_override.split(' ') + eval_env.attention_radius[(node_type1, node_type2)] = float(attention_radius) + + if eval_env.robot_type is None and hyperparams['incl_robot_node']: + eval_env.robot_type = eval_env.NodeType[0] # TODO: Make more general, allow the user to specify? + for scene in eval_env.scenes: + scene.add_robot_from_nodes(eval_env.robot_type) + + eval_scenes = eval_env.scenes + eval_scenes_sample_probs = eval_env.scenes_freq_mult_prop if args.scene_freq_mult_eval else None + + eval_dataset = EnvironmentDataset(eval_env, + hyperparams['state'], + hyperparams['pred_state'], + scene_freq_mult=hyperparams['scene_freq_mult_eval'], + node_freq_mult=hyperparams['node_freq_mult_eval'], + hyperparams=hyperparams, + min_history_timesteps=hyperparams['minimum_history_length'], + min_future_timesteps=hyperparams['prediction_horizon'], + return_robot=not args.incl_robot_node) + eval_data_loader = dict() + for node_type_data_set in eval_dataset: + node_type_dataloader = utils.data.DataLoader(node_type_data_set, + collate_fn=collate, + pin_memory=False if args.eval_device is 'cpu' else True, + batch_size=args.eval_batch_size, + shuffle=True, + num_workers=args.preprocess_workers) + eval_data_loader[node_type_data_set.node_type] = node_type_dataloader + + print(f"Loaded evaluation data from {eval_data_path}") + + # Offline Calculate Scene Graph + if hyperparams['offline_scene_graph'] == 'yes': + print(f"Offline calculating scene graphs") + for i, scene in enumerate(train_scenes): + scene.calculate_scene_graph(train_env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) + print(f"Created Scene Graph for Training Scene {i}") + + for i, scene in enumerate(eval_scenes): + scene.calculate_scene_graph(eval_env.attention_radius, + hyperparams['edge_addition_filter'], + hyperparams['edge_removal_filter']) + print(f"Created Scene Graph for Evaluation Scene {i}") + + model_registrar = ModelRegistrar(model_dir, args.device) + + trajectron = Trajectron(model_registrar, + hyperparams, + log_writer, + args.device) + + trajectron.set_environment(train_env) + trajectron.set_annealing_params() + print('Created Training Model.') + + eval_trajectron = None + if args.eval_every is not None or args.vis_every is not None: + eval_trajectron = Trajectron(model_registrar, + hyperparams, + log_writer, + args.eval_device) + eval_trajectron.set_environment(eval_env) + eval_trajectron.set_annealing_params() + print('Created Evaluation Model.') + + optimizer = dict() + lr_scheduler = dict() + for node_type in train_env.NodeType: + if node_type not in hyperparams['pred_state']: + continue + optimizer[node_type] = optim.Adam([{'params': model_registrar.get_all_but_name_match('map_encoder').parameters()}, + {'params': model_registrar.get_name_match('map_encoder').parameters(), 'lr':0.0008}], lr=hyperparams['learning_rate']) + # Set Learning Rate + if hyperparams['learning_rate_style'] == 'const': + lr_scheduler[node_type] = optim.lr_scheduler.ExponentialLR(optimizer[node_type], gamma=1.0) + elif hyperparams['learning_rate_style'] == 'exp': + lr_scheduler[node_type] = optim.lr_scheduler.ExponentialLR(optimizer[node_type], + gamma=hyperparams['learning_decay_rate']) + + ################################# + # TRAINING # + ################################# + curr_iter_node_type = {node_type: 0 for node_type in train_data_loader.keys()} + for epoch in range(1, args.train_epochs + 1): + model_registrar.to(args.device) + train_dataset.augment = args.augment + for node_type, data_loader in train_data_loader.items(): + curr_iter = curr_iter_node_type[node_type] + pbar = tqdm(data_loader, ncols=80) + for batch in pbar: + trajectron.set_curr_iter(curr_iter) + trajectron.step_annealers(node_type) + optimizer[node_type].zero_grad() + train_loss = trajectron.train_loss(batch, node_type) + pbar.set_description(f"Epoch {epoch}, {node_type} L: {train_loss.item():.2f}") + train_loss.backward() + # Clipping gradients. + if hyperparams['grad_clip'] is not None: + nn.utils.clip_grad_value_(model_registrar.parameters(), hyperparams['grad_clip']) + optimizer[node_type].step() + + # Stepping forward the learning rate scheduler and annealers. + lr_scheduler[node_type].step() + + if not args.debug: + log_writer.add_scalar(f"{node_type}/train/learning_rate", + lr_scheduler[node_type].get_lr()[0], + curr_iter) + log_writer.add_scalar(f"{node_type}/train/loss", train_loss, curr_iter) + + curr_iter += 1 + curr_iter_node_type[node_type] = curr_iter + train_dataset.augment = False + if args.eval_every is not None or args.vis_every is not None: + eval_trajectron.set_curr_iter(epoch) + + ################################# + # VISUALIZATION # + ################################# + if args.vis_every is not None and not args.debug and epoch % args.vis_every == 0 and epoch > 0: + max_hl = hyperparams['maximum_history_length'] + ph = hyperparams['prediction_horizon'] + with torch.no_grad(): + # Predict random timestep to plot for train data set + if args.scene_freq_mult_viz: + scene = np.random.choice(train_scenes, p=train_scenes_sample_probs) + else: + scene = np.random.choice(train_scenes) + timestep = scene.sample_timesteps(1, min_future_timesteps=ph) + predictions = trajectron.predict(scene, + timestep, + ph, + z_mode=True, + gmm_mode=True, + all_z_sep=False, + full_dist=False) + + # Plot predicted timestep for random scene + fig, ax = plt.subplots(figsize=(10, 10)) + visualization.visualize_prediction(ax, + predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + map=scene.map['VISUALIZATION'] if scene.map is not None else None) + ax.set_title(f"{scene.name}-t: {timestep}") + log_writer.add_figure('train/prediction', fig, epoch) + + model_registrar.to(args.eval_device) + # Predict random timestep to plot for eval data set + if args.scene_freq_mult_viz: + scene = np.random.choice(eval_scenes, p=eval_scenes_sample_probs) + else: + scene = np.random.choice(eval_scenes) + timestep = scene.sample_timesteps(1, min_future_timesteps=ph) + predictions = eval_trajectron.predict(scene, + timestep, + ph, + num_samples=20, + min_future_timesteps=ph, + z_mode=False, + full_dist=False) + + # Plot predicted timestep for random scene + fig, ax = plt.subplots(figsize=(10, 10)) + visualization.visualize_prediction(ax, + predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + map=scene.map['VISUALIZATION'] if scene.map is not None else None) + ax.set_title(f"{scene.name}-t: {timestep}") + log_writer.add_figure('eval/prediction', fig, epoch) + + # Predict random timestep to plot for eval data set + predictions = eval_trajectron.predict(scene, + timestep, + ph, + min_future_timesteps=ph, + z_mode=True, + gmm_mode=True, + all_z_sep=True, + full_dist=False) + + # Plot predicted timestep for random scene + fig, ax = plt.subplots(figsize=(10, 10)) + visualization.visualize_prediction(ax, + predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + map=scene.map['VISUALIZATION'] if scene.map is not None else None) + ax.set_title(f"{scene.name}-t: {timestep}") + log_writer.add_figure('eval/prediction_all_z', fig, epoch) + + ################################# + # EVALUATION # + ################################# + if args.eval_every is not None and not args.debug and epoch % args.eval_every == 0 and epoch > 0: + max_hl = hyperparams['maximum_history_length'] + ph = hyperparams['prediction_horizon'] + model_registrar.to(args.eval_device) + with torch.no_grad(): + # Calculate evaluation loss + for node_type, data_loader in eval_data_loader.items(): + eval_loss = [] + print(f"Starting Evaluation @ epoch {epoch} for node type: {node_type}") + pbar = tqdm(data_loader, ncols=80) + for batch in pbar: + eval_loss_node_type = eval_trajectron.eval_loss(batch, node_type) + pbar.set_description(f"Epoch {epoch}, {node_type} L: {eval_loss_node_type.item():.2f}") + eval_loss.append({node_type: {'nll': [eval_loss_node_type]}}) + del batch + + evaluation.log_batch_errors(eval_loss, + log_writer, + f"{node_type}/eval_loss", + epoch) + + # Predict batch timesteps for evaluation dataset evaluation + eval_batch_errors = [] + for scene in tqdm(eval_scenes, desc='Sample Evaluation', ncols=80): + timesteps = scene.sample_timesteps(args.eval_batch_size) + + predictions = eval_trajectron.predict(scene, + timesteps, + ph, + num_samples=50, + min_future_timesteps=ph, + full_dist=False) + + eval_batch_errors.append(evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + node_type_enum=eval_env.NodeType, + map=scene.map)) + + evaluation.log_batch_errors(eval_batch_errors, + log_writer, + 'eval', + epoch, + bar_plot=['kde'], + box_plot=['ade', 'fde']) + + # Predict maximum likelihood batch timesteps for evaluation dataset evaluation + eval_batch_errors_ml = [] + for scene in tqdm(eval_scenes, desc='MM Evaluation', ncols=80): + timesteps = scene.sample_timesteps(scene.timesteps) + + predictions = eval_trajectron.predict(scene, + timesteps, + ph, + num_samples=1, + min_future_timesteps=ph, + z_mode=True, + gmm_mode=True, + full_dist=False) + + eval_batch_errors_ml.append(evaluation.compute_batch_statistics(predictions, + scene.dt, + max_hl=max_hl, + ph=ph, + map=scene.map, + node_type_enum=eval_env.NodeType, + kde=False)) + + evaluation.log_batch_errors(eval_batch_errors_ml, + log_writer, + 'eval/ml', + epoch) + + if args.save_every is not None and args.debug is False and epoch % args.save_every == 0: + model_registrar.save_models(epoch) + + +if __name__ == '__main__': + main() diff --git a/trajectron/utils/__init__.py b/trajectron/utils/__init__.py new file mode 100644 index 0000000..b50caf0 --- /dev/null +++ b/trajectron/utils/__init__.py @@ -0,0 +1,3 @@ +from .trajectory_utils import prediction_output_to_trajectories +from .matrix_utils import block_diag, tile +from .os_utils import maybe_makedirs \ No newline at end of file diff --git a/trajectron/utils/matrix_utils.py b/trajectron/utils/matrix_utils.py new file mode 100644 index 0000000..87c4efa --- /dev/null +++ b/trajectron/utils/matrix_utils.py @@ -0,0 +1,41 @@ +import numpy as np +import torch + + +def attach_dim(v, n_dim_to_prepend=0, n_dim_to_append=0): + return v.reshape( + torch.Size([1] * n_dim_to_prepend) + + v.shape + + torch.Size([1] * n_dim_to_append)) + + +def block_diag(m): + """ + Make a block diagonal matrix along dim=-3 + EXAMPLE: + block_diag(torch.ones(4,3,2)) + should give a 12 x 8 matrix with blocks of 3 x 2 ones. + Prepend batch dimensions if needed. + You can also give a list of matrices. + :type m: torch.Tensor, list + :rtype: torch.Tensor + """ + if type(m) is list: + m = torch.cat([m1.unsqueeze(-3) for m1 in m], -3) + + d = m.dim() + n = m.shape[-3] + siz0 = m.shape[:-3] + siz1 = m.shape[-2:] + m2 = m.unsqueeze(-2) + eye = attach_dim(torch.eye(n, device=m.device).unsqueeze(-2), d - 3, 1) + return (m2 * eye).reshape(siz0 + torch.Size(torch.tensor(siz1) * n)) + + +def tile(a, dim, n_tile, device='cpu'): + init_dim = a.size(dim) + repeat_idx = [1] * a.dim() + repeat_idx[dim] = n_tile + a = a.repeat(*(repeat_idx)) + order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])).to(device) + return torch.index_select(a, dim, order_index) \ No newline at end of file diff --git a/trajectron/utils/os_utils.py b/trajectron/utils/os_utils.py new file mode 100644 index 0000000..0383426 --- /dev/null +++ b/trajectron/utils/os_utils.py @@ -0,0 +1,16 @@ +import os + + +def maybe_makedirs(path_to_create): + """This function will create a directory, unless it exists already, + at which point the function will return. + The exception handling is necessary as it prevents a race condition + from occurring. + Inputs: + path_to_create - A string path to a directory you'd like created. + """ + try: + os.makedirs(path_to_create) + except OSError: + if not os.path.isdir(path_to_create): + raise \ No newline at end of file diff --git a/code/utils/trajectory_utils.py b/trajectron/utils/trajectory_utils.py similarity index 55% rename from code/utils/trajectory_utils.py rename to trajectron/utils/trajectory_utils.py index 822e8b9..e355822 100644 --- a/code/utils/trajectory_utils.py +++ b/trajectron/utils/trajectory_utils.py @@ -1,17 +1,4 @@ import numpy as np -from scipy.integrate import cumtrapz - - -def integrate(f, dx, F0=0.): - N = f.shape[0] - return F0 + np.hstack((np.zeros((N, 1)), cumtrapz(f, axis=1, dx=dx))) - - -def integrate_trajectory(v, x0, dt): - xd_ = integrate(v[..., 0], dx=dt, F0=x0[0]) - yd_ = integrate(v[..., 1], dx=dt, F0=x0[1]) - integrated = np.stack([xd_, yd_], axis=2) - return integrated def prediction_output_to_trajectories(prediction_output_dict, @@ -19,7 +6,6 @@ def prediction_output_to_trajectories(prediction_output_dict, max_h, ph, map=None, - gmm_agg='mean', prune_ph_to_future=False): prediction_timesteps = prediction_output_dict.keys() @@ -36,29 +22,19 @@ def prediction_output_to_trajectories(prediction_output_dict, for node in prediction_nodes: predictions_output = prediction_output_dict[t][node] position_state = {'position': ['x', 'y']} - velocity_state = {'velocity': ['x', 'y']} - acceleration_state = {'acceleration': ['m']} + history = node.get(np.array([t - max_h, t]), position_state) # History includes current pos history = history[~np.isnan(history.sum(axis=1))] future = node.get(np.array([t + 1, t + ph]), position_state) future = future[~np.isnan(future.sum(axis=1))] - current_pos = node.get(t, position_state)[0] # List with single item - current_vel = node.get(t, velocity_state)[0] # List with single item - - predictions_output = getattr(predictions_output, gmm_agg)(axis=1) - if prune_ph_to_future: - predictions_output = predictions_output[:, :future.shape[0]] - if predictions_output.shape[1] == 0: + predictions_output = predictions_output[:, :, :future.shape[0]] + if predictions_output.shape[2] == 0: continue - vel_broad = np.expand_dims(np.broadcast_to(current_vel, - (predictions_output.shape[0], - current_vel.shape[-1])), axis=-2) - vel = np.concatenate((vel_broad, predictions_output), axis=1) - trajectory = integrate_trajectory(vel, current_pos, dt=dt)[:, 1:] + trajectory = predictions_output if map is None: histories_dict[t][node] = history diff --git a/code/visualization/__init__.py b/trajectron/visualization/__init__.py similarity index 100% rename from code/visualization/__init__.py rename to trajectron/visualization/__init__.py diff --git a/code/visualization/visualization.py b/trajectron/visualization/visualization.py similarity index 52% rename from code/visualization/visualization.py rename to trajectron/visualization/visualization.py index 118a432..50ea2c5 100644 --- a/code/visualization/visualization.py +++ b/trajectron/visualization/visualization.py @@ -1,6 +1,8 @@ from utils import prediction_output_to_trajectories import matplotlib.pyplot as plt import matplotlib.patheffects as pe +import numpy as np +import seaborn as sns def plot_trajectories(ax, @@ -11,7 +13,9 @@ def plot_trajectories(ax, line_width=0.2, edge_width=2, circle_edge_width=0.5, - node_circle_size=0.3): + node_circle_size=0.3, + batch_num=0, + kde=False): cmap = ['k', 'b', 'y', 'g', 'r'] @@ -20,21 +24,32 @@ def plot_trajectories(ax, future = futures_dict[node] predictions = prediction_dict[node] - ax.plot(history[:, 1], history[:, 0], 'k--') + if np.isnan(history[-1]).any(): + continue - for sample_num in range(prediction_dict[node].shape[0]): - ax.plot(predictions[sample_num, :, 1], predictions[sample_num, :, 0], + ax.plot(history[:, 0], history[:, 1], 'k--') + + for sample_num in range(prediction_dict[node].shape[1]): + + if kde and predictions.shape[1] >= 50: + line_alpha = 0.2 + for t in range(predictions.shape[2]): + sns.kdeplot(predictions[batch_num, :, t, 0], predictions[batch_num, :, t, 1], + ax=ax, shade=True, shade_lowest=False, + color=np.random.choice(cmap), alpha=0.8) + + ax.plot(predictions[batch_num, sample_num, :, 0], predictions[batch_num, sample_num, :, 1], color=cmap[node.type.value], linewidth=line_width, alpha=line_alpha) - ax.plot(future[:, 1], - future[:, 0], + ax.plot(future[:, 0], + future[:, 1], 'w--', path_effects=[pe.Stroke(linewidth=edge_width, foreground='k'), pe.Normal()]) # Current Node Position - circle = plt.Circle((history[-1, 1], - history[-1, 0]), + circle = plt.Circle((history[-1, 0], + history[-1, 1]), node_circle_size, facecolor='g', edgecolor='k', @@ -42,34 +57,7 @@ def plot_trajectories(ax, zorder=3) ax.add_artist(circle) - # Robot Node # TODO Robot Node - # if robot_node is not None: - # prefix_earliest_idx = max(0, t_predict - predict_horizon) - # robot_prefix = inputs[robot_node][0, prefix_earliest_idx : t_predict + 1, 0:2].cpu().numpy() - # robot_future = inputs[robot_node][0, t_predict + 1 : min(t_predict + predict_horizon + 1, traj_length), 0:2].cpu().numpy() - # - # prefix_all_zeros = not np.any(robot_prefix) - # future_all_zeros = not np.any(robot_future) - # if not (prefix_all_zeros and future_all_zeros): - # ax.plot(robot_prefix[:, 0], robot_prefix[:, 1], 'k--') - # ax.plot(robot_future[:, 0], robot_future[:, 1], 'w--', - # path_effects=[pe.Stroke(linewidth=edge_width, foreground='k'), pe.Normal()]) - # - # circle = plt.Circle((robot_prefix[-1, 0], - # robot_prefix[-1, 1]), - # node_circle_size, - # facecolor='g', - # edgecolor='k', - # lw=circle_edge_width, - # zorder=3) - # ax.add_artist(circle) - # - # # Radius of influence - # if robot_circle: - # circle = plt.Circle((robot_prefix[-1, 0], robot_prefix[-1, 1]), test_stg.hyperparams['edge_radius'], - # fill=False, color='r', linestyle='--', zorder=3) - # ax.plot([], [], 'r--', label='Edge Radius') - # ax.add_artist(circle) + ax.axis('equal') def visualize_prediction(ax, @@ -97,5 +85,5 @@ def visualize_prediction(ax, futures_dict = futures_dict[ts_key] if map is not None: - ax.imshow(map.fdata, origin='lower', alpha=0.5) + ax.imshow(map.as_image(), origin='lower', alpha=0.5) plot_trajectories(ax, prediction_dict, histories_dict, futures_dict, *kwargs) \ No newline at end of file diff --git a/code/visualization/visualization_utils.py b/trajectron/visualization/visualization_utils.py similarity index 100% rename from code/visualization/visualization_utils.py rename to trajectron/visualization/visualization_utils.py