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authors = ["Ruben van de Ven "] readme = "README.md" +license = "MIT" + +packages = [ + { include = "trajectron" }, +] + +[tool.poetry.scripts] +trajectron_train = "trajectron.train:main" [tool.poetry.dependencies] python = "^3.9,<3.12" @@ -28,8 +36,6 @@ matplotlib = "^3.5" #scikit-learn = "0.22.1" #filterpy = "^1.4.5" tqdm = "^4.65.0" -#ipywidgets = "^8.0.6" -#deep-sort-realtime = "^1.3.2" scipy = "^1.11.3" pandas = "^2.1.1" orjson = "^3.9.7" @@ -41,7 +47,7 @@ notebook = "^7.0.4" scikit-learn = "^1.3.1" seaborn = "^0.13.0" setuptools = "^68.2.2" -tensorboard = "1.14" +tensorboard = "^2.11" tensorboardx = "^2.6.2.2" diff --git a/trajectron/argument_parser.py b/trajectron/argument_parser.py index 3eb5f04..8da1fd9 100644 --- a/trajectron/argument_parser.py +++ b/trajectron/argument_parser.py @@ -4,7 +4,7 @@ parser = argparse.ArgumentParser() parser.add_argument("--conf", help="path to json config file for hyperparameters", type=str, - default='../config/config.json') + default='./config/config.json') parser.add_argument("--debug", help="disable all disk writing processes.", @@ -97,7 +97,7 @@ parser.add_argument('--no_edge_encoding', parser.add_argument("--data_dir", help="what dir to look in for data", type=str, - default='../experiments/processed') + default='./experiments/processed') parser.add_argument("--train_data_dict", help="what file to load for training data", diff --git a/trajectron/evaluation/evaluation.py b/trajectron/evaluation/evaluation.py index 27e79a6..f096630 100644 --- a/trajectron/evaluation/evaluation.py +++ b/trajectron/evaluation/evaluation.py @@ -3,7 +3,7 @@ from scipy.interpolate import RectBivariateSpline from scipy.ndimage import binary_dilation from scipy.stats import gaussian_kde from trajectron.utils import prediction_output_to_trajectories -import trajectron.visualization +import trajectron.visualization as visualization from matplotlib import pyplot as plt diff --git a/trajectron/train.py b/trajectron/train.py index 0e40b5b..a783728 100644 --- a/trajectron/train.py +++ b/trajectron/train.py @@ -9,14 +9,14 @@ import random import pathlib import warnings from tqdm import tqdm -import visualization -import evaluation +import trajectron.visualization as visualization +import trajectron.evaluation as 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 trajectron.argument_parser import args +from trajectron.model.trajectron import Trajectron +from trajectron.model.model_registrar import ModelRegistrar +from trajectron.model.model_utils import cyclical_lr +from trajectron.model.dataset import EnvironmentDataset, collate from tensorboardX import SummaryWriter # torch.autograd.set_detect_anomaly(True)