security_vision/Trajectron-plus-plus
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Logging for output to hunt OOM

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Ruben van de Ven 2024-12-29 20:39:21 +01:00
parent dd64170ac3
commit 95da3d6136
1 changed files with 9 additions and 3 deletions
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12
trajectron/train.py
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@ -1,3 +1,5 @@
import logging
from typing import List
import torch
from torch import nn, optim, utils
import numpy as np
@ -17,6 +19,8 @@ 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 trajectron.environment import Environment, Scene, Node
from tensorboardX import SummaryWriter
# torch.autograd.set_detect_anomaly(True)
@ -134,7 +138,7 @@ def main():
min_future_timesteps=hyperparams['prediction_horizon'],
return_robot=not args.incl_robot_node)
train_data_loader = dict()
print(train_scenes)
logging.debug(f"{train_scenes=}")
for node_type_data_set in train_dataset:
if len(node_type_data_set) == 0:
continue
@ -165,7 +169,7 @@ def main():
for scene in eval_env.scenes:
scene.add_robot_from_nodes(eval_env.robot_type)
eval_scenes = eval_env.scenes
eval_scenes: List[Scene] = 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,
@ -178,6 +182,7 @@ def main():
min_future_timesteps=hyperparams['prediction_horizon'],
return_robot=not args.incl_robot_node)
eval_data_loader = dict()
logging.debug(f"{eval_scenes=}")
for node_type_data_set in eval_dataset:
if len(node_type_data_set) == 0:
continue
@ -387,6 +392,7 @@ def main():
# Predict batch timesteps for evaluation dataset evaluation
eval_batch_errors = []
for scene in tqdm(eval_scenes, desc='Sample Evaluation', ncols=80):
logging.debug(f"{scene}, {scene.timesteps=}, {len(scene.nodes)}")
timesteps = scene.sample_timesteps(args.eval_batch_size)
predictions = eval_trajectron.predict(scene,
@ -413,6 +419,7 @@ def main():
# Predict maximum likelihood batch timesteps for evaluation dataset evaluation
eval_batch_errors_ml = []
for scene in tqdm(eval_scenes, desc='MM Evaluation', ncols=80):
logging.debug(f"{scene}, {scene.timesteps=}, {len(scene.nodes)}")
timesteps = scene.sample_timesteps(scene.timesteps)
predictions = eval_trajectron.predict(scene,
@ -423,7 +430,6 @@ def main():
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,