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making expert data save s, a, sp. making dataloader also load batches thisway. renaming state to ego_state. converting path_x and path_y to single path variable. making number of samples for ray an argument. adjusting metrics, policy, and other functions to be able to handle this

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Arec
2021-08-04 09:45:36 -07:00
parent 7ae01f73a2
commit f9729b0a9d
7 changed files with 89 additions and 70 deletions
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9
src/main.py
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@@ -48,8 +48,8 @@ def main(config, method='bc', train=False, test=False, loc=0, datadir='./expert_
# make policy, train and test datasets, and send to
policy = policy_class(config)
train_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[0,1,2])
cv_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[3])
train_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['train_tracks'])
cv_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['cv_tracks'])
train_fn(config, policy, train_dataset, cv_dataset, filestr, **kwargs)
if test:
@@ -59,11 +59,10 @@ def main(config, method='bc', train=False, test=False, loc=0, datadir='./expert_
policy.eval()
# simulate policy
track = 4
simulate_policy(policy, loc=loc, track=track, filestr=filestr, nframes=kwargs['nframes'], graph=kwargs['graph'])
simulate_policy(policy, loc=loc, track=kwargs['test_tracks'][0], filestr=filestr, nframes=kwargs['nframes'], graph=kwargs['graph'])
# run test metrics
test_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[track])
test_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=kwargs['test_tracks'])
writer = SummaryWriter(filestr)
info = metrics(filestr, test_dataset, policy)
for k, m in info.items():