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@@ -1,3 +1,17 @@
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""This scripts demonstrates how to train Diffusion Policy on the PushT environment.
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Once you have trained a model with this script, you can try to evaluate it on
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@@ -85,7 +99,7 @@ def main():
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done = False
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while not done:
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for batch in dataloader:
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batch = {k: v.to(device, non_blocking=True) for k, v in batch.items()}
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batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
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loss, _ = policy.forward(batch)
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loss.backward()
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optimizer.step()
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