[pre-commit.ci] auto fixes from pre-commit.com hooks
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pre-commit-ci[bot]
committed by
Michel Aractingi
Michel Aractingi
parent
2abbd60a0d
commit
0ea27704f6
@@ -46,9 +46,7 @@ def train_evaluate_multiclass_classifier():
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logging.info(
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f"Start multiclass classifier train eval with {DEVICE} device, batch size {BATCH_SIZE}, learning rate {LR}"
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)
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multiclass_config = ClassifierConfig(
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model_name="microsoft/resnet-18", device=DEVICE, num_classes=10
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)
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multiclass_config = ClassifierConfig(model_name="microsoft/resnet-18", device=DEVICE, num_classes=10)
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multiclass_classifier = Classifier(multiclass_config)
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trainset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
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@@ -119,18 +117,10 @@ def train_evaluate_multiclass_classifier():
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test_probs = torch.stack(test_probs)
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accuracy = Accuracy(task="multiclass", num_classes=multiclass_num_classes)
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precision = Precision(
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task="multiclass", average="weighted", num_classes=multiclass_num_classes
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)
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recall = Recall(
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task="multiclass", average="weighted", num_classes=multiclass_num_classes
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)
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f1 = F1Score(
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task="multiclass", average="weighted", num_classes=multiclass_num_classes
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)
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auroc = AUROC(
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task="multiclass", num_classes=multiclass_num_classes, average="weighted"
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)
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precision = Precision(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
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recall = Recall(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
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f1 = F1Score(task="multiclass", average="weighted", num_classes=multiclass_num_classes)
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auroc = AUROC(task="multiclass", num_classes=multiclass_num_classes, average="weighted")
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# Calculate metrics
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acc = accuracy(test_predictions, test_labels)
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@@ -159,28 +149,18 @@ def train_evaluate_binary_classifier():
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new_label = float(1.0) if label == target_class else float(0.0)
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new_targets.append(new_label)
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dataset.targets = (
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new_targets # Replace the original labels with the binary ones
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)
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dataset.targets = new_targets # Replace the original labels with the binary ones
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return dataset
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binary_train_dataset = CIFAR10(
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root="data", train=True, download=True, transform=ToTensor()
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)
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binary_test_dataset = CIFAR10(
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root="data", train=False, download=True, transform=ToTensor()
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)
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binary_train_dataset = CIFAR10(root="data", train=True, download=True, transform=ToTensor())
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binary_test_dataset = CIFAR10(root="data", train=False, download=True, transform=ToTensor())
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# Apply one-vs-rest labeling
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binary_train_dataset = one_vs_rest(binary_train_dataset, target_binary_class)
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binary_test_dataset = one_vs_rest(binary_test_dataset, target_binary_class)
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binary_trainloader = DataLoader(
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binary_train_dataset, batch_size=BATCH_SIZE, shuffle=True
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)
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binary_testloader = DataLoader(
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binary_test_dataset, batch_size=BATCH_SIZE, shuffle=False
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)
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binary_trainloader = DataLoader(binary_train_dataset, batch_size=BATCH_SIZE, shuffle=True)
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binary_testloader = DataLoader(binary_test_dataset, batch_size=BATCH_SIZE, shuffle=False)
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binary_epoch = 1
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@@ -196,13 +196,9 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
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# Test updating the policy (and test that it does not mutate the batch)
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batch_ = deepcopy(batch)
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policy.forward(batch)
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assert set(batch) == set(batch_), (
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"Batch keys are not the same after a forward pass."
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)
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assert set(batch) == set(batch_), "Batch keys are not the same after a forward pass."
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assert all(
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torch.equal(batch[k], batch_[k])
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if isinstance(batch[k], torch.Tensor)
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else batch[k] == batch_[k]
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torch.equal(batch[k], batch_[k]) if isinstance(batch[k], torch.Tensor) else batch[k] == batch_[k]
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for k in batch
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), "Batch values are not the same after a forward pass."
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@@ -214,9 +210,7 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
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observation = preprocess_observation(observation)
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# send observation to device/gpu
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observation = {
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key: observation[key].to(DEVICE, non_blocking=True) for key in observation
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}
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observation = {key: observation[key].to(DEVICE, non_blocking=True) for key in observation}
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# get the next action for the environment (also check that the observation batch is not modified)
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observation_ = deepcopy(observation)
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@@ -241,12 +235,8 @@ def test_act_backbone_lr():
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cfg = TrainPipelineConfig(
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# TODO(rcadene, aliberts): remove dataset download
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dataset=DatasetConfig(
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repo_id="lerobot/aloha_sim_insertion_scripted", episodes=[0]
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),
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policy=make_policy_config(
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"act", optimizer_lr=0.01, optimizer_lr_backbone=0.001
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),
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dataset=DatasetConfig(repo_id="lerobot/aloha_sim_insertion_scripted", episodes=[0]),
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policy=make_policy_config("act", optimizer_lr=0.01, optimizer_lr_backbone=0.001),
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)
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cfg.validate() # Needed for auto-setting some parameters
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@@ -269,9 +259,7 @@ def test_policy_defaults(dummy_dataset_metadata, policy_name: str):
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policy_cls = get_policy_class(policy_name)
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policy_cfg = make_policy_config(policy_name)
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features = dataset_to_policy_features(dummy_dataset_metadata.features)
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policy_cfg.output_features = {
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key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION
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}
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policy_cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
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policy_cfg.input_features = {
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key: ft for key, ft in features.items() if key not in policy_cfg.output_features
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}
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@@ -283,9 +271,7 @@ def test_save_and_load_pretrained(dummy_dataset_metadata, tmp_path, policy_name:
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policy_cls = get_policy_class(policy_name)
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policy_cfg = make_policy_config(policy_name)
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features = dataset_to_policy_features(dummy_dataset_metadata.features)
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policy_cfg.output_features = {
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key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION
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}
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policy_cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
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policy_cfg.input_features = {
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key: ft for key, ft in features.items() if key not in policy_cfg.output_features
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}
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@@ -294,9 +280,7 @@ def test_save_and_load_pretrained(dummy_dataset_metadata, tmp_path, policy_name:
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save_dir = tmp_path / f"test_save_and_load_pretrained_{policy_cls.__name__}"
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policy.save_pretrained(save_dir)
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loaded_policy = policy_cls.from_pretrained(save_dir, config=policy_cfg)
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torch.testing.assert_close(
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list(policy.parameters()), list(loaded_policy.parameters()), rtol=0, atol=0
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)
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torch.testing.assert_close(list(policy.parameters()), list(loaded_policy.parameters()), rtol=0, atol=0)
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@pytest.mark.parametrize("insert_temporal_dim", [False, True])
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@@ -436,9 +420,7 @@ def test_normalize(insert_temporal_dim):
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# pass if it's run on another platform due to floating point errors
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@require_x86_64_kernel
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@require_cpu
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def test_backward_compatibility(
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ds_repo_id: str, policy_name: str, policy_kwargs: dict, file_name_extra: str
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):
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def test_backward_compatibility(ds_repo_id: str, policy_name: str, policy_kwargs: dict, file_name_extra: str):
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"""
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NOTE: If this test does not pass, and you have intentionally changed something in the policy:
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1. Inspect the differences in policy outputs and make sure you can account for them. Your PR should
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@@ -452,17 +434,13 @@ def test_backward_compatibility(
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6. Remember to stage and commit the resulting changes to `tests/artifacts`.
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"""
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ds_name = ds_repo_id.split("/")[-1]
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artifact_dir = (
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Path("tests/artifacts/policies") / f"{ds_name}_{policy_name}_{file_name_extra}"
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)
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artifact_dir = Path("tests/artifacts/policies") / f"{ds_name}_{policy_name}_{file_name_extra}"
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saved_output_dict = load_file(artifact_dir / "output_dict.safetensors")
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saved_grad_stats = load_file(artifact_dir / "grad_stats.safetensors")
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saved_param_stats = load_file(artifact_dir / "param_stats.safetensors")
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saved_actions = load_file(artifact_dir / "actions.safetensors")
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output_dict, grad_stats, param_stats, actions = get_policy_stats(
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ds_repo_id, policy_name, policy_kwargs
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)
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output_dict, grad_stats, param_stats, actions = get_policy_stats(ds_repo_id, policy_name, policy_kwargs)
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for key in saved_output_dict:
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torch.testing.assert_close(output_dict[key], saved_output_dict[key])
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@@ -471,12 +449,8 @@ def test_backward_compatibility(
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for key in saved_param_stats:
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torch.testing.assert_close(param_stats[key], saved_param_stats[key])
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for key in saved_actions:
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rtol, atol = (
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(2e-3, 5e-6) if policy_name == "diffusion" else (None, None)
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) # HACK
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torch.testing.assert_close(
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actions[key], saved_actions[key], rtol=rtol, atol=atol
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)
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rtol, atol = (2e-3, 5e-6) if policy_name == "diffusion" else (None, None) # HACK
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torch.testing.assert_close(actions[key], saved_actions[key], rtol=rtol, atol=atol)
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def test_act_temporal_ensembler():
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@@ -502,9 +476,7 @@ def test_act_temporal_ensembler():
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batch_size = batch_seq.shape[0]
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# Exponential weighting (normalized). Unsqueeze once to match the position of the `episode_length`
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# dimension of `batch_seq`.
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weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)).unsqueeze(
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-1
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)
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weights = torch.exp(-temporal_ensemble_coeff * torch.arange(chunk_size)).unsqueeze(-1)
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# Simulate stepping through a rollout and computing a batch of actions with model on each step.
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for i in range(episode_length):
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@@ -527,8 +499,7 @@ def test_act_temporal_ensembler():
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episode_step_indices = torch.arange(i + 1)[-len(chunk_indices) :]
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seq_slice = batch_seq[:, episode_step_indices, chunk_indices]
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offline_avg = (
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einops.reduce(seq_slice * weights[: i + 1], "b s 1 -> b 1", "sum")
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/ weights[: i + 1].sum()
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einops.reduce(seq_slice * weights[: i + 1], "b s 1 -> b 1", "sum") / weights[: i + 1].sum()
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)
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# Sanity check. The average should be between the extrema.
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assert torch.all(einops.reduce(seq_slice, "b s 1 -> b 1", "min") <= offline_avg)
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