add for rest of policies

AI_POLICY.md

@@ -1,25 +0,0 @@
-# AI Usage Policy
-
-The LeRobot project welcomes contributions from everyone, and we have a few guidelines regarding AI usage to ensure high code quality, clear communication, and a healthy open-source ecosystem:
-
-- **Please disclose significant AI assistance.** If you used AI tools (e.g., Copilot, Claude, Cursor, ChatGPT) to generate a substantial portion of your code or text, let us know in your PR description. Transparency helps us review your changes more effectively.
-- **Own your code (The Human-in-the-Loop).** You must fully understand all the changes you are proposing. If you cannot explain what your AI-assisted code does or how it interacts with LeRobot's broader architecture, please take the time to learn and test it before submitting.
-- **Keep issues and discussions focused.** You are welcome to use AI to help draft issues or PR descriptions, but please review and edit them carefully before posting. AI can often be overly verbose; trimming the noise and getting straight to the point helps our maintainers address your needs faster.
-
-Our core maintainers also use AI tools to aid their workflows, but they do so while bringing deep contextual knowledge of the LeRobot codebase to validate the output. We ask all contributors to apply that same level of rigor.
-
-## Remember the Human Maintainers
-
-Please remember that LeRobot is maintained by a dedicated team of humans.
-
-Every discussion, issue, and pull request is read and reviewed by real people. While AI tools can generate thousands of lines of code in seconds, reviewing that code still takes human time and energy. Submitting unverified or low-effort AI output puts an unfair burden on our maintainers.
-
-Today, the quality of the AI output still heavily depends on the developer driving the tool. We ask that you respect our maintainers' time by thoroughly vetting, testing, and refining your submissions.
-
-## AI is Welcome Here
-
-LeRobot operates at the cutting edge of AI and robotics, and many of our maintainers actively embrace AI coding assistants as valuable productivity tools. We are a pro-AI project!
-
-Our reason for having an AI policy is not an anti-AI stance. Rather, it exists to ensure that AI is used to enhance human contributions, not replace them with unverified noise. It's about how the tools are used, not the tools themselves.
-
-We value the unique human insight you bring to the LeRobot community. Let AI empower your workflow, but always let your own judgment take the wheel.

CONTRIBUTING.md

@@ -2,7 +2,7 @@
 
 Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out, and improving the documentation are immensely valuable.
 
-Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md) and our [AI policy](./AI_POLICY.md).
+Whichever way you choose to contribute, please be mindful to respect our [code of conduct](./CODE_OF_CONDUCT.md).
 
 ## Ways to Contribute
 

examples/backward_compatibility/replay.py

@@ -57,7 +57,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30

pyproject.toml

@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"
 
 [project]
 name = "lerobot"
-version = "0.4.5"
+version = "0.4.4"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }

src/lerobot/configs/default.py

@@ -27,7 +27,7 @@ class DatasetConfig:
     # "dataset_index" into the returned item. The index mapping is made according to the order in which the
     # datasets are provided.
     repo_id: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | None = None
     episodes: list[int] | None = None
     image_transforms: ImageTransformsConfig = field(default_factory=ImageTransformsConfig)

src/lerobot/datasets/lerobot_dataset.py

@@ -664,11 +664,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         for the README).
 
         Args:
-            repo_id (str): This is the repo id that will be used to fetch the dataset.
-            root (Path | None, optional): Local directory where the dataset will be downloaded and
-                stored. If set, all dataset files will be stored directly under this path. If not set, the
-                dataset files will be stored under $HF_LEROBOT_HOME/repo_id (configurable via the
-                HF_LEROBOT_HOME environment variable).
+            repo_id (str): This is the repo id that will be used to fetch the dataset. Locally, the dataset
+                will be stored under root/repo_id.
+            root (Path | None, optional): Local directory to use for downloading/writing files. You can also
+                set the HF_LEROBOT_HOME environment variable to point to a different location. Defaults to
+                '~/.cache/huggingface/lerobot'.
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
             image_transforms (Callable | None, optional): You can pass standard v2 image transforms from

src/lerobot/motors/dynamixel/dynamixel.py

@@ -122,14 +122,12 @@ class DynamixelMotorsBus(SerialMotorsBus):
         port: str,
         motors: dict[str, Motor],
         calibration: dict[str, MotorCalibration] | None = None,
-        protocol_version: int = PROTOCOL_VERSION,
     ):
         super().__init__(port, motors, calibration)
         import dynamixel_sdk as dxl
 
         self.port_handler = dxl.PortHandler(self.port)
-        self.packet_handler = dxl.PacketHandler(protocol_version)
-        print(f"Using protocol version {protocol_version}")
+        self.packet_handler = dxl.PacketHandler(PROTOCOL_VERSION)
         self.sync_reader = dxl.GroupSyncRead(self.port_handler, self.packet_handler, 0, 0)
         self.sync_writer = dxl.GroupSyncWrite(self.port_handler, self.packet_handler, 0, 0)
         self._comm_success = dxl.COMM_SUCCESS

src/lerobot/motors/dynamixel/tables.py

@@ -33,58 +33,6 @@
 # 2. We can change the value of the MyControlTableKey enums without impacting the client code
 
 
-# {data_name: (address, size_byte)}
-# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#control-table
-AX_SERIES_CONTROL_TABLE = {
-    # EEPROM Area
-    "Model_Number": (0, 2),
-    "Firmware_Version": (2, 1),
-    "ID": (3, 1),
-    "Baud_Rate": (4, 1),
-    "Return_Delay_Time": (5, 1),
-    "CW_Angle_Limit": (6, 2),
-    "CCW_Angle_Limit": (8, 2),
-    "Temperature_Limit": (11, 1),
-    "Min_Voltage_Limit": (12, 1),
-    "Max_Voltage_Limit": (13, 1),
-    "Max_Torque": (14, 2),
-    "Status_Return_Level": (16, 1),
-    "Alarm_LED": (17, 1),
-    "Shutdown": (18, 1),
-    # RAM Area
-    "Torque_Enable": (24, 1),
-    "LED": (25, 1),
-    "CW_Compliance_Margin": (26, 1),
-    "CCW_Compliance_Margin": (27, 1),
-    "CW_Compliance_Slope": (28, 1),
-    "CCW_Compliance_Slope": (29, 1),
-    "Goal_Position": (30, 2),
-    "Moving_Speed": (32, 2),
-    "Torque_Limit": (34, 2),
-    "Present_Position": (36, 2),
-    "Present_Speed": (38, 2),
-    "Present_Load": (40, 2),
-    "Present_Voltage": (42, 1),
-    "Present_Temperature": (43, 1),
-    "Registered": (44, 1),
-    "Moving": (46, 1),
-    "Lock": (47, 1),
-    "Punch": (48, 2),
-}
-
-# https://emanual.robotis.com/docs/en/dxl/ax/{MODEL}/#baud-rate4
-AX_SERIES_BAUDRATE_TABLE = {
-    9_600: 207,
-    19_200: 103,
-    57_600: 34,
-    115_200: 16,
-    200_000: 9,
-    250_000: 7,
-    400_000: 4,
-    500_000: 3,
-    1_000_000: 1,
-}
-
 # {data_name: (address, size_byte)}
 # https://emanual.robotis.com/docs/en/dxl/x/{MODEL}/#control-table
 X_SERIES_CONTROL_TABLE = {
@@ -166,14 +114,6 @@ X_SERIES_ENCODINGS_TABLE = {
     "Present_Velocity": X_SERIES_CONTROL_TABLE["Present_Velocity"][1],
 }
 
-# {data_name: size_byte}
-AX_SERIES_ENCODINGS_TABLE = {
-    "Goal_Position": AX_SERIES_CONTROL_TABLE["Goal_Position"][1],
-    "Moving_Speed": AX_SERIES_CONTROL_TABLE["Moving_Speed"][1],
-    "Present_Position": AX_SERIES_CONTROL_TABLE["Present_Position"][1],
-    "Present_Speed": AX_SERIES_CONTROL_TABLE["Present_Speed"][1],
-}
-
 MODEL_ENCODING_TABLE = {
     "x_series": X_SERIES_ENCODINGS_TABLE,
     "xl330-m077": X_SERIES_ENCODINGS_TABLE,
@@ -182,8 +122,6 @@ MODEL_ENCODING_TABLE = {
     "xm430-w350": X_SERIES_ENCODINGS_TABLE,
     "xm540-w270": X_SERIES_ENCODINGS_TABLE,
     "xc430-w150": X_SERIES_ENCODINGS_TABLE,
-    "ax_series": AX_SERIES_ENCODINGS_TABLE,
-    "ax-12a": AX_SERIES_ENCODINGS_TABLE,
 }
 
 # {model: model_resolution}
@@ -196,8 +134,6 @@ MODEL_RESOLUTION = {
     "xm430-w350": 4096,
     "xm540-w270": 4096,
     "xc430-w150": 4096,
-    "ax_series": 1024,
-    "ax-12a": 1024,
 }
 
 # {model: model_number}
@@ -209,7 +145,6 @@ MODEL_NUMBER_TABLE = {
     "xm430-w350": 1020,
     "xm540-w270": 1120,
     "xc430-w150": 1070,
-    "ax-12a": 12,
 }
 
 # {model: available_operating_modes}
@@ -231,8 +166,6 @@ MODEL_CONTROL_TABLE = {
     "xm430-w350": X_SERIES_CONTROL_TABLE,
     "xm540-w270": X_SERIES_CONTROL_TABLE,
     "xc430-w150": X_SERIES_CONTROL_TABLE,
-    "ax_series": AX_SERIES_CONTROL_TABLE,
-    "ax-12a": AX_SERIES_CONTROL_TABLE,
 }
 
 MODEL_BAUDRATE_TABLE = {
@@ -243,8 +176,6 @@ MODEL_BAUDRATE_TABLE = {
     "xm430-w350": X_SERIES_BAUDRATE_TABLE,
     "xm540-w270": X_SERIES_BAUDRATE_TABLE,
     "xc430-w150": X_SERIES_BAUDRATE_TABLE,
-    "ax_series": AX_SERIES_BAUDRATE_TABLE,
-    "ax-12a": AX_SERIES_BAUDRATE_TABLE,
 }
 
 AVAILABLE_BAUDRATES = [

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -55,16 +55,10 @@ class DiffusionConfig(PreTrainedConfig):
         normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
             a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
-        resize_shape: (H, W) shape to resize images to as a preprocessing step for the vision
-            backbone. If None, no resizing is done and the original image resolution is used.
-        crop_ratio: Ratio in (0, 1] used to derive the crop size from resize_shape
-            (crop_h = int(resize_shape[0] * crop_ratio), likewise for width).
-            Set to 1.0 to disable cropping. Only takes effect when resize_shape is not None.
-        crop_shape: (H, W) shape to crop images to. When resize_shape is set and crop_ratio < 1.0,
-            this is computed automatically. Can also be set directly for legacy configs that use
-            crop-only (without resize). If None and no derivation applies, no cropping is done.
-        crop_is_random: Whether the crop should be random at training time (it's always a center
-            crop in eval mode).
+        crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
+            within the image size. If None, no cropping is done.
+        crop_is_random: Whether the crop should be random at training time (it's always a center crop in eval
+            mode).
         pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
@@ -120,9 +114,7 @@ class DiffusionConfig(PreTrainedConfig):
     # Architecture / modeling.
     # Vision backbone.
     vision_backbone: str = "resnet18"
-    resize_shape: tuple[int, int] | None = None
-    crop_ratio: float = 1.0
-    crop_shape: tuple[int, int] | None = None
+    crop_shape: tuple[int, int] | None = (84, 84)
     crop_is_random: bool = True
     pretrained_backbone_weights: str | None = None
     use_group_norm: bool = True
@@ -183,25 +175,6 @@ class DiffusionConfig(PreTrainedConfig):
                 f"Got {self.noise_scheduler_type}."
             )
 
-        if self.resize_shape is not None and (
-            len(self.resize_shape) != 2 or any(d <= 0 for d in self.resize_shape)
-        ):
-            raise ValueError(f"`resize_shape` must be a pair of positive integers. Got {self.resize_shape}.")
-        if not (0 < self.crop_ratio <= 1.0):
-            raise ValueError(f"`crop_ratio` must be in (0, 1]. Got {self.crop_ratio}.")
-
-        if self.resize_shape is not None:
-            if self.crop_ratio < 1.0:
-                self.crop_shape = (
-                    int(self.resize_shape[0] * self.crop_ratio),
-                    int(self.resize_shape[1] * self.crop_ratio),
-                )
-            else:
-                # Explicitly disable cropping for resize+ratio path when crop_ratio == 1.0.
-                self.crop_shape = None
-        if self.crop_shape is not None and (self.crop_shape[0] <= 0 or self.crop_shape[1] <= 0):
-            raise ValueError(f"`crop_shape` must have positive dimensions. Got {self.crop_shape}.")
-
         # Check that the horizon size and U-Net downsampling is compatible.
         # U-Net downsamples by 2 with each stage.
         downsampling_factor = 2 ** len(self.down_dims)
@@ -229,12 +202,13 @@ class DiffusionConfig(PreTrainedConfig):
         if len(self.image_features) == 0 and self.env_state_feature is None:
             raise ValueError("You must provide at least one image or the environment state among the inputs.")
 
-        if self.resize_shape is None and self.crop_shape is not None:
+        if self.crop_shape is not None:
             for key, image_ft in self.image_features.items():
                 if self.crop_shape[0] > image_ft.shape[1] or self.crop_shape[1] > image_ft.shape[2]:
                     raise ValueError(
-                        f"`crop_shape` should fit within the image shapes. Got {self.crop_shape} "
-                        f"for `crop_shape` and {image_ft.shape} for `{key}`."
+                        f"`crop_shape` should fit within the images shapes. Got {self.crop_shape} "
+                        f"for `crop_shape` and {image_ft.shape} for "
+                        f"`{key}`."
                     )
 
         # Check that all input images have the same shape.

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -454,18 +454,12 @@ class DiffusionRgbEncoder(nn.Module):
     def __init__(self, config: DiffusionConfig):
         super().__init__()
         # Set up optional preprocessing.
-        if config.resize_shape is not None:
-            self.resize = torchvision.transforms.Resize(config.resize_shape)
-        else:
-            self.resize = None
-
-        crop_shape = config.crop_shape
-        if crop_shape is not None:
+        if config.crop_shape is not None:
             self.do_crop = True
             # Always use center crop for eval
-            self.center_crop = torchvision.transforms.CenterCrop(crop_shape)
+            self.center_crop = torchvision.transforms.CenterCrop(config.crop_shape)
             if config.crop_is_random:
-                self.maybe_random_crop = torchvision.transforms.RandomCrop(crop_shape)
+                self.maybe_random_crop = torchvision.transforms.RandomCrop(config.crop_shape)
             else:
                 self.maybe_random_crop = self.center_crop
         else:
@@ -491,16 +485,13 @@ class DiffusionRgbEncoder(nn.Module):
 
         # Set up pooling and final layers.
         # Use a dry run to get the feature map shape.
-        # The dummy shape mirrors the runtime preprocessing order: resize -> crop.
+        # The dummy input should take the number of image channels from `config.image_features` and it should
+        # use the height and width from `config.crop_shape` if it is provided, otherwise it should use the
+        # height and width from `config.image_features`.
 
         # Note: we have a check in the config class to make sure all images have the same shape.
         images_shape = next(iter(config.image_features.values())).shape
-        if config.crop_shape is not None:
-            dummy_shape_h_w = config.crop_shape
-        elif config.resize_shape is not None:
-            dummy_shape_h_w = config.resize_shape
-        else:
-            dummy_shape_h_w = images_shape[1:]
+        dummy_shape_h_w = config.crop_shape if config.crop_shape is not None else images_shape[1:]
         dummy_shape = (1, images_shape[0], *dummy_shape_h_w)
         feature_map_shape = get_output_shape(self.backbone, dummy_shape)[1:]
 
@@ -516,10 +507,7 @@ class DiffusionRgbEncoder(nn.Module):
         Returns:
             (B, D) image feature.
         """
-        # Preprocess: resize if configured, then crop if configured.
-
-        if self.resize is not None:
-            x = self.resize(x)
+        # Preprocess: maybe crop (if it was set up in the __init__).
         if self.do_crop:
             if self.training:  # noqa: SIM108
                 x = self.maybe_random_crop(x)

src/lerobot/policies/pi0/modeling_pi0.py

@@ -995,7 +995,14 @@ class PI0Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        model = cls(config, **kwargs)
+        if _transformers_available:
+            from transformers.modeling_utils import no_init_weights
+
+            with no_init_weights():
+                model = cls(config, **kwargs)
+            model.model.paligemma_with_expert.paligemma.tie_weights()
+        else:
+            model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/pi05/modeling_pi05.py

@@ -967,7 +967,14 @@ class PI05Policy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        model = cls(config, **kwargs)
+        if _transformers_available:
+            from transformers.modeling_utils import no_init_weights
+
+            with no_init_weights():
+                model = cls(config, **kwargs)
+            model.model.paligemma_with_expert.paligemma.tie_weights()
+        else:
+            model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -895,7 +895,14 @@ class PI0FastPolicy(PreTrainedPolicy):
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs
-        model = cls(config, **kwargs)
+        if _transformers_available:
+            from transformers.modeling_utils import no_init_weights
+
+            with no_init_weights():
+                model = cls(config, **kwargs)
+            model.model.paligemma_with_expert.paligemma.tie_weights()
+        else:
+            model = cls(config, **kwargs)
 
         # Now manually load and remap the state dict
         try:

src/lerobot/policies/smolvla/configuration_smolvla.py

@@ -106,9 +106,6 @@ class SmolVLAConfig(PreTrainedConfig):
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 
-    compile_model: bool = False  # Whether to use torch.compile for model optimization
-    compile_mode: str = "max-autotune"  # Torch compile mode
-
     def __post_init__(self):
         super().__post_init__()
 

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -593,12 +593,6 @@ class VLAFlowMatching(nn.Module):
         self.prefix_length = self.config.prefix_length
         self.rtc_processor = rtc_processor
 
-        # Compile model if requested
-        if config.compile_model:
-            torch.set_float32_matmul_precision("high")
-            self.sample_actions = torch.compile(self.sample_actions, mode=config.compile_mode)
-            self.forward = torch.compile(self.forward, mode=config.compile_mode)
-
     def _rtc_enabled(self):
         return self.config.rtc_config is not None and self.config.rtc_config.enabled
 

src/lerobot/scripts/lerobot_record.py

@@ -155,7 +155,7 @@ class DatasetRecordConfig:
     repo_id: str
     # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
     single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | Path | None = None
     # Limit the frames per second.
     fps: int = 30

src/lerobot/scripts/lerobot_replay.py

@@ -80,7 +80,7 @@ class DatasetReplayConfig:
     repo_id: str
     # Episode to replay.
     episode: int
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    # Root directory where the dataset will be stored (e.g. 'dataset/path').
     root: str | Path | None = None
     # Limit the frames per second. By default, uses the policy fps.
     fps: int = 30

src/lerobot/scripts/lerobot_train.py

@@ -380,10 +380,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         "dataloading_s": AverageMeter("data_s", ":.3f"),
     }
 
-    # Keep global batch size for logging; MetricsTracker handles world size internally.
+    # Use effective batch size for proper epoch calculation in distributed training
     effective_batch_size = cfg.batch_size * accelerator.num_processes
     train_tracker = MetricsTracker(
-        cfg.batch_size,
+        effective_batch_size,
         dataset.num_frames,
         dataset.num_episodes,
         train_metrics,

src/lerobot/utils/logging_utils.py

@@ -104,10 +104,9 @@ class MetricsTracker:
         self.metrics = metrics
 
         self.steps = initial_step
-        world_size = accelerator.num_processes if accelerator else 1
         # A sample is an (observation,action) pair, where observation and action
         # can be on multiple timestamps. In a batch, we have `batch_size` number of samples.
-        self.samples = self.steps * self._batch_size * world_size
+        self.samples = self.steps * self._batch_size
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
         self.accelerator = accelerator
@@ -133,8 +132,7 @@ class MetricsTracker:
         Updates metrics that depend on 'step' for one step.
         """
         self.steps += 1
-        world_size = self.accelerator.num_processes if self.accelerator else 1
-        self.samples += self._batch_size * world_size
+        self.samples += self._batch_size * (self.accelerator.num_processes if self.accelerator else 1)
         self.episodes = self.samples / self._avg_samples_per_ep
         self.epochs = self.samples / self._num_frames
 

tests/artifacts/policies/pusht_diffusion_/actions.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:54aecbc1af72a4cd5e9261492f5e7601890517516257aacdf2a0ffb3ce281f1b
+oid sha256:19eaaa85f66ba4aa6388dbb83819ffad6ea4363247208f871a8dc385689f6fc8
 size 992

tests/artifacts/policies/pusht_diffusion_/grad_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:88a9c3775a2aa1e90a08850521970070a4fcf0f6b82aab43cd8ccc5cf77e0013
+oid sha256:227296eaeeb54acdc3dae2eb8af3d4d08fb87e245337624447140b1e91cfd002
 size 47424

tests/artifacts/policies/pusht_diffusion_/output_dict.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91a2635e05a75fe187a5081504c5f35ce3417378813fa2deaf9ca4e8200e1819
+oid sha256:271b00cb2f0cd5fd26b1d53463638e3d1a6e92692ec625fcffb420ca190869e5
 size 68

tests/artifacts/policies/pusht_diffusion_/param_stats.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:645bff922ac7bea63ad018ebf77c303c0e4cd2c1c0dc5ef3192865281bef3dc6
+oid sha256:778fddbbaa64248cee35cb377c02cc2b6076f7ce5855146de677128900617ddf
 size 47424

tests/utils/test_logging_utils.py

@@ -24,11 +24,6 @@ def mock_metrics():
     return {"loss": AverageMeter("loss", ":.3f"), "accuracy": AverageMeter("accuracy", ":.2f")}
 
 
-class MockAccelerator:
-    def __init__(self, num_processes: int):
-        self.num_processes = num_processes
-
-
 def test_average_meter_initialization():
     meter = AverageMeter("loss", ":.2f")
     assert meter.name == "loss"
@@ -87,37 +82,6 @@ def test_metrics_tracker_step(mock_metrics):
     assert tracker.epochs == tracker.samples / 1000
 
 
-def test_metrics_tracker_initialization_with_accelerator(mock_metrics):
-    tracker = MetricsTracker(
-        batch_size=32,
-        num_frames=1000,
-        num_episodes=50,
-        metrics=mock_metrics,
-        initial_step=10,
-        accelerator=MockAccelerator(num_processes=2),
-    )
-    assert tracker.steps == 10
-    assert tracker.samples == 10 * 32 * 2
-    assert tracker.episodes == tracker.samples / (1000 / 50)
-    assert tracker.epochs == tracker.samples / 1000
-
-
-def test_metrics_tracker_step_with_accelerator(mock_metrics):
-    tracker = MetricsTracker(
-        batch_size=32,
-        num_frames=1000,
-        num_episodes=50,
-        metrics=mock_metrics,
-        initial_step=5,
-        accelerator=MockAccelerator(num_processes=2),
-    )
-    tracker.step()
-    assert tracker.steps == 6
-    assert tracker.samples == (5 * 32 * 2) + (32 * 2)
-    assert tracker.episodes == tracker.samples / (1000 / 50)
-    assert tracker.epochs == tracker.samples / 1000
-
-
 def test_metrics_tracker_getattr(mock_metrics):
     tracker = MetricsTracker(batch_size=32, num_frames=1000, num_episodes=50, metrics=mock_metrics)
     assert tracker.loss == mock_metrics["loss"]