feat(depth stats): enforcing all depth stats to be in millimeters (default unit) for consistency

src/lerobot/datasets/compute_stats.py

@@ -22,6 +22,7 @@ import numpy as np
 from lerobot.processor import RelativeActionsProcessorStep
 from lerobot.utils.constants import ACTION, OBS_STATE
 
+from .depth_utils import MM_PER_METRE
 from .io_utils import load_image_as_numpy
 
 DEFAULT_QUANTILES = [0.01, 0.10, 0.50, 0.90, 0.99]
@@ -508,8 +509,8 @@ def compute_episode_stats(
     Note:
         For 'image'/'video' features, stats are computed per channel and kept with a
         leading channel axis (e.g. shape (3, 1, 1) for RGB). RGB stats are divided by
-        255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) skip
+        255 to land in [0, 1]; depth maps (features flagged with ``is_depth_map``) are
-        this rescaling and remain in their stored units.
+        instead canonicalized to millimetres regardless of the raw frame unit.
     """
     if quantile_list is None:
         quantile_list = DEFAULT_QUANTILES
@@ -533,9 +534,14 @@ def compute_episode_stats(
         )
 
         if features[key]["dtype"] in ["image", "video"]:
-            normalization_factor = (
+            if (features[key].get("info") or {}).get("is_depth_map", False):
-                255.0 if not (features[key].get("info") or {}).get("is_depth_map", False) else 1.0
+                # Depth stats are canonically stored in millimetres; metre (float) depth is
-            )
+                # scaled up, integer (millimetre) depth is left as-is.
+                normalization_factor = (
+                    1.0 / MM_PER_METRE if np.issubdtype(ep_ft_array.dtype, np.floating) else 1.0
+                )
+            else:
+                normalization_factor = 255.0
             ep_stats[key] = {
                 k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0)
                 for k, v in ep_stats[key].items()

src/lerobot/datasets/depth_utils.py

@@ -39,7 +39,7 @@ from lerobot.configs.video import (
 from .image_writer import squeeze_single_channel
 from .pyav_utils import write_u16_plane
 
-_MM_PER_METRE = 1000.0
+MM_PER_METRE = 1000.0
 _UINT16_MAX = 65535
 
 
@@ -126,12 +126,12 @@ def quantize_depth(
 
     # Convert depth_min, depth_max, and shift to the resolved input unit.
     depth_min_u = (
-        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * _MM_PER_METRE)
+        np.float32(depth_min) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_min * MM_PER_METRE)
     )
     depth_max_u = (
-        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * _MM_PER_METRE)
+        np.float32(depth_max) if resolved_unit == DEPTH_METER_UNIT else np.float32(depth_max * MM_PER_METRE)
     )
-    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * _MM_PER_METRE)
+    shift_u = np.float32(shift) if resolved_unit == DEPTH_METER_UNIT else np.float32(shift * MM_PER_METRE)
 
     # Normalization and quantization is performed in the resolved input unit.
     if use_log:
@@ -236,7 +236,7 @@ def dequantize_depth(
 
         # mm path: round + clamp in float32, skipping the uint16 round-trip
         # when returning a tensor (torch.uint16 is poorly supported).
-        buf.mul_(_MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
+        buf.mul_(MM_PER_METRE).round_().clamp_(0.0, _UINT16_MAX)
         if output_tensor:
             return buf
         return buf.cpu().numpy().astype(np.uint16, copy=False)
@@ -259,7 +259,7 @@ def dequantize_depth(
     if output_unit == DEPTH_METER_UNIT:
         return torch.from_numpy(buf) if output_tensor else buf
 
-    np.multiply(buf, _MM_PER_METRE, out=buf)
+    np.multiply(buf, MM_PER_METRE, out=buf)
     np.rint(buf, out=buf)
     np.clip(buf, 0.0, _UINT16_MAX, out=buf)
     if output_tensor:

src/lerobot/datasets/video_utils.py

@@ -47,7 +47,7 @@ from lerobot.configs import (
 )
 from lerobot.utils.import_utils import get_safe_default_video_backend
 
-from .depth_utils import quantize_depth
+from .depth_utils import MM_PER_METRE, quantize_depth
 from .pyav_utils import get_pix_fmt_channels
 
 logger = logging.getLogger(__name__)
@@ -848,6 +848,9 @@ class _CameraEncoderThread(threading.Thread):
                 # Reshape CHW to (H*W, C) for per-channel stats
                 channels = img_downsampled.shape[0]
                 img_for_stats = img_downsampled.transpose(1, 2, 0).reshape(-1, channels)
+                # Depth stats are canonically stored in millimetres; metre (float) depth is scaled up.
+                if self.is_depth and np.issubdtype(frame_data.dtype, np.floating):
+                    img_for_stats = img_for_stats * MM_PER_METRE
                 stats_tracker.update(img_for_stats)
 
                 frame_count += 1