early training loss as expected

lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py

@@ -1,15 +1,37 @@
 import copy
 from typing import Dict, Tuple, Union
 
+import timm
 import torch
 import torch.nn as nn
 import torchvision
+from robomimic.models.base_nets import SpatialSoftmax
 
 from lerobot.common.policies.diffusion.model.crop_randomizer import CropRandomizer
 from lerobot.common.policies.diffusion.model.module_attr_mixin import ModuleAttrMixin
 from lerobot.common.policies.diffusion.pytorch_utils import replace_submodules
 
 
+class RgbEncoder(nn.Module):
+    """Following `VisualCore` from Robomimic 0.2.0."""
+
+    def __init__(self, input_shape, model_name="resnet18", pretrained=False, num_keypoints=32):
+        """
+        resnet_name: a timm model name.
+        pretrained: whether to use timm pretrained weights.
+        num_keypoints: Number of keypoints for SpatialSoftmax (default value of 32 matches PushT Image).
+        """
+        super().__init__()
+        self.backbone = timm.create_model(model_name, pretrained, num_classes=0, global_pool="")
+        # Figure out the feature map shape.
+        with torch.inference_mode():
+            feat_map_shape = tuple(self.backbone(torch.zeros(size=(1, *input_shape))).shape[1:])
+        self.pool = SpatialSoftmax(feat_map_shape, num_kp=num_keypoints)
+
+    def forward(self, x):
+        return torch.flatten(self.pool(self.backbone(x)), start_dim=1)
+
+
 class MultiImageObsEncoder(ModuleAttrMixin):
     def __init__(
         self,
@@ -101,7 +123,8 @@ class MultiImageObsEncoder(ModuleAttrMixin):
                 if imagenet_norm:
                     # TODO(rcadene): move normalizer to dataset and env
                     this_normalizer = torchvision.transforms.Normalize(
-                        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                        mean=[127.5, 127.5, 127.5],
+                        std=[127.5, 127.5, 127.5],
                     )
 
                 this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)

lerobot/common/policies/diffusion/policy.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 
 from lerobot.common.policies.diffusion.diffusion_unet_image_policy import DiffusionUnetImagePolicy
 from lerobot.common.policies.diffusion.model.lr_scheduler import get_scheduler
-from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder
+from lerobot.common.policies.diffusion.model.multi_image_obs_encoder import MultiImageObsEncoder, RgbEncoder
 
 
 class DiffusionPolicy(nn.Module):
@@ -38,7 +38,7 @@ class DiffusionPolicy(nn.Module):
         self.cfg = cfg
 
         noise_scheduler = hydra.utils.instantiate(cfg_noise_scheduler)
-        rgb_model = hydra.utils.instantiate(cfg_rgb_model)
+        rgb_model = RgbEncoder(input_shape=shape_meta.obs.image.shape, **cfg_rgb_model)
         obs_encoder = MultiImageObsEncoder(
             rgb_model=rgb_model,
             **cfg_obs_encoder,

lerobot/configs/policy/diffusion.yaml

@@ -84,9 +84,9 @@ obs_encoder:
   imagenet_norm: True
 
 rgb_model:
-  _target_: lerobot.common.policies.diffusion.pytorch_utils.get_resnet
+  model_name: resnet18
-  name: resnet18
+  pretrained: false
-  weights: null
+  num_keypoints: 32
 
 ema:
   _target_: lerobot.common.policies.diffusion.model.ema_model.EMAModel

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -573,6 +573,16 @@ files = [
 [package.dependencies]
 six = ">=1.4.0"
 
+[[package]]
+name = "egl-probe"
+version = "1.0.2"
+description = ""
+optional = false
+python-versions = "*"
+files = [
+    {file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
+]
+
 [[package]]
 name = "einops"
 version = "0.7.0"
@@ -769,6 +779,72 @@ files = [
 [package.extras]
 preview = ["glfw-preview"]
 
+[[package]]
+name = "grpcio"
+version = "1.62.1"
+description = "HTTP/2-based RPC framework"
+optional = false
+python-versions = ">=3.7"
+files = [
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+    {file = "grpcio-1.62.1.tar.gz", hash = "sha256:6c455e008fa86d9e9a9d85bb76da4277c0d7d9668a3bfa70dbe86e9f3c759947"},
+]
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.62.1)"]
+
 [[package]]
 name = "gym"
 version = "0.26.2"
@@ -1076,6 +1152,21 @@ files = [
     {file = "llvmlite-0.42.0.tar.gz", hash = "sha256:f92b09243c0cc3f457da8b983f67bd8e1295d0f5b3746c7a1861d7a99403854a"},
 ]
 
+[[package]]
+name = "markdown"
+version = "3.5.2"
+description = "Python implementation of John Gruber's Markdown."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "Markdown-3.5.2-py3-none-any.whl", hash = "sha256:d43323865d89fc0cb9b20c75fc8ad313af307cc087e84b657d9eec768eddeadd"},
+    {file = "Markdown-3.5.2.tar.gz", hash = "sha256:e1ac7b3dc550ee80e602e71c1d168002f062e49f1b11e26a36264dafd4df2ef8"},
+]
+
+[package.extras]
+docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
+testing = ["coverage", "pyyaml"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -2259,6 +2350,30 @@ urllib3 = ">=1.21.1,<3"
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
+[[package]]
+name = "robomimic"
+version = "0.2.0"
+description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
+]
+
+[package.dependencies]
+egl_probe = ">=1.0.1"
+h5py = "*"
+imageio = "*"
+imageio-ffmpeg = "*"
+numpy = ">=1.13.3"
+psutil = "*"
+tensorboard = "*"
+tensorboardX = "*"
+termcolor = "*"
+torch = "*"
+torchvision = "*"
+tqdm = "*"
+
 [[package]]
 name = "safetensors"
 version = "0.4.2"
@@ -2746,6 +2861,55 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tensorboard"
+version = "2.16.2"
+description = "TensorBoard lets you watch Tensors Flow"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
+]
+
+[package.dependencies]
+absl-py = ">=0.4"
+grpcio = ">=1.48.2"
+markdown = ">=2.6.8"
+numpy = ">=1.12.0"
+protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
+setuptools = ">=41.0.0"
+six = ">1.9"
+tensorboard-data-server = ">=0.7.0,<0.8.0"
+werkzeug = ">=1.0.1"
+
+[[package]]
+name = "tensorboard-data-server"
+version = "0.7.2"
+description = "Fast data loading for TensorBoard"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[[package]]
+name = "tensorboardx"
+version = "2.6.2.2"
+description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
+optional = false
+python-versions = "*"
+files = [
+    {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
+    {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
+]
+
+[package.dependencies]
+numpy = "*"
+packaging = "*"
+protobuf = ">=3.20"
+
 [[package]]
 name = "tensordict"
 version = "0.4.0+551331d"
@@ -2802,6 +2966,24 @@ numpy = "*"
 [package.extras]
 all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
 
+[[package]]
+name = "timm"
+version = "0.9.16"
+description = "PyTorch Image Models"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "timm-0.9.16-py3-none-any.whl", hash = "sha256:bf5704014476ab011589d3c14172ee4c901fd18f9110a928019cac5be2945914"},
+    {file = "timm-0.9.16.tar.gz", hash = "sha256:891e54f375d55adf31a71ab0c117761f0e472f9f3971858ecdd1e7376b7071e6"},
+]
+
+[package.dependencies]
+huggingface_hub = "*"
+pyyaml = "*"
+safetensors = "*"
+torch = "*"
+torchvision = "*"
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -3086,6 +3268,23 @@ perf = ["orjson"]
 reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
+[[package]]
+name = "werkzeug"
+version = "3.0.1"
+description = "The comprehensive WSGI web application library."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "werkzeug-3.0.1-py3-none-any.whl", hash = "sha256:90a285dc0e42ad56b34e696398b8122ee4c681833fb35b8334a095d82c56da10"},
+    {file = "werkzeug-3.0.1.tar.gz", hash = "sha256:507e811ecea72b18a404947aded4b3390e1db8f826b494d76550ef45bb3b1dcc"},
+]
+
+[package.dependencies]
+MarkupSafe = ">=2.1.1"
+
+[package.extras]
+watchdog = ["watchdog (>=2.3)"]
+
 [[package]]
 name = "zarr"
 version = "2.17.0"
@@ -3125,4 +3324,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "c4d83579aed1c8c2e54cad7c8ec81b95a09ab8faff74fc9a4cb20bd00e4ddec6"
+content-hash = "adc2cbe447c2ebe4a7273a4a849d725f6df56106e0f6bf178cf798de5d6337e2"

pyproject.toml

@@ -48,6 +48,8 @@ opencv-python = "^4.9.0.80"
 diffusers = "^0.26.3"
 torchvision = "^0.17.1"
 h5py = "^3.10.0"
+robomimic = "0.2.0"
+timm = "^0.9.16"
 
 
 [tool.poetry.group.dev.dependencies]