Merge branch 'user/alexander-soare/train_pusht' into user/alexander-soare/multistep_policy_and_serial_env

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

@@ -1,15 +1,37 @@
 import copy
-from typing import Dict, Tuple, Union
+from typing import Dict, Optional, 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,
@@ -24,7 +46,7 @@ class MultiImageObsEncoder(ModuleAttrMixin):
         share_rgb_model: bool = False,
         # renormalize rgb input with imagenet normalization
         # assuming input in [0,1]
-        imagenet_norm: bool = False,
+        norm_mean_std: Optional[tuple[float, float]] = None,
     ):
         """
         Assumes rgb input: B,C,H,W
@@ -98,10 +120,9 @@ class MultiImageObsEncoder(ModuleAttrMixin):
                         this_normalizer = torchvision.transforms.CenterCrop(size=(h, w))
                 # configure normalizer
                 this_normalizer = nn.Identity()
-                if imagenet_norm:
+                if norm_mean_std is not None:
-                    # 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=norm_mean_std[0], std=norm_mean_std[1]
                     )
 
                 this_transform = nn.Sequential(this_resizer, this_randomizer, this_normalizer)

lerobot/common/policies/diffusion/policy.py

@@ -7,7 +7,7 @@ import torch
 from lerobot.common.policies.abstract import AbstractPolicy
 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(AbstractPolicy):
@@ -38,7 +38,7 @@ class DiffusionPolicy(AbstractPolicy):
         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

@@ -42,8 +42,8 @@ policy:
   num_inference_steps: 100
   obs_as_global_cond: ${obs_as_global_cond}
   # crop_shape: null
-  diffusion_step_embed_dim: 256  # before 128
+  diffusion_step_embed_dim: 128
-  down_dims: [256, 512, 1024]  # before [512, 1024, 2048]
+  down_dims: [512, 1024, 2048]
   kernel_size: 5
   n_groups: 8
   cond_predict_scale: True
@@ -81,12 +81,12 @@ obs_encoder:
   # random_crop: True
   use_group_norm: True
   share_rgb_model: False
-  imagenet_norm: True
+  norm_mean_std: [0.5, 0.5]  # for PushT the original impl normalizes to [-1, 1] (maybe not the case for robomimic envs)
 
 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
@@ -109,13 +109,13 @@ training:
   debug: False
   resume: True
   # optimization
-  # lr_scheduler: cosine
+  lr_scheduler: cosine
-  # lr_warmup_steps: 500
+  lr_warmup_steps: 500
-  num_epochs: 8000
+  num_epochs: 500
   # gradient_accumulate_every: 1
   # EMA destroys performance when used with BatchNorm
   # replace BatchNorm with GroupNorm.
-  # use_ema: True
+  use_ema: True
   freeze_encoder: False
   # training loop control
   # in epochs

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"
@@ -604,6 +604,16 @@ 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"
@@ -763,6 +773,72 @@ 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"
@@ -1192,6 +1268,21 @@ html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
 source = ["Cython (>=3.0.7)"]
 
+[[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"},
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+]
+
+[package.extras]
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+testing = ["coverage", "pyyaml"]
+
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -2387,6 +2478,30 @@ 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"},
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+
+[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"
@@ -2874,6 +2989,55 @@ 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 = [
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+]
+
+[package.dependencies]
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+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 = [
+    {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
+    {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
+]
+
+[[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"
@@ -2930,6 +3094,24 @@ 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"
@@ -3215,6 +3397,23 @@ 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 = "2.17.1"
@@ -3254,4 +3453,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "3d82309a7b2388d774b56ceb6f6906ef0732d8cedda0d76cc84a30e239949be8"
+content-hash = "d7c551c44380ac26e784390f077d902287c40582127db91f9a85ab5d7707ad59"

pyproject.toml

@@ -49,6 +49,8 @@ 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"
 dm-control = "1.0.14"