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Port SpatialSoftmax and remove Robomimic dependency (#182) 

Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
This commit is contained in:
Akshay Kashyap 2024-05-16 10:34:10 -04:00 committed by GitHub
parent 68c1b13406
commit c9069df9f1
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7 changed files with 75 additions and 179 deletions
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74
lerobot/common/policies/diffusion/modeling_diffusion.py
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@ -17,7 +17,6 @@
"""Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
TODO(alexander-soare):
- Remove reliance on Robomimic for SpatialSoftmax.
- Remove reliance on diffusers for DDPMScheduler and LR scheduler.
- Make compatible with multiple image keys.
"""
@ -27,13 +26,13 @@ from collections import deque
from typing import Callable
import einops
import numpy as np
import torch
import torch.nn.functional as F # noqa: N812
import torchvision
from diffusers.schedulers.scheduling_ddim import DDIMScheduler
from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
from huggingface_hub import PyTorchModelHubMixin
from robomimic.models.base_nets import SpatialSoftmax
from torch import Tensor, nn
from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
@ -312,6 +311,77 @@ class DiffusionModel(nn.Module):
return loss.mean()
class SpatialSoftmax(nn.Module):
"""
Spatial Soft Argmax operation described in "Deep Spatial Autoencoders for Visuomotor Learning" by Finn et al.
(https://arxiv.org/pdf/1509.06113). A minimal port of the robomimic implementation.
At a high level, this takes 2D feature maps (from a convnet/ViT) and returns the "center of mass"
of activations of each channel, i.e., keypoints in the image space for the policy to focus on.
Example: take feature maps of size (512x10x12). We generate a grid of normalized coordinates (10x12x2):
-----------------------------------------------------
| (-1., -1.) | (-0.82, -1.) | ... | (1., -1.) |
| (-1., -0.78) | (-0.82, -0.78) | ... | (1., -0.78) |
| ... | ... | ... | ... |
| (-1., 1.) | (-0.82, 1.) | ... | (1., 1.) |
-----------------------------------------------------
This is achieved by applying channel-wise softmax over the activations (512x120) and computing the dot
product with the coordinates (120x2) to get expected points of maximal activation (512x2).
The example above results in 512 keypoints (corresponding to the 512 input channels). We can optionally
provide num_kp != None to control the number of keypoints. This is achieved by a first applying a learnable
linear mapping (in_channels, H, W) -> (num_kp, H, W).
"""
def __init__(self, input_shape, num_kp=None):
"""
Args:
input_shape (list): (C, H, W) input feature map shape.
num_kp (int): number of keypoints in output. If None, output will have the same number of channels as input.
"""
super().__init__()
assert len(input_shape) == 3
self._in_c, self._in_h, self._in_w = input_shape
if num_kp is not None:
self.nets = torch.nn.Conv2d(self._in_c, num_kp, kernel_size=1)
self._out_c = num_kp
else:
self.nets = None
self._out_c = self._in_c
# we could use torch.linspace directly but that seems to behave slightly differently than numpy
# and causes a small degradation in pc_success of pre-trained models.
pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h))
pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float()
pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float()
# register as buffer so it's moved to the correct device.
self.register_buffer("pos_grid", torch.cat([pos_x, pos_y], dim=1))
def forward(self, features: Tensor) -> Tensor:
"""
Args:
features: (B, C, H, W) input feature maps.
Returns:
(B, K, 2) image-space coordinates of keypoints.
"""
if self.nets is not None:
features = self.nets(features)
# [B, K, H, W] -> [B * K, H * W] where K is number of keypoints
features = features.reshape(-1, self._in_h * self._in_w)
# 2d softmax normalization
attention = F.softmax(features, dim=-1)
# [B * K, H * W] x [H * W, 2] -> [B * K, 2] for spatial coordinate mean in x and y dimensions
expected_xy = attention @ self.pos_grid
# reshape to [B, K, 2]
feature_keypoints = expected_xy.view(-1, self._out_c, 2)
return feature_keypoints
class DiffusionRgbEncoder(nn.Module):
"""Encoder an RGB image into a 1D feature vector.

179
poetry.lock generated
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@ -4,7 +4,7 @@
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version = "2.1.0"
description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
optional = false
optional = true
python-versions = ">=3.7"
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@ -767,16 +767,6 @@ files = [
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six = ">=1.4.0"
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python-versions = "*"
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]
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name = "einops"
version = "0.8.0"
@ -1037,64 +1027,6 @@ files = [
[package.extras]
preview = ["glfw-preview"]
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name = "grpcio"
version = "1.63.0"
description = "HTTP/2-based RPC framework"
optional = false
python-versions = ">=3.8"
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protobuf = ["grpcio-tools (>=1.63.0)"]
[[package]]
name = "gym-aloha"
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@ -1740,21 +1671,6 @@ html5 = ["html5lib"]
htmlsoup = ["BeautifulSoup4"]
source = ["Cython (>=3.0.10)"]
[[package]]
name = "markdown"
version = "3.6"
description = "Python implementation of John Gruber's Markdown."
optional = false
python-versions = ">=3.8"
files = [
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testing = ["coverage", "pyyaml"]
[[package]]
name = "markupsafe"
version = "2.1.5"
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@ -3224,30 +3141,6 @@ typing-extensions = ">=4.5"
[package.extras]
tests = ["pytest (==7.1.2)"]
[[package]]
name = "robomimic"
version = "0.2.0"
description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
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python-versions = ">=3"
files = [
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egl_probe = ">=1.0.1"
h5py = "*"
imageio = "*"
imageio-ffmpeg = "*"
numpy = ">=1.13.3"
psutil = "*"
tensorboard = "*"
tensorboardX = "*"
termcolor = "*"
torch = "*"
torchvision = "*"
tqdm = "*"
[[package]]
name = "safetensors"
version = "0.4.3"
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[[package]]
name = "tensorboard"
version = "2.16.2"
description = "TensorBoard lets you watch Tensors Flow"
optional = false
python-versions = ">=3.9"
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grpcio = ">=1.48.2"
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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 = "termcolor"
version = "2.4.0"
@ -4064,23 +3908,6 @@ perf = ["orjson"]
reports = ["pydantic (>=2.0.0)"]
sweeps = ["sweeps (>=0.2.0)"]
[[package]]
name = "werkzeug"
version = "3.0.3"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
{file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
{file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
]
[package.dependencies]
MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog (>=2.3)"]
[[package]]
name = "xxhash"
version = "3.4.1"
@ -4348,4 +4175,4 @@ xarm = ["gym-xarm"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
content-hash = "2f0d2cbf4a2dec546e25b29b9b108ff1f97b4c278b718360b3f7f6a2bf9dcef8"
content-hash = "e3e3c306a5519e4f716a1ac086ad9b734efedcac077a0ec71e5bc16349a1e559"

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pyproject.toml
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@ -44,7 +44,6 @@ diffusers = "^0.27.2"
torchvision = ">=0.18.0"
h5py = ">=3.10.0"
huggingface-hub = ">=0.21.4"
robomimic = "0.2.0"
gymnasium = ">=0.29.1"
cmake = ">=3.29.0.1"
gym-pusht = { version = ">=0.1.3", optional = true}

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