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Remove warnings (#111)

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- Replace `use_pretrained_backbone` with `pretrained_backbone_weights`
- Bump diffusers' minimum version `0.26.3` -> `0.27.2`
- Add ignore flags in CI's pytest
- Change Box observation spaces in simulation environments
- Set `version_base="1.2"` in Hydra initializations
- Bump einops' minimum version `0.7.0` -> `0.8.0`
This commit is contained in:
Simon Alibert
2024-04-29 00:31:33 +02:00
committed by GitHub
parent 55dc9f7f51
commit 791506dfb8
14 changed files with 52 additions and 36 deletions
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11
.github/workflows/test.yml vendored
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@@ -53,7 +53,14 @@ jobs:
poetry install --all-extras
- name: Test with pytest
run: pytest -v --cov=./lerobot --durations=0 tests
run: |
pytest tests -v --cov=./lerobot --durations=0 \
-W ignore::DeprecationWarning:imageio_ffmpeg._utils:7 \
-W ignore::UserWarning:torch.utils.data.dataloader:558 \
-W ignore::UserWarning:gymnasium.utils.env_checker:247 \
&& rm -rf tests/outputs outputs
- name: Test end-to-end
run: make test-end-to-end
run: |
make test-end-to-end \
&& rm -rf outputs

6
lerobot/common/policies/act/configuration_act.py
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@@ -33,8 +33,8 @@ class ActionChunkingTransformerConfig:
deviation and "min_max" which rescale in a [-1, 1] range.
unnormalize_output_modes: Similar dictionary as `normalize_input_modes`, but to unormalize in original scale.
vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
use_pretrained_backbone: Whether the backbone should be initialized with pretrained weights from
torchvision.
pretrained_backbone_weights: Pretrained weights from torchvision to initalize the backbone.
`None` means no pretrained weights.
replace_final_stride_with_dilation: Whether to replace the ResNet's final 2x2 stride with a dilated
convolution.
pre_norm: Whether to use "pre-norm" in the transformer blocks.
@@ -90,7 +90,7 @@ class ActionChunkingTransformerConfig:
# Architecture.
# Vision backbone.
vision_backbone: str = "resnet18"
use_pretrained_backbone: bool = True
pretrained_backbone_weights: str | None = "ResNet18_Weights.IMAGENET1K_V1"
replace_final_stride_with_dilation: int = False
# Transformer layers.
pre_norm: bool = False

2
lerobot/common/policies/act/modeling_act.py
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@@ -104,7 +104,7 @@ class ActionChunkingTransformerPolicy(nn.Module):
# Backbone for image feature extraction.
backbone_model = getattr(torchvision.models, cfg.vision_backbone)(
replace_stride_with_dilation=[False, False, cfg.replace_final_stride_with_dilation],
pretrained=cfg.use_pretrained_backbone,
weights=cfg.pretrained_backbone_weights,
norm_layer=FrozenBatchNorm2d,
)
# Note: The assumption here is that we are using a ResNet model (and hence layer4 is the final feature

6
lerobot/common/policies/diffusion/configuration_diffusion.py
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@@ -35,8 +35,8 @@ class DiffusionConfig:
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).
use_pretrained_backbone: Whether the backbone should be initialized with pretrained weights from
torchvision.
pretrained_backbone_weights: Pretrained weights from torchvision to initalize the backbone.
`None` means no pretrained weights.
use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
The group sizes are set to be about 16 (to be precise, feature_dim // 16).
spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax.
@@ -96,7 +96,7 @@ class DiffusionConfig:
vision_backbone: str = "resnet18"
crop_shape: tuple[int, int] | None = (84, 84)
crop_is_random: bool = True
use_pretrained_backbone: bool = False
pretrained_backbone_weights: str | None = None
use_group_norm: bool = True
spatial_softmax_num_keypoints: int = 32
# Unet.

4
lerobot/common/policies/diffusion/modeling_diffusion.py
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@@ -378,13 +378,13 @@ class _RgbEncoder(nn.Module):
# Set up backbone.
backbone_model = getattr(torchvision.models, cfg.vision_backbone)(
pretrained=cfg.use_pretrained_backbone
weights=cfg.pretrained_backbone_weights
)
# Note: This assumes that the layer4 feature map is children()[-3]
# TODO(alexander-soare): Use a safer alternative.
self.backbone = nn.Sequential(*(list(backbone_model.children())[:-2]))
if cfg.use_group_norm:
if cfg.use_pretrained_backbone:
if cfg.pretrained_backbone_weights:
raise ValueError(
"You can't replace BatchNorm in a pretrained model without ruining the weights!"
)

12
lerobot/common/utils/io_utils.py Normal file
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@@ -0,0 +1,12 @@
import warnings
import imageio
def write_video(video_path, stacked_frames, fps):
# Filter out DeprecationWarnings raised from pkg_resources
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", "pkg_resources is deprecated as an API", category=DeprecationWarning
)
imageio.mimsave(video_path, stacked_frames, fps=fps)

3
lerobot/common/utils/utils.py
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@@ -92,7 +92,8 @@ def init_hydra_config(config_path: str, overrides: list[str] | None = None) -> D
hydra.core.global_hydra.GlobalHydra.instance().clear()
# Hydra needs a path relative to this file.
hydra.initialize(
str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent))
str(_relative_path_between(Path(config_path).absolute().parent, Path(__file__).absolute().parent)),
version_base="1.2",
)
cfg = hydra.compose(Path(config_path).stem, overrides)
return cfg

2
lerobot/configs/policy/act.yaml
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@@ -45,7 +45,7 @@ policy:
# Architecture.
# Vision backbone.
vision_backbone: resnet18
use_pretrained_backbone: true
pretrained_backbone_weights: ResNet18_Weights.IMAGENET1K_V1
replace_final_stride_with_dilation: false
# Transformer layers.
pre_norm: false

2
lerobot/configs/policy/diffusion.yaml
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@@ -61,7 +61,7 @@ policy:
vision_backbone: resnet18
crop_shape: [84, 84]
crop_is_random: True
use_pretrained_backbone: false
pretrained_backbone_weights: null
use_group_norm: True
spatial_softmax_num_keypoints: 32
# Unet.

6
lerobot/scripts/eval.py
View File

@@ -38,7 +38,6 @@ from pathlib import Path
import einops
import gymnasium as gym
import imageio
import numpy as np
import torch
from datasets import Dataset, Features, Image, Sequence, Value
@@ -51,13 +50,10 @@ from lerobot.common.envs.factory import make_env
from lerobot.common.envs.utils import postprocess_action, preprocess_observation
from lerobot.common.logger import log_output_dir
from lerobot.common.policies.factory import make_policy
from lerobot.common.utils.io_utils import write_video
from lerobot.common.utils.utils import get_safe_torch_device, init_hydra_config, init_logging, set_global_seed
def write_video(video_path, stacked_frames, fps):
imageio.mimsave(video_path, stacked_frames, fps=fps)
def eval_policy(
env: gym.vector.VectorEnv,
policy: torch.nn.Module,

2
lerobot/scripts/train.py
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@@ -22,7 +22,7 @@ from lerobot.common.utils.utils import (
from lerobot.scripts.eval import eval_policy
@hydra.main(version_base=None, config_name="default", config_path="../configs")
@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
def train_cli(cfg: dict):
train(
cfg,

2
lerobot/scripts/visualize_dataset.py
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@@ -16,7 +16,7 @@ MAX_NUM_STEPS = 1000
FIRST_FRAME = 0
@hydra.main(version_base=None, config_name="default", config_path="../configs")
@hydra.main(version_base="1.2", config_name="default", config_path="../configs")
def visualize_dataset_cli(cfg: dict):
visualize_dataset(cfg, out_dir=hydra.core.hydra_config.HydraConfig.get().runtime.output_dir)

26
poetry.lock generated
View File

@@ -597,13 +597,13 @@ files = [
[[package]]
name = "diffusers"
version = "0.26.3"
version = "0.27.2"
description = "State-of-the-art diffusion in PyTorch and JAX."
optional = false
python-versions = ">=3.8.0"
files = [
{file = "diffusers-0.26.3-py3-none-any.whl", hash = "sha256:f8f5710c8f9170e9749f0b104f50fc4a1259f8aff3effed99598409a5ea9b1cd"},
{file = "diffusers-0.26.3.tar.gz", hash = "sha256:e217ea39e85b0bd34fee11f8b39fd00116680b05ff7a70c0b4fdab5351ae4f96"},
{file = "diffusers-0.27.2-py3-none-any.whl", hash = "sha256:85da5cd1098ab428535d592136973ec0c3f12f78148c94b379cb9f02d2414e75"},
{file = "diffusers-0.27.2.tar.gz", hash = "sha256:6cefd7770d7fc1d139614233aa17cdcd639c138d0c3517b8d8bbc8cf573050a0"},
]
[package.dependencies]
@@ -617,12 +617,12 @@ requests = "*"
safetensors = ">=0.3.1"
[package.extras]
dev = ["GitPython (<3.1.19)", "Jinja2", "accelerate (>=0.11.0)", "compel (==0.1.8)", "datasets", "flax (>=0.4.1)", "hf-doc-builder (>=0.3.0)", "invisible-watermark (>=0.2.0)", "isort (>=5.5.4)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "ruff (==0.1.5)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "torch (>=1.4,<2.2.0)", "torchvision (<0.17)", "transformers (>=4.25.1)", "urllib3 (<=2.0.0)"]
dev = ["GitPython (<3.1.19)", "Jinja2", "accelerate (>=0.11.0)", "compel (==0.1.8)", "datasets", "flax (>=0.4.1)", "hf-doc-builder (>=0.3.0)", "invisible-watermark (>=0.2.0)", "isort (>=5.5.4)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "ruff (==0.1.5)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "torch (>=1.4)", "torchvision", "transformers (>=4.25.1)", "urllib3 (<=2.0.0)"]
docs = ["hf-doc-builder (>=0.3.0)"]
flax = ["flax (>=0.4.1)", "jax (>=0.4.1)", "jaxlib (>=0.4.1)"]
quality = ["hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<=2.0.0)"]
test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisible-watermark (>=0.2.0)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "torchvision (<0.17)", "transformers (>=4.25.1)"]
torch = ["accelerate (>=0.11.0)", "torch (>=1.4,<2.2.0)"]
test = ["GitPython (<3.1.19)", "Jinja2", "compel (==0.1.8)", "datasets", "invisible-watermark (>=0.2.0)", "k-diffusion (>=0.0.12)", "librosa", "parameterized", "pytest", "pytest-timeout", "pytest-xdist", "requests-mock (==1.10.0)", "safetensors (>=0.3.1)", "scipy", "sentencepiece (>=0.1.91,!=0.1.92)", "torchvision", "transformers (>=4.25.1)"]
torch = ["accelerate (>=0.11.0)", "torch (>=1.4)"]
training = ["Jinja2", "accelerate (>=0.11.0)", "datasets", "peft (>=0.6.0)", "protobuf (>=3.20.3,<4)", "tensorboard"]
[[package]]
@@ -779,13 +779,13 @@ files = [
[[package]]
name = "einops"
version = "0.7.0"
version = "0.8.0"
description = "A new flavour of deep learning operations"
optional = false
python-versions = ">=3.8"
files = [
{file = "einops-0.7.0-py3-none-any.whl", hash = "sha256:0f3096f26b914f465f6ff3c66f5478f9a5e380bb367ffc6493a68143fbbf1fd1"},
{file = "einops-0.7.0.tar.gz", hash = "sha256:b2b04ad6081a3b227080c9bf5e3ace7160357ff03043cd66cc5b2319eb7031d1"},
{file = "einops-0.8.0-py3-none-any.whl", hash = "sha256:9572fb63046264a862693b0a87088af3bdc8c068fde03de63453cbbde245465f"},
{file = "einops-0.8.0.tar.gz", hash = "sha256:63486517fed345712a8385c100cb279108d9d47e6ae59099b07657e983deae85"},
]
[[package]]
@@ -1121,7 +1121,7 @@ mujoco = "^2.3.7"
type = "git"
url = "git@github.com:huggingface/gym-aloha.git"
reference = "HEAD"
resolved_reference = "c636f05ba0d1760df94537da84c860be1487e17f"
resolved_reference = "12c8171d6708ec6d8547c310fe736d8d494195c5"
[[package]]
name = "gym-pusht"
@@ -1144,7 +1144,7 @@ shapely = "^2.0.3"
type = "git"
url = "git@github.com:huggingface/gym-pusht.git"
reference = "HEAD"
resolved_reference = "080d4ce4d8d3140b2fd204ed628bda14dc58ff06"
resolved_reference = "e0684ff988d223808c0a9dcfaba9dc4991791370"
[[package]]
name = "gym-xarm"
@@ -1164,7 +1164,7 @@ mujoco = "^2.3.7"
type = "git"
url = "git@github.com:huggingface/gym-xarm.git"
reference = "HEAD"
resolved_reference = "27e65c981f9a8d252eca8f157f83508ba6149db7"
resolved_reference = "415811fc34863d349ed113eab77e756726c03525"
[[package]]
name = "gymnasium"
@@ -4299,4 +4299,4 @@ xarm = ["gym-xarm"]
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
content-hash = "8bd1352973c6104e52f50b68f7387d26ced9b07a52e889540b73d132865cda38"
content-hash = "0f72eb92ac8817a46f0659b4d72647a6b76f6e4ba762d11b280f8a88e6cd4371"

4
pyproject.toml
View File

@@ -34,13 +34,13 @@ wandb = "^0.16.3"
imageio = {extras = ["ffmpeg"], version = "^2.34.0"}
gdown = "^5.1.0"
hydra-core = "^1.3.2"
einops = "^0.7.0"
einops = "^0.8.0"
pymunk = "^6.6.0"
zarr = "^2.17.0"
numba = "^0.59.0"
torch = "^2.2.1"
opencv-python = "^4.9.0.80"
diffusers = "^0.26.3"
diffusers = "^0.27.2"
torchvision = "^0.18.0"
h5py = "^3.10.0"
huggingface-hub = "^0.21.4"