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

This commit is contained in:
Alexander Soare 2024-03-14 16:06:21 +00:00
parent 4822d63dbe 736bc969ca
commit a222c88c99
5 changed files with 241 additions and 19 deletions
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31
lerobot/common/policies/diffusion/model/multi_image_obs_encoder.py
View File

@ -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:
# TODO(rcadene): move normalizer to dataset and env
if norm_mean_std is not None:
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)

4
lerobot/common/policies/diffusion/policy.py
View File

@ -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,

20
lerobot/configs/policy/diffusion.yaml
View File

@ -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
down_dims: [256, 512, 1024] # before [512, 1024, 2048]
diffusion_step_embed_dim: 128
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
name: resnet18
weights: null
model_name: resnet18
pretrained: false
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_warmup_steps: 500
num_epochs: 8000
lr_scheduler: cosine
lr_warmup_steps: 500
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

203
poetry.lock generated
View File

@ -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"
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]
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name = "einops"
version = "0.7.0"
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preview = ["glfw-preview"]
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@ -1192,6 +1268,21 @@ html5 = ["html5lib"]
htmlsoup = ["BeautifulSoup4"]
source = ["Cython (>=3.0.7)"]
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@ -2387,6 +2478,30 @@ urllib3 = ">=1.21.1,<3"
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files = [
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{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"

2
pyproject.toml
View File

@ -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"