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Alexander Soare 2024-04-02 19:11:53 +01:00
parent 11cbf1bea1
commit 2b928eedd4
8 changed files with 314 additions and 37 deletions
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26
lerobot/common/datasets/factory.py
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@ -125,6 +125,32 @@ def make_offline_buffer(
# TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std # TODO(rcadene): remove this and put it in config. Ideally we want to reproduce SOTA results just with mean_std
normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max" normalization_mode = "mean_std" if cfg.env.name == "aloha" else "min_max"
# TODO(now): These stats are needed to use their pretrained model for sim_transfer_cube_human.
# (Pdb) stats['observation']['state']['mean']
# tensor([-0.0071, -0.6293, 1.0351, -0.0517, -0.4642, -0.0754, 0.4751, -0.0373,
# -0.3324, 0.9034, -0.2258, -0.3127, -0.2412, 0.6866])
stats['observation', 'state', 'mean'] = torch.tensor([-0.00740268, -0.63187766, 1.0356655 , -0.05027218, -0.46199223,
-0.07467502, 0.47467607, -0.03615446, -0.33203387, 0.9038929 ,
-0.22060776, -0.31011587, -0.23484458, 0.6842416 ])
# (Pdb) stats['observation']['state']['std']
# tensor([0.0022, 0.0520, 0.0291, 0.0092, 0.0267, 0.0145, 0.0563, 0.0179, 0.0494,
# 0.0326, 0.0476, 0.0535, 0.0956, 0.0513])
stats['observation', 'state', 'std'] = torch.tensor([0.01219023, 0.2975381 , 0.16728032, 0.04733803, 0.1486037 ,
0.08788499, 0.31752336, 0.1049916 , 0.27933604, 0.18094037,
0.26604933, 0.30466506, 0.5298686 , 0.25505227])
# (Pdb) stats['action']['mean']
# tensor([-0.0075, -0.6346, 1.0353, -0.0465, -0.4686, -0.0738, 0.3723, -0.0396,
# -0.3184, 0.8991, -0.2065, -0.3182, -0.2338, 0.5593])
stats['action']['mean'] = torch.tensor([-0.00756444, -0.6281845 , 1.0312834 , -0.04664314, -0.47211358,
-0.074527 , 0.37389806, -0.03718753, -0.3261143 , 0.8997205 ,
-0.21371077, -0.31840396, -0.23360962, 0.551947])
# (Pdb) stats['action']['std']
# tensor([0.0023, 0.0514, 0.0290, 0.0086, 0.0263, 0.0143, 0.0593, 0.0185, 0.0510,
# 0.0328, 0.0478, 0.0531, 0.0945, 0.0794])
stats['action']['std'] = torch.tensor([0.01252818, 0.2957442 , 0.16701928, 0.04584508, 0.14833844,
0.08763024, 0.30665937, 0.10600077, 0.27572668, 0.1805853 ,
0.26304692, 0.30708534, 0.5305411 , 0.38381037])
transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode)) transforms.append(NormalizeTransform(stats, in_keys, mode=normalization_mode))
offline_buffer.set_transform(transforms) offline_buffer.set_transform(transforms)

79
lerobot/common/policies/act/detr_vae.py
View File

@ -2,6 +2,7 @@ import numpy as np
import torch import torch
from torch import nn from torch import nn
from torch.autograd import Variable from torch.autograd import Variable
from transformers import DetrForObjectDetection
from .backbone import build_backbone from .backbone import build_backbone
from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer from .transformer import TransformerEncoder, TransformerEncoderLayer, build_transformer
@ -24,31 +25,57 @@ def get_sinusoid_encoding_table(n_position, d_hid):
return torch.FloatTensor(sinusoid_table).unsqueeze(0) return torch.FloatTensor(sinusoid_table).unsqueeze(0)
class DETRVAE(nn.Module): class ActionChunkingTransformer(nn.Module):
"""This is the DETR module that performs object detection""" """
Action Chunking Transformer as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
(https://arxiv.org/abs/2304.13705).
Note: In this code we use the symbols `vae_encoder`, 'encoder', `decoder`. The meanings are as follows.
- The `vae_encoder` is, as per the literature around conditional variational auto-encoders (cVAE), the
part of the model that encodes the target data (here, a sequence of actions), and the condition
(here, we include the robot joint-space state as an input to the encoder).
- The `transformer` is the cVAE's decoder. But since we have an option to train this model without the
variational objective (in which case we drop the `vae_encoder` altogether), we don't call it the
`vae_decoder`.
# TODO(now): remove the following
- The `encoder` is actually a component of the cVAE's "decoder". But we refer to it as an "encoder"
because, in terms of the transformer with cross-attention that forms the cVAE's decoder, it is the
"encoder" part. We drop the `vae_` prefix because we have an option to train this model without the
variational objective (in which case we drop the `vae_encoder` altogether), and nothing about this
model has anything to do with a VAE).
- The `decoder` is a building block of the VAE decoder, and is just the "decoder" part of a
transformer with cross-attention. For the same reasoning behind the naming of `encoder`, we make
this term agnostic to the option to use a variational objective for training.
"""
def __init__( def __init__(
self, backbones, transformer, encoder, state_dim, action_dim, num_queries, camera_names, vae self, backbones, transformer, vae_encoder, state_dim, action_dim, horizon, camera_names, vae
): ):
"""Initializes the model. """Initializes the model.
Parameters: Parameters:
backbones: torch module of the backbone to be used. See backbone.py backbones: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py transformer: torch module of the transformer architecture. See transformer.py
state_dim: robot state dimension of the environment state_dim: robot state dimension of the environment
num_queries: number of object queries, ie detection slot. This is the maximal number of objects horizon: number of object queries, ie detection slot. This is the maximal number of objects
DETR can detect in a single image. For COCO, we recommend 100 queries. DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
Args:
state_dim: Robot positional state dimension.
action_dim: Action dimension.
horizon: The number of actions to generate in one forward pass.
vae: Whether to use the variational objective. TODO(now): Give more details.
""" """
super().__init__() super().__init__()
self.num_queries = num_queries
self.camera_names = camera_names self.camera_names = camera_names
self.transformer = transformer self.transformer = transformer
self.encoder = encoder self.vae_encoder = vae_encoder
self.vae = vae self.vae = vae
hidden_dim = transformer.d_model hidden_dim = transformer.d_model
self.action_head = nn.Linear(hidden_dim, action_dim) self.action_head = nn.Linear(hidden_dim, action_dim)
self.is_pad_head = nn.Linear(hidden_dim, 1) self.is_pad_head = nn.Linear(hidden_dim, 1)
self.query_embed = nn.Embedding(num_queries, hidden_dim) # Positional embedding to be used as input to the latent vae_encoder (if applicable) and for the
self.pos_embed = nn.Embedding(horizon, hidden_dim)
if backbones is not None: if backbones is not None:
self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1) self.input_proj = nn.Conv2d(backbones[0].num_channels, hidden_dim, kernel_size=1)
self.backbones = nn.ModuleList(backbones) self.backbones = nn.ModuleList(backbones)
@ -61,16 +88,16 @@ class DETRVAE(nn.Module):
self.pos = torch.nn.Embedding(2, hidden_dim) self.pos = torch.nn.Embedding(2, hidden_dim)
self.backbones = None self.backbones = None
# encoder extra parameters # vae_encoder extra parameters
self.latent_dim = 32 # final size of latent z # TODO tune self.latent_dim = 32 # final size of latent z # TODO tune
self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding self.cls_embed = nn.Embedding(1, hidden_dim) # extra cls token embedding
self.encoder_action_proj = nn.Linear(14, hidden_dim) # project action to embedding self.vae_encoder_action_proj = nn.Linear(14, hidden_dim) # project action to embedding
self.encoder_joint_proj = nn.Linear(14, hidden_dim) # project qpos to embedding self.vae_encoder_joint_proj = nn.Linear(14, hidden_dim) # project qpos to embedding
self.latent_proj = nn.Linear( self.latent_proj = nn.Linear(
hidden_dim, self.latent_dim * 2 hidden_dim, self.latent_dim * 2
) # project hidden state to latent std, var ) # project hidden state to latent std, var
self.register_buffer( self.register_buffer(
"pos_table", get_sinusoid_encoding_table(1 + 1 + num_queries, hidden_dim) "pos_table", get_sinusoid_encoding_table(1 + 1 + horizon, hidden_dim)
) # [CLS], qpos, a_seq ) # [CLS], qpos, a_seq
# decoder extra parameters # decoder extra parameters
@ -91,15 +118,15 @@ class DETRVAE(nn.Module):
### Obtain latent z from action sequence ### Obtain latent z from action sequence
if self.vae and is_training: if self.vae and is_training:
# project action sequence to embedding dim, and concat with a CLS token # project action sequence to embedding dim, and concat with a CLS token
action_embed = self.encoder_action_proj(actions) # (bs, seq, hidden_dim) action_embed = self.vae_encoder_action_proj(actions) # (bs, seq, hidden_dim)
qpos_embed = self.encoder_joint_proj(qpos) # (bs, hidden_dim) qpos_embed = self.vae_encoder_joint_proj(qpos) # (bs, hidden_dim)
qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim) qpos_embed = torch.unsqueeze(qpos_embed, axis=1) # (bs, 1, hidden_dim)
cls_embed = self.cls_embed.weight # (1, hidden_dim) cls_embed = self.cls_embed.weight # (1, hidden_dim)
cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim) cls_embed = torch.unsqueeze(cls_embed, axis=0).repeat(bs, 1, 1) # (bs, 1, hidden_dim)
encoder_input = torch.cat( vae_encoder_input = torch.cat(
[cls_embed, qpos_embed, action_embed], axis=1 [cls_embed, qpos_embed, action_embed], axis=1
) # (bs, seq+1, hidden_dim) ) # (bs, seq+1, hidden_dim)
encoder_input = encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim) vae_encoder_input = vae_encoder_input.permute(1, 0, 2) # (seq+1, bs, hidden_dim)
# do not mask cls token # do not mask cls token
# cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding # cls_joint_is_pad = torch.full((bs, 2), False).to(qpos.device) # False: not a padding
# is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1) # is_pad = torch.cat([cls_joint_is_pad, is_pad], axis=1) # (bs, seq+1)
@ -107,9 +134,9 @@ class DETRVAE(nn.Module):
pos_embed = self.pos_table.clone().detach() pos_embed = self.pos_table.clone().detach()
pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim) pos_embed = pos_embed.permute(1, 0, 2) # (seq+1, 1, hidden_dim)
# query model # query model
encoder_output = self.encoder(encoder_input, pos=pos_embed) # , src_key_padding_mask=is_pad) vae_encoder_output = self.vae_encoder(vae_encoder_input, pos=pos_embed) # , src_key_padding_mask=is_pad)
encoder_output = encoder_output[0] # take cls output only vae_encoder_output = vae_encoder_output[0] # take cls output only
latent_info = self.latent_proj(encoder_output) latent_info = self.latent_proj(vae_encoder_output)
mu = latent_info[:, : self.latent_dim] mu = latent_info[:, : self.latent_dim]
logvar = latent_info[:, self.latent_dim :] logvar = latent_info[:, self.latent_dim :]
latent_sample = reparametrize(mu, logvar) latent_sample = reparametrize(mu, logvar)
@ -137,7 +164,7 @@ class DETRVAE(nn.Module):
hs = self.transformer( hs = self.transformer(
src, src,
None, None,
self.query_embed.weight, self.pos_embed.weight,
pos, pos,
latent_input, latent_input,
proprio_input, proprio_input,
@ -147,7 +174,7 @@ class DETRVAE(nn.Module):
qpos = self.input_proj_robot_state(qpos) qpos = self.input_proj_robot_state(qpos)
env_state = self.input_proj_env_state(env_state) env_state = self.input_proj_env_state(env_state)
transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2 transformer_input = torch.cat([qpos, env_state], axis=1) # seq length = 2
hs = self.transformer(transformer_input, None, self.query_embed.weight, self.pos.weight)[0] hs = self.transformer(transformer_input, None, self.pos_embed.weight, self.pos.weight)[0]
a_hat = self.action_head(hs) a_hat = self.action_head(hs)
is_pad_hat = self.is_pad_head(hs) is_pad_hat = self.is_pad_head(hs)
return a_hat, is_pad_hat, [mu, logvar] return a_hat, is_pad_hat, [mu, logvar]
@ -165,7 +192,7 @@ def mlp(input_dim, hidden_dim, output_dim, hidden_depth):
return trunk return trunk
def build_encoder(args): def build_vae_encoder(args):
d_model = args.hidden_dim # 256 d_model = args.hidden_dim # 256
dropout = args.dropout # 0.1 dropout = args.dropout # 0.1
nhead = args.nheads # 8 nhead = args.nheads # 8
@ -193,15 +220,15 @@ def build(args):
transformer = build_transformer(args) transformer = build_transformer(args)
encoder = build_encoder(args) vae_encoder = build_vae_encoder(args)
model = DETRVAE( model = ActionChunkingTransformer(
backbones, backbones,
transformer, transformer,
encoder, vae_encoder,
state_dim=args.state_dim, state_dim=args.state_dim,
action_dim=args.action_dim, action_dim=args.action_dim,
num_queries=args.num_queries, horizon=args.num_queries,
camera_names=args.camera_names, camera_names=args.camera_names,
vae=args.vae, vae=args.vae,
) )

40
lerobot/common/policies/act/policy.py
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@ -42,9 +42,28 @@ def kl_divergence(mu, logvar):
class ActionChunkingTransformerPolicy(AbstractPolicy): class ActionChunkingTransformerPolicy(AbstractPolicy):
"""
Action Chunking Transformer as per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
(https://arxiv.org/abs/2304.13705).
"""
name = "act" name = "act"
def __init__(self, cfg, device, n_action_steps=1): def __init__(self, cfg, device, n_action_steps=1):
"""
Args:
vae: Whether to use the variational objective. TODO(now): Give more details.
temporal_agg: Whether to do temporal aggregation. For each timestep during rollout, the action
returned as an exponential moving average of previously generated actions for that timestep.
n_obs_steps: Number of time steps worth of observation to use as input.
horizon: The number of actions to generate in one forward pass.
kl_weight: Weight for KL divergence. Defaults to None. Only applicable when using the variational
objective.
batch_size: Training batch size.
grad_clip_norm: Optionally clip the gradients to have this value as the norm at most. Defaults to
None meaning gradient clipping is not applied.
lr: Learning rate.
"""
super().__init__(n_action_steps) super().__init__(n_action_steps)
self.cfg = cfg self.cfg = cfg
self.n_action_steps = n_action_steps self.n_action_steps = n_action_steps
@ -57,8 +76,6 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
def update(self, replay_buffer, step): def update(self, replay_buffer, step):
del step del step
start_time = time.time()
self.train() self.train()
num_slices = self.cfg.batch_size num_slices = self.cfg.batch_size
@ -104,10 +121,13 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
} }
return out return out
start_time = time.time()
batch = replay_buffer.sample(batch_size) batch = replay_buffer.sample(batch_size)
batch = process_batch(batch, self.cfg.horizon, num_slices) batch = process_batch(batch, self.cfg.horizon, num_slices)
data_s = time.time() - start_time data_s = time.time() - start_time
print(data_s)
loss = self.compute_loss(batch) loss = self.compute_loss(batch)
loss.backward() loss.backward()
@ -151,9 +171,6 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
@torch.no_grad() @torch.no_grad()
def select_actions(self, observation, step_count): def select_actions(self, observation, step_count):
if observation["image"].shape[0] != 1:
raise NotImplementedError("Batch size > 1 not handled")
# TODO(rcadene): remove unused step_count # TODO(rcadene): remove unused step_count
del step_count del step_count
@ -167,7 +184,17 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
"image": observation["image", "top"], "image": observation["image", "top"],
"agent_pos": observation["state"], "agent_pos": observation["state"],
} }
action = self._forward(qpos=obs_dict["agent_pos"], image=obs_dict["image"]) # qpos = obs_dict["agent_pos"]
# img = obs_dict["image"]
# qpos_ = torch.load('/tmp/qpos.pth')
# img_ = torch.load('/tmp/curr_image.pth')
# out_ = torch.load('/tmp/out.pth')
# import cv2, numpy as np
# cv2.imwrite("ours.png", (obs_dict["image"][0, 0].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
# cv2.imwrite("theirs.png", (img_[0, 0].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
# out = self._forward(qpos_, img_)
# breakpoint()
action = self._forward(qpos=obs_dict["agent_pos"] * 0.182, image=obs_dict["image"])
if self.cfg.temporal_agg: if self.cfg.temporal_agg:
# TODO(rcadene): implement temporal aggregation # TODO(rcadene): implement temporal aggregation
@ -197,6 +224,7 @@ class ActionChunkingTransformerPolicy(AbstractPolicy):
if is_pad is not None: if is_pad is not None:
is_pad = is_pad[:, : self.model.num_queries] is_pad = is_pad[:, : self.model.num_queries]
breakpoint()
a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad) a_hat, is_pad_hat, (mu, logvar) = self.model(qpos, image, env_state, actions, is_pad)
all_l1 = F.l1_loss(actions, a_hat, reduction="none") all_l1 = F.l1_loss(actions, a_hat, reduction="none")

2
lerobot/common/policies/factory.py
View File

@ -1,5 +1,5 @@
def make_policy(cfg): def make_policy(cfg):
if cfg.policy.name != "diffusion" and cfg.rollout_batch_size > 1: if cfg.policy.name not in ["diffusion", "act"] and cfg.rollout_batch_size > 1:
raise NotImplementedError("Only diffusion policy supports rollout_batch_size > 1 for the time being.") raise NotImplementedError("Only diffusion policy supports rollout_batch_size > 1 for the time being.")
if cfg.policy.name == "tdmpc": if cfg.policy.name == "tdmpc":

3
lerobot/configs/policy/act.yaml
View File

@ -1,6 +1,6 @@
# @package _global_ # @package _global_
offline_steps: 1344000 offline_steps: 2000
online_steps: 0 online_steps: 0
eval_episodes: 1 eval_episodes: 1
@ -24,7 +24,6 @@ policy:
weight_decay: 1e-4 weight_decay: 1e-4
grad_clip_norm: 10 grad_clip_norm: 10
backbone: resnet18 backbone: resnet18
num_queries: ${horizon} # chunk_size
horizon: ${horizon} # chunk_size horizon: ${horizon} # chunk_size
kl_weight: 10 kl_weight: 10
hidden_dim: 512 hidden_dim: 512

1
lerobot/scripts/train.py
View File

@ -151,6 +151,7 @@ def train(cfg: dict, out_dir=None, job_name=None):
logging.info("make_policy") logging.info("make_policy")
policy = make_policy(cfg) policy = make_policy(cfg)
policy.save("act.pt")
num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad) num_learnable_params = sum(p.numel() for p in policy.parameters() if p.requires_grad)
num_total_params = sum(p.numel() for p in policy.parameters()) num_total_params = sum(p.numel() for p in policy.parameters())

197
poetry.lock generated
View File

@ -3248,6 +3248,133 @@ numpy = "*"
[package.extras] [package.extras]
all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"] all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
[[package]]
name = "tokenizers"
version = "0.15.2"
description = ""
optional = false
python-versions = ">=3.7"
files = [
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dev = ["tokenizers[testing]"]
docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
[[package]] [[package]]
name = "tomli" name = "tomli"
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@ -3413,6 +3540,74 @@ notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"] slack = ["slack-sdk"]
telegram = ["requests"] telegram = ["requests"]
[[package]]
name = "transformers"
version = "4.39.3"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false
python-versions = ">=3.8.0"
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tokenizers = ">=0.14,<0.19"
tqdm = ">=4.27"
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flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
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ftfy = ["ftfy"]
integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
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sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"]
sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
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tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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tokenizers = ["tokenizers (>=0.14,<0.19)"]
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torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
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video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]] [[package]]
name = "triton" name = "triton"
version = "2.2.0" version = "2.2.0"
@ -3589,4 +3784,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "174c7d42f8039eedd2c447a4e6cae5169782cbd94346b5606572a0010194ca05" content-hash = "5ebd02dac0322efe1236eb9fec84c471edd0c5373cc8967b1982314164b3bf50"

1
pyproject.toml
View File

@ -52,6 +52,7 @@ robomimic = "0.2.0"
gymnasium-robotics = "^1.2.4" gymnasium-robotics = "^1.2.4"
gymnasium = "^0.29.1" gymnasium = "^0.29.1"
cmake = "^3.29.0.1" cmake = "^3.29.0.1"
transformers = "^4.39.3"
[tool.poetry.group.dev.dependencies] [tool.poetry.group.dev.dependencies]