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import os
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from configs.state_vec import STATE_VEC_IDX_MAPPING
from models.multimodal_encoder.siglip_encoder import SiglipVisionTower
from models.multimodal_encoder.t5_encoder import T5Embedder
from models.rdt_runner import RDTRunner
MANISKILL_INDICES = [
STATE_VEC_IDX_MAPPING[f"right_arm_joint_{i}_pos"] for i in range(7)
] + [
STATE_VEC_IDX_MAPPING[f"right_gripper_open"]
]
def create_model(args, pretrained, **kwargs):
model = RoboticDiffusionTransformerModel(args, **kwargs)
if pretrained is not None:
model.load_pretrained_weights(pretrained)
return model
DATA_STAT = {'state_min': [-0.7463043928146362, -0.0801204964518547, -0.4976441562175751, -2.657780647277832, -0.5742632150650024, 1.8309762477874756, -2.2423808574676514, 0.0], 'state_max': [0.7645499110221863, 1.4967026710510254, 0.4650936424732208, -0.3866899907588959, 0.5505855679512024, 3.2900545597076416, 2.5737812519073486, 0.03999999910593033], 'action_min': [-0.7472005486488342, -0.08631071448326111, -0.4995281398296356, -2.658363103866577, -0.5751323103904724, 1.8290787935256958, -2.245187997817993, -1.0], 'action_max': [0.7654682397842407, 1.4984270334243774, 0.46786263585090637, -0.38181185722351074, 0.5517147779464722, 3.291581630706787, 2.575840711593628, 1.0]}
class RoboticDiffusionTransformerModel(object):
"""A wrapper for the RDT model, which handles
1. Model initialization
2. Encodings of instructions
3. Model inference
"""
def __init__(
self, args,
device='cuda',
dtype=torch.bfloat16,
image_size=None,
control_frequency=25,
pretrained_text_encoder_name_or_path=None,
pretrained_vision_encoder_name_or_path=None,
):
self.args = args
self.dtype = dtype
self.image_size = image_size
self.device = device
self.control_frequency = control_frequency
self.text_tokenizer, self.text_model = self.get_text_encoder(pretrained_text_encoder_name_or_path)
self.image_processor, self.vision_model = self.get_vision_encoder(pretrained_vision_encoder_name_or_path)
self.policy = self.get_policy()
self.state_min = torch.tensor(DATA_STAT['state_min']).to(device)
self.state_max = torch.tensor(DATA_STAT['state_max']).to(device)
self.action_min = torch.tensor(DATA_STAT['action_min']).to(device)
self.action_max = torch.tensor(DATA_STAT['action_max']).to(device)
self.reset()
def get_policy(self):
"""Initialize the model."""
# Initialize model with arguments
img_cond_len = (self.args["common"]["img_history_size"]
* self.args["common"]["num_cameras"]
* self.vision_model.num_patches)
_model = RDTRunner(
action_dim=self.args["common"]["state_dim"],
pred_horizon=self.args["common"]["action_chunk_size"],
config=self.args["model"],
lang_token_dim=self.args["model"]["lang_token_dim"],
img_token_dim=self.args["model"]["img_token_dim"],
state_token_dim=self.args["model"]["state_token_dim"],
max_lang_cond_len=self.args["dataset"]["tokenizer_max_length"],
img_cond_len=img_cond_len,
img_pos_embed_config=[
# No initial pos embed in the last grid size
# since we've already done in ViT
("image", (self.args["common"]["img_history_size"],
self.args["common"]["num_cameras"],
-self.vision_model.num_patches)),
],
lang_pos_embed_config=[
# Similarly, no initial pos embed for language
("lang", -self.args["dataset"]["tokenizer_max_length"]),
],
dtype=self.dtype,
)
return _model
def get_text_encoder(self, pretrained_text_encoder_name_or_path):
text_embedder = T5Embedder(from_pretrained=pretrained_text_encoder_name_or_path,
model_max_length=self.args["dataset"]["tokenizer_max_length"],
device=self.device)
tokenizer, text_encoder = text_embedder.tokenizer, text_embedder.model
return tokenizer, text_encoder
def get_vision_encoder(self, pretrained_vision_encoder_name_or_path):
vision_encoder = SiglipVisionTower(vision_tower=pretrained_vision_encoder_name_or_path, args=None)
image_processor = vision_encoder.image_processor
return image_processor, vision_encoder
def reset(self):
"""Set model to evaluation mode.
"""
device = self.device
weight_dtype = self.dtype
self.policy.eval()
self.text_model.eval()
self.vision_model.eval()
self.policy = self.policy.to(device, dtype=weight_dtype)
self.text_model = self.text_model.to(device, dtype=weight_dtype)
self.vision_model = self.vision_model.to(device, dtype=weight_dtype)
def load_pretrained_weights(self, pretrained=None):
if pretrained is None:
return
print(f'Loading weights from {pretrained}')
filename = os.path.basename(pretrained)
if filename.endswith('.pt'):
checkpoint = torch.load(pretrained)
self.policy.load_state_dict(checkpoint["module"])
elif filename.endswith('.safetensors'):
from safetensors.torch import load_model
load_model(self.policy, pretrained)
else:
raise NotImplementedError(f"Unknown checkpoint format: {pretrained}")
def encode_instruction(self, instruction, device="cuda"):
"""Encode string instruction to latent embeddings.
Args:
instruction: a string of instruction
device: a string of device
Returns:
pred: a tensor of latent embeddings of shape (text_max_length, 512)
"""
tokens = self.text_tokenizer(
instruction, return_tensors="pt",
padding="longest",
truncation=True
)["input_ids"].to(device)
tokens = tokens.view(1, -1)
with torch.no_grad():
pred = self.text_model(tokens).last_hidden_state.detach()
return pred
def _format_joint_to_state(self, joints):
"""
Format the robot joint state into the unified state vector.
Args:
joints (torch.Tensor): The joint state to be formatted.
qpos ([B, N, 14]).
Returns:
state (torch.Tensor): The formatted state for RDT ([B, N, 128]).
"""
# Rescale the gripper
# joints = joints / torch.tensor(
# [[[1, 1, 1, 1, 1, 1, 4.7908, 1, 1, 1, 1, 1, 1, 4.7888]]],
# device=joints.device, dtype=joints.dtype
# )
# normalize to -1,1
joints = (joints - self.state_min) / (self.state_max - self.state_min) * 2 - 1
B, N, _ = joints.shape
state = torch.zeros(
(B, N, self.args["model"]["state_token_dim"]),
device=joints.device, dtype=joints.dtype
)
# assemble the unifed state vector
state[:, :, MANISKILL_INDICES] = joints
state_elem_mask = torch.zeros(
(B, self.args["model"]["state_token_dim"]),
device=joints.device, dtype=joints.dtype
)
state_elem_mask[:, MANISKILL_INDICES] = 1
return state, state_elem_mask
def _unformat_action_to_joint(self, action):
action_indices = MANISKILL_INDICES
joints = action[:, :, action_indices]
# denormalize to action space
joints = (joints + 1) / 2 * (self.action_max - self.action_min) + self.action_min
return joints
@torch.no_grad()
def step(self, proprio, images, text_embeds):
"""
Args:
proprio: proprioceptive states
images: RGB images
text_embeds: instruction embeddings
Returns:
action: predicted action
"""
device = self.device
dtype = self.dtype
background_color = np.array([
int(x*255) for x in self.image_processor.image_mean
], dtype=np.uint8).reshape(1, 1, 3)
background_image = np.ones((
self.image_processor.size["height"],
self.image_processor.size["width"], 3), dtype=np.uint8
) * background_color
image_tensor_list = []
for image in images:
if image is None:
# Replace it with the background image
image = Image.fromarray(background_image)
if self.image_size is not None:
image = transforms.Resize(self.data_args.image_size)(image)
if self.args["dataset"].get("auto_adjust_image_brightness", False):
pixel_values = list(image.getdata())
average_brightness = sum(sum(pixel) for pixel in pixel_values) / (len(pixel_values) * 255.0 * 3)
if average_brightness <= 0.15:
image = transforms.ColorJitter(brightness=(1.75,1.75))(image)
if self.args["dataset"].get("image_aspect_ratio", "pad") == 'pad':
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
image = expand2square(image, tuple(int(x*255) for x in self.image_processor.image_mean))
image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
image_tensor_list.append(image)
image_tensor = torch.stack(image_tensor_list, dim=0).to(device, dtype=dtype)
image_embeds = self.vision_model(image_tensor).detach()
image_embeds = image_embeds.reshape(-1, self.vision_model.hidden_size).unsqueeze(0)
# history of actions
joints = proprio.to(device).unsqueeze(0) # (1, 1, 14)
states, state_elem_mask = self._format_joint_to_state(joints) # (1, 1, 128), (1, 128)
states, state_elem_mask = states.to(device, dtype=dtype), state_elem_mask.to(device, dtype=dtype)
states = states[:, -1:, :] # (1, 1, 128)
ctrl_freqs = torch.tensor([self.control_frequency]).to(device)
text_embeds = text_embeds.to(device, dtype=dtype)
trajectory = self.policy.predict_action(
lang_tokens=text_embeds,
lang_attn_mask=torch.ones(
text_embeds.shape[:2], dtype=torch.bool,
device=text_embeds.device),
img_tokens=image_embeds,
state_tokens=states,
action_mask=state_elem_mask.unsqueeze(1),
ctrl_freqs=ctrl_freqs
)
trajectory = self._unformat_action_to_joint(trajectory).to(torch.float32)
return trajectory
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