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import os
import re
import torch, os, imageio, argparse, imageio_ffmpeg
from torchvision.transforms import v2
from einops import rearrange
import lightning as pl
import pandas as pd
from diffsynth import WanVideoPipeline, ModelManager, load_state_dict
import torchvision
from PIL import Image
import numpy as np
import random
import json
import torch.nn as nn
import torch.nn.functional as F
import shutil
import open3d as o3d
import h5py
import cv2
import math
from robotwin_utils import get_instructions_rlbench, rgbd_to_pointcloud, image_to_float_array, sample_frame_indices, find_rlbench_data
def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
"""
Convert rotations given as quaternions to rotation matrices.
Args:
quaternions: quaternions with real part first,
as tensor of shape (..., 4).
Returns:
Rotation matrices as tensor of shape (..., 3, 3).
"""
r, i, j, k = torch.unbind(quaternions, -1)
# pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
two_s = 2.0 / (quaternions * quaternions).sum(-1)
o = torch.stack(
(
1 - two_s * (j * j + k * k),
two_s * (i * j - k * r),
two_s * (i * k + j * r),
two_s * (i * j + k * r),
1 - two_s * (i * i + k * k),
two_s * (j * k - i * r),
two_s * (i * k - j * r),
two_s * (j * k + i * r),
1 - two_s * (i * i + j * j),
),
-1,
)
return o.reshape(quaternions.shape[:-1] + (3, 3))
def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor:
"""
Converts rotation matrices to 6D rotation representation by Zhou et al. [1]
by dropping the last row. Note that 6D representation is not unique.
Args:
matrix: batch of rotation matrices of size (*, 3, 3)
Returns:
6D rotation representation, of size (*, 6)
[1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H.
On the Continuity of Rotation Representations in Neural Networks.
IEEE Conference on Computer Vision and Pattern Recognition, 2019.
Retrieved from http://arxiv.org/abs/1812.07035
"""
batch_dim = matrix.size()[:-2]
return matrix[..., :2, :].clone().reshape(batch_dim + (6,))
class RLBenchTextVideoDataset(torch.utils.data.Dataset):
def __init__(self, base_path, metadata_path, max_num_frames=81, frame_interval=1, num_frames=81, height=240, width=320, is_i2v=False):
# metadata = pd.read_csv(metadata_path)
# self.path = [os.path.join(base_path, "train", file_name) for file_name in metadata["file_name"]]
# self.text = metadata["text"].to_list()
self.base_path = base_path
self.data_list = find_rlbench_data(base_path)
self.max_num_frames = max_num_frames
self.frame_interval = frame_interval
self.num_frames = num_frames
self.height = height
self.width = width
self.is_i2v = is_i2v
self.frame_process = v2.Compose([
# v2.CenterCrop(size=(height, width)),
# v2.Resize(size=(height, width), antialias=True),
v2.ToTensor(),
v2.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
def _task_name(self, data_path: str) -> str:
rel = os.path.relpath(data_path, self.base_path)
return rel.split(os.sep)[0]
def get_task_name(self, index: int) -> str:
return self._task_name(self.data_list[index]["data_path"])
def __getitem__(self, data_id):
data_dict = self.data_list[data_id]
instructions_file_path = data_dict["instructions_path"]
instructions_list = get_instructions_rlbench(instructions_file_path)
text = instructions_list[0]
# video_path = os.path.join(data_dict["data_path"], data_dict["episode_name"])
# text = self.text[data_id]
# path = self.data_list[data_id]
# print(1)
# metadata = pd.read_json(os.path.join(data_dict["data_path"], f"{data_dict['episode_name']}.json"))
out_dict=dict()
out_dict["cache_path"] = data_dict["cache_path"]
out_dict["text"] = text
out_dict["cam"] = dict()
scene_data = np.load(data_dict["scene_data_path"], allow_pickle=True).items()
# for key, value in scene_data:
# print(f"scene_data key: {key}, value type: {type(value)}")
# scene_data key: left_shoulder_camera, value type: <class 'numpy.ndarray'>
# scene_data key: right_shoulder_camera, value type: <class 'numpy.ndarray'>
# scene_data key: front_camera, value type: <class 'numpy.ndarray'>
# scene_data key: wrist_camera, value type: <class 'numpy.ndarray'>
# scene_data key: overhead_camera, value type: <class 'numpy.ndarray'>
# scene_data key: cam_0, value type: <class 'numpy.ndarray'>
# scene_data key: cam_1, value type: <class 'numpy.ndarray'>
# scene_data key: cam_2, value type: <class 'numpy.ndarray'>
# scene_data key: cam_3, value type: <class 'numpy.ndarray'>
# scene_data key: cam_4, value type: <class 'numpy.ndarray'>
# scene_data key: cam_5, value type: <class 'numpy.ndarray'>
# scene_data key: cam_6, value type: <class 'numpy.ndarray'>
# scene_data key: cam_7, value type: <class 'numpy.ndarray'>
# scene_data key: cam_8, value type: <class 'numpy.ndarray'>
# scene_data key: cam_9, value type: <class 'numpy.ndarray'>
# scene_data key: cam_10, value type: <class 'numpy.ndarray'>
# scene_data key: cam_11, value type: <class 'numpy.ndarray'>
# print(f"cache_path: {out_dict['cache_path']}, text: {text}")
# print(f"scene_data keys: {scene_data}")
# assert 0
# num_frames = int(metadata[list(metadata.keys())[0]]["num_frames"])
# index_list = sample_frame_indices(num_frames, self.num_frames)
# index_list= np.arange(0, self.num_frames, self.frame_interval)
# index_list = list(range(0, self.num_frames, self.frame_interval))
jointdata = np.load(data_dict["jointdata_path"], allow_pickle=True)
# for key, value in jointdata:
# print(f"jointdata key: {key}, value type: {type(value)}")
# jointdata key: random_seed, value type: <class 'numpy.ndarray'>
# jointdata key: num_reset_attempts, value type: <class 'numpy.ndarray'>
# jointdata key: observations, value type: <class 'numpy.ndarray'>
# assert 0
joint_action = jointdata["observations"].item()
# joint_action keys: dict_keys(['vector', 'joint_velocities', 'joint_positions', 'joint_forces', 'gripper_pose', 'gripper_joint_positions', 'gripper_touch_forces', 'task_low_dim_state'])
# print(joint_action)
# print(f"joint_action keys: {joint_action.keys()}")
# left_arm = np.stack(joint_action["left_arm"], axis=0).astype(np.float32)
# left_gripper = np.stack(joint_action["left_gripper"], axis=0).astype(np.float32)[..., None]
# right_arm = np.stack(joint_action["right_arm"], axis=0).astype(np.float32)
# right_gripper = np.stack(joint_action["right_gripper"], axis=0).astype(np.float32)[..., None]
# action = np.concatenate([left_arm, left_gripper, right_arm, right_gripper], axis=-1)
endpose7d = joint_action["gripper_pose"] # xyz quaternion
gripper_open = joint_action["gripper_open"] # 0 1
# gripper_joint_positions = joint_action["gripper_joint_positions"]
xyz = endpose7d[..., :3]
quaternion = endpose7d[..., 3:]
matrix = quaternion_to_matrix(torch.from_numpy(quaternion).float()) # N x 3 x 3
rotation_6d = matrix_to_rotation_6d(matrix).numpy() # N x 6
action = np.concatenate([xyz, rotation_6d, gripper_open[..., None]], axis=-1).astype(np.float32)
num_frames = action.shape[0]
index_list= sample_frame_indices(num_frames, self.num_frames)
# print(f"joint_positions shape: {np.array(joint_positions).shape}") # joint_positions shape: (58, 7)
# print(f"gripper_positions shape: {np.array(gripper_positions).shape}") # gripper_positions shape: (58, 2)
# print(f"gripper_joint_positions shape: {np.array(gripper_joint_positions).shape}") # gripper_joint_positions shape: (58, 2)
# print(f"joint_positions[30:35]: {joint_positions[30:35]}")
# print(f"gripper_positions[30:35]: {gripper_positions[30:35]}")
# print(f"gripper_joint_positions[30:35]: {gripper_joint_positions[30:35]}")
# assert 0
action = action[index_list]
out_dict["action"] = action
for cam_name, cam_data in scene_data:
# print(f"cam_name: {cam_name}, cam_data type: {type(cam_data)}")
if not cam_name.startswith("cam_"):
continue
# print(f"Processing camera: {cam_name}")
# print(f"Camera data keys: {cam_data}")
# Camera data keys: {'intrinsics': array([[-439.596401, 0. , 120. ],
# [ 0. , -439.596401, 160. ],
# [ 0. , 0. , 1. ]]), 'near_plane': 0.009999999776482582, 'far_plane': 3.200000047683716, 'extrinsics': array([[ 1.73648179e-01, 8.92538846e-01, 4.16198105e-01,
# -1.74999714e-01],
# [ 9.84807789e-01, -1.57378674e-01, -7.33871460e-02,
# 2.00000003e-01],
# [-1.78813934e-07, 4.22618657e-01, -9.06307697e-01,
# 1.97999895e+00],
# [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
# 1.00000000e+00]])}
# assert 0
# if cam_name == "surround_camera_11":
cam_data = cam_data.item()
far_plane = cam_data['far_plane']
near_plane = cam_data['near_plane']
intrinsic_cv = np.array(cam_data["intrinsics"]).reshape(3, 3)
intrinsic_cv = np.abs(intrinsic_cv)
extrinsic_cv = np.eye(4, dtype=np.float64)
extrinsic_cv[:3,:] = np.array(cam_data["extrinsics"])[:3,:]
C = np.expand_dims(extrinsic_cv[:3, 3], 0).T
R = extrinsic_cv[:3, :3]
R_inv = R.T # inverse of rot matrix is transpose
tf = np.array([[-1,0,0],
[0,-1,0],
[0,0,1]], dtype=np.float64)
R_inv_C = np.matmul(R_inv, C)
extrinsic_cv[:3,:] = tf @ np.concatenate((R_inv, -R_inv_C), -1)[:3,:]
# print(f"Original extrinsic: {extrinsic_cv}")
# print(f"extrinsic.shape: {extrinsic_cv.shape}")
# rotate 90 degrees around y
# tf0 = np.array([[0, 0, 1, 0],
# [0, 1, 0, 0],
# [-1, 0, 0, 0],
# [0, 0, 0, 1]], dtype=np.float64)
# tf = np.array([[-1, 0, 0, 0],
# [0, -1, 0, 0],
# [0, 0, 1, 0],
# [0, 0, 0, 1]], dtype=np.float64)
# extrinsic_cv = np.linalg.inv(extrinsic_cv)
# extrinsic_cv = tf @ extrinsic_cv
# extrinsic_cv[:3, :3] = tf[:3, :3] @ extrinsic_cv[:3, :3]
# extrinsic_cv[:3, 3] = -extrinsic_cv[:3, 3]
# opengl to opencv
# extrinsic_cv[3, :2] *= -1
# extrinsic_cv[1:3, :] *= -1
# extrinsic_cv = extrinsic_cv
rgb_mp4_path = os.path.join(data_dict["data_path"], f"cam_rgb_{cam_name.split('_')[-1]}.mp4")
depth_mp4_path = os.path.join(data_dict["data_path"], f"cam_depth_{cam_name.split('_')[-1]}.mp4")
if not os.path.exists(rgb_mp4_path) or not os.path.exists(depth_mp4_path):
print(f"RGBD video path {rgb_mp4_path} or {depth_mp4_path} does not exist, skip.")
raise FileNotFoundError(f"RGBD video path {rgb_mp4_path} or {depth_mp4_path} does not exist, skip.")
# import time
# start_time = time.time()
# input_params = [
# "-hwaccel", "cuvid",
# "-c:v", "h264_cuvid"
# ]
input_params = []
# rgb_reader = imageio.get_reader(rgb_mp4_path, 'ffmpeg')
rgb_reader = imageio_ffmpeg.read_frames(rgb_mp4_path, input_params=input_params)
# depth_reader = imageio.get_reader(depth_mp4_path, 'ffmpeg')
depth_reader = imageio_ffmpeg.read_frames(depth_mp4_path, input_params=input_params)
rgb_frames = np.zeros((self.num_frames, self.height, self.width, 3), dtype=np.float32)
depth_frames = np.zeros((self.num_frames, self.height, self.width, 3), dtype=np.float32)
rgb_meta, depth_meta = next(rgb_reader), next(depth_reader)
# print(rgb_meta, depth_meta) # {'ffmpeg_version': '7.0.2-static https://johnvansickle.com/ffmpeg/ built with gcc 8 (Debian 8.3.0-6)', 'codec': 'h264', 'pix_fmt': 'yuv420p(progressive)', 'fps': 30.0, 'source_size': (832, 480), 'size': (832, 480), 'rotate': 0, 'duration': 4.73}
rgb_size = (rgb_meta["size"][1], rgb_meta["size"][0], 3)
depth_size = (depth_meta["size"][1], depth_meta["size"][0], 3)
assert rgb_size == (self.height, self.width, 3), f"RGB size {rgb_size} does not match expected size {(self.height, self.width, 3)}"
assert depth_size == (self.height, self.width, 3), f"Depth size {depth_size} does not match expected size {(self.height, self.width, 3)}"
for rgb_frame, depth_frame, i in zip(rgb_reader, depth_reader, range(num_frames)):
# read frame from the generator
# print(rgb_frame)
if i in index_list:
select_idx = index_list.index(i)
rgb_frames[select_idx] = np.frombuffer(rgb_frame, dtype=np.uint8).reshape(rgb_size)
depth_frames[select_idx] = np.frombuffer(depth_frame, dtype=np.uint8).reshape(depth_size)
# end_time = time.time()
# print(f"Reading frames time: {end_time - start_time:.5f}s")
rgb_first_frame = rgb_frames[0].copy().astype(np.uint8) # H W C
rgb_frames = torch.from_numpy(rgb_frames).float().permute(0, 3, 1, 2) # T H W C -> T C H W
rgb_frames /= 255.0
rgb_frames = self.frame_process(rgb_frames) # Normalize
rgb_frames = rgb_frames.permute(1, 0, 2, 3) # T C H W -> C T H W
depth_frames = image_to_float_array(depth_frames, scale_factor=256000.0)
depth_frames = near_plane + depth_frames * (far_plane - near_plane)
# depth_frames = torch.from_numpy(depth_frames).unsqueeze(0) # T H W -> 1 T H W
depth_frames = np.expand_dims(depth_frames, axis=0) # T H W -> 1 T H W
# print(f"depth min , max before clipping: {depth_frames.min()}, {depth_frames.max()}")
# break
cam_dict = dict()
cam_dict["intrinsic"] = intrinsic_cv
cam_dict["extrinsic"] = extrinsic_cv
# print(f"Camera {cam_name} extrinsic: {extrinsic_cv}")
## used to load original rgb and depth when testing ##
cam_dict["rgb_mp4_path"] = rgb_mp4_path
cam_dict["depth_mp4_path"] = depth_mp4_path
cam_dict["num_frames"] = int(self.num_frames)
cam_dict["frame_interval"] = int(self.frame_interval)
# print(f"cam {cam_name} depth min {depth_frames.min()}, max {depth_frames.max()}")
cam_dict["rgb"] = rgb_frames
cam_dict["rgb_first_frame"] = rgb_first_frame
cam_dict["depth"] = depth_frames # [1, 81, 480, 832]
# print(type(depth_frames), depth_frames.shape, depth_frames.min(), depth_frames.max())
# assert 0
out_dict["cam"][cam_name] = cam_dict
# def axis_map_to_T4(newx_from, sx, newy_from, sy, newz_from, sz):
# """
# construct a 4x4 homogeneous matrix T such that:
# v_new = R @ v_old
# v_old/v_new are both [x,y,z]^T
# newx_from/newy_from/newz_from take 'x'/'y'/'z'
# sx/sy/sz take +1 or -1
# """
# idx = {'x': 0, 'y': 1, 'z': 2}
# R = np.zeros((3, 3), dtype=float)
# R[0, idx[newx_from]] = float(sx)
# R[1, idx[newy_from]] = float(sy)
# R[2, idx[newz_from]] = float(sz)
# T = np.eye(4, dtype=float)
# T[:3, :3] = R
# return T
# def generate_T4_list_axis_permute_sign(include_det_pos=True, include_det_neg=True):
# """
# return list[np.ndarray], each element is 4x4.
# - include_det_pos=True: includes the 24 matrices with det=+1
# - include_det_neg=True: includes the 24 matrices with det=-1
# """
# Ts = []
# # 6 axis permutations
# perms = [
# ('x','y','z'),
# ('x','z','y'),
# ('y','x','z'),
# ('y','z','x'),
# ('z','x','y'),
# ('z','y','x'),
# ]
# # 8 sign combinations
# signs = [
# (+1,+1,+1),
# (+1,+1,-1),
# (+1,-1,+1),
# (+1,-1,-1),
# (-1,+1,+1),
# (-1,+1,-1),
# (-1,-1,+1),
# (-1,-1,-1),
# ]
# for a, b, c in perms:
# for sx, sy, sz in signs:
# T = axis_map_to_T4(a, sx, b, sy, c, sz)
# det = int(round(np.linalg.det(T[:3, :3])))
# if det == 1 and include_det_pos:
# Ts.append(T)
# elif det == -1 and include_det_neg:
# Ts.append(T)
# return Ts
# # directly get list[np.ndarray], each is 4x4
# T_list = generate_T4_list_axis_permute_sign()
# print(T_list[6])
# print(T_list[6])
# print(T_list[6])
# print(T_list[6])
# for i in range(len(T_list)):
# T = T_list[i]
# pointcloud = []
# # print(f"T {i} det: {det}")
# for cam_name, cam_data in out_dict["cam"].items():
# first_rgb = cam_data["rgb_first_frame"]
# first_depth = cam_data["depth"][0,0]
# intrinsic = cam_data["intrinsic"]
# extrinsic = cam_data["extrinsic"]
# extrinsic = T @ cam_data["extrinsic"]
# # print dtype
# print(f"first_rgb dtype: {first_rgb.dtype}, first_depth dtype: {first_depth.dtype}, intrinsic dtype: {intrinsic.dtype}, extrinsic dtype: {extrinsic.dtype}")
# # first_rgb = first_rgb.numpy()
# # first_depth = first_depth.numpy()
# # first_depth = torch.from_numpy(first_depth).float()
# # intrinsic = torch.from_numpy(intrinsic).float()
# # extrinsic = torch.from_numpy(extrinsic).float()
# pc = rgbd_to_pointcloud(
# rgb=first_rgb,
# depth=first_depth,
# intrinsic=intrinsic,
# extrinsic=extrinsic)
# pc = np.concatenate([pc[0], pc[1]], axis=-1) # N x 6
# # print(f"Pointcloud from {cam_name} has shape {pc.shape}")
# pointcloud.append(pc)
# # all_pc = torch.cat(pointcloud, dim=0)
# all_pc = np.concatenate(pointcloud, axis=0)
# print(f"shape {all_pc.shape}")
# import open3d as o3d
# o3d_pc = o3d.geometry.PointCloud()
# o3d_pc.points = o3d.utility.Vector3dVector(all_pc[:, :3].astype(np.float64))
# if all_pc.shape[1] >= 6:
# o3d_pc.colors = o3d.utility.Vector3dVector((all_pc[:, 3:6] / 255.0).astype(np.float64))
# xyz = all_pc[:, :3]
# x_range = -xyz[:, 0].min()+ xyz[:, 0].max()
# y_range = -xyz[:, 1].min()+ xyz[:, 1].max()
# z_range = -xyz[:, 2].min()+ xyz[:, 2].max()
# o3d.io.write_point_cloud(f"./llll/debug_pc_{i}_{x_range:.2f}_{y_range:.2f}_{z_range:.2f}.ply", o3d_pc)
# # take a picture of the pointcloud
# print(f"Saved pointcloud with {all_pc.shape[0]} points to debug_pc_{i}.ply")
# # # assert 0
return out_dict
def __len__(self):
return len(self.data_list)
def robust_depth_range(depth_tensor: torch.Tensor, q_low=1.0, q_high=99.0):
depth_tensor = depth_tensor.to(torch.float32)
valid = depth_tensor[depth_tensor > 1e-6]
if valid.numel() == 0:
return 1e-3, 1.0
lo = torch.nanquantile(valid, q_low / 100.0).item()
hi = torch.nanquantile(valid, q_high / 100.0).item()
if hi - lo < 1e-6:
hi = lo + 1e-3
return max(lo, 1e-4), max(hi, lo + 1e-3)
class LightningModelForDataProcess(pl.LightningModule):
def __init__(self, text_encoder_path, vae_path, image_encoder_path=None, tiled=False, tile_size=(34, 34), tile_stride=(18, 16), overwrite=True):
super().__init__()
model_path = [text_encoder_path, vae_path]
if image_encoder_path is not None:
model_path.append(image_encoder_path)
model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu")
model_manager.load_models(model_path)
self.pipe = WanVideoPipeline.from_model_manager(model_manager)
self.pipe.torch_dtype = model_manager.torch_dtype
self.pipe.vae.dtype = model_manager.torch_dtype
self.pipe.vae.to(model_manager.device)
print(f"Loaded pipeline to {model_manager.device} with dtype {model_manager.torch_dtype}")
# assert 0
self.overwrite = overwrite
self.tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
self.debug_flag = True
def test_step(self, batch, batch_idx):
self.pipe.device = self.device
# print(f"batch.keys() {batch.keys()}") # batch.keys() dict_keys(['cache_path', 'text', 'cam'])
# for key, value in batch.items():
# print(f"{key}: {type(value)}")
# cache_path: <class 'list'>
# text: <class 'list'>
# cam: <class 'dict'>
text = batch["text"][0]
cam = batch["cam"]
cache_path = batch["cache_path"][0]
action = batch["action"]
# data/pp_tasks_/place_a2b_left/ARX-X5+ARX-X5/demo_randomized/cache/episode0/ to data/pp_tasks_/place_a2b_left/ARX-X5+ARX-X5/demo_randomized/cache_wan2_2/episode0/
# .replace("/cache/", "/cache_rlbench_wan2_2/")
cache_path = cache_path.replace("/cache/episode", "/cache_rlbench_wan2_2/episode")
if self.debug_flag:
print(f"Processing data for text: {text}, cache_path: {cache_path}")
os.makedirs(cache_path, exist_ok=True)
prompt_emb_pth_path = os.path.join(cache_path, "prompt_emb.pth")
# if not os.path.exists(prompt_emb_pth_path):
# prompt
# delete the torch.save file if exists
if self.overwrite and os.path.exists(prompt_emb_pth_path):
os.remove(prompt_emb_pth_path)
prompt_emb = self.pipe.encode_prompt(text)
torch.save(prompt_emb, prompt_emb_pth_path)
depth_min = 0.01
depth_max = 6.0000
log_depth_bounds = (math.log(depth_min), math.log(depth_max))
for cam_name, cam_data in cam.items():
cam_emb_pth_path = os.path.join(cache_path, f"{cam_name}_emb.pth")
if self.overwrite or not os.path.exists(cam_emb_pth_path):
# video
video = cam_data["rgb"]
# print("video shape : ", video.shape)
rgb_mp4_path = cam_data["rgb_mp4_path"]
depth_mp4_path = cam_data["depth_mp4_path"]
num_frames = cam_data["num_frames"]
frame_interval = cam_data["frame_interval"]
video = video.to(dtype=self.pipe.torch_dtype, device=self.pipe.device)
#### encode videos ####
video_frames = video[0] # [3, T, H, W]
num_frames = video_frames.shape[1]
height, width = video_frames.shape[2], video_frames.shape[3]
rgb_latent = self.pipe.vae.encode(videos=[video_frames])[0]
depth_videos = cam_data["depth"].to(dtype=self.pipe.torch_dtype, device=self.pipe.device) # [1, 1, T, H, W]
depth_frames = depth_videos.squeeze(0).clamp_min(1e-4)
log_depth = torch.log(depth_frames)
log_depth = torch.clamp(log_depth, min=log_depth_bounds[0], max=log_depth_bounds[1])
denom = (log_depth_bounds[1] - log_depth_bounds[0]) + 1e-6
depth_norm = (log_depth - log_depth_bounds[0]) / denom
depth_norm = depth_norm * 2 - 1
# assert 0
depth_video_frames = depth_norm.repeat(3, 1, 1, 1) # [3, T, H, W]
# print(depth_video_frames.shape)
# print(depth_video_frames.min())
# print(depth_video_frames.max())
# assert 0
# def decode_video(latent, to_numpy=True):
# frames = self.pipe.decode_video(latent)
# # if self.
# # print(f"Decoded frames shape (C H W T): {frames.shape}")
# frames = rearrange(frames, "N C T H W -> N T C H W")
# frames = ((frames.float() + 1) * 127.5).clip(0, 255)
# if to_numpy:
# frames = frames.cpu().numpy().astype(np.uint8)
# return frames
depth_latent = self.pipe.vae.encode(videos=[depth_video_frames])[0]
# print("depth_latent.shape : ",depth_latent.shape) # 48 21 15 20
# depth_recon = decode_video([depth_latent])
# # print(depth_recon.shape)
# # print(depth_video_frames.shape)
# # assert 0
# # def tensor_to_uint8(img_tensor):
# # img_tensor = img_tensor.detach().cpu().float()
# # img_tensor = (img_tensor + 1) / 2 # back to [0,1]
# # img_tensor = img_tensor.clamp(0, 1)
# # return (img_tensor * 255).byte()
# print(" depth video frames shape ", depth_video_frames.shape)
# print("depth_recon : ", depth_recon.shape)
# def save_depth_video(tensor_frames, name, fps=5):
# frames = tensor_frames.float().cpu().numpy() # T H W C
# # input()
# video_path = os.path.join(f"{name}.mp4")
# imageio.mimwrite(video_path, frames, fps=fps)
# save_depth_video(depth_video_frames.permute(1,2,3,0), "depth_input")
# save_depth_video(torch.tensor(depth_recon).squeeze(0).permute(0,2,3,1), "depth_recon")
# assert 0
data = {
"rgb_latent": rgb_latent, # [C_vace, T_latent, H_latent, W_latent]
"depth_latent": depth_latent, # [C_vace, T_latent, H_latent, W_latent]
# "rgb_original": video_frames, # 3, T, H, W
# "depth_original": depth_videos, # 1, T , H, W before log norm
"rgb_mp4_path":rgb_mp4_path,
"depth_mp4_path":depth_mp4_path,
"num_frames": num_frames,
"frame_interval": frame_interval,
"intrinsic": cam_data["intrinsic"][0], # [3, 3]
"extrinsic": cam_data["extrinsic"][0], # [4, 4]
"log_depth_bounds": log_depth_bounds, # tuple
"action": action
}
if self.debug_flag == True:
for key, value in data.items():
if isinstance(value, torch.Tensor):
print(f"{key}: type {type(value)}, shape {value.shape}, dtype {value.dtype}, device {value.device}")
else:
print(f"{key}: type {type(value)}, value {value}")
self.debug_flag = False
# assert 0
# print(f"Saving camera embeddings to {cam_emb_pth_path}")
if self.overwrite and os.path.exists(cam_emb_pth_path):
os.remove(cam_emb_pth_path)
torch.save(data, cam_emb_pth_path)
else:
print(f"File {cam_emb_pth_path} already exists, skipping.")
class Camera(object):
def __init__(self, c2w):
c2w_mat = np.array(c2w).reshape(4, 4)
self.c2w_mat = c2w_mat
self.w2c_mat = np.linalg.inv(c2w_mat)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description="Preprocess RLBench (depth) videos into Wan2.2 latent cache (*.pth).")
parser.add_argument("--data_dir", required=True,
help="Extracted dataset root for this source (see the dataset README).")
parser.add_argument("--model_dir", default="./model_weights/Wan-AI/Wan2.2-TI2V-5B",
help="Dir holding Wan2.2 VAE (Wan2.2_VAE.pth), the T5 text encoder "
"(models_t5_umt5-xxl-enc-bf16.safetensors or .pth) and the "
"google/umt5-xxl/ tokenizer folder. Download from Wan-AI/Wan2.2-TI2V-5B.")
parser.add_argument("--num_frames", type=int, default=41)
parser.add_argument("--height", type=int, default=240)
parser.add_argument("--width", type=int, default=320)
parser.add_argument("--num_workers", type=int, default=1)
args = parser.parse_args()
# locate the T5 text-encoder weight (.safetensors preferred, .pth fallback)
text_encoder_path = None
for _name in ("models_t5_umt5-xxl-enc-bf16.safetensors", "models_t5_umt5-xxl-enc-bf16.pth"):
_p = os.path.join(args.model_dir, _name)
if os.path.exists(_p):
text_encoder_path = _p
break
if text_encoder_path is None:
raise FileNotFoundError(f"T5 text encoder not found under {args.model_dir}")
vae_path = os.path.join(args.model_dir, "Wan2.2_VAE.pth")
text_video_dataset = RLBenchTextVideoDataset(
base_path=args.data_dir,
metadata_path="",
max_num_frames=args.num_frames,
frame_interval=1,
num_frames=args.num_frames,
height=args.height,
width=args.width,
is_i2v=False,
)
text_video_dataloader = torch.utils.data.DataLoader(
text_video_dataset,
shuffle=False,
batch_size=1,
num_workers=args.num_workers,
prefetch_factor=1,
pin_memory=True,
pin_memory_device="cuda",
)
data_preprocess_model = LightningModelForDataProcess(
text_encoder_path=text_encoder_path,
vae_path=vae_path,
image_encoder_path=None,
)
trainer = pl.Trainer(
accelerator="gpu",
devices="auto",
default_root_dir=args.data_dir,
)
trainer.test(data_preprocess_model, text_video_dataloader)
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