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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 rgbd_to_pointcloud, image_to_float_array, sample_frame_indices, get_instructions, find_data
class TextVideoDataset(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.data_list = find_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 __getitem__(self, data_id):
data_dict = self.data_list[data_id]
instructions_file_path = data_dict["instructions_path"]
instructions_list = get_instructions(instructions_file_path, num=3)
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()
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)
joint_action = jointdata["joint_action"].item()
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)
action = action[index_list]
out_dict["action"] = action
for cam_name, cam_data in metadata.items():
# if cam_name == "surround_camera_11":
intrinsic_cv = np.array(cam_data["intrinsic_cv"]).reshape(3, 3)
extrinsic_cv = np.eye(4, dtype=np.float64)
extrinsic_cv[:3,:] = np.array(cam_data["extrinsic_cv"])[:3,:]
rgb_mp4_path = os.path.join(data_dict["data_path"], data_dict["episode_name"], f"{cam_name}.mp4")
depth_mp4_path = os.path.join(data_dict["data_path"], data_dict["episode_name"], f"{cam_name}_depth.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 = torch.from_numpy(depth_frames).unsqueeze(0) # T H W -> 1 T H W
# break
cam_dict = dict()
cam_dict["intrinsic"] = intrinsic_cv
cam_dict["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)
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
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/
cache_path = cache_path.replace("/cache/episode", "/cache_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 = 0.3328 #3327
depth_max = 3.0000
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 and os.path.exists(cam_emb_pth_path):
os.remove(cam_emb_pth_path)
if 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
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 RobotWin (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=81)
parser.add_argument("--height", type=int, default=240)
parser.add_argument("--width", type=int, default=320)
parser.add_argument("--num_workers", type=int, default=2)
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 = TextVideoDataset(
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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