Spaces:
Runtime error
Runtime error
File size: 4,392 Bytes
0daa129 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
import json
import random
from typing import List
import numpy as np
import pandas as pd
import torch
import torchvision.transforms as transforms
from decord import VideoReader
from PIL import Image
from torch.utils.data import Dataset
from transformers import CLIPImageProcessor
class HumanDanceVideoDataset(Dataset):
def __init__(
self,
sample_rate,
n_sample_frames,
width,
height,
img_scale=(1.0, 1.0),
img_ratio=(0.9, 1.0),
drop_ratio=0.1,
data_meta_paths=["./data/fashion_meta.json"],
):
super().__init__()
self.sample_rate = sample_rate
self.n_sample_frames = n_sample_frames
self.width = width
self.height = height
self.img_scale = img_scale
self.img_ratio = img_ratio
vid_meta = []
for data_meta_path in data_meta_paths:
vid_meta.extend(json.load(open(data_meta_path, "r")))
self.vid_meta = vid_meta
self.clip_image_processor = CLIPImageProcessor()
self.pixel_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(height, width),
scale=self.img_scale,
ratio=self.img_ratio,
interpolation=transforms.InterpolationMode.BILINEAR,
),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.cond_transform = transforms.Compose(
[
transforms.RandomResizedCrop(
(height, width),
scale=self.img_scale,
ratio=self.img_ratio,
interpolation=transforms.InterpolationMode.BILINEAR,
),
transforms.ToTensor(),
]
)
self.drop_ratio = drop_ratio
def augmentation(self, images, transform, state=None):
if state is not None:
torch.set_rng_state(state)
if isinstance(images, List):
transformed_images = [transform(img) for img in images]
ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w)
else:
ret_tensor = transform(images) # (c, h, w)
return ret_tensor
def __getitem__(self, index):
video_meta = self.vid_meta[index]
video_path = video_meta["video_path"]
kps_path = video_meta["kps_path"]
video_reader = VideoReader(video_path)
kps_reader = VideoReader(kps_path)
assert len(video_reader) == len(
kps_reader
), f"{len(video_reader) = } != {len(kps_reader) = } in {video_path}"
video_length = len(video_reader)
clip_length = min(
video_length, (self.n_sample_frames - 1) * self.sample_rate + 1
)
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(
start_idx, start_idx + clip_length - 1, self.n_sample_frames, dtype=int
).tolist()
# read frames and kps
vid_pil_image_list = []
pose_pil_image_list = []
for index in batch_index:
img = video_reader[index]
vid_pil_image_list.append(Image.fromarray(img.asnumpy()))
img = kps_reader[index]
pose_pil_image_list.append(Image.fromarray(img.asnumpy()))
ref_img_idx = random.randint(0, video_length - 1)
ref_img = Image.fromarray(video_reader[ref_img_idx].asnumpy())
# transform
state = torch.get_rng_state()
pixel_values_vid = self.augmentation(
vid_pil_image_list, self.pixel_transform, state
)
pixel_values_pose = self.augmentation(
pose_pil_image_list, self.cond_transform, state
)
pixel_values_ref_img = self.augmentation(ref_img, self.pixel_transform, state)
clip_ref_img = self.clip_image_processor(
images=ref_img, return_tensors="pt"
).pixel_values[0]
sample = dict(
video_dir=video_path,
pixel_values_vid=pixel_values_vid,
pixel_values_pose=pixel_values_pose,
pixel_values_ref_img=pixel_values_ref_img,
clip_ref_img=clip_ref_img,
)
return sample
def __len__(self):
return len(self.vid_meta)
|