Spaces:
Running
on
Zero
Running
on
Zero
File size: 12,976 Bytes
eadd7b4 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 |
import os
import random
from PIL import Image
import numpy as np
import torch
from torchvision.datasets.folder import default_loader, IMG_EXTENSIONS
from torch.utils.data import Dataset
from diffusers.utils.torch_utils import randn_tensor
from torchvision import transforms as T
from diffusion.data.builder import get_data_path, DATASETS
from diffusion.utils.logger import get_root_logger
import json
@DATASETS.register_module()
class InternalData(Dataset):
def __init__(self,
root,
image_list_json='data_info.json',
transform=None,
resolution=256,
sample_subset=None,
load_vae_feat=False,
input_size=32,
patch_size=2,
mask_ratio=0.0,
load_mask_index=False,
max_length=120,
config=None,
**kwargs):
self.root = get_data_path(root)
self.transform = transform
self.load_vae_feat = load_vae_feat
self.ori_imgs_nums = 0
self.resolution = resolution
self.N = int(resolution // (input_size // patch_size))
self.mask_ratio = mask_ratio
self.load_mask_index = load_mask_index
self.max_lenth = max_length
self.meta_data_clean = []
self.img_samples = []
self.txt_feat_samples = []
self.vae_feat_samples = []
self.mask_index_samples = []
self.prompt_samples = []
image_list_json = image_list_json if isinstance(image_list_json, list) else [image_list_json]
for json_file in image_list_json:
meta_data = self.load_json(os.path.join(self.root, 'partition', json_file))
self.ori_imgs_nums += len(meta_data)
meta_data_clean = [item for item in meta_data if item['ratio'] <= 4]
self.meta_data_clean.extend(meta_data_clean)
self.img_samples.extend([os.path.join(self.root.replace('InternData', "InternImgs"), item['path']) for item in meta_data_clean])
self.txt_feat_samples.extend([os.path.join(self.root, 'caption_feature_wmask', '_'.join(item['path'].rsplit('/', 1)).replace('.png', '.npz')) for item in meta_data_clean])
self.vae_feat_samples.extend([os.path.join(self.root, f'img_vae_features_{resolution}resolution/noflip', '_'.join(item['path'].rsplit('/', 1)).replace('.png', '.npy')) for item in meta_data_clean])
self.prompt_samples.extend([item['prompt'] for item in meta_data_clean])
# Set loader and extensions
if load_vae_feat:
self.transform = None
self.loader = self.vae_feat_loader
else:
self.loader = default_loader
if sample_subset is not None:
self.sample_subset(sample_subset) # sample dataset for local debug
logger = get_root_logger() if config is None else get_root_logger(os.path.join(config.work_dir, 'train_log.log'))
logger.info(f"T5 max token length: {self.max_lenth}")
def getdata(self, index):
img_path = self.img_samples[index]
npz_path = self.txt_feat_samples[index]
npy_path = self.vae_feat_samples[index]
prompt = self.prompt_samples[index]
data_info = {
'img_hw': torch.tensor([torch.tensor(self.resolution), torch.tensor(self.resolution)], dtype=torch.float32),
'aspect_ratio': torch.tensor(1.)
}
img = self.loader(npy_path) if self.load_vae_feat else self.loader(img_path)
txt_info = np.load(npz_path)
txt_fea = torch.from_numpy(txt_info['caption_feature']) # 1xTx4096
attention_mask = torch.ones(1, 1, txt_fea.shape[1]) # 1x1xT
if 'attention_mask' in txt_info.keys():
attention_mask = torch.from_numpy(txt_info['attention_mask'])[None]
if txt_fea.shape[1] != self.max_lenth:
txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_lenth-txt_fea.shape[1], 1)], dim=1)
attention_mask = torch.cat([attention_mask, torch.zeros(1, 1, self.max_lenth-attention_mask.shape[-1])], dim=-1)
if self.transform:
img = self.transform(img)
data_info['prompt'] = prompt
return img, txt_fea, attention_mask, data_info
def __getitem__(self, idx):
for _ in range(20):
try:
return self.getdata(idx)
except Exception as e:
print(f"Error details: {str(e)}")
idx = np.random.randint(len(self))
raise RuntimeError('Too many bad data.')
def get_data_info(self, idx):
data_info = self.meta_data_clean[idx]
return {'height': data_info['height'], 'width': data_info['width']}
@staticmethod
def vae_feat_loader(path):
# [mean, std]
mean, std = torch.from_numpy(np.load(path)).chunk(2)
sample = randn_tensor(mean.shape, generator=None, device=mean.device, dtype=mean.dtype)
return mean + std * sample
def load_ori_img(self, img_path):
# 加载图像并转换为Tensor
transform = T.Compose([
T.Resize(256), # Image.BICUBIC
T.CenterCrop(256),
T.ToTensor(),
])
return transform(Image.open(img_path))
def load_json(self, file_path):
with open(file_path, 'r') as f:
meta_data = json.load(f)
return meta_data
def sample_subset(self, ratio):
sampled_idx = random.sample(list(range(len(self))), int(len(self) * ratio))
self.img_samples = [self.img_samples[i] for i in sampled_idx]
def __len__(self):
return len(self.img_samples)
def __getattr__(self, name):
if name == "set_epoch":
return lambda epoch: None
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
@DATASETS.register_module()
class InternalDataSigma(Dataset):
def __init__(self,
root,
image_list_json='data_info.json',
transform=None,
resolution=256,
sample_subset=None,
load_vae_feat=False,
load_t5_feat=False,
input_size=32,
patch_size=2,
mask_ratio=0.0,
mask_type='null',
load_mask_index=False,
real_prompt_ratio=1.0,
max_length=300,
config=None,
**kwargs):
self.root = get_data_path(root)
self.transform = transform
self.load_vae_feat = load_vae_feat
self.load_t5_feat = load_t5_feat
self.ori_imgs_nums = 0
self.resolution = resolution
self.N = int(resolution // (input_size // patch_size))
self.mask_ratio = mask_ratio
self.load_mask_index = load_mask_index
self.mask_type = mask_type
self.real_prompt_ratio = real_prompt_ratio
self.max_lenth = max_length
self.meta_data_clean = []
self.img_samples = []
self.txt_samples = []
self.sharegpt4v_txt_samples = []
self.txt_feat_samples = []
self.vae_feat_samples = []
self.mask_index_samples = []
self.gpt4v_txt_feat_samples = []
self.weight_dtype = torch.float16 if self.real_prompt_ratio > 0 else torch.float32
logger = get_root_logger() if config is None else get_root_logger(os.path.join(config.work_dir, 'train_log.log'))
logger.info(f"T5 max token length: {self.max_lenth}")
logger.info(f"ratio of real user prompt: {self.real_prompt_ratio}")
image_list_json = image_list_json if isinstance(image_list_json, list) else [image_list_json]
for json_file in image_list_json:
meta_data = self.load_json(os.path.join(self.root, json_file))
logger.info(f"{json_file} data volume: {len(meta_data)}")
self.ori_imgs_nums += len(meta_data)
meta_data_clean = [item for item in meta_data if item['ratio'] <= 4.5]
self.meta_data_clean.extend(meta_data_clean)
self.img_samples.extend([
os.path.join(self.root.replace('InternData', 'InternImgs'), item['path']) for item in meta_data_clean
])
self.txt_samples.extend([item['prompt'] for item in meta_data_clean])
self.sharegpt4v_txt_samples.extend([item['sharegpt4v'] if 'sharegpt4v' in item else '' for item in meta_data_clean])
self.txt_feat_samples.extend([
os.path.join(
self.root,
'caption_features_new',
item['path'].rsplit('/', 1)[-1].replace('.png', '.npz')
) for item in meta_data_clean
])
self.gpt4v_txt_feat_samples.extend([
os.path.join(
self.root,
'sharegpt4v_caption_features_new',
item['path'].rsplit('/', 1)[-1].replace('.png', '.npz')
) for item in meta_data_clean
])
self.vae_feat_samples.extend(
[
os.path.join(
self.root,
f'img_sdxl_vae_features_{resolution}resolution_new',
item['path'].rsplit('/', 1)[-1].replace('.png', '.npy')
) for item in meta_data_clean
])
# Set loader and extensions
if load_vae_feat:
self.transform = None
self.loader = self.vae_feat_loader
else:
self.loader = default_loader
if sample_subset is not None:
self.sample_subset(sample_subset) # sample dataset for local debug
def getdata(self, index):
img_path = self.img_samples[index]
real_prompt = random.random() < self.real_prompt_ratio
npz_path = self.txt_feat_samples[index] if real_prompt else self.gpt4v_txt_feat_samples[index]
txt = self.txt_samples[index] if real_prompt else self.sharegpt4v_txt_samples[index]
npy_path = self.vae_feat_samples[index]
data_info = {'img_hw': torch.tensor([torch.tensor(self.resolution), torch.tensor(self.resolution)], dtype=torch.float32),
'aspect_ratio': torch.tensor(1.)}
if self.load_vae_feat:
img = self.loader(npy_path)
else:
img = self.loader(img_path)
attention_mask = torch.ones(1, 1, self.max_lenth) # 1x1xT
if self.load_t5_feat:
txt_info = np.load(npz_path)
txt_fea = torch.from_numpy(txt_info['caption_feature']) # 1xTx4096
if 'attention_mask' in txt_info.keys():
attention_mask = torch.from_numpy(txt_info['attention_mask'])[None]
if txt_fea.shape[1] != self.max_lenth:
txt_fea = torch.cat([txt_fea, txt_fea[:, -1:].repeat(1, self.max_lenth-txt_fea.shape[1], 1)], dim=1)
attention_mask = torch.cat([attention_mask, torch.zeros(1, 1, self.max_lenth-attention_mask.shape[-1])], dim=-1)
else:
txt_fea = txt
if self.transform:
img = self.transform(img)
data_info["mask_type"] = self.mask_type
return img, txt_fea, attention_mask.to(torch.int16), data_info
def __getitem__(self, idx):
for _ in range(20):
try:
data = self.getdata(idx)
return data
except Exception as e:
print(f"Error details {self.img_samples[idx]}: {str(e)}")
idx = np.random.randint(len(self))
raise RuntimeError('Too many bad data.')
def get_data_info(self, idx):
data_info = self.meta_data_clean[idx]
return {'height': data_info['height'], 'width': data_info['width']}
@staticmethod
def vae_feat_loader(path):
# [mean, std]
mean, std = torch.from_numpy(np.load(path)).chunk(2)
sample = randn_tensor(mean.shape, generator=None, device=mean.device, dtype=mean.dtype)
return mean + std * sample
def load_ori_img(self, img_path):
# 加载图像并转换为Tensor
transform = T.Compose([
T.Resize(256), # Image.BICUBIC
T.CenterCrop(256),
T.ToTensor(),
])
img = transform(Image.open(img_path))
return img
def load_json(self, file_path):
with open(file_path, 'r') as f:
meta_data = json.load(f)
return meta_data
def sample_subset(self, ratio):
sampled_idx = random.sample(list(range(len(self))), int(len(self) * ratio))
self.img_samples = [self.img_samples[i] for i in sampled_idx]
def __len__(self):
return len(self.img_samples)
def __getattr__(self, name):
if name == "set_epoch":
return lambda epoch: None
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
|