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619344d | 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 | # Copyright (c) 2025 Hansheng Chen
import os
import numpy as np
import torch
import mmcv
from io import BytesIO
from PIL import Image
from torch.utils.data import Dataset
from mmcv.fileio import FileClient
from mmcv.parallel import DataContainer as DC
from mmgen.datasets.builder import DATASETS
from mmgen.utils import get_root_logger
def image_preproc(pil_image, image_size, random_flip=False):
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(tuple(x // 2 for x in pil_image.size), resample=Image.BOX)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
arr = arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size]
if random_flip and np.random.rand() < 0.5:
arr = np.ascontiguousarray(arr[:, ::-1])
if arr.ndim == 2:
arr = np.stack([arr] * 3, axis=-1)
elif arr.ndim == 3:
if arr.shape[2] == 1:
arr = np.concatenate([arr] * 3, axis=-1)
elif arr.shape[2] == 4:
arr = arr[:, :, :3]
else:
assert arr.shape[2] == 3
else:
raise ValueError(f'Unexpected number of dimensions: {arr.ndim}')
return arr
@DATASETS.register_module()
class ImageNet(Dataset):
def __init__(
self,
data_root='data/imagenet/train',
datalist_path='data/imagenet/train.txt',
label2name_path='data/imagenet/imagenet1000_clsidx_to_labels.txt',
random_flip=True,
negative_label=1000,
image_size=256,
latent_size=(4, 32, 32),
test_label_repeat=1,
test_mode=False,
num_test_images=50000):
super().__init__()
self.data_root = data_root
self.file_client = FileClient.infer_client(uri=self.data_root)
self.datalist_path = datalist_path
self.label2name_path = label2name_path
self.random_flip = random_flip
self.negative_label = negative_label
self.image_size = image_size
self.latent_size = latent_size
self.test_label_repeat = test_label_repeat
self.test_mode = test_mode
self.num_test_images = num_test_images
self.label2name = {}
label2name_text = FileClient.infer_client(uri=self.label2name_path).get_text(self.label2name_path)
for line in label2name_text.split('\n'):
line = line.strip()
if len(line) == 0:
continue
idx, name = line.split(':')
idx, name = idx.strip(), name.strip()
if name[-1] == ',':
name = name[:-1]
if name[0] == '"' and name[-1] == '"':
name = name[1:-1]
if name[0] == "'" and name[-1] == "'":
name = name[1:-1]
self.label2name[int(idx)] = name
if not test_mode:
self.all_paths = []
self.all_labels = []
datalist_text = FileClient.infer_client(uri=self.datalist_path).get_text(self.datalist_path)
for line in datalist_text.split('\n'):
line = line.strip()
if len(line) == 0:
continue
path_label = line.split(' ')
self.all_paths.append(path_label[0])
if len(path_label) > 1:
self.all_labels.append(int(path_label[1]))
logger = get_root_logger()
mmcv.print_log(f'Data root: {self.data_root}', logger=logger)
mmcv.print_log(f'Data list path: {self.datalist_path}', logger=logger)
mmcv.print_log(f'Number of images: {len(self.all_paths)}', logger=logger)
def __len__(self):
return self.num_test_images if self.test_mode else len(self.all_paths)
def __getitem__(self, idx):
data = dict(ids=DC(idx, cpu_only=True))
if self.test_mode:
label_generator = torch.Generator().manual_seed(idx // self.test_label_repeat)
label = torch.randint(0, 1000, (), generator=label_generator).long()
noise_generator = torch.Generator().manual_seed(idx + 1000)
noise = torch.randn(self.latent_size, generator=noise_generator)
data.update(noise=noise)
else:
rel_data_path = self.all_paths[idx]
data.update(paths=DC(rel_data_path, cpu_only=True))
data_path = self.file_client.join_path(self.data_root, rel_data_path)
data_bytesio = BytesIO(self.file_client.get(data_path))
ext = os.path.splitext(data_path)[-1]
if ext.lower() in ('.pth', '.pt'):
torch_data = torch.load(data_bytesio, map_location='cpu')
label = torch_data['y'].long()
data.update(latents=torch_data['x'].float())
elif ext.lower() in ('.jpg', '.jpeg', '.png'):
label = torch.tensor(self.all_labels[idx], dtype=torch.long)
img_data = Image.open(data_bytesio)
data.update(
images=torch.from_numpy(image_preproc(
img_data, self.image_size, random_flip=self.random_flip)).float().permute(2, 0, 1) / 255.0)
else:
raise ValueError(f'Unsupported file extension: {ext}')
name = self.label2name[label.item()]
data.update(labels=label, name=DC(name, cpu_only=True))
if self.negative_label is not None:
if isinstance(self.negative_label, int):
data.update(negative_labels=torch.tensor(self.negative_label, dtype=torch.long))
else:
raise ValueError(f'Unsupported negative label: {self.negative_label}')
return data
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