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on
Zero
Running
on
Zero
import os | |
import time | |
from mmcv import Registry, build_from_cfg | |
from torch.utils.data import DataLoader | |
from diffusion.data.transforms import get_transform | |
from diffusion.utils.logger import get_root_logger | |
DATASETS = Registry('datasets') | |
DATA_ROOT = '/cache/data' | |
def set_data_root(data_root): | |
global DATA_ROOT | |
DATA_ROOT = data_root | |
def get_data_path(data_dir): | |
if os.path.isabs(data_dir): | |
return data_dir | |
global DATA_ROOT | |
return os.path.join(DATA_ROOT, data_dir) | |
def build_dataset(cfg, resolution=224, **kwargs): | |
logger = get_root_logger() | |
dataset_type = cfg.get('type') | |
logger.info(f"Constructing dataset {dataset_type}...") | |
t = time.time() | |
transform = cfg.pop('transform', 'default_train') | |
transform = get_transform(transform, resolution) | |
dataset = build_from_cfg(cfg, DATASETS, default_args=dict(transform=transform, resolution=resolution, **kwargs)) | |
logger.info(f"Dataset {dataset_type} constructed. time: {(time.time() - t):.2f} s, length (use/ori): {len(dataset)}/{dataset.ori_imgs_nums}") | |
return dataset | |
def build_dataloader(dataset, batch_size=256, num_workers=4, shuffle=True, **kwargs): | |
if 'batch_sampler' in kwargs: | |
dataloader = DataLoader(dataset, batch_sampler=kwargs['batch_sampler'], num_workers=num_workers, pin_memory=True) | |
else: | |
dataloader = DataLoader(dataset, | |
batch_size=batch_size, | |
shuffle=shuffle, | |
num_workers=num_workers, | |
pin_memory=True, | |
**kwargs) | |
return dataloader | |