external/Grounded-Segment-Anything/recognize-anything/ram/data/__init__.py

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from .dataset import pretrain_dataset, finetune_dataset
from .randaugment import RandomAugment

def create_dataset(dataset, config, min_scale=0.5):
    
    normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))

    transform_train = transforms.Compose([                        
            transforms.RandomResizedCrop(config['image_size'],scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC),
            transforms.RandomHorizontalFlip(),
            RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize',
                                              'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),     
            transforms.ToTensor(),
            normalize,
        ])        

    transform_inputsize_224 = transforms.Compose([                        
            transforms.RandomResizedCrop(224,scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC),
            transforms.RandomHorizontalFlip(),
            RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize',
                                              'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']),     
            transforms.ToTensor(),
            normalize,
        ])     

    if dataset=='pretrain':
        dataset = pretrain_dataset(config['train_file'], transform_train, class_num=config['class_num'], root=config['image_path_root'])              
        return dataset  

    elif dataset=='finetune':   
        dataset = finetune_dataset(config['train_file'], transform_train, transform_inputsize_224, class_num=config['class_num'], root=config['image_path_root'])              
        return dataset  


    
    
def create_sampler(datasets, shuffles, num_tasks, global_rank):
    samplers = []
    for dataset,shuffle in zip(datasets,shuffles):
        sampler = torch.utils.data.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle)
        samplers.append(sampler)
    return samplers     


def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns):
    loaders = []
    for dataset,sampler,bs,n_worker,is_train,collate_fn in zip(datasets,samplers,batch_size,num_workers,is_trains,collate_fns):
        if is_train:
            shuffle = (sampler is None)
            drop_last = True
        else:
            shuffle = False
            drop_last = False
        loader = DataLoader(
            dataset,
            batch_size=bs,
            num_workers=n_worker,
            pin_memory=True,
            sampler=sampler,
            shuffle=shuffle,
            collate_fn=collate_fn,
            drop_last=drop_last,
        )              
        loaders.append(loader)
    return loaders