training/data/build_data.py

# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------

import queue
from typing import Dict, Sequence
import warnings
import os
import torch
import numpy as np
import torch.distributed as dist
from torchvision import datasets, transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import Mixup
from timm.data import create_transform


from .samplers import SubsetRandomSampler

def build_loader(config):

    config.defrost()
    dataset_train, _ = build_dataset(is_train=True, config=config)
    config.freeze()
    print(f"global rank {dist.get_rank()} successfully build train dataset")


    sampler_train = torch.utils.data.DistributedSampler(
        dataset_train,  shuffle=True
    )

    data_loader_train = torch.utils.data.DataLoader(
        dataset_train, sampler=sampler_train,
        batch_size=config.DATA.BATCH_SIZE,
        num_workers=config.DATA.NUM_WORKERS,
        pin_memory=config.DATA.PIN_MEMORY,
        drop_last=True,
        persistent_workers=True
    )

    #-----------------------------------Val Dataset-----------------------------------

    dataset_val, _ = build_dataset(is_train=False, config=config)
    print(f"global rank {dist.get_rank()} successfully build val dataset")

    indices = np.arange(dist.get_rank(), len(dataset_val), dist.get_world_size())
    sampler_val = SubsetRandomSampler(indices)

    data_loader_val = torch.utils.data.DataLoader(
        dataset_val, sampler=sampler_val,
        batch_size=config.DATA.BATCH_SIZE,
        shuffle=False,
        num_workers=config.DATA.NUM_WORKERS,
        pin_memory=config.DATA.PIN_MEMORY,
        drop_last=False,
        persistent_workers=True
    )
    
    # setup mixup / cutmix
    mixup_fn = None
    mixup_active = config.AUG.MIXUP > 0 or config.AUG.CUTMIX > 0. or config.AUG.CUTMIX_MINMAX is not None
    if mixup_active:
        mixup_fn = Mixup(
            mixup_alpha=config.AUG.MIXUP, cutmix_alpha=config.AUG.CUTMIX, cutmix_minmax=config.AUG.CUTMIX_MINMAX,
            prob=config.AUG.MIXUP_PROB, switch_prob=config.AUG.MIXUP_SWITCH_PROB, mode=config.AUG.MIXUP_MODE,
            label_smoothing=config.MODEL.LABEL_SMOOTHING, num_classes=config.MODEL.NUM_CLASSES)

    return dataset_train, dataset_val, data_loader_train, data_loader_val, mixup_fn


def build_dataset(is_train, config):
    transform = build_transform(is_train, config)
    if config.DATA.DATASET == 'imagenet':
        prefix = 'train' if is_train else 'val'
        root = os.path.join(config.DATA.DATA_PATH, prefix)
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 1000
    elif config.DATA.DATASET == 'imagenet22K':
        if is_train:
            root = config.DATA.DATA_PATH
        else:
            root = config.DATA.EVAL_DATA_PATH
        dataset = datasets.ImageFolder(root, transform=transform)
        nb_classes = 21841
    else:
        raise NotImplementedError("We only support ImageNet Now.")

    return dataset, nb_classes


def build_transform(is_train, config):
    resize_im = config.DATA.IMG_SIZE > 32
    if is_train:
        # this should always dispatch to transforms_imagenet_train
        transform = create_transform(
            input_size=config.DATA.IMG_SIZE,
            is_training=True,
            color_jitter=config.AUG.COLOR_JITTER if config.AUG.COLOR_JITTER > 0 else None,
            auto_augment=config.AUG.AUTO_AUGMENT if config.AUG.AUTO_AUGMENT != 'none' else None,
            re_prob=config.AUG.REPROB,
            re_mode=config.AUG.REMODE,
            re_count=config.AUG.RECOUNT,
            interpolation=config.DATA.INTERPOLATION,
        )
        if not resize_im:
            # replace RandomResizedCropAndInterpolation with
            # RandomCrop
            transform.transforms[0] = transforms.RandomCrop(config.DATA.IMG_SIZE, padding=4)
        return transform

    t = []
    if resize_im:
        if config.DATA.IMG_SIZE > 224:  
            t.append(
            transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE), 
                            interpolation=transforms.InterpolationMode.BICUBIC), 
        )
            print(f"Warping {config.DATA.IMG_SIZE} size input images...")
        elif config.TEST.CROP:
            size = int((256 / 224) * config.DATA.IMG_SIZE)
            t.append(
                transforms.Resize(size, interpolation=transforms.InterpolationMode.BICUBIC),
                # to maintain same ratio w.r.t. 224 images
            )
            t.append(transforms.CenterCrop(config.DATA.IMG_SIZE))
        else:
            t.append(
                transforms.Resize((config.DATA.IMG_SIZE, config.DATA.IMG_SIZE),
                                  interpolation=transforms.InterpolationMode.BICUBIC)
            )

    t.append(transforms.ToTensor())
    t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
    return transforms.Compose(t)