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# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# Based on timm, DINO and DeiT code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit/
# https://github.com/facebookresearch/dino
# --------------------------------------------------------'
import argparse
import os
import torch
import random
from torchvision import datasets, transforms
from timm.data.constants import \
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from transforms import RandomResizedCropAndInterpolationWithTwoPic, _pil_interp
from timm.data import create_transform, ImageDataset
from masking_generator import MaskingGenerator
from dataset_folder import ImageFolder
class DataAugmentationForBEiT(object):
def __init__(self, args):
imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
# oringinal beit data augmentation
self.common_transform = transforms.Compose([
transforms.ColorJitter(0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(p=0.5),
RandomResizedCropAndInterpolationWithTwoPic(
size=args.input_size, second_size=args.second_input_size, scale=(args.min_crop_scale, 1.0),
interpolation=args.train_interpolation, second_interpolation=args.second_interpolation,
),
])
self.patch_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor(mean),
std=torch.tensor(std))
])
self.visual_token_transform = transforms.Compose([
transforms.ToTensor(),])
self.masked_position_generator = MaskingGenerator(
args.window_size, num_masking_patches=args.num_mask_patches,
max_num_patches=args.max_mask_patches_per_block,
min_num_patches=args.min_mask_patches_per_block,
)
def __call__(self, image):
for_patches, for_visual_tokens = self.common_transform(image)
return \
self.patch_transform(for_patches), self.visual_token_transform(for_visual_tokens), \
self.masked_position_generator()
def __repr__(self):
repr = "(DataAugmentationForBEiT,\n"
repr += " common_transform = %s,\n" % str(self.common_transform)
repr += " patch_transform = %s,\n" % str(self.patch_transform)
repr += " visual_tokens_transform = %s,\n" % str(self.visual_token_transform)
repr += " Masked position generator = %s,\n" % str(self.masked_position_generator)
repr += ")"
return repr
def build_beit_pretraining_dataset(args):
transform = DataAugmentationForBEiT(args)
print("Data Aug = %s" % str(transform))
return ImageFolder(args.data_path, transform=transform)
############################################### Dataset and Transforms for Tokenizer Training #########################################################
def build_vqkd_dataset(is_train, args):
if is_train:
t = []
if args.color_jitter > 0.:
t.append(transforms.ColorJitter(args.color_jitter, args.color_jitter, args.color_jitter))
t.append(transforms.RandomResizedCrop(args.input_size, scale=(args.min_crop_scale, 1.0), interpolation=_pil_interp(args.train_interpolation)))
t.append(transforms.RandomHorizontalFlip(0.5))
t.append(transforms.ToTensor())
transform = transforms.Compose(t)
else:
t = []
if args.input_size < 384:
args.crop_pct = 224 / 256
else:
args.crop_pct = 1.0
size = int(args.input_size / args.crop_pct)
t.append(
transforms.Resize(size, interpolation=_pil_interp(args.train_interpolation)), # to maintain same ratio w.r.t. 224 images
)
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
transform = transforms.Compose(t)
print(f"{'Train' if is_train else 'Test'} Data Aug: {str(transform)}")
if args.data_set == 'image_folder':
if is_train:
return ImageFolder(args.data_path, transform=transform)
else:
if args.eval_data_path == '':
return ImageFolder(args.data_path, transform=transform)
else:
return ImageFolder(args.eval_data_path, transform=transform)
else:
raise NotImplementedError()
############################################### Dataset and Transforms for Ft #########################################################
def build_dataset(is_train, args):
transform = build_transform(is_train, args)
print("Transform = ")
if isinstance(transform, tuple):
for trans in transform:
print(" - - - - - - - - - - ")
for t in trans.transforms:
print(t)
else:
for t in transform.transforms:
print(t)
print("---------------------------")
if args.data_set == 'CIFAR':
dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform)
nb_classes = 100
elif args.data_set == 'IMNET':
root = os.path.join(args.data_path, 'train' if is_train else 'val')
dataset = datasets.ImageFolder(root, transform=transform)
nb_classes = 1000
elif args.data_set == "image_folder":
root = args.data_path if is_train else args.eval_data_path
index_file = args.image_folder_class_index_file
dataset = ImageFolder(root, transform=transform, index_file=index_file)
nb_classes = args.nb_classes
assert len(dataset.class_to_idx) == nb_classes
else:
raise NotImplementedError()
assert nb_classes == args.nb_classes
print("Number of the class = %d" % args.nb_classes)
return dataset, nb_classes
def build_transform(is_train, args):
resize_im = args.input_size > 32
imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
if is_train:
# this should always dispatch to transforms_imagenet_train
transform = create_transform(
input_size=args.input_size,
is_training=True,
color_jitter=args.color_jitter,
auto_augment=args.aa,
interpolation=args.train_interpolation,
re_prob=args.reprob,
re_mode=args.remode,
re_count=args.recount,
mean=mean,
std=std,
)
if not resize_im:
# replace RandomResizedCropAndInterpolation with
# RandomCrop
transform.transforms[0] = transforms.RandomCrop(
args.input_size, padding=4)
return transform
t = []
if resize_im:
if args.crop_pct is None:
if args.input_size < 384:
args.crop_pct = 224 / 256
else:
args.crop_pct = 1.0
size = int(args.input_size / args.crop_pct)
t.append(
transforms.Resize(size, interpolation=3), # to maintain same ratio w.r.t. 224 images
)
t.append(transforms.CenterCrop(args.input_size))
t.append(transforms.ToTensor())
t.append(transforms.Normalize(mean, std))
return transforms.Compose(t)
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