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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 code bases
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# --------------------------------------------------------'
import math
import random
import warnings
import torchvision.transforms.functional as F
from timm.data.transforms import interp_mode_to_str, _RANDOM_INTERPOLATION, str_to_interp_mode
class RandomResizedCropAndInterpolationWithTwoPic:
"""Crop the given PIL Image to random size and aspect ratio with random interpolation.
A crop of random size (default: of 0.08 to 1.0) of the original size and a random
aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.
Args:
size: expected output size of each edge
scale: range of size of the origin size cropped
ratio: range of aspect ratio of the origin aspect ratio cropped
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, second_size=None, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.),
interpolation='bilinear', second_interpolation='lanczos'):
if isinstance(size, tuple):
self.size = size
else:
self.size = (size, size)
if second_size is not None:
if isinstance(second_size, tuple):
self.second_size = second_size
else:
self.second_size = (second_size, second_size)
else:
self.second_size = None
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("range should be of kind (min, max)")
if interpolation == 'random':
self.interpolation = _RANDOM_INTERPOLATION
else:
self.interpolation = str_to_interp_mode(interpolation)
self.second_interpolation = str_to_interp_mode(second_interpolation)
self.scale = scale
self.ratio = ratio
@staticmethod
def get_params(img, scale, ratio):
"""Get parameters for ``crop`` for a random sized crop.
Args:
img (PIL Image): Image to be cropped.
scale (tuple): range of size of the origin size cropped
ratio (tuple): range of aspect ratio of the origin aspect ratio cropped
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for a random
sized crop.
"""
area = img.size[0] * img.size[1]
for attempt in range(10):
target_area = random.uniform(*scale) * area
log_ratio = (math.log(ratio[0]), math.log(ratio[1]))
aspect_ratio = math.exp(random.uniform(*log_ratio))
w = int(round(math.sqrt(target_area * aspect_ratio)))
h = int(round(math.sqrt(target_area / aspect_ratio)))
if w <= img.size[0] and h <= img.size[1]:
i = random.randint(0, img.size[1] - h)
j = random.randint(0, img.size[0] - w)
return i, j, h, w
# Fallback to central crop
in_ratio = img.size[0] / img.size[1]
if in_ratio < min(ratio):
w = img.size[0]
h = int(round(w / min(ratio)))
elif in_ratio > max(ratio):
h = img.size[1]
w = int(round(h * max(ratio)))
else: # whole image
w = img.size[0]
h = img.size[1]
i = (img.size[1] - h) // 2
j = (img.size[0] - w) // 2
return i, j, h, w
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped and resized.
Returns:
PIL Image: Randomly cropped and resized image.
"""
i, j, h, w = self.get_params(img, self.scale, self.ratio)
if isinstance(self.interpolation, (tuple, list)):
interpolation = random.choice(self.interpolation)
else:
interpolation = self.interpolation
if self.second_size is None:
return F.resized_crop(img, i, j, h, w, self.size, interpolation)
else:
return F.resized_crop(img, i, j, h, w, self.size, interpolation), \
F.resized_crop(img, i, j, h, w, self.second_size, self.second_interpolation)
def __repr__(self):
if isinstance(self.interpolation, (tuple, list)):
interpolate_str = ' '.join([interp_mode_to_str(x) for x in self.interpolation])
else:
interpolate_str = interp_mode_to_str(self.interpolation)
format_string = self.__class__.__name__ + '(size={0}'.format(self.size)
format_string += ', scale={0}'.format(tuple(round(s, 4) for s in self.scale))
format_string += ', ratio={0}'.format(tuple(round(r, 4) for r in self.ratio))
format_string += ', interpolation={0}'.format(interpolate_str)
if self.second_size is not None:
format_string += ', second_size={0}'.format(self.second_size)
format_string += ', second_interpolation={0}'.format(interp_mode_to_str(self.second_interpolation))
format_string += ')'
return format_string |