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import torch
import torchvision.transforms.functional as F
import warnings
import random
import numpy as np
import torchvision
from PIL import Image, ImageOps
import numbers
class GroupRandomCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, img_tuple):
img_group, label = img_tuple
w, h = img_group[0].size
th, tw = self.size
out_images = list()
x1 = random.randint(0, w - tw)
y1 = random.randint(0, h - th)
for img in img_group:
assert(img.size[0] == w and img.size[1] == h)
if w == tw and h == th:
out_images.append(img)
else:
out_images.append(img.crop((x1, y1, x1 + tw, y1 + th)))
return (out_images, label)
class GroupCenterCrop(object):
def __init__(self, size):
self.worker = torchvision.transforms.CenterCrop(size)
def __call__(self, img_tuple):
img_group, label = img_tuple
return ([self.worker(img) for img in img_group], label)
class GroupRandomHorizontalFlip(object):
def __init__(self, flip=False):
self.flip = flip
def __call__(self, img_tuple):
v = random.random()
if self.flip and v < 0.5:
img_group, label = img_tuple
ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group]
return (ret, label)
else:
return img_tuple
class GroupNormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor_tuple):
tensor, label = tensor_tuple
rep_mean = self.mean * (tensor.size()[0]//len(self.mean))
rep_std = self.std * (tensor.size()[0]//len(self.std))
# TODO: make efficient
for t, m, s in zip(tensor, rep_mean, rep_std):
t.sub_(m).div_(s)
return (tensor,label)
class GroupGrayScale(object):
def __init__(self, size):
self.worker = torchvision.transforms.Grayscale(size)
def __call__(self, img_tuple):
img_group, label = img_tuple
return ([self.worker(img) for img in img_group], label)
class GroupColorJitter(object):
def __init__(self, size):
self.worker = torchvision.transforms.ColorJitter(
brightness=size, contrast=size, saturation=size
)
def __call__(self, img_tuple):
img_group, label = img_tuple
return ([self.worker(img) for img in img_group], label)
class GroupScale(object):
""" Rescales the input PIL.Image to the given 'size'.
'size' will be the size of the smaller edge.
For example, if height > width, then image will be
rescaled to (size * height / width, size)
size: size of the smaller edge
interpolation: Default: PIL.Image.BILINEAR
"""
def __init__(self, size, interpolation=Image.BILINEAR):
self.worker = torchvision.transforms.Resize(size, interpolation)
def __call__(self, img_tuple):
img_group, label = img_tuple
return ([self.worker(img) for img in img_group], label)
class GroupMultiScaleCrop(object):
def __init__(self, input_size, scales=None, max_distort=1, fix_crop=True, more_fix_crop=True):
self.scales = scales if scales is not None else [1, 875, .75, .66]
self.max_distort = max_distort
self.fix_crop = fix_crop
self.more_fix_crop = more_fix_crop
self.input_size = input_size if not isinstance(input_size, int) else [input_size, input_size]
self.interpolation = Image.BILINEAR
def __call__(self, img_tuple):
img_group, label = img_tuple
im_size = img_group[0].size
crop_w, crop_h, offset_w, offset_h = self._sample_crop_size(im_size)
crop_img_group = [img.crop((offset_w, offset_h, offset_w + crop_w, offset_h + crop_h)) for img in img_group]
ret_img_group = [img.resize((self.input_size[0], self.input_size[1]), self.interpolation) for img in crop_img_group]
return (ret_img_group, label)
def _sample_crop_size(self, im_size):
image_w, image_h = im_size[0], im_size[1]
# find a crop size
base_size = min(image_w, image_h)
crop_sizes = [int(base_size * x) for x in self.scales]
crop_h = [self.input_size[1] if abs(x - self.input_size[1]) < 3 else x for x in crop_sizes]
crop_w = [self.input_size[0] if abs(x - self.input_size[0]) < 3 else x for x in crop_sizes]
pairs = []
for i, h in enumerate(crop_h):
for j, w in enumerate(crop_w):
if abs(i - j) <= self.max_distort:
pairs.append((w, h))
crop_pair = random.choice(pairs)
if not self.fix_crop:
w_offset = random.randint(0, image_w - crop_pair[0])
h_offset = random.randint(0, image_h - crop_pair[1])
else:
w_offset, h_offset = self._sample_fix_offset(image_w, image_h, crop_pair[0], crop_pair[1])
return crop_pair[0], crop_pair[1], w_offset, h_offset
def _sample_fix_offset(self, image_w, image_h, crop_w, crop_h):
offsets = self.fill_fix_offset(self.more_fix_crop, image_w, image_h, crop_w, crop_h)
return random.choice(offsets)
@staticmethod
def fill_fix_offset(more_fix_crop, image_w, image_h, crop_w, crop_h):
w_step = (image_w - crop_w) // 4
h_step = (image_h - crop_h) // 4
ret = list()
ret.append((0, 0)) # upper left
ret.append((4 * w_step, 0)) # upper right
ret.append((0, 4 * h_step)) # lower left
ret.append((4 * w_step, 4 * h_step)) # lower right
ret.append((2 * w_step, 2 * h_step)) # center
if more_fix_crop:
ret.append((0, 2 * h_step)) # center left
ret.append((4 * w_step, 2 * h_step)) # center right
ret.append((2 * w_step, 4 * h_step)) # lower center
ret.append((2 * w_step, 0 * h_step)) # upper center
ret.append((1 * w_step, 1 * h_step)) # upper left quarter
ret.append((3 * w_step, 1 * h_step)) # upper right quarter
ret.append((1 * w_step, 3 * h_step)) # lower left quarter
ret.append((3 * w_step, 3 * h_step)) # lower righ quarter
return ret
class Stack(object):
def __init__(self, roll=False):
self.roll = roll
def __call__(self, img_tuple):
img_group, label = img_tuple
if img_group[0].mode == 'L':
return (np.concatenate([np.expand_dims(x, 2) for x in img_group], axis=2), label)
elif img_group[0].mode == 'RGB':
if self.roll:
return (np.concatenate([np.array(x)[:, :, ::-1] for x in img_group], axis=2), label)
else:
return (np.concatenate(img_group, axis=2), label)
class ToTorchFormatTensor(object):
""" Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
def __init__(self, div=True):
self.div = div
def __call__(self, pic_tuple):
pic, label = pic_tuple
if isinstance(pic, np.ndarray):
# handle numpy array
img = torch.from_numpy(pic).permute(2, 0, 1).contiguous()
else:
# handle PIL Image
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
img = img.view(pic.size[1], pic.size[0], len(pic.mode))
# put it from HWC to CHW format
# yikes, this transpose takes 80% of the loading time/CPU
img = img.transpose(0, 1).transpose(0, 2).contiguous()
return (img.float().div(255.) if self.div else img.float(), label)
class IdentityTransform(object):
def __call__(self, data):
return data
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