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 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 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