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Running
on
Zero
| 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) | |
| 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 | |