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98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
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### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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import torch.utils.data as data
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from PIL import Image
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import torchvision.transforms as transforms
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import numpy as np
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import random
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class BaseDataset(data.Dataset):
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def __init__(self):
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super(BaseDataset, self).__init__()
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def name(self):
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return 'BaseDataset'
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def initialize(self, opt):
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pass
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def get_params(opt, size):
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w, h = size
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new_h = h
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new_w = w
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if opt.resize_or_crop == 'resize_and_crop':
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new_h = new_w = opt.loadSize
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elif opt.resize_or_crop == 'scale_width_and_crop':
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new_w = opt.loadSize
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new_h = opt.loadSize * h // w
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x = random.randint(0, np.maximum(0, new_w - opt.fineSize))
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y = random.randint(0, np.maximum(0, new_h - opt.fineSize))
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#flip = random.random() > 0.5
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flip = 0
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return {'crop_pos': (x, y), 'flip': flip}
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def get_transform(opt, params, method=Image.BICUBIC, normalize=True, normalize_mask=False):
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transform_list = []
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if 'resize' in opt.resize_or_crop:
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osize = [opt.loadSize, opt.loadSize]
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transform_list.append(transforms.Scale(osize, method))
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elif 'scale_width' in opt.resize_or_crop:
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transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.loadSize, method)))
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if 'crop' in opt.resize_or_crop:
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transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.fineSize)))
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if opt.resize_or_crop == 'none':
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base = float(2 ** opt.n_downsample_global)
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if opt.netG == 'local':
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base *= (2 ** opt.n_local_enhancers)
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transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))
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if opt.isTrain and not opt.no_flip:
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transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
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transform_list += [transforms.ToTensor()]
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if normalize:
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transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
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(0.5, 0.5, 0.5))]
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if normalize_mask:
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transform_list += [transforms.Normalize((0, 0, 0),
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(1 / 255., 1 / 255., 1 / 255.))]
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return transforms.Compose(transform_list)
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def normalize():
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return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
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def __make_power_2(img, base, method=Image.BICUBIC):
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ow, oh = img.size
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h = int(round(oh / base) * base)
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w = int(round(ow / base) * base)
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if (h == oh) and (w == ow):
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return img
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return img.resize((w, h), method)
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def __scale_width(img, target_width, method=Image.BICUBIC):
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ow, oh = img.size
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if (ow == target_width):
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return img
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w = target_width
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h = int(target_width * oh / ow)
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return img.resize((w, h), method)
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def __crop(img, pos, size):
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ow, oh = img.size
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x1, y1 = pos
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tw = th = size
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if (ow > tw or oh > th):
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return img.crop((x1, y1, x1 + tw, y1 + th))
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return img
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def __flip(img, flip):
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if flip:
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return img.transpose(Image.FLIP_LEFT_RIGHT)
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return img
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