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import torch |
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import os.path |
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import torchvision.transforms as transforms |
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from data.base_dataset import BaseDataset, get_transform |
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from data.image_folder import make_dataset |
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import random |
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from PIL import Image |
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import PIL |
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from pdb import set_trace as st |
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class UnalignedDataset(BaseDataset): |
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def initialize(self, opt): |
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self.opt = opt |
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self.root = opt.dataroot |
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self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') |
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self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') |
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self.A_paths = make_dataset(self.dir_A) |
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self.B_paths = make_dataset(self.dir_B) |
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self.A_paths = sorted(self.A_paths) |
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self.B_paths = sorted(self.B_paths) |
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self.A_size = len(self.A_paths) |
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self.B_size = len(self.B_paths) |
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transform_list = [transforms.ToTensor(), |
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transforms.Normalize((0.5, 0.5, 0.5), |
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(0.5, 0.5, 0.5))] |
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self.transform = transforms.Compose(transform_list) |
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def __getitem__(self, index): |
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A_path = self.A_paths[index % self.A_size] |
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B_path = self.B_paths[index % self.B_size] |
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A_img = Image.open(A_path).convert('RGB') |
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B_img = Image.open(B_path).convert('RGB') |
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A_size = A_img.size |
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B_size = B_img.size |
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A_size = A_size = (A_size[0]//16*16, A_size[1]//16*16) |
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B_size = B_size = (B_size[0]//16*16, B_size[1]//16*16) |
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A_img = A_img.resize(A_size, Image.BICUBIC) |
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B_img = B_img.resize(B_size, Image.BICUBIC) |
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A_img = self.transform(A_img) |
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B_img = self.transform(B_img) |
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if self.opt.resize_or_crop == 'no': |
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pass |
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else: |
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w = A_img.size(2) |
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h = A_img.size(1) |
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size = [8,16,22] |
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from random import randint |
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size_index = randint(0,2) |
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Cropsize = size[size_index]*16 |
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w_offset = random.randint(0, max(0, w - Cropsize - 1)) |
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h_offset = random.randint(0, max(0, h - Cropsize - 1)) |
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A_img = A_img[:, h_offset:h_offset + Cropsize, |
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w_offset:w_offset + Cropsize] |
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if (not self.opt.no_flip) and random.random() < 0.5: |
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idx = [i for i in range(A_img.size(2) - 1, -1, -1)] |
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idx = torch.LongTensor(idx) |
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A_img = A_img.index_select(2, idx) |
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B_img = B_img.index_select(2, idx) |
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if (not self.opt.no_flip) and random.random() < 0.5: |
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idx = [i for i in range(A_img.size(1) - 1, -1, -1)] |
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idx = torch.LongTensor(idx) |
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A_img = A_img.index_select(1, idx) |
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B_img = B_img.index_select(1, idx) |
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return {'A': A_img, 'B': B_img, |
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'A_paths': A_path, 'B_paths': B_path} |
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def __len__(self): |
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return max(self.A_size, self.B_size) |
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def name(self): |
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return 'UnalignedDataset' |
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