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