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| import os | |
| from data.base_dataset import BaseDataset, get_transform | |
| from data.image_folder import make_dataset | |
| from PIL import Image | |
| import random | |
| class UnalignedDataset(BaseDataset): | |
| """ | |
| This dataset class can load unaligned/unpaired datasets. | |
| It requires two directories to host training images from domain A '/path/to/data/trainA' | |
| and from domain B '/path/to/data/trainB' respectively. | |
| You can train the model with the dataset flag '--dataroot /path/to/data'. | |
| Similarly, you need to prepare two directories: | |
| '/path/to/data/testA' and '/path/to/data/testB' during test time. | |
| """ | |
| def __init__(self, opt): | |
| """Initialize this dataset class. | |
| Parameters: | |
| opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions | |
| """ | |
| BaseDataset.__init__(self, opt) | |
| self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA' | |
| self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB' | |
| self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA' | |
| self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB' | |
| self.A_size = len(self.A_paths) # get the size of dataset A | |
| self.B_size = len(self.B_paths) # get the size of dataset B | |
| btoA = self.opt.direction == 'BtoA' | |
| input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image | |
| output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image | |
| self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1)) | |
| self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1)) | |
| def __getitem__(self, index): | |
| """Return a data point and its metadata information. | |
| Parameters: | |
| index (int) -- a random integer for data indexing | |
| Returns a dictionary that contains A, B, A_paths and B_paths | |
| A (tensor) -- an image in the input domain | |
| B (tensor) -- its corresponding image in the target domain | |
| A_paths (str) -- image paths | |
| B_paths (str) -- image paths | |
| """ | |
| A_path = self.A_paths[index % self.A_size] # make sure index is within then range | |
| if self.opt.serial_batches: # make sure index is within then range | |
| index_B = index % self.B_size | |
| else: # randomize the index for domain B to avoid fixed pairs. | |
| index_B = random.randint(0, self.B_size - 1) | |
| B_path = self.B_paths[index_B] | |
| A_img = Image.open(A_path).convert('RGB') | |
| B_img = Image.open(B_path).convert('RGB') | |
| # apply image transformation | |
| A = self.transform_A(A_img) | |
| B = self.transform_B(B_img) | |
| return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path} | |
| def __len__(self): | |
| """Return the total number of images in the dataset. | |
| As we have two datasets with potentially different number of images, | |
| we take a maximum of | |
| """ | |
| return max(self.A_size, self.B_size) | |