import os from pathlib import Path 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 them """ return max(self.A_size, self.B_size)