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RefineDNet/data/unaligned_dataset.py

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+import os.path
+from data.base_dataset import BaseDataset, get_transform, get_params
+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')
+
+        params_A = get_params(self.opt, A_img.size)
+        params_B = get_params(self.opt, B_img.size)
+
+        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
+        transform_A = get_transform(self.opt, params=params_A, grayscale=(input_nc == 1))
+        transform_B = get_transform(self.opt, params=params_B, grayscale=(output_nc == 1))
+        # apply image transformation
+        A = transform_A(A_img)
+        B = 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)