1 files changed, 63 insertions, 0 deletions
diff --git a/data/unaligned_data_loader.py b/data/unaligned_data_loader.py
new file mode 100644
index 0000000..95d9ac7
--- /dev/null
+++ b/data/unaligned_data_loader.py
@@ -0,0 +1,63 @@
+import torch.utils.data
+import torchvision.transforms as transforms
+from data.base_data_loader import BaseDataLoader
+from data.image_folder import ImageFolder
+from builtins import object
+
+
+class PairedData(object):
+    def __init__(self, data_loader_A, data_loader_B):
+        self.data_loader_A = data_loader_A
+        self.data_loader_B = data_loader_B
+
+    def __iter__(self):
+        self.data_loader_A_iter = iter(self.data_loader_A)
+        self.data_loader_B_iter = iter(self.data_loader_B)
+        return self
+
+    def __next__(self):
+        A, A_paths = next(self.data_loader_A_iter)
+        B, B_paths = next(self.data_loader_B_iter)
+        return {'A': A, 'A_paths': A_paths,
+                'B': B, 'B_paths': B_paths}
+
+
+class UnalignedDataLoader(BaseDataLoader):
+    def initialize(self, opt):
+        BaseDataLoader.initialize(self, opt)
+        transform = transforms.Compose([
+                                       transforms.Scale(opt.loadSize),
+                                       transforms.CenterCrop(opt.fineSize),
+                                       transforms.ToTensor(),
+                                       transforms.Normalize((0.5, 0.5, 0.5),
+                                                            (0.5, 0.5, 0.5))])
+
+        # Dataset A
+        dataset_A = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'A',
+                                transform=transform, return_paths=True)
+        data_loader_A = torch.utils.data.DataLoader(
+            dataset_A,
+            batch_size=self.opt.batchSize,
+            shuffle=not self.opt.serial_batches,
+            num_workers=int(self.opt.nThreads))
+
+        # Dataset B
+        dataset_B = ImageFolder(root=opt.dataroot + '/' + opt.phase + 'B',
+                                transform=transform, return_paths=True)
+        data_loader_B = torch.utils.data.DataLoader(
+            dataset_B,
+            batch_size=self.opt.batchSize,
+            shuffle=not self.opt.serial_batches,
+            num_workers=int(self.opt.nThreads))
+        self.dataset_A = dataset_A
+        self.dataset_B = dataset_B
+        self.paired_data = PairedData(data_loader_A, data_loader_B)
+
+    def name(self):
+        return 'UnalignedDataLoader'
+
+    def load_data(self):
+        return self.paired_data
+
+    def __len__(self):
+        return len(self.dataset_A)