1 files changed, 69 insertions, 0 deletions
diff --git a/data/aligned_data_loader.py b/data/aligned_data_loader.py
new file mode 100644
index 0000000..01dbf89
--- /dev/null
+++ b/data/aligned_data_loader.py
@@ -0,0 +1,69 @@
+import random
+import torch.utils.data
+import torchvision.transforms as transforms
+from data.base_data_loader import BaseDataLoader
+from data.image_folder import ImageFolder
+from pdb import set_trace as st
+from builtins import object
+
+class PairedData(object):
+    def __init__(self, data_loader, fineSize):
+        self.data_loader = data_loader
+        self.fineSize = fineSize
+        # st()
+
+    def __iter__(self):
+        self.data_loader_iter = iter(self.data_loader)
+        return self
+
+    def __next__(self):
+        # st()
+        AB, AB_paths = next(self.data_loader_iter)
+        # st()
+        w_total = AB.size(3)
+        w = int(w_total / 2)
+        h = AB.size(2)
+
+        w_offset = random.randint(0, max(0, w - self.fineSize - 1))
+        h_offset = random.randint(0, max(0, h - self.fineSize - 1))
+
+        A = AB[:, :, h_offset:h_offset + self.fineSize,
+               w_offset:w_offset + self.fineSize]
+        B = AB[:, :, h_offset:h_offset + self.fineSize,
+               w + w_offset:w + w_offset + self.fineSize]
+
+        return {'A': A, 'A_paths': AB_paths, 'B': B, 'B_paths': AB_paths}
+
+
+class AlignedDataLoader(BaseDataLoader):
+    def initialize(self, opt):
+        BaseDataLoader.initialize(self, opt)
+        self.fineSize = opt.fineSize
+        transform = transforms.Compose([
+            # TODO: Scale
+            #transforms.Scale((opt.loadSize * 2, 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 = ImageFolder(root=opt.dataroot + '/' + opt.phase,
+                              transform=transform, return_paths=True)
+        data_loader = torch.utils.data.DataLoader(
+            dataset,
+            batch_size=self.opt.batchSize,
+            shuffle=not self.opt.serial_batches,
+            num_workers=int(self.opt.nThreads))
+            
+        self.dataset = dataset
+        self.paired_data = PairedData(data_loader, opt.fineSize)
+
+    def name(self):
+        return 'AlignedDataLoader'
+
+    def load_data(self):
+        return self.paired_data
+
+    def __len__(self):
+        return len(self.dataset)