first commit

6 files changed, 225 insertions, 0 deletions

diff --git a/data/__init__.py b/data/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/data/__init__.py
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
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+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)
diff --git a/data/base_data_loader.py b/data/base_data_loader.py
new file mode 100644
index 0000000..0e1deb5
--- /dev/null
+++ b/data/base_data_loader.py
@@ -0,0 +1,14 @@
+
+class BaseDataLoader():
+    def __init__(self):
+        pass
+    
+    def initialize(self, opt):
+        self.opt = opt
+        pass
+
+    def load_data():
+        return None
+
+        
+        
diff --git a/data/data_loader.py b/data/data_loader.py
new file mode 100644
index 0000000..69035ea
--- /dev/null
+++ b/data/data_loader.py
@@ -0,0 +1,12 @@
+
+def CreateDataLoader(opt):
+    data_loader = None
+    if opt.align_data > 0:
+        from data.aligned_data_loader import AlignedDataLoader
+        data_loader = AlignedDataLoader()
+    else:
+        from data.unaligned_data_loader import UnalignedDataLoader
+        data_loader = UnalignedDataLoader()
+    print(data_loader.name())
+    data_loader.initialize(opt)
+    return data_loader
diff --git a/data/image_folder.py b/data/image_folder.py
new file mode 100644
index 0000000..44e15cb
--- /dev/null
+++ b/data/image_folder.py
@@ -0,0 +1,67 @@
+################################################################################
+# Code from
+# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
+# Modified the original code so that it also loads images from the current
+# directory as well as the subdirectories
+################################################################################
+
+import torch.utils.data as data
+
+from PIL import Image
+import os
+import os.path
+
+IMG_EXTENSIONS = [
+    '.jpg', '.JPG', '.jpeg', '.JPEG',
+    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
+]
+
+
+def is_image_file(filename):
+    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def make_dataset(dir):
+    images = []
+    assert os.path.isdir(dir), '%s is not a valid directory' % dir
+
+    for root, _, fnames in sorted(os.walk(dir)):
+        for fname in fnames:
+            if is_image_file(fname):
+                path = os.path.join(root, fname)
+                images.append(path)
+
+    return images
+
+
+def default_loader(path):
+    return Image.open(path).convert('RGB')
+
+
+class ImageFolder(data.Dataset):
+
+    def __init__(self, root, transform=None, return_paths=False,
+                 loader=default_loader):
+        imgs = make_dataset(root)
+        if len(imgs) == 0:
+            raise(RuntimeError("Found 0 images in: " + root + "\n"
+                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
+
+        self.root = root
+        self.imgs = imgs
+        self.transform = transform
+        self.return_paths = return_paths
+        self.loader = loader
+
+    def __getitem__(self, index):
+        path = self.imgs[index]
+        img = self.loader(path)
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.return_paths:
+            return img, path
+        else:
+            return img
+
+    def __len__(self):
+        return len(self.imgs)
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)