Tensorflow updated to 2

.gitignore

@@ -142,3 +142,4 @@ experiment/
 upload/
 models_src/
 photo2cartoon_resources.zip
+.DS_Store

dataset.py

@@ -1,108 +1,108 @@
-import torch.utils.data as data
-
-from PIL import Image
-
-import os
-import os.path
-
-
-def has_file_allowed_extension(filename, extensions):
-    """Checks if a file is an allowed extension.
-
-    Args:
-        filename (string): path to a file
-
-    Returns:
-        bool: True if the filename ends with a known image extension
-    """
-    filename_lower = filename.lower()
-    return any(filename_lower.endswith(ext) for ext in extensions)
-
-
-def find_classes(dir):
-    classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
-    classes.sort()
-    class_to_idx = {classes[i]: i for i in range(len(classes))}
-    return classes, class_to_idx
-
-
-def make_dataset(dir, extensions):
-    images = []
-    for root, _, fnames in sorted(os.walk(dir)):
-        for fname in sorted(fnames):
-            if has_file_allowed_extension(fname, extensions):
-                path = os.path.join(root, fname)
-                item = (path, 0)
-                images.append(item)
-
-    return images
-
-
-class DatasetFolder(data.Dataset):
-    def __init__(self, root, loader, extensions, transform=None, target_transform=None):
-        # classes, class_to_idx = find_classes(root)
-        samples = make_dataset(root, extensions)
-        if len(samples) == 0:
-            raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"
-                               "Supported extensions are: " + ",".join(extensions)))
-
-        self.root = root
-        self.loader = loader
-        self.extensions = extensions
-        self.samples = samples
-
-        self.transform = transform
-        self.target_transform = target_transform
-
-    def __getitem__(self, index):
-        """
-        Args:
-            index (int): Index
-
-        Returns:
-            tuple: (sample, target) where target is class_index of the target class.
-        """
-        path, target = self.samples[index]
-        sample = self.loader(path)
-        if self.transform is not None:
-            sample = self.transform(sample)
-        if self.target_transform is not None:
-            target = self.target_transform(target)
-
-        return sample, target
-
-    def __len__(self):
-        return len(self.samples)
-
-    def __repr__(self):
-        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
-        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
-        fmt_str += '    Root Location: {}\n'.format(self.root)
-        tmp = '    Transforms (if any): '
-        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        tmp = '    Target Transforms (if any): '
-        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
-        return fmt_str
-
-
-IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
-
-
-def pil_loader(path):
-    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
-    with open(path, 'rb') as f:
-        img = Image.open(f)
-        return img.convert('RGB')
-
-
-def default_loader(path):
-    return pil_loader(path)
-
-
-class ImageFolder(DatasetFolder):
-    def __init__(self, root, transform=None, target_transform=None,
-                 loader=default_loader):
-        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
-                                          transform=transform,
-                                          target_transform=target_transform)
-        self.imgs = self.samples
+import torch.utils.data as data
+
+from PIL import Image
+
+import os
+import os.path
+
+
+def has_file_allowed_extension(filename, extensions):
+    """Checks if a file is an allowed extension.
+
+    Args:
+        filename (string): path to a file
+
+    Returns:
+        bool: True if the filename ends with a known image extension
+    """
+    filename_lower = filename.lower()
+    return any(filename_lower.endswith(ext) for ext in extensions)
+
+
+def find_classes(dir):
+    classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]
+    classes.sort()
+    class_to_idx = {classes[i]: i for i in range(len(classes))}
+    return classes, class_to_idx
+
+
+def make_dataset(dir, extensions):
+    images = []
+    for root, _, fnames in sorted(os.walk(dir)):
+        for fname in sorted(fnames):
+            if has_file_allowed_extension(fname, extensions):
+                path = os.path.join(root, fname)
+                item = (path, 0)
+                images.append(item)
+
+    return images
+
+
+class DatasetFolder(data.Dataset):
+    def __init__(self, root, loader, extensions, transform=None, target_transform=None):
+        # classes, class_to_idx = find_classes(root)
+        samples = make_dataset(root, extensions)
+        if len(samples) == 0:
+            raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n"
+                               "Supported extensions are: " + ",".join(extensions)))
+
+        self.root = root
+        self.loader = loader
+        self.extensions = extensions
+        self.samples = samples
+
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (sample, target) where target is class_index of the target class.
+        """
+        path, target = self.samples[index]
+        sample = self.loader(path)
+        if self.transform is not None:
+            sample = self.transform(sample)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return sample, target
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+
+IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif']
+
+
+def pil_loader(path):
+    # open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
+    with open(path, 'rb') as f:
+        img = Image.open(f)
+        return img.convert('RGB')
+
+
+def default_loader(path):
+    return pil_loader(path)
+
+
+class ImageFolder(DatasetFolder):
+    def __init__(self, root, transform=None, target_transform=None,
+                 loader=default_loader):
+        super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,
+                                          transform=transform,
+                                          target_transform=target_transform)
+        self.imgs = self.samples