training loader

Global/data/base_data_loader.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+class BaseDataLoader():
+    def __init__(self):
+        pass
+
+    def initialize(self, opt):
+        self.opt = opt
+        pass
+
+    def load_data():
+        return None
+
+
+

Global/data/base_dataset.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch.utils.data as data
+from PIL import Image
+import torchvision.transforms as transforms
+import numpy as np
+import random
+
+class BaseDataset(data.Dataset):
+    def __init__(self):
+        super(BaseDataset, self).__init__()
+
+    def name(self):
+        return 'BaseDataset'
+
+    def initialize(self, opt):
+        pass
+
+def get_params(opt, size):
+    w, h = size
+    new_h = h
+    new_w = w
+    if opt.resize_or_crop == 'resize_and_crop':
+        new_h = new_w = opt.loadSize
+
+    if opt.resize_or_crop == 'scale_width_and_crop': # we scale the shorter side into 256
+
+        if w<h:
+            new_w = opt.loadSize
+            new_h = opt.loadSize * h // w
+        else:
+            new_h=opt.loadSize
+            new_w = opt.loadSize * w // h
+
+    if opt.resize_or_crop=='crop_only':
+        pass
+
+
+    x = random.randint(0, np.maximum(0, new_w - opt.fineSize))
+    y = random.randint(0, np.maximum(0, new_h - opt.fineSize))
+
+    flip = random.random() > 0.5
+    return {'crop_pos': (x, y), 'flip': flip}
+
+def get_transform(opt, params, method=Image.BICUBIC, normalize=True):
+    transform_list = []
+    if 'resize' in opt.resize_or_crop:
+        osize = [opt.loadSize, opt.loadSize]
+        transform_list.append(transforms.Scale(osize, method))   
+    elif 'scale_width' in opt.resize_or_crop:
+    #    transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.loadSize, method)))  ## Here , We want the shorter side to match 256, and Scale will finish it.
+        transform_list.append(transforms.Scale(256,method))
+
+    if 'crop' in opt.resize_or_crop:
+        if opt.isTrain:
+            transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.fineSize)))
+        else:
+            if opt.test_random_crop:
+                transform_list.append(transforms.RandomCrop(opt.fineSize))
+            else:
+                transform_list.append(transforms.CenterCrop(opt.fineSize))
+
+    ## when testing, for ablation study, choose center_crop directly.
+
+
+
+    if opt.resize_or_crop == 'none':
+        base = float(2 ** opt.n_downsample_global)
+        if opt.netG == 'local':
+            base *= (2 ** opt.n_local_enhancers)
+        transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))
+
+    if opt.isTrain and not opt.no_flip:
+        transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
+
+    transform_list += [transforms.ToTensor()]
+
+    if normalize:
+        transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
+                                                (0.5, 0.5, 0.5))]
+    return transforms.Compose(transform_list)
+
+def normalize():    
+    return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+
+def __make_power_2(img, base, method=Image.BICUBIC):
+    ow, oh = img.size        
+    h = int(round(oh / base) * base)
+    w = int(round(ow / base) * base)
+    if (h == oh) and (w == ow):
+        return img
+    return img.resize((w, h), method)
+
+def __scale_width(img, target_width, method=Image.BICUBIC):
+    ow, oh = img.size
+    if (ow == target_width):
+        return img    
+    w = target_width
+    h = int(target_width * oh / ow)    
+    return img.resize((w, h), method)
+
+def __crop(img, pos, size):
+    ow, oh = img.size
+    x1, y1 = pos
+    tw = th = size
+    if (ow > tw or oh > th):        
+        return img.crop((x1, y1, x1 + tw, y1 + th))
+    return img
+
+def __flip(img, flip):
+    if flip:
+        return img.transpose(Image.FLIP_LEFT_RIGHT)
+    return img

Global/data/custom_dataset_data_loader.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch.utils.data
+import random
+from data.base_data_loader import BaseDataLoader
+from data import online_dataset_for_old_photos as dts_ray_bigfile
+
+
+def CreateDataset(opt):
+    dataset = None
+    if opt.training_dataset=='domain_A' or opt.training_dataset=='domain_B':
+        dataset = dts_ray_bigfile.UnPairOldPhotos_SR()
+    if opt.training_dataset=='mapping':
+        if opt.random_hole:
+            dataset = dts_ray_bigfile.PairOldPhotos_with_hole()
+        else:
+            dataset = dts_ray_bigfile.PairOldPhotos()
+    print("dataset [%s] was created" % (dataset.name()))
+    dataset.initialize(opt)
+    return dataset
+
+class CustomDatasetDataLoader(BaseDataLoader):
+    def name(self):
+        return 'CustomDatasetDataLoader'
+
+    def initialize(self, opt):
+        BaseDataLoader.initialize(self, opt)
+        self.dataset = CreateDataset(opt)
+        self.dataloader = torch.utils.data.DataLoader(
+            self.dataset,
+            batch_size=opt.batchSize,
+            shuffle=not opt.serial_batches,
+            num_workers=int(opt.nThreads),
+            drop_last=True)
+
+    def load_data(self):
+        return self.dataloader
+
+    def __len__(self):
+        return min(len(self.dataset), self.opt.max_dataset_size)

Global/data/data_loader.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+def CreateDataLoader(opt):
+    from data.custom_dataset_data_loader import CustomDatasetDataLoader
+    data_loader = CustomDatasetDataLoader()
+    print(data_loader.name())
+    data_loader.initialize(opt)
+    return data_loader

Global/data/image_folder.py

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+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch.utils.data as data
+from PIL import Image
+import os
+
+IMG_EXTENSIONS = [
+    '.jpg', '.JPG', '.jpeg', '.JPEG',
+    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff'
+]
+
+
+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)