init

data/base_dataset.py

@@ -0,0 +1,90 @@
+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            
+    elif opt.resize_or_crop == 'scale_width_and_crop':
+        new_w = opt.loadSize
+        new_h = opt.loadSize * h // w
+
+    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)))
+        
+    if 'crop' in opt.resize_or_crop:
+        transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.fineSize)))
+
+    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