first push

cirtorch/datasets/datahelpers.py

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+import os
+from PIL import Image
+
+import torch
+
+def cid2filename(cid, prefix):
+    """
+    Creates a training image path out of its CID name
+    
+    Arguments
+    ---------
+    cid      : name of the image
+    prefix   : root directory where images are saved
+    
+    Returns
+    -------
+    filename : full image filename
+    """
+    return os.path.join(prefix, cid[-2:], cid[-4:-2], cid[-6:-4], cid)
+
+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 accimage_loader(path):
+    import accimage
+    try:
+        return accimage.Image(path)
+    except IOError:
+        # Potentially a decoding problem, fall back to PIL.Image
+        return pil_loader(path)
+
+def default_loader(path):
+    from torchvision import get_image_backend
+    if get_image_backend() == 'accimage':
+        return accimage_loader(path)
+    else:
+        return pil_loader(path)
+
+def imresize(img, imsize):
+    img.thumbnail((imsize, imsize), Image.ANTIALIAS)
+    return img
+
+def flip(x, dim):
+    xsize = x.size()
+    dim = x.dim() + dim if dim < 0 else dim
+    x = x.view(-1, *xsize[dim:])
+    x = x.view(x.size(0), x.size(1), -1)[:, getattr(torch.arange(x.size(1)-1, -1, -1), ('cpu','cuda')[x.is_cuda])().long(), :]
+    return x.view(xsize)
+
+def collate_tuples(batch):
+    if len(batch) == 1:
+        return [batch[0][0]], [batch[0][1]]
+    return [batch[i][0] for i in range(len(batch))], [batch[i][1] for i in range(len(batch))]