chainer-fast-neuralstyle
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bigger image size
I get this error
epoch 0
Traceback (most recent call last):
File "train.py", line 135, in
Expect: in_types[0].shape == in_types[1].shape Actual: (1, 256, 119, 119) != (1, 256, 118, 118)
what are the restrictions on image size?
The network subsamples your image twice during transformation, that is, the dimensions shrink 4x at a certain point in the process. So if your resolution is not divisible by 4 it won't necessarily turn out the same size. In fact, it will always be less.
If you really need to use 119, you can explicitly pass outsize
argument to Deconvolution2D
upon initialization, but it makes the operation less convenient.