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I managed to test EDSR and SRGAN on a custom dataset with the pre-trained weights with no problem.
However, I when I load the WDSR weights, I get the error: ValueError: axes don't match array
I don't know if it is related, but, I was getting the following error, when calling the wdsr_b model:
`~/.pyenv/versions/3.6.8/envs/srgan/lib/python3.6/site-packages/keras/layers/wrappers.py in init(self, layer, **kwargs)
44
45 def init(self, layer, **kwargs):
---> 46 assert isinstance(layer, Layer)
47 self.layer = layer
48 super(Wrapper, self).init(**kwargs)
Hello everyone.
I managed to test EDSR and SRGAN on a custom dataset with the pre-trained weights with no problem.
However, I when I load the WDSR weights, I get the error:
ValueError: axes don't match array
I don't know if it is related, but, I was getting the following error, when calling the wdsr_b model:
`~/.pyenv/versions/3.6.8/envs/srgan/lib/python3.6/site-packages/keras/layers/wrappers.py in init(self, layer, **kwargs)
44
45 def init(self, layer, **kwargs):
---> 46 assert isinstance(layer, Layer)
47 self.layer = layer
48 super(Wrapper, self).init(**kwargs)
AssertionError:`
Thus, I changed
conv2d_weightnorm
function (as mentioned in https://www.reddit.com/r/tensorflow/comments/dn0hjv/applying_weight_normalization_layer_in_tf_2/ in the following way:def conv2d_weightnorm(filters, kernel_size, padding='same', activation=None, **kwargs): return Conv2D(filters, kernel_size, padding=padding, activation=activation, **kwargs)
Does anyone have an idea what I am doing wrong?
Thank you in advance!
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