def _invert_Conv2DLayer(self,layer,feeder):
# Warning they are swapped here
feeder = self._put_rectifiers(feeder,layer)
feeder = self._get_normalised_relevance_layer(layer,feeder)
f_s = layer.filter_size
if layer.pad == 'same':
pad = 'same'
elif layer.pad == 'valid' or layer.pad == (0, 0):
pad = 'full'
else:
raise RuntimeError("Define your padding as full or same.")
# By definition the
# Flip filters must be on to be a proper deconvolution.
num_filters = L.get_output_shape(layer.input_layer)[1]
if layer.stride == (4,4):
# Todo: similar code gradient based explainers. Merge.
feeder = L.Upscale2DLayer(feeder, layer.stride, mode='dilate')
output_layer = L.Conv2DLayer(feeder,
num_filters=num_filters,
filter_size=f_s,
stride=1,
pad=pad,
nonlinearity=None,
b=None,
flip_filters=True)
conv_layer = output_layer
tmp = L.SliceLayer(output_layer, slice(0, -3), axis=3)
output_layer = L.SliceLayer(tmp, slice(0, -3), axis=2)
output_layer.W = conv_layer.W
else:
output_layer = L.Conv2DLayer(feeder,
num_filters=num_filters,
filter_size=f_s,
stride=1,
pad=pad,
nonlinearity=None,
b=None,
flip_filters=True)
W = output_layer.W
# Do the multiplication.
x_layer = L.ReshapeLayer(layer.input_layer,
(-1,)+L.get_output_shape(output_layer)[1:])
output_layer = L.ElemwiseMergeLayer(incomings=[x_layer, output_layer],
merge_function=T.mul)
output_layer.W = W
return output_layer
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