def get_conv_xy_all(layer, deterministic=True):
w_np = layer.W.get_value()
w = layer.W
if layer.flip_filters:
w = w[:, :, ::-1, ::-1]
input_layer = layer.input_layer
if layer.pad == 'same':
input_layer = L.PadLayer(layer.input_layer,
width=np.array(w_np.shape[2:])//2,
batch_ndim=2)
input_shape = L.get_output_shape(input_layer)
output_shape = L.get_output_shape(layer)
max_x = input_shape[2] - w_np.shape[2]+1
max_y = input_shape[3] - w_np.shape[3]+1
#print("input_shape shape: ", input_shape)
#print("output_shape shape: ", output_shape,np.prod(output_shape[2:]))
#print("pad: \"%s\""%layer.pad)
#print(" stride: " ,layer.stride)
#print("max_x %d max_y %d"%(max_x,max_y))
x_orig = L.get_output(input_layer, deterministic=True)
x = theano.tensor.nnet.neighbours.images2neibs(x_orig,
neib_shape=layer.filter_size,
neib_step=layer.stride,
mode='valid')
x = T.reshape(x, (x_orig.shape[0], -1,
np.prod(output_shape[2:]), np.prod(w_np.shape[2:])))
x = T.transpose(x, (0, 2, 1, 3))
x = T.reshape(x, (-1, T.prod(x.shape[2:])))
w = T.flatten(w, outdim=2).T # D,O
y = T.dot(x, w) # N,O
if layer.b is not None:
y += T.shape_padaxis(layer.b, axis=0)
return x, y
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