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AutoEncoderLayers.py
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AutoEncoderLayers.py
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from keras.engine import Layer, InputSpec
from keras import activations, constraints, initializations, regularizers
from keras import backend as K
from theano.tensor import as_tensor_variable
from theano.tensor.nnet.neighbours import images2neibs, neibs2images
import theano.tensor as T
import theano
dtensor5 = T.TensorType('float32', (False,) * 5)
class DeConvLayer(Layer):
"""
upsampling layer
"""
def __init__(self, nb_filter, nb_row, nb_col,
init='glorot_uniform', activation='linear', weights=None, dim_ordering='th',
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None, **kwargs):
self.nb_filter = nb_filter
self.nb_row = nb_row
self.nb_col = nb_col
self.init = initializations.get(init, dim_ordering=dim_ordering)
self.activation = activations.get(activation)
self.dim_ordering = dim_ordering
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(DeConvLayer, self).__init__(**kwargs)
def build(self, input_shape):
if self.dim_ordering == 'th':
stack_size = input_shape[1]
self.W_shape = (self.nb_filter, self.nb_row, self.nb_col)
elif self.dim_ordering == 'tf':
stack_size = input_shape[3]
self.W_shape = (self.nb_row, self.nb_col, self.nb_filter)
else:
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)
self.W = self.init(self.W_shape, name='{}_W'.format(self.name))
self.b = K.zeros((self.nb_filter,), name='{}_b'.format(self.name))
self.trainable_weights = [self.W, self.b]
self.regularizers = []
if self.W_regularizer:
self.W_regularizer.set_param(self.W)
self.regularizers.append(self.W_regularizer)
if self.b_regularizer:
self.b_regularizer.set_param(self.b)
self.regularizers.append(self.b_regularizer)
if self.activity_regularizer:
self.activity_regularizer.set_layer(self)
self.regularizers.append(self.activity_regularizer)
self.constraints = {}
if self.W_constraint:
self.constraints[self.W] = self.W_constraint
if self.b_constraint:
self.constraints[self.b] = self.b_constraint
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
def get_output_shape_for(self, input_shape):
if self.dim_ordering == 'th':
rows = input_shape[2]
cols = input_shape[3]
elif self.dim_ordering == 'tf':
rows = input_shape[1]
cols = input_shape[2]
else:
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)
# rows = rows
# cols = cols
if self.dim_ordering == 'th':
return (1, self.nb_filter, None, self.nb_row*self.nb_col)
elif self.dim_ordering == 'tf':
return (1, self.nb_filter, None, self.nb_row*self.nb_col)
else:
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)
def call(self, x, mask=None):
x = K.sum(x, axis=-1, keepdims=True)
x = K.resize_images(x, self.nb_row, self.nb_col, self.dim_ordering)
conv_out = self.deconv2d(x=x, kernel=self.W, dim_ordering=self.dim_ordering, filter_shape=self.W_shape)
if self.dim_ordering == 'th':
output = conv_out + K.reshape(self.b, (self.nb_filter, 1, 1))
elif self.dim_ordering == 'tf':
output = conv_out + K.reshape(self.b, (self.nb_filter, 1, 1))
# output = conv_out + self.b
else:
raise Exception('Invalid dim_ordering: ' + self.dim_ordering)
output = self.activation(output)
# return output
results_shape = output.shape
return output.reshape((1, results_shape[0], results_shape[1], results_shape[2]))
def deconv2d(self, x, kernel, dim_ordering='th',
image_shape=None, filter_shape=None):
if dim_ordering not in {'th', 'tf'}:
raise Exception('Unknown dim_ordering ' + str(dim_ordering))
if dim_ordering == 'tf':
x = x.dimshuffle((0, 3, 1, 2))
kernel = kernel.dimshuffle((2, 0, 1))
if image_shape:
image_shape = (image_shape[0], image_shape[3],
image_shape[1], image_shape[2])
if filter_shape:
filter_shape = (filter_shape[2],
filter_shape[0], filter_shape[1])
def int_or_none(value):
try:
return int(value)
except TypeError:
return None
if image_shape is not None:
image_shape = tuple(int_or_none(v) for v in image_shape)
if filter_shape is not None:
filter_shape = tuple(int_or_none(v) for v in filter_shape)
inputs = as_tensor_variable(x)
filters = as_tensor_variable(kernel)
# conv_out = get_conv_out(inputs, filters)
# filters = K.resize_images(filters, 86, 86, dim_ordering="tf")
conv_out = get_deconv_out_new(inputs, filters)
# if dim_ordering == 'tf':
# conv_out = conv_out.dimshuffle((0, 2, 3, 1))
return conv_out
def get_single_deconv_out(input, filter):
# reshape inputs
img = input.reshape((1, 1, input.shape[0], input.shape[1]))
kernel = filter.reshape((1, K.prod(filter.shape)))
# construct split function
# image = T.tensor4("image")
neibs = images2neibs(img, neib_shape=filter.shape, neib_step=filter.shape)
# window_function = theano.function([image], neibs)
#
# neibs_val = window_function(img_val)
neibs = neibs*kernel
# construct merge function
img_new = neibs2images(neibs, filter.shape, img.shape)
return img_new[0][0]
def get_single_deconv_out_new(input, filter):
output_shape = (1, 1, input.shape[0]*filter.shape[0], input.shape[1]*filter.shape[1])
# reshape inputs
image = input.reshape((1, K.prod(input.shape)))
kernel = filter.reshape((1, K.prod(filter.shape)))
# neibs = images2neibs(output, neib_shape=filter.shape, neib_step=filter.shape)
def fn(i, k):
return i*k
results, updates = theano.scan(fn=fn, sequences=image[0], non_sequences=kernel[0])
# neibs = neibs*results
img_new = neibs2images(results, filter.shape, output_shape)
return img_new[0][0]
def inner_loop(n, fil, r, b, ipt, deconv_out):
patch = get_single_deconv_out_new(ipt, fil)
r = r + T.set_subtensor(deconv_out[b, n, :, :], patch)
return r
def outer_loop(b, ipt, r, filters, deconv_out):
results, _ = theano.scan(fn=inner_loop, sequences=[T.arange(filters.shape[0]), filters],
outputs_info=r, non_sequences=[b, ipt, deconv_out])
final_result = results[-1]
return final_result
def get_deconv_out(inputs, filters):
output_shape = (inputs.shape[0], filters.shape[0], inputs.shape[1]*filters.shape[1], inputs.shape[2]*filters.shape[2])
deconv_out = T.zeros(output_shape)
results, _ = theano.scan(fn=outer_loop, sequences=[T.arange(inputs.shape[0]), inputs],
outputs_info=T.zeros_like(deconv_out),
non_sequences=[filters, deconv_out])
return results[-1]
def get_deconv_out_new(inputs, filters):
inputs_shape = inputs.shape
filters_shape = filters.shape
# img = K.reshape(inputs, (inputs_shape[0], 1, inputs_shape[1], inputs_shape[2]))
kernel = K.reshape(filters, (filters_shape[0], 1, filters_shape[1]*filters_shape[2]))
# def fn(i, k):
# i = K.reshape(i, (1, 1, i.shape[0], i.shape[1]))
# neibs = images2neibs(i, neib_shape=(filters_shape[-2], filters_shape[-1]),
# neib_step=(filters_shape[-2], filters_shape[-1]))
# neibs = neibs*k
# return neibs
# results,_ = theano.scan(fn=fn, sequences=inputs, non_sequences=kernel)
neibs = images2neibs(inputs, neib_shape=(filters_shape[1], filters_shape[2]),
neib_step=(filters_shape[1], filters_shape[2]))
def fn(k, n):
return n*k
results,_ = theano.scan(fn=fn, sequences=kernel, non_sequences=neibs)
# results = neibs * kernel
# results_shape = results.shape
return results