def call(self, inputs, *args, **kwargs):
outputs = super(Convolution2DTranspose,
self).call(inputs, *args, **kwargs)
if not outputs.get_shape().is_fully_defined():
input_shape = K.int_shape(inputs)
if self.data_format == "channels_last":
height, width = input_shape[1], input_shape[2]
else:
height, width = input_shape[2], input_shape[3]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
batch_size = inputs.get_shape()[0]
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height, stride_h, kernel_h,
self.padding)
out_width = conv_utils.deconv_length(width, stride_w, kernel_w,
self.padding)
if self.data_format == "channels_last":
output_shape = (batch_size, out_height, out_width,
self.filters)
else:
output_shape = (batch_size, self.filters, out_height,
out_width)
outputs.set_shape(output_shape)
return outputs
def compute_output_shape(self, input_shape):
if self.data_format == 'channels_first':
h_axis, w_axis, d_axis = 2, 3, 4
else:
h_axis, w_axis, d_axis = 1, 2, 3
height, width, depth = input_shape[h_axis], input_shape[w_axis], input_shape[d_axis]
kernel_h, kernel_w, kernel_d = self.kernel_size
stride_h, stride_w, stride_d = self.strides
batch_size = input_shape[0]
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(
height, stride_h, kernel_h, self.padding)
out_width = conv_utils.deconv_length(
width, stride_w, kernel_w, self.padding)
out_depth = conv_utils.deconv_length(
depth, stride_d, kernel_d, self.padding)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width, out_depth)
else:
output_shape = (batch_size, out_height, out_width, out_depth, self.filters)
# print("in compute_output_shape")
# print("output_shape:{}".format(output_shape))
return output_shape
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_length(output_shape[h_axis],
stride_h,
kernel_h,
self.padding,
out_pad_h,
self.dilation_rate[0])
output_shape[w_axis] = conv_utils.deconv_length(output_shape[w_axis],
stride_w,
kernel_w,
self.padding,
out_pad_w,
self.dilation_rate[1])
return tuple(output_shape)
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
output_shape[1] = deconv_length(output_shape[1], self.scaling, self.kernel_size, self.padding)
output_shape[2] = deconv_length(output_shape[2], self.scaling, self.kernel_size, self.padding)
output_shape[3] = self.num_capsule
output_shape[4] = self.num_atoms
return tuple(output_shape)
def Unpool(self,pooled_Maps, indices,
Rectified_FM = None,
pool_size = [1,2,2,1],
strides = [1,1,1,1],
padding = 'valid',
scope = 'unpool'):
"""
Unpooling layer after max_pool_with_argmax.
The name of args cited from this paper(Fig.1 at https://arxiv.org/pdf/1311.2901.pdf)
Args:
pooled_Maps : max pooled output tensor
indices : argmax indices
Rectified_FM : Rectified Feature Maps
Return:
unpool: unpooling tensor
"""
with tf.variable_scope(scope):
input_shape = tf.shape(pooled_Maps)
if Rectified_FM is None:
# Calculate output shape
height, width, filters = input_shape[1:]
kernel_h, kernel_w = pool_size[1:3]
stride_h, stride_w = strides[1:3]
out_height = conv_utils.deconv_length(height, stride_h, kernel_h, padding)
out_width = conv_utils.deconv_length(width, stride_h, kernel_h, padding)
output_shape = [input_shape[0], out_height, out_width, filters]
else:
output_shape = Rectified_FM.get_shape().as_list()
if output_shape[0] is None:
output_shape = [input_shape[0], ] + output_shape[1:]
flat_input_size = tf.reduce_prod(input_shape)
flat_output_shape = [output_shape[0], output_shape[1] * output_shape[2] * output_shape[3]]
flat_pooled = tf.reshape(pooled_Maps, tf.stack([flat_input_size]))
batch_range = tf.reshape(tensor = tf.range(tf.cast(input_shape[0], tf.int64),
dtype = indices.dtype),
shape = [input_shape[0], 1, 1, 1])
b = tf.ones_like(indices) * batch_range
b = tf.reshape(b, tf.stack([flat_input_size, 1]))
flat_indices = tf.reshape(indices, tf.stack([flat_input_size, 1]))
flat_indices = tf.concat([b, flat_indices], 1)
#Switches is Fig.1 at https://arxiv.org/pdf/1311.2901.pdf
Switches = tf.scatter_nd(flat_indices, tf.ones_like(flat_pooled), shape=tf.cast(flat_output_shape, tf.int64))
Switches = tf.reshape(Switches, [-1])
Switches = tf.greater(Switches, tf.zeros_like(Switches))
Switches = tf.reshape(Switches, tf.stack(output_shape))
img = tf.image.resize_nearest_neighbor(pooled_Maps, output_shape[1:3])
Unpooled_Maps = tf.multiply(img, tf.cast(Switches, img.dtype))
return Unpooled_Maps
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
c_axis = 1
else:
h_axis, w_axis = 1, 2
c_axis = 3
##BTEK
kernel = self.U()
in_channels = input_shape[c_axis]
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height, stride_h, kernel_h,
self.padding, out_pad_h,
self.dilation_rate[0])
out_width = conv_utils.deconv_length(width, stride_w, kernel_w,
self.padding, out_pad_w,
self.dilation_rate[1])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
##BTEK
kernel = self.U()
print("kernel shape in output:", kernel.shape)
print("channel axis")
kernel = K.repeat_elements(kernel, self.input_channels, axis=c_axis)
print("kernel reshaped :", kernel.shape)
outputs = K.conv2d_transpose(inputs,
kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
if self.transpose_output_shape is None:
input_shape = K.shape(inputs)
input_shape_list = inputs.get_shape().as_list()
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape_list[h_axis], input_shape_list[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height, stride_h, kernel_h,
self.padding)
out_width = conv_utils.deconv_length(width, stride_w, kernel_w,
self.padding)
if self.data_format == 'channels_first':
self.transpose_output_shape = (batch_size, self.filters,
out_height, out_width)
else:
self.transpose_output_shape = (batch_size, out_height,
out_width, self.filters)
shape = self.transpose_output_shape
else:
shape = self.transpose_output_shape
if self.data_format == 'channels_first':
shape = (shape[0], shape[2], shape[3], shape[1])
if shape[0] is None:
shape = (tf.shape(inputs)[0], ) + tuple(shape[1:])
shape = tf.stack(list(shape))
outputs = K.conv2d_transpose(x=inputs,
kernel=self.kernel,
output_shape=shape,
strides=self.strides,
padding=self.padding,
data_format=self.data_format)
outputs = tf.reshape(outputs, shape)
# if self.bias:
# outputs = K.bias_add(
# outputs,
# self.bias,
# data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h,
self.dilation_rate[0])
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding,
out_pad_w,
self.dilation_rate[1])
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
outputs = inputs
if self.use_bias:
outputs = K.bias_add(
outputs,
-self.bias,
data_format=self.data_format)
outputs = K.conv2d_transpose(
outputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shapes):
input_shape = input_shapes[0]
output_shape = list(input_shape)
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_length(
output_shape[h_axis], stride_h, kernel_h, self.padding, None)
output_shape[w_axis] = conv_utils.deconv_length(
output_shape[w_axis], stride_w, kernel_w, self.padding, None)
return tuple(output_shape)
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
else:
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_length(output_shape[d_axis], stride_d, kernel_d, self.padding)
output_shape[h_axis] = conv_utils.deconv_length(output_shape[h_axis], stride_h, kernel_h, self.padding)
output_shape[w_axis] = conv_utils.deconv_length(output_shape[w_axis], stride_w, kernel_w, self.padding)
#print('compute_outptu_shape', output_shape)
return tuple(output_shape)
def call(self, input_tensor, training=None):
input_transposed = tf.transpose(input_tensor, [4, 0, 1, 2, 3, 5])
input_shape = K.shape(input_transposed)
input_tensor_reshaped = K.reshape(input_transposed, [
input_shape[1] * input_shape[0], self.input_height, self.input_width, self.input_depth, self.input_num_atoms])
input_tensor_reshaped.set_shape((None, self.input_height, self.input_width, self.input_depth, self.input_num_atoms))
if self.upsamp_type == 'resize':
# added 1 more self.scaling
upsamp = K.resize_images(input_tensor_reshaped, self.scaling, self.scaling, self.scaling, 'channels_last')
outputs = K.conv3d(upsamp, kernel=self.W, strides=(1, 1, 1), padding=self.padding, data_format='channels_last')
elif self.upsamp_type == 'subpix':
conv = K.conv3d(input_tensor_reshaped, kernel=self.W, strides=(1, 1, 1), padding='same',
data_format='channels_last')
outputs = tf.depth_to_space(conv, self.scaling)
else:
batch_size = input_shape[1] * input_shape[0]
# Infer the dynamic output shape:
out_height = deconv_length(self.input_height, self.scaling, self.kernel_size, self.padding)
out_width = deconv_length(self.input_width, self.scaling, self.kernel_size, self.padding)
out_depth = deconv_length(self.input_depth, self.scaling, self.kernel_size, self.padding)
output_shape = (batch_size, out_height, out_width, out_depth, self.num_capsule * self.num_atoms)
outputs = K.conv3d_transpose(input_tensor_reshaped, self.W, output_shape, (self.scaling, self.scaling, self.scaling),
padding=self.padding, data_format='channels_last')
votes_shape = K.shape(outputs)
_, conv_height, conv_width, conv_depth, _ = outputs.get_shape()
votes = K.reshape(outputs, [input_shape[2], input_shape[1], input_shape[0], votes_shape[1], votes_shape[2],
self.num_capsule, self.num_atoms])
votes.set_shape((None, self.input_num_capsule, conv_height.value, conv_width.value, conv_depth.value,
self.num_capsule, self.num_atoms))
logit_shape = K.stack([
input_shape[1], input_shape[0], votes_shape[1], votes_shape[2], votes_shape[3], self.num_capsule])
biases_replicated = K.tile(self.b, [votes_shape[1], votes_shape[2], votes_shape[3], 1, 1])
activations = update_routing(
votes=votes,
biases=biases_replicated,
logit_shape=logit_shape,
num_dims=7,
input_dim=self.input_num_capsule,
output_dim=self.num_capsule,
num_routing=self.routings)
return activations
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
# Infer the dynamic output shape:
if self._output_shape is None:
out_height = deconv_length(height, stride_h, kernel_h, self.padding)
out_width = deconv_length(width, stride_w, kernel_w, self.padding)
if self.data_format == 'channels_first':
output_shape = (
batch_size, self.filters, out_height, out_width
)
else:
output_shape = (
batch_size, out_height, out_width, self.filters
)
else:
output_shape = (batch_size,) + self._output_shape
outputs = K.conv2d_transpose(
inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format
)
if self.bias:
outputs = K.bias_add(
outputs, self.bias, data_format=self.data_format
)
if self.activation is not None:
return self.activation(outputs)
return outputs
def test_deconv_length():
assert conv_utils.deconv_length(None, 1, 7, 'same') is None
assert conv_utils.deconv_length(224, 1, 7, 'same') == 224
assert conv_utils.deconv_length(224, 2, 7, 'same') == 448
assert conv_utils.deconv_length(32, 1, 5, 'valid') == 36
assert conv_utils.deconv_length(32, 2, 5, 'valid') == 67
assert conv_utils.deconv_length(32, 1, 5, 'full') == 28
assert conv_utils.deconv_length(32, 2, 5, 'full') == 59
def compute_output_shape(self, input_shape):
if self.unpooling_output_shape is None:
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.pool_size
stride_h, stride_w = self.strides
output_shape[c_axis] = input_shape[c_axis]
output_shape[h_axis] = conv_utils.deconv_length(
output_shape[h_axis], stride_h, kernel_h, self.padding)
output_shape[w_axis] = conv_utils.deconv_length(
output_shape[w_axis], stride_w, kernel_w, self.padding)
else:
output_shape = self.unpooling_output_shape
return tuple(output_shape)
def conv2d_transpose(
inputs,
filter, # pylint: disable=redefined-builtin
kernel_size=None,
filters=None,
strides=(1, 1),
padding='same',
output_padding=None,
data_format='channels_last'):
"""Compatibility layer for K.conv2d_transpose
Take a filter defined for forward convolution and adjusts it for a
transposed convolution."""
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = kernel_size
stride_h, stride_w = strides
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height, stride_h, kernel_h, padding,
output_padding)
out_width = conv_utils.deconv_length(width, stride_w, kernel_w, padding,
output_padding)
if data_format == 'channels_first':
output_shape = (batch_size, filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, filters)
filter = K.permute_dimensions(filter, (0, 1, 3, 2))
return K.conv2d_transpose(inputs,
filter,
output_shape,
strides,
padding=padding,
data_format=data_format)
def conv_transpose_output_length(
input_length, filter_size, padding, stride, dilation=1):
"""Rearrange arguments for compatibility with conv_output_length."""
if dilation != 1:
msg = f"Dilation must be 1 for transposed convolution. "
msg += f"Got dilation = {dilation}"
raise ValueError(msg)
return conv_utils.deconv_length(
input_length, # dim_size
stride, # stride_size
filter_size, # kernel_size
padding, # padding
)
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self._output_shape is None:
output_shape[c_axis] = self.filters
output_shape[h_axis] = deconv_length(
output_shape[h_axis], stride_h, kernel_h, self.padding
)
output_shape[w_axis] = deconv_length(
output_shape[w_axis], stride_w, kernel_w, self.padding
)
else:
output_shape[1:] = self._output_shape
return tuple(output_shape)
def compute_output_shape(self, input_shape):
if self.transpose_output_shape is None:
output_shape = list(input_shape)
if self.data_format == 'channels_first':
c_axis, h_axis, w_axis = 1, 2, 3
else:
c_axis, h_axis, w_axis = 3, 1, 2
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[h_axis] = conv_utils.deconv_length(
output_shape[h_axis], stride_h, kernel_h, self.padding)
output_shape[w_axis] = conv_utils.deconv_length(
output_shape[w_axis], stride_w, kernel_w, self.padding)
else:
if not isinstance(self.transpose_output_shape, (list, tuple)):
output_shape = self.transpose_output_shape
else:
output_shape = self.transpose_output_shape
return tuple(output_shape)
def call(self, inputs):
input_shape = K.shape(inputs)
output_shape = [0]*5
output_shape[0] = input_shape[0]
if self.data_format == 'channels_first':
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
else:
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
output_shape[c_axis] = self.filters
output_shape[d_axis] = conv_utils.deconv_length(input_shape[d_axis], stride_d, kernel_d, self.padding)
output_shape[h_axis] = conv_utils.deconv_length(input_shape[h_axis], stride_h, kernel_h, self.padding)
output_shape[w_axis] = conv_utils.deconv_length(input_shape[w_axis], stride_w, kernel_w, self.padding)
#print('call',output_shape)
outputs = K.deconv3d(inputs, self.kernel, output_shape, strides=self.strides,
padding=self.padding, data_format=self.data_format)
if self.bias:
outputs = K.bias_add(outputs, self.bias, data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
d_axis, h_axis, w_axis = 2, 3, 4
else:
d_axis, h_axis, w_axis = 1, 2, 3
depth, height, width = input_shape[d_axis], input_shape[h_axis], input_shape[w_axis]
kernel_d, kernel_h, kernel_w = self.kernel_size
stride_d, stride_h, stride_w = self.strides
# Infer the dynamic output shape:
out_depth = conv_utils.deconv_length(depth, stride_h, kernel_h, self.padding, None)
out_height = conv_utils.deconv_length(height, stride_h, kernel_h, self.padding, None)
out_width = conv_utils.deconv_length(width, stride_w, kernel_w, self.padding, None)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_depth, out_height, out_width)
else:
output_shape = (batch_size, out_depth, out_height, out_width, self.filters)
outputs = conv3d_transpose(
inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
input_shape = K.shape(inputs)
input_shape = tuple(d.value for d in inputs.shape)
batch_size = input_shape[0]
if self.data_format == 'channel_first':
w_axis = 2
else:
w_axis = 1
width = input_shape[w_axis]
kernel_w, = self.kernel_size
stride_w, = self.strides
out_pad = self.output_padding
# Infer dynamic output shape
out_width = conv_utils.deconv_length(
dim_size=width,
stride_size=stride_w,
kernel_size=kernel_w,
padding=self.padding,
# output_padding=out_pad # Does not exists in earlier keras versions
)
# Define output shape based on channel index position
if self.data_format == 'channel_first':
output_shape = (batch_size, self.filters, out_width)
else:
output_shape = (batch_size, out_width, self.filters)
# output_shape = np.asarray( output_shape )
# Apply tensorflow's conv1d transpose function
outputs = conv1d_transpose(value=inputs,
filter=self.kernel,
output_shape=output_shape,
stride=stride_w,
padding=self.padding,
data_format=self.data_format)
# Add bias if enabled
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
# Apply activation function if provided
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
#if self.data_format == 'channels_first':
# s_axis = 2
#else:
s_axis = 1
steps = input_shape[s_axis]
kernel_w, = self.kernel_size
stride, = self.strides
if self.output_padding is None:
out_pad_w = None
else:
out_pad_w, = self.output_padding
# Infer the dynamic output shape:
out_width = conv_utils.deconv_length(steps, stride, kernel_w,
self.padding, out_pad_w,
self.dilation_rate[0])
#if self.data_format == 'channels_first':
# output_shape = (batch_size, self.filters, 1, out_width)
# inputs = K.expand_dims(inputs, axis=2)
#else:
output_shape = (batch_size, 1, out_width, self.filters)
inputs = K.expand_dims(inputs, axis=1)
outputs = K.conv2d_transpose(inputs,
K.expand_dims(self.kernel, axis=0),
output_shape, (1, self.strides[0]),
padding=self.padding,
data_format=self.data_format,
dilation_rate=(1, self.dilation_rate[0]))
#if self.data_format == 'channels_first':
# outputs = K.squeeze(outputs, axis=2)
#else:
outputs = K.squeeze(outputs, axis=1)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
#if self.data_format == 'channels_first':
# c_axis, w_axis = 1, 2
#else:
c_axis, w_axis = 2, 1
kernel_w, = self.kernel_size
stride, = self.strides
if self.output_padding is None:
out_pad_w = None
else:
out_pad_w, = self.output_padding
output_shape[c_axis] = self.filters
output_shape[w_axis] = conv_utils.deconv_length(
output_shape[w_axis], stride, kernel_w, self.padding, out_pad_w,
self.dilation_rate[0])
return tuple(output_shape)
def call(self, inputs):
revcomp_kernel =\
K.concatenate([self.kernel,
self.kernel[::-1,::-1,::-1]],axis=-2)
if (self.use_bias):
revcomp_bias = K.concatenate([self.bias, self.bias[::-1]], axis=-1)
input_shape = K.shape(inputs)
batch_size = input_shape[0]
s_axis = 1
steps = input_shape[s_axis]
kernel_w, = self.kernel_size
stride, = self.strides
if self.output_padding is None:
out_pad_w = None
else:
out_pad_w, = self.output_padding
# Infer the dynamic output shape:
out_width = conv_utils.deconv_length(steps, stride, kernel_w,
self.padding, out_pad_w,
self.dilation_rate[0])
output_shape = (batch_size, 1, out_width, 2 * self.filters)
inputs = K.expand_dims(inputs, axis=1)
outputs = K.conv2d_transpose(inputs,
K.expand_dims(revcomp_kernel, axis=0),
output_shape, (1, self.strides[0]),
padding=self.padding,
data_format=self.data_format,
dilation_rate=(1, self.dilation_rate[0]))
outputs = K.squeeze(outputs, axis=1)
if self.use_bias:
outputs = K.bias_add(outputs,
revcomp_bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def compute_output_shape(self, input_shape):
output_shape = list(input_shape)
if self.data_format == 'channel_first':
c_axis, w_axis = 1, 2
else:
c_axis, w_axis = 2, 1
kernel_w, = self.kernel_size
stride_w, = self.strides
out_pad = self.output_padding
out_width = conv_utils.deconv_length(
dim_size=output_shape[w_axis],
stride_size=stride_w,
kernel_size=kernel_w,
padding=self.padding,
# output_padding=out_pad # Does not exists in earlier keras versions
)
output_shape[c_axis] = self.filters
output_shape[w_axis] = out_width
return tuple(output_shape)
def compute_output_shape(self, input_shape):
space = input_shape[3:]
if self.op == "conv":
new_space = []
for i in range(len(space)):
new_dim = conv_output_length(
space[i],
self.kernel_size,
padding=self.padding,
stride=self.strides,
dilation=1)
new_space.append(new_dim)
elif self.op == 'deconv':
new_space = []
for i in range(len(space)):
new_dim = deconv_length(space[i], self.strides, self.kernel_size, self.padding,
output_padding=None)
new_space.append(new_dim)
else:
raise ValueError("Wrong type of operation for capsule")
return (input_shape[0],) + (self.num_capsule, np.product(self.app_dim) + np.product(self.pos_dim)) + \
tuple(new_space)
def call(self, inputs, training=None):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height,
stride_h, kernel_h,
self.padding,
out_pad_h)
out_width = conv_utils.deconv_length(width,
stride_w, kernel_w,
self.padding,
out_pad_w)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
w_shape = self.kernel.shape.as_list()
# Flatten the Tensor
w_reshaped = K.reshape(self.kernel, [-1, w_shape[-1]])
_u, _v = power_iteration(w_reshaped, self.u)
# Calculate Sigma
sigma = K.dot(_v, w_reshaped)
sigma = K.dot(sigma, K.transpose(_u))
# normalize it
w_bar = w_reshaped / sigma
# reshape weight tensor
if not training:
w_bar = K.reshape(w_bar, w_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
w_bar = K.reshape(w_bar, w_shape)
outputs = K.conv2d_transpose(
inputs,
w_bar,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def call(self, input_tensor, training=None):
z_app = np.product(self.app_dim)
z_pos = np.product(self.pos_dim)
# For each child (input) capsule (t_0) project into all parent (output) capsule domain (t_1)
idx_all = 0
u_hat_t_list = []
# Split input_tensor by capsule types
u_t_list = [tf.squeeze(u_t, axis=1) for u_t in tf.split(input_tensor, self.input_num_capsule, axis=1)]
for u_t in u_t_list:
u_t = tf.reshape(u_t, [self.batch * self.channels, self.input_height, self.input_width, 1])
u_t.set_shape((None, self.input_height, self.input_width, 1))
# Apply spatial kernel
# Incorporating local neighborhood information by learning a convolution kernel of size k x k for the pose
# and appearance matrices Pi and Ai
if self.op == "conv":
u_spat_t = K.conv2d(u_t, self.W[idx_all], (self.strides, self.strides),
padding=self.padding, data_format='channels_last')
elif self.op == "deconv":
out_height = deconv_length(self.input_height, self.strides, self.kernel_size, self.padding,
output_padding=None)
out_width = deconv_length(self.input_width, self.strides, self.kernel_size, self.padding,
output_padding=None)
output_shape = (self.batch * self.channels, out_height, out_width, self.num_capsule)
u_spat_t = K.conv2d_transpose(u_t, self.W[idx_all], output_shape, (self.strides, self.strides),
padding=self.padding, data_format='channels_last')
else:
raise ValueError("Wrong type of operation for capsule")
# Some shape operation
H_1 = u_spat_t.get_shape()[1]
W_1 = u_spat_t.get_shape()[2]
# H_1 = tf.shape(u_spat_t)[1]
# W_1 = tf.shape(u_spat_t)[2]
u_spat_t = tf.reshape(u_spat_t, [self.batch, self.channels, H_1, W_1, self.num_capsule])
u_spat_t = tf.transpose(u_spat_t, (0, 2, 3, 4, 1))
u_spat_t = tf.reshape(u_spat_t, [self.batch, H_1, W_1, self.num_capsule * self.channels])
# Split convolution output of input_tensor to Pose and Appearance matrices
u_t_pos, u_t_app = tf.split(u_spat_t, [self.num_capsule * z_pos, self.num_capsule * z_app], axis=-1)
u_t_pos = tf.reshape(u_t_pos, [self.batch, H_1, W_1, self.num_capsule, self.pos_dim[0], self.pos_dim[1]])
u_t_app = tf.reshape(u_t_app, [self.batch, H_1, W_1, self.num_capsule, self.app_dim[0], self.app_dim[1]])
# Gather projection matrices and bias
# Take appropriate capsule type
mult_pos = tf.gather(self.W_pos, idx_all, axis=0)
mult_pos = tf.reshape(mult_pos, [self.num_capsule, self.pos_dim[1], self.pos_dim[1]])
mult_app = tf.gather(self.W_app, idx_all, axis=0)
mult_app = tf.reshape(mult_app, [self.num_capsule, self.app_dim[1], self.app_dim[1]])
bias = tf.reshape(tf.gather(self.b_app, idx_all, axis=0), (1, 1, 1, self.num_capsule, 1, 1))
u_t_app += bias
# Prepare the pose projection matrix
mult_pos = K.l2_normalize(mult_pos, axis=-2)
if self.coord_add:
mult_pos = coordinate_addition(mult_pos,
[1, H_1, W_1, self.num_capsule, self.pos_dim[1], self.pos_dim[1]])
u_t_pos = mat_mult_2d(u_t_pos, mult_pos)
u_t_app = mat_mult_2d(u_t_app, mult_app)
# Store the result
u_hat_t_pos = tf.reshape(u_t_pos, [self.batch, H_1, W_1, self.num_capsule, z_pos])
u_hat_t_app = tf.reshape(u_t_app, [self.batch, H_1, W_1, self.num_capsule, z_app])
u_hat_t = tf.concat([u_hat_t_pos, u_hat_t_app], axis=-1)
u_hat_t_list.append(u_hat_t)
idx_all += 1
u_hat_t_list = tf.stack(u_hat_t_list, axis=-2)
u_hat_t_list = tf.transpose(u_hat_t_list,
[0, 5, 1, 2, 4, 3]) # [N, H, W, t_1, t_0, z] = > [N, z, H_1, W_1, t_0, t_1]
# Routing operation
if self.routings > 0:
if self.routing_type is 'dynamic':
if type(self.routings) is list:
self.routings = self.routings[-1]
c_t_list = routing2d(routing=self.routings, t_0=self.input_num_capsule,
u_hat_t_list=u_hat_t_list) # [T1][N,H,W,to]
elif self.routing_type is 'dual':
if type(self.routings) is list:
self.routings = self.routings[-1]
c_t_list = dual_routing(routing=self.routings, t_0=self.input_num_capsule, u_hat_t_list=u_hat_t_list,
z_app=z_app,
z_pos=z_pos) # [T1][N,H,W,to]
else:
raise ValueError(self.routing_type + ' is an invalid routing; try dynamic or dual')
else:
self.routing_type = 'NONE'
c = tf.ones([self.batch, H_1, W_1, self.input_num_capsule, self.num_capsule])
c_t_list = [tf.squeeze(c_t, axis=-1) for c_t in tf.split(c, self.num_capsule, axis=-1)]
# Form each parent capsule through the weighted sum of all child capsules
r_t_mul_u_hat_t_list = []
u_hat_t_list_ = [(tf.squeeze(u_hat_t, axis=-1)) for u_hat_t in tf.split(u_hat_t_list, self.num_capsule, axis=-1)]
for c_t, u_hat_t in zip(c_t_list, u_hat_t_list_):
r_t = tf.expand_dims(c_t, axis=1)
r_t_mul_u_hat_t_list.append(tf.reduce_sum(r_t * u_hat_t, axis=-1))
p = r_t_mul_u_hat_t_list
p = tf.stack(p, axis=1)
p_pos, p_app = tf.split(p, [z_pos, z_app], axis=2)
# Squash the weighted sum to form the final parent capsule
v_pos = Psquash(p_pos, axis=2)
v_app = matwo_squash(p_app, axis=2)
outputs = tf.concat([v_pos, v_app], axis=2)
return outputs
def call(self, inputs):
input_shape = K.shape(inputs)
batch_size = input_shape[0]
if self.data_format == 'channels_first':
h_axis, w_axis = 2, 3
else:
h_axis, w_axis = 1, 2
height, width = input_shape[h_axis], input_shape[w_axis]
kernel_h, kernel_w = self.kernel_size
stride_h, stride_w = self.strides
if self.output_padding is None:
out_pad_h = out_pad_w = None
else:
out_pad_h, out_pad_w = self.output_padding
# Infer the dynamic output shape:
out_height = conv_utils.deconv_length(height, stride_h, kernel_h,
self.padding, out_pad_h)
out_width = conv_utils.deconv_length(width, stride_w, kernel_w,
self.padding, out_pad_w)
if self.data_format == 'channels_first':
output_shape = (batch_size, self.filters, out_height, out_width)
else:
output_shape = (batch_size, out_height, out_width, self.filters)
#Spectral Normalization
def _l2normalize(v, eps=1e-12):
return v / (K.sum(v**2)**0.5 + eps)
def power_iteration(W, u):
#Accroding the paper, we only need to do power iteration one time.
_u = u
_v = _l2normalize(K.dot(_u, K.transpose(W)))
_u = _l2normalize(K.dot(_v, W))
return _u, _v
W_shape = self.kernel.shape.as_list()
#Flatten the Tensor
W_reshaped = K.reshape(self.kernel, [-1, W_shape[-1]])
_u, _v = power_iteration(W_reshaped, self.u)
#Calculate Sigma
sigma = K.dot(_v, W_reshaped)
sigma = K.dot(sigma, K.transpose(_u))
#normalize it
W_bar = W_reshaped / sigma
#reshape weight tensor
if training in {0, False}:
W_bar = K.reshape(W_bar, W_shape)
else:
with tf.control_dependencies([self.u.assign(_u)]):
W_bar = K.reshape(W_bar, W_shape)
self.kernel = W_bar
outputs = K.conv2d_transpose(inputs,
self.kernel,
output_shape,
self.strides,
padding=self.padding,
data_format=self.data_format)
if self.use_bias:
outputs = K.bias_add(outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
def test_deconv_length():
assert conv_utils.deconv_length(None, 1, 7, 'same', None) is None
assert conv_utils.deconv_length(224, 1, 7, 'same', None) == 224
assert conv_utils.deconv_length(224, 2, 7, 'same', None) == 448
assert conv_utils.deconv_length(32, 1, 5, 'valid', None) == 36
assert conv_utils.deconv_length(32, 2, 5, 'valid', None) == 67
assert conv_utils.deconv_length(32, 1, 5, 'full', None) == 28
assert conv_utils.deconv_length(32, 2, 5, 'full', None) == 59
assert conv_utils.deconv_length(224, 1, 7, 'same', 0) == 224
assert conv_utils.deconv_length(224, 2, 7, 'same', 0) == 447
assert conv_utils.deconv_length(224, 2, 7, 'same', 1) == 448
assert conv_utils.deconv_length(32, 1, 5, 'valid', 0) == 36
assert conv_utils.deconv_length(32, 2, 5, 'valid', 0) == 67
assert conv_utils.deconv_length(32, 2, 5, 'valid', 1) == 68
assert conv_utils.deconv_length(6, 1, 3, 'full', 0) == 4
assert conv_utils.deconv_length(6, 2, 3, 'full', 1) == 10
assert conv_utils.deconv_length(6, 2, 3, 'full', 2) == 11
def test_deconv_length():
assert conv_utils.deconv_length(None, 1, 7, 'same', None) is None
assert conv_utils.deconv_length(224, 1, 7, 'same', None) == 224
assert conv_utils.deconv_length(224, 2, 7, 'same', None) == 448
assert conv_utils.deconv_length(32, 1, 5, 'valid', None) == 36
assert conv_utils.deconv_length(32, 2, 5, 'valid', None) == 67
assert conv_utils.deconv_length(32, 1, 5, 'full', None) == 28
assert conv_utils.deconv_length(32, 2, 5, 'full', None) == 59
assert conv_utils.deconv_length(224, 1, 7, 'same', 0) == 224
assert conv_utils.deconv_length(224, 2, 7, 'same', 0) == 447
assert conv_utils.deconv_length(224, 2, 7, 'same', 1) == 448
assert conv_utils.deconv_length(32, 1, 5, 'valid', 0) == 36
assert conv_utils.deconv_length(32, 2, 5, 'valid', 0) == 67
assert conv_utils.deconv_length(32, 2, 5, 'valid', 1) == 68
assert conv_utils.deconv_length(6, 1, 3, 'full', 0) == 4
assert conv_utils.deconv_length(6, 2, 3, 'full', 1) == 10
assert conv_utils.deconv_length(6, 2, 3, 'full', 2) == 11