def _test_deconv_output_shape(self, rank, param_dict, output_shape):
if rank == 2:
input_data = self.get_2d_input()
elif rank == 3:
input_data = self.get_3d_input()
deconv_layer = DeconvLayer(**param_dict)
output_data = deconv_layer(input_data)
print(deconv_layer)
with self.cached_session() as sess:
sess.run(tf.global_variables_initializer())
output_value = sess.run(output_data)
self.assertAllClose(output_shape, output_value.shape)
def layer_op(self, main_flow, bypass_flow):
"""
:param main_flow: tensor, input to the VNet block
:param bypass_flow: tensor, input from skip connection
:return: res_flow is tensor before final block operation (for residual connections),
main_flow is final output tensor
"""
for i in range(self.n_conv):
main_flow = ConvLayer(name='conv_{}'.format(i),
n_output_chns=self.n_feature_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=5)(main_flow)
if i < self.n_conv - 1: # no activation for the last conv layer
main_flow = ActiLayer(
func=self.acti_func,
regularizer=self.regularizers['w'])(main_flow)
res_flow = ElementwiseLayer('SUM')(main_flow, bypass_flow)
if self.func == 'DOWNSAMPLE':
main_flow = ConvLayer(name='downsample',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=2,
stride=2,
with_bias=True)(res_flow)
elif self.func == 'UPSAMPLE':
main_flow = DeconvLayer(name='upsample',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=2,
stride=2,
with_bias=True)(res_flow)
elif self.func == 'SAME':
main_flow = ConvLayer(name='conv_1x1x1',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
kernel_size=1,
with_bias=True)(res_flow)
main_flow = ActiLayer(self.acti_func)(main_flow)
print(self)
return res_flow, main_flow
def layer_op(self, main_flow, bypass_flow):
for i in range(self.n_conv):
main_flow = ConvLayer(name='conv_{}'.format(i),
n_output_chns=self.n_feature_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=5)(main_flow)
if i < self.n_conv - 1: # no activation for the last conv layer
main_flow = ActiLayer(
func=self.acti_func,
regularizer=self.regularizers['w'])(main_flow)
res_flow = ElementwiseLayer('SUM')(main_flow, bypass_flow)
if self.func == 'DOWNSAMPLE':
main_flow = ConvLayer(name='downsample',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=2,
stride=2,
with_bias=True)(res_flow)
elif self.func == 'UPSAMPLE':
main_flow = DeconvLayer(name='upsample',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
kernel_size=2,
stride=2,
with_bias=True)(res_flow)
elif self.func == 'SAME':
main_flow = ConvLayer(name='conv_1x1x1',
n_output_chns=self.n_output_chns,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
kernel_size=1,
with_bias=True)(res_flow)
main_flow = ActiLayer(self.acti_func)(main_flow)
print(self)
print('VNet is running')
return res_flow, main_flow
def layer_op(self, input_tensor):
spatial_rank = layer_util.infer_spatial_rank(input_tensor)
output_tensor = input_tensor
if self.func == 'REPLICATE':
if self.kernel_size != self.stride:
raise ValueError(
"`kernel_size` != `stride` currently not"
"supported in `REPLICATE` mode. Please"
"consider using `CHANNELWISE_DECONV` operation.")
# simply replicate input values to
# local regions of (kernel_size ** spatial_rank) element
kernel_size_all_dims = layer_util.expand_spatial_params(
self.kernel_size, spatial_rank)
pixel_num = np.prod(kernel_size_all_dims)
repmat = np.hstack((pixel_num, [1] * spatial_rank, 1)).flatten()
output_tensor = tf.tile(input=input_tensor, multiples=repmat)
output_tensor = tf.batch_to_space_nd(
input=output_tensor,
block_shape=kernel_size_all_dims,
crops=[[0, 0]] * spatial_rank)
elif self.func == 'CHANNELWISE_DECONV':
output_tensor = [
tf.expand_dims(x, -1)
for x in tf.unstack(input_tensor, axis=-1)
]
output_tensor = [
DeconvLayer(n_output_chns=1,
kernel_size=self.kernel_size,
stride=self.stride,
padding='SAME',
with_bias=self.with_bias,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
b_initializer=self.initializers['b'],
b_regularizer=self.regularizers['b'],
name='deconv_{}'.format(i))(x)
for (i, x) in enumerate(output_tensor)
]
output_tensor = tf.concat(output_tensor, axis=-1)
return output_tensor