def _test_upsample_shape(self, rank, param_dict, output_shape):
if rank == 3:
input_data = self.get_3d_input()
elif rank == 2:
input_data = self.get_2d_input()
upsample_layer = UpSampleLayer(**param_dict)
output_data = upsample_layer(input_data)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(output_data)
self.assertAllClose(output_shape, output.shape)
def layer_op(self, codes, is_training):
# Define the decoding fully-connected layers
decoders_fc = []
for i in range(0, len(self.layer_sizes_decoder)):
decoders_fc.append(FullyConnectedLayer(
n_output_chns=self.layer_sizes_decoder[i],
with_bias=True,
with_bn=True,
acti_func=self.acti_func_decoder[i],
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='decoder_fc_{}'.format(self.layer_sizes_decoder[i])))
print(decoders_fc[-1])
# Define the decoding convolutional layers
decoders_cnn = []
decoders_upsamplers = []
for i in range(0, len(self.trans_conv_output_channels)):
if self.upsampling_mode == 'DECONV':
decoders_upsamplers.append(DeconvolutionalLayer(
n_output_chns=self.trans_conv_output_channels[i],
kernel_size=self.trans_conv_unpooling_factors[i],
stride=self.trans_conv_unpooling_factors[i],
padding='SAME',
with_bias=True,
with_bn=True,
w_initializer=self.initializers['w'],
w_regularizer=None,
acti_func=None,
name='decoder_upsampler_{}_{}'.format(
self.trans_conv_unpooling_factors[i],
self.trans_conv_unpooling_factors[i])))
print(decoders_upsamplers[-1])
decoders_cnn.append(DeconvolutionalLayer(
n_output_chns=self.trans_conv_output_channels[i],
kernel_size=self.trans_conv_kernel_sizes[i],
stride=1,
padding='SAME',
with_bias=True,
with_bn=True,
#with_bn=not (i == len(self.trans_conv_output_channels) - 1),
# No BN on output
w_initializer=self.initializers['w'],
w_regularizer=None,
acti_func=self.acti_func_trans_conv[i],
name='decoder_trans_conv_{}_{}'.format(
self.trans_conv_kernel_sizes[i],
self.trans_conv_output_channels[i])))
print(decoders_cnn[-1])
# Fully-connected decoder layers
flow = codes
for i in range(0, len(self.layer_sizes_decoder)):
flow = decoders_fc[i](flow, is_training)
# Reconstitute the feature maps
flow = tf.reshape(flow, [-1] + self.downsampled_shape)
# Convolutional decoder layers
for i in range(0, len(self.trans_conv_output_channels)):
if self.upsampling_mode == 'DECONV':
flow = decoders_upsamplers[i](flow, is_training)
elif self.upsampling_mode == 'CHANNELWISE_DECONV':
flow = UpSampleLayer(
'CHANNELWISE_DECONV',
kernel_size=self.trans_conv_unpooling_factors[i],
stride=self.trans_conv_unpooling_factors[i])(flow)
elif self.upsampling_mode == 'REPLICATE':
flow = UpSampleLayer(
'REPLICATE',
kernel_size=self.trans_conv_unpooling_factors[i],
stride=self.trans_conv_unpooling_factors[i])(flow)
flow = decoders_cnn[i](flow, is_training)
return flow
def layer_op(self, input_tensor, is_training, layer_id=-1):
layer_instances = []
scores_instances = []
first_conv_layer = ConvolutionalLayer(
n_output_chns=self.num_features[0],
with_bn=True,
kernel_size=3,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
acti_func=self.acti_func,
name='conv_1_1')
flow = first_conv_layer(input_tensor, is_training)
layer_instances.append((first_conv_layer, flow))
# SCALE 1
with DilatedTensor(flow, dilation_factor=1) as dilated:
for j in range(self.num_res_blocks[0]):
res_block = HighResBlock(self.num_features[0],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='%s_%d' % ('res_1', j))
dilated.tensor = res_block(dilated.tensor, is_training)
layer_instances.append((res_block, dilated.tensor))
flow = dilated.tensor
score_layer_scale1 = ScoreLayer(
num_features=self.num_fea_score_layers[0],
num_classes=self.num_classes)
score_1 = score_layer_scale1(flow, is_training)
scores_instances.append(score_1)
# if is_training:
# loss_s1 = WGDL(score_1, labels)
# tf.add_to_collection('multiscale_loss', loss_s1/num_scales)
# # SCALE 2
with DilatedTensor(flow, dilation_factor=2) as dilated:
for j in range(self.num_res_blocks[1]):
res_block = HighResBlock(self.num_features[1],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='%s_%d' % ('res_2', j))
dilated.tensor = res_block(dilated.tensor, is_training)
layer_instances.append((res_block, dilated.tensor))
flow = dilated.tensor
score_layer_scale2 = ScoreLayer(
num_features=self.num_fea_score_layers[1],
num_classes=self.num_classes)
score_2 = score_layer_scale2(flow, is_training)
# score_2 = self.score_layer(flow, self.num_fea_score_layers[1])
up_score_2 = score_2
scores_instances.append(up_score_2)
# if is_training:
# loss_s2 = self.WGDL(score_2, labels)
# # loss_s2 = self.new_dice_loss(score_2, labels)
# tf.add_to_collection('multiscale_loss', loss_s2/num_scales)
# SCALE 3
## dowsampling factor = 2
downsample_scale3 = DownSampleLayer(func='AVG',
kernel_size=2,
stride=2)
flow = downsample_scale3(flow)
layer_instances.append((downsample_scale3, flow))
with DilatedTensor(flow, dilation_factor=1) as dilated:
for j in range(self.num_res_blocks[2]):
res_block = HighResBlock(self.num_features[2],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='%s_%d' % ('res_3', j))
dilated.tensor = res_block(dilated.tensor, is_training)
layer_instances.append((res_block, dilated.tensor))
flow = dilated.tensor
score_layer_scale3 = ScoreLayer(
num_features=self.num_fea_score_layers[2],
num_classes=self.num_classes)
score_3 = score_layer_scale3(flow, is_training)
upsample_indep_scale3 = UpSampleLayer(
func='CHANNELWISE_DECONV',
kernel_size=2,
stride=2,
w_initializer=tf.constant_initializer(1.0, dtype=tf.float32))
up_score_3 = upsample_indep_scale3(score_3)
scores_instances.append(up_score_3)
# up_score_3 = self.feature_indep_upsample_conv(score_3, factor=2)
# if is_training:
# loss_s3 = self.WGDL(up_score_3, labels)
# # loss_s3 = self.new_dice_loss(up_score_3, labels)
# tf.add_to_collection('multiscale_loss', loss_s3/num_scales)
# SCALE 4
with DilatedTensor(flow, dilation_factor=2) as dilated:
for j in range(self.num_res_blocks[3]):
res_block = HighResBlock(self.num_features[3],
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='%s_%d' % ('res_4', j))
dilated.tensor = res_block(dilated.tensor, is_training)
layer_instances.append((res_block, dilated.tensor))
flow = dilated.tensor
score_layer_scale4 = ScoreLayer(
num_features=self.num_fea_score_layers[3],
num_classes=self.num_classes)
score_4 = score_layer_scale4(flow, self.num_fea_score_layers[3],
is_training)
upsample_indep_scale4 = UpSampleLayer(
func='CHANNELWISE_DECONV',
kernel_size=1,
stride=2,
w_initializer=tf.constant_initializer(1.0, dtype=tf.float32))
up_score_4 = upsample_indep_scale4(score_4)
scores_instances.append(up_score_4)
# if is_training:
# loss_s4 = self.WGDL(up_score_4, labels)
# # loss_s4 = self.new_dice_loss(up_score_4, labels)
# tf.add_to_collection('multiscale_loss', loss_s4/num_scales)
# FUSED SCALES
merge_layer = MergeLayer('WEIGHTED_AVERAGE')
soft_scores = []
for s in scores_instances:
soft_scores.append(tf.nn.softmax(s))
fused_score = merge_layer(soft_scores)
scores_instances.append(fused_score)
if is_training:
return scores_instances
return fused_score
def layer_op(self, images, is_training, layer_id=-1, **unused_kwargs):
"""
:param images: tensor, input to the network, size should be divisible by d_factor
:param is_training: boolean, True if network is in training mode
:param layer_id: not in use
:param unused_kwargs:
:return: tensor, network output
"""
# image_size is defined as the largest context, then:
# downsampled path size: image_size / d_factor
# downsampled path output: image_size / d_factor - 16
# to make sure same size of feature maps from both pathways:
# normal path size: (image_size / d_factor - 16) * d_factor + 16
# normal path output: (image_size / d_factor - 16) * d_factor
# where 16 is fixed by the receptive field of conv layers
# TODO: make sure label_size = image_size/d_factor - 16
# image_size has to be an odd number and divisible by 3 and
# smaller than the smallest image size of the input volumes
# label_size should be (image_size/d_factor - 16) * d_factor
assert self.d_factor % 2 == 1 # to make the downsampling centered
assert (layer_util.check_spatial_dims(
images, lambda x: x % self.d_factor == 0))
assert (layer_util.check_spatial_dims(images, lambda x: x % 2 == 1)
) # to make the crop centered
assert (layer_util.check_spatial_dims(images,
lambda x: x > self.d_factor * 16)
) # required by receptive field
# crop 25x25x25 from 57x57x57
crop_op = CropLayer(border=self.crop_diff, name='cropping_input')
normal_path = crop_op(images)
print(crop_op)
# downsample 19x19x19 from 57x57x57
downsample_op = DownSampleLayer(func='CONSTANT',
kernel_size=self.d_factor,
stride=self.d_factor,
padding='VALID',
name='downsample_input')
downsample_path = downsample_op(images)
print(downsample_op)
# convolutions for both pathways
for n_features in self.conv_features:
# normal pathway convolutions
conv_path_1 = ConvolutionalLayer(
n_output_chns=n_features,
kernel_size=3,
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
acti_func=self.acti_func,
name='normal_conv')
normal_path = conv_path_1(normal_path, is_training)
print(conv_path_1)
# downsampled pathway convolutions
conv_path_2 = ConvolutionalLayer(
n_output_chns=n_features,
kernel_size=3,
padding='VALID',
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
acti_func=self.acti_func,
name='downsample_conv')
downsample_path = conv_path_2(downsample_path, is_training)
print(conv_path_2)
# upsampling the downsampled pathway
downsample_path = UpSampleLayer('REPLICATE',
kernel_size=self.d_factor,
stride=self.d_factor)(downsample_path)
# concatenate both pathways
output_tensor = ElementwiseLayer('CONCAT')(normal_path,
downsample_path)
# 1x1x1 convolution layer
for n_features in self.fc_features:
conv_fc = ConvolutionalLayer(
n_output_chns=n_features,
kernel_size=1,
acti_func=self.acti_func,
w_initializer=self.initializers['w'],
w_regularizer=self.regularizers['w'],
name='conv_1x1x1_{}'.format(n_features))
output_tensor = conv_fc(output_tensor, is_training)
print(conv_fc)
return output_tensor
