def layer_op(self, input_tensor, is_training, layer_id=-1):
hp = self.hyperparameters
if is_training and hp['augmentation_scale'] > 0:
aug = Affine3DAugmentationLayer(hp['augmentation_scale'], 'LINEAR',
'ZERO')
input_tensor = aug(input_tensor)
channel_dim = len(input_tensor.get_shape()) - 1
input_size = input_tensor.get_shape().as_list()
spatial_rank = len(input_size) - 2
modulo = 2**(len(hp['dilation_rates']))
assert layer_util.check_spatial_dims(input_tensor,
lambda x: x % modulo == 0)
downsample_channels = list(hp['n_input_channels'][1:]) + [None]
v_params = zip(hp['n_dense_channels'], hp['n_seg_channels'],
downsample_channels, hp['dilation_rates'],
range(len(downsample_channels)))
downsampled_img = BNLayer()(
tf.nn.avg_pool3d(input_tensor, [1] + [3] * spatial_rank + [1],
[1] + [2] * spatial_rank + [1], 'SAME'),
is_training=is_training)
all_segmentation_features = [downsampled_img]
output_shape = downsampled_img.get_shape().as_list()[1:-1]
initial_features = ConvolutionalLayer(hp['n_input_channels'][0],
kernel_size=5,
stride=2)(
input_tensor,
is_training=is_training)
down = tf.concat([downsampled_img, initial_features], channel_dim)
## ADDED to prevent dropout at inference
if is_training is False:
hp['p_channels_selected'] = 1
######
for dense_ch, seg_ch, down_ch, dil_rate, idx in v_params:
sd = DenseFeatureStackBlockWithSkipAndDownsample(
dense_ch,
3,
dil_rate,
seg_ch,
down_ch,
self.architecture_parameters['use_bdo'],
acti_func='relu')
skip, down = sd(down,
is_training=is_training,
keep_prob=hp['p_channels_selected'])
all_segmentation_features.append(image_resize(skip, output_shape))
segmentation = ConvolutionalLayer(
10, kernel_size=hp['final_kernel'], with_bn=False,
with_bias=True)(tf.concat(all_segmentation_features, channel_dim),
is_training=is_training)
if self.architecture_parameters['use_prior']:
segmentation = segmentation + \
SpatialPriorBlock([12] * spatial_rank, output_shape)
if is_training and hp['augmentation_scale'] > 0:
inverse_aug = aug.inverse()
segmentation = inverse_aug(segmentation)
segmentation = image_resize(segmentation, input_size[1:-1])
#seg_summary = tf.to_float(tf.expand_dims(tf.argmax(segmentation,-1),-1)) * (255./self.num_classes-1)
###########
# =============================================================================
k = segmentation.get_shape().as_list()
segmentation = tf.nn.max_pool3d(segmentation, [1, 1, 1, k[3], 1],
[1, 1, 1, 1, 1],
'VALID',
data_format='NDHWC')
segmentation = tf.reshape(segmentation, [k[0], k[1], k[2], k[-1]])
segmentation = tf.layers.conv2d(
segmentation,
filters=10,
kernel_size=(3, 3),
strides=1,
padding='SAME',
use_bias=True,
kernel_initializer=tf.variance_scaling_initializer(),
activation=tf.nn.relu,
data_format='channels_last')
segmentation = tf.layers.conv2d(
segmentation,
filters=2,
kernel_size=(3, 3),
strides=1,
padding='SAME',
use_bias=True,
kernel_initializer=tf.variance_scaling_initializer(),
activation=tf.nn.relu,
data_format='channels_last')
# =============================================================================
###########
#segmentation = tf.transpose(segmentation,[0,3,1,2])
return segmentation
def layer_op(self, input_tensor, is_training, layer_id=-1):
hp = self.hyperparameters
if is_training and hp['augmentation_scale'] > 0:
aug = Affine3DAugmentationLayer(hp['augmentation_scale'], 'LINEAR',
'ZERO')
input_tensor = aug(input_tensor)
channel_dim = len(input_tensor.get_shape()) - 1
input_size = input_tensor.get_shape().as_list()
spatial_rank = len(input_size) - 2
modulo = 2**(len(hp['dilation_rates']))
assert layer_util.check_spatial_dims(input_tensor,
lambda x: x % modulo == 0)
downsample_channels = list(hp['n_input_channels'][1:]) + [None]
v_params = zip(hp['n_dense_channels'], hp['n_seg_channels'],
downsample_channels, hp['dilation_rates'],
range(len(downsample_channels)))
downsampled_img = BNLayer()(
tf.nn.avg_pool3d(input_tensor, [1] + [3] * spatial_rank + [1],
[1] + [2] * spatial_rank + [1], 'SAME'),
is_training=is_training)
all_segmentation_features = [downsampled_img]
output_shape = downsampled_img.get_shape().as_list()[1:-1]
initial_features = ConvolutionalLayer(hp['n_input_channels'][0],
kernel_size=5,
stride=2)(
input_tensor,
is_training=is_training)
down = tf.concat([downsampled_img, initial_features], channel_dim)
for dense_ch, seg_ch, down_ch, dil_rate, idx in v_params:
sd = DenseFeatureStackBlockWithSkipAndDownsample(
dense_ch,
3,
dil_rate,
seg_ch,
down_ch,
self.architecture_parameters['use_bdo'],
acti_func='relu')
skip, down = sd(down,
is_training=is_training,
keep_prob=hp['p_channels_selected'])
all_segmentation_features.append(image_resize(skip, output_shape))
segmentation = ConvolutionalLayer(
self.num_classes,
kernel_size=hp['final_kernel'],
with_bn=False,
with_bias=True)(tf.concat(all_segmentation_features, channel_dim),
is_training=is_training)
if self.architecture_parameters['use_prior']:
segmentation = segmentation + \
SpatialPriorBlock([12] * spatial_rank, output_shape)
if is_training and hp['augmentation_scale'] > 0:
inverse_aug = aug.inverse()
segmentation = inverse_aug(segmentation)
segmentation = image_resize(segmentation, input_size[1:-1])
seg_summary = tf.to_float(
tf.expand_dims(tf.argmax(segmentation, -1),
-1)) * (255. / self.num_classes - 1)
m, v = tf.nn.moments(input_tensor, axes=[1, 2, 3], keep_dims=True)
img_summary = tf.minimum(
255.,
tf.maximum(0., (tf.to_float(input_tensor - m) /
(tf.sqrt(v) * 2.) + 1.) * 127.))
image3_axial('imgseg', tf.concat([img_summary, seg_summary], 1), 5,
[tf.GraphKeys.SUMMARIES])
return segmentation
