def network(input, is_training, num_outputs_embedding, actual_network, filters=64, levels=7, activation='relu', normalize=False, data_format='channels_first', padding='same'):
if activation == 'selu':
activation = tf.nn.selu
kernel_initializer = selu_initializer
elif activation == 'relu':
activation = tf.nn.relu
kernel_initializer = he_initializer
elif activation == 'tanh':
activation = tf.nn.tanh
kernel_initializer = selu_initializer
padding = padding
embedding_axis = 1 if data_format == 'channels_first' else 4
if normalize:
embeddings_activation = lambda x, name: tf.nn.l2_normalize(x, dim=embedding_axis, name=name, epsilon=1e-4)
else:
if activation == tf.nn.selu:
embeddings_activation = tf.nn.selu
else:
embeddings_activation = None
embeddings_normalization = lambda x, name: tf.nn.l2_normalize(x, dim=embedding_axis, name=name, epsilon=1e-4)
with tf.variable_scope('unet_0'):
unet = actual_network(num_filters_base=filters, kernel=[3, 3, 3], num_levels=levels, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet', padding=padding)
unet_out = unet(input, is_training)
embeddings = conv3d(unet_out, kernel_size=[1, 1, 1], name='embeddings', filters=num_outputs_embedding, kernel_initializer=kernel_initializer, activation=embeddings_activation, data_format=data_format, is_training=is_training, padding=padding)
with tf.variable_scope('unet_1'):
normalized_embeddings = embeddings_normalization(embeddings, 'embeddings_normalized')
input_concat = concat_channels([input, normalized_embeddings], name='input_concat', data_format=data_format)
unet = actual_network(num_filters_base=filters, kernel=[3, 3, 3], num_levels=levels, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet', padding=padding)
unet_out = unet(input_concat, is_training)
embeddings_2 = conv3d(unet_out, kernel_size=[1, 1, 1], name='embeddings', filters=num_outputs_embedding, kernel_initializer=kernel_initializer, activation=embeddings_activation, data_format=data_format, is_training=is_training, padding=padding)
return embeddings, embeddings_2
def network(input, is_training, num_outputs_embedding, data_format='channels_first'):
kernel_initializer = he_initializer
activation = tf.nn.relu
with tf.variable_scope('unet_0'):
unet = UnetParallel2D(num_filters_base=64, kernel=[3, 3], num_levels=7, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet')
embeddings = conv2d(unet(input, is_training), kernel_size=[3, 3], name='embeddings', filters=num_outputs_embedding, kernel_initializer=kernel_initializer, activation=None, data_format=data_format)
with tf.variable_scope('unet_1'):
embeddings_normalized = tf.nn.l2_normalize(embeddings, axis=1)
input_concat = concat_channels([input, embeddings_normalized], name='input_concat', data_format=data_format)
unet = UnetParallel2D(num_filters_base=64, kernel=[3, 3], num_levels=7, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet')
embeddings_2 = conv2d(unet(input_concat, is_training), kernel_size=[3, 3], name='embeddings', filters=num_outputs_embedding, kernel_initializer=kernel_initializer, activation=None, data_format=data_format)
return embeddings, embeddings_2
def network2d(input, is_training, num_outputs_embedding, actual_network, filters=64, levels=5, activation='relu', normalize=True, data_format='channels_first', padding='same'):
if activation == 'selu':
activation = tf.nn.selu
kernel_initializer = selu_initializer
elif activation == 'relu':
activation = tf.nn.relu
kernel_initializer = he_initializer
elif activation == 'tanh':
activation = tf.nn.tanh
kernel_initializer = selu_initializer
padding = padding
embedding_axis = 1 if data_format == 'channels_first' else 4
if normalize:
embeddings_activation = lambda x, name: tf.nn.l2_normalize(x, dim=embedding_axis, name=name, epsilon=1e-4)
else:
if activation == tf.nn.selu:
embeddings_activation = tf.nn.selu
else:
embeddings_activation = None
embeddings_normalization = lambda x, name: tf.nn.l2_normalize(x, dim=embedding_axis, name=name, epsilon=1e-4)
batch_size, channels, (num_frames, height, width) = get_batch_channel_image_size(input, data_format=data_format)
with tf.variable_scope('unet_0'):
unet = actual_network(num_filters_base=filters, kernel=[3, 3], num_levels=levels, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet', padding=padding)
unet_out = unet(input[:, 0, :, :, :], is_training)
embeddings_2d = conv2d(unet_out, kernel_size=[1, 1], name='embeddings', filters=num_outputs_embedding * num_frames, kernel_initializer=kernel_initializer, activation=embeddings_activation, data_format=data_format, is_training=is_training, padding=padding)
embeddings = tf.reshape(embeddings_2d, [batch_size, num_outputs_embedding, num_frames, height, width])
with tf.variable_scope('unet_1'):
normalized_embeddings = embeddings_normalization(embeddings, 'embeddings_normalized')
normalized_embeddings_2d = tf.reshape(embeddings_2d, [batch_size, num_outputs_embedding * num_frames, height, width])
input_concat = concat_channels([input[:, 0, :, :, :], normalized_embeddings_2d], name='input_concat', data_format=data_format)
unet = actual_network(num_filters_base=filters, kernel=[3, 3], num_levels=levels, data_format=data_format, kernel_initializer=kernel_initializer, activation=activation, is_training=is_training, name='unet', padding=padding)
unet_out = unet(input_concat, is_training)
embeddings_2_2d = conv2d(unet_out, kernel_size=[1, 1], name='embeddings', filters=num_outputs_embedding * num_frames, kernel_initializer=kernel_initializer, activation=embeddings_activation, data_format=data_format, is_training=is_training, padding=padding)
embeddings_2 = tf.reshape(embeddings_2_2d, [batch_size, num_outputs_embedding, num_frames, height, width])
return embeddings, embeddings_2
def combine(self, parallel_node, upsample_node, current_level,
is_training):
return concat_channels([parallel_node, upsample_node],
name='concat' + str(current_level),
data_format=self.data_format)
def combine(self, parallel_node, upsample_node, current_level, is_training):
node = concat_channels([parallel_node, upsample_node], name='concatenate' + str(current_level))
return node
