def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
skip_output_id = start_node_id
for layer in self._get_pooling_layers(start_node_id, end_node_id):
new_layer = deepcopy(layer)
if is_layer(new_layer, 'Conv'):
filters = self.node_list[start_node_id].shape[-1]
new_layer = get_conv_class(self.n_dim)(filters, filters, 1, layer.stride)
if self.weighted:
init_conv_weight(new_layer)
else:
new_layer = deepcopy(layer)
skip_output_id = self.add_layer(new_layer, skip_output_id)
skip_output_id = self.add_layer(StubReLU(), skip_output_id)
return skip_output_id
def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
skip_output_id = start_node_id
for layer in self._get_pooling_layers(start_node_id, end_node_id):
new_layer = deepcopy(layer)
if is_layer(new_layer, 'Conv'):
filters = self.node_list[start_node_id].shape[-1]
kernel_size = layer.kernel_size if layer.padding != int(
layer.kernel_size / 2) or layer.stride != 1 else 1
new_layer = get_conv_class(self.n_dim)(filters, filters, kernel_size, layer.stride,
padding=layer.padding)
if self.weighted:
init_conv_weight(new_layer)
else:
new_layer = deepcopy(layer)
skip_output_id = self.add_layer(new_layer, skip_output_id)
skip_output_id = self.add_layer(StubReLU(), skip_output_id)
return skip_output_id
def _insert_pooling_layer_chain(self, start_node_id, end_node_id):
skip_output_id = start_node_id
for layer in self._get_pooling_layers(start_node_id, end_node_id):
new_layer = deepcopy(layer)
if is_layer(new_layer, LayerType.CONV):
filters = self.node_list[start_node_id].shape[-1]
kernel_size = layer.kernel_size if layer.padding != int(
layer.kernel_size / 2) or layer.stride != 1 else 1
new_layer = get_conv_class(self.n_dim)(filters, filters, kernel_size, layer.stride,
padding=layer.padding)
if self.weighted:
init_conv_weight(new_layer)
else:
new_layer = deepcopy(layer)
skip_output_id = self.add_layer(new_layer, skip_output_id)
skip_output_id = self.add_layer(StubReLU(), skip_output_id)
return skip_output_id
def to_deeper_model(self, target_id, new_layer):
"""Insert a relu-conv-bn block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the block.
new_layer: An instance of StubLayer subclasses.
"""
self.operation_history.append(('to_deeper_model', target_id, new_layer))
input_id = self.layer_id_to_input_node_ids[target_id][0]
output_id = self.layer_id_to_output_node_ids[target_id][0]
if self.weighted:
if is_layer(new_layer, 'Dense'):
init_dense_weight(new_layer)
elif is_layer(new_layer, 'Conv'):
init_conv_weight(new_layer)
elif is_layer(new_layer, 'BatchNormalization'):
init_bn_weight(new_layer)
self._insert_new_layers([new_layer], input_id, output_id)
def to_deeper_model(self, target_id, new_layer):
"""Insert a relu-conv-bn block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the block.
new_layer: An instance of StubLayer subclasses.
"""
self.operation_history.append(('to_deeper_model', target_id, new_layer))
input_id = self.layer_id_to_input_node_ids[target_id][0]
output_id = self.layer_id_to_output_node_ids[target_id][0]
if self.weighted:
if is_layer(new_layer, LayerType.DENSE):
init_dense_weight(new_layer)
elif is_layer(new_layer, LayerType.CONV):
init_conv_weight(new_layer)
elif is_layer(new_layer, LayerType.BATCH_NORM):
init_bn_weight(new_layer)
self._insert_new_layers([new_layer], input_id, output_id)
