def get_add_skip_model():
graph = Graph((32, 32, 3), False)
output_node_id = 0
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
temp_node_id = output_node_id
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
temp_node_id = graph.add_layer(StubReLU(), temp_node_id)
temp_node_id = graph.add_layer(StubConv2d(3, 3, 1), temp_node_id)
temp_node_id = graph.add_layer(StubBatchNormalization2d(3), temp_node_id)
output_node_id = graph.add_layer(StubAdd(), [output_node_id, temp_node_id])
temp_node_id = output_node_id
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
temp_node_id = graph.add_layer(StubReLU(), temp_node_id)
temp_node_id = graph.add_layer(StubConv2d(3, 3, 1), temp_node_id)
temp_node_id = graph.add_layer(StubBatchNormalization2d(3), temp_node_id)
output_node_id = graph.add_layer(StubAdd(), [output_node_id, temp_node_id])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubConv2d(3, 3, 3), output_node_id)
output_node_id = graph.add_layer(StubBatchNormalization2d(3),
output_node_id)
output_node_id = graph.add_layer(StubFlatten(), output_node_id)
output_node_id = graph.add_layer(StubDropout2d(Constant.CONV_DROPOUT_RATE),
output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(
StubDense(graph.node_list[output_node_id].shape[0], 5), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(StubDense(5, 5), output_node_id)
graph.add_layer(StubSoftmax(), output_node_id)
graph.produce_model().set_weight_to_graph()
return graph
def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection.
"""
self.operation_history.append(('to_add_skip_model', start_id, end_id))
conv_block_input_id = self._conv_block_end_node(start_id)
conv_block_input_id = self.adj_list[conv_block_input_id][0][0]
block_last_layer_input_id = self._conv_block_end_node(end_id)
# Add the pooling layer chain.
layer_list = self._get_pooling_layers(conv_block_input_id, block_last_layer_input_id)
skip_output_id = conv_block_input_id
for index, layer_id in enumerate(layer_list):
skip_output_id = self.add_layer(deepcopy(self.layer_list[layer_id]), skip_output_id)
# Add the conv layer
new_relu_layer = StubReLU()
skip_output_id = self.add_layer(new_relu_layer, skip_output_id)
new_conv_layer = self.conv(self.layer_list[start_id].filters, self.layer_list[end_id].filters, 1)
skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
new_bn_layer = self.batch_norm(self.layer_list[end_id].filters)
skip_output_id = self.add_layer(new_bn_layer, skip_output_id)
# Add the add layer.
block_last_layer_output_id = self.adj_list[block_last_layer_input_id][0][0]
add_input_node_id = self._add_node(deepcopy(self.node_list[block_last_layer_output_id]))
add_layer = StubAdd()
self._redirect_edge(block_last_layer_input_id, block_last_layer_output_id, add_input_node_id)
self._add_edge(add_layer, add_input_node_id, block_last_layer_output_id)
self._add_edge(add_layer, skip_output_id, block_last_layer_output_id)
add_layer.input = [self.node_list[add_input_node_id], self.node_list[skip_output_id]]
add_layer.output = self.node_list[block_last_layer_output_id]
self.node_list[block_last_layer_output_id].shape = add_layer.output_shape
# Set weights to the additional conv layer.
if self.weighted:
filters_end = self.layer_list[end_id].filters
filters_start = self.layer_list[start_id].filters
filter_shape = (1,) * (len(self.layer_list[end_id].get_weights()[0].shape) - 2)
weights = np.zeros((filters_end, filters_start) + filter_shape)
bias = np.zeros(filters_end)
new_conv_layer.set_weights((add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
n_filters = filters_end
new_weights = [add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1]))]
new_bn_layer.set_weights(new_weights)
def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection.
"""
self.operation_history.append(('to_add_skip_model', start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(
start_node_id, end_node_id)
# Add the conv layer
new_conv_layer = get_conv_class(self.n_dim)(filters_start, filters_end,
1)
skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
# Add the add layer.
add_input_node_id = self._add_node(
deepcopy(self.node_list[end_node_id]))
add_layer = StubAdd()
self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
self._add_edge(add_layer, add_input_node_id, end_node_id)
self._add_edge(add_layer, skip_output_id, end_node_id)
add_layer.input = [
self.node_list[add_input_node_id], self.node_list[skip_output_id]
]
add_layer.output = self.node_list[end_node_id]
self.node_list[end_node_id].shape = add_layer.output_shape
# Set weights to the additional conv layer.
if self.weighted:
filter_shape = (1, ) * self.n_dim
weights = np.zeros((filters_end, filters_start) + filter_shape)
bias = np.zeros(filters_end)
new_conv_layer.set_weights((add_noise(weights, np.array([0, 1])),
add_noise(bias, np.array([0, 1]))))
def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection.
"""
self.operation_history.append(('to_add_skip_model', start_id, end_id))
filters_end = self.layer_list[end_id].output.shape[-1]
filters_start = self.layer_list[start_id].output.shape[-1]
start_node_id = self.layer_id_to_output_node_ids[start_id][0]
pre_end_node_id = self.layer_id_to_input_node_ids[end_id][0]
end_node_id = self.layer_id_to_output_node_ids[end_id][0]
skip_output_id = self._insert_pooling_layer_chain(start_node_id, end_node_id)
# Add the conv layer
new_conv_layer = get_conv_class(self.n_dim)(filters_start,
filters_end,
1)
skip_output_id = self.add_layer(new_conv_layer, skip_output_id)
# Add the add layer.
add_input_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
add_layer = StubAdd()
self._redirect_edge(pre_end_node_id, end_node_id, add_input_node_id)
self._add_edge(add_layer, add_input_node_id, end_node_id)
self._add_edge(add_layer, skip_output_id, end_node_id)
add_layer.input = [self.node_list[add_input_node_id], self.node_list[skip_output_id]]
add_layer.output = self.node_list[end_node_id]
self.node_list[end_node_id].shape = add_layer.output_shape
# Set weights to the additional conv layer.
if self.weighted:
filter_shape = (1,) * self.n_dim
weights = np.zeros((filters_end, filters_start) + filter_shape)
bias = np.zeros(filters_end)
new_conv_layer.set_weights((add_noise(weights, np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
def _make_block(self, graph, inplanes, planes, node_id, downsample=None):
residual_node_id = node_id
out = graph.add_layer(StubReLU(), node_id)
out = graph.add_layer(self.conv(inplanes, planes, kernel_size=1), out)
out = graph.add_layer(self.batch_norm(planes), out)
out = graph.add_layer(StubReLU(), out)
out = graph.add_layer(self.conv(planes, planes, kernel_size=3), out)
out = graph.add_layer(self.batch_norm(planes), out)
if downsample is not None:
downsample_out = graph.add_layer(StubReLU(), node_id)
downsample_out = graph.add_layer(downsample[0], downsample_out)
residual_node_id = graph.add_layer(downsample[1], downsample_out)
out = graph.add_layer(StubAdd(), (out, residual_node_id))
return out
def _make_block(self, graph, in_planes, planes, node_id, stride=1):
out = graph.add_layer(self.batch_norm(in_planes), node_id)
out = graph.add_layer(StubReLU(), out)
residual_node_id = out
out = graph.add_layer(self.conv(in_planes, planes, kernel_size=3, stride=stride), out)
out = graph.add_layer(self.batch_norm(planes), out)
out = graph.add_layer(StubReLU(), out)
out = graph.add_layer(self.conv(planes, planes, kernel_size=3), out)
residual_node_id = graph.add_layer(StubReLU(), residual_node_id)
residual_node_id = graph.add_layer(self.conv(in_planes,
planes * self.block_expansion,
kernel_size=1,
stride=stride), residual_node_id)
out = graph.add_layer(StubAdd(), (out, residual_node_id))
return out
def _make_block(self, graph, in_planes, out_planes, expansion, node_id, stride):
planes = expansion * in_planes
output_node_id = graph.add_layer(self.conv(in_planes,
planes,
kernel_size=1,
stride=1,
padding=0), node_id)
output_node_id = graph.add_layer(self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(self.conv(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
groups=planes), output_node_id)
output_node_id = graph.add_layer(self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
output_node_id = graph.add_layer(self.conv(planes,
out_planes,
kernel_size=1,
stride=1,
padding=0), output_node_id)
output_node_id = graph.add_layer(self.batch_norm(graph.node_list[output_node_id].shape[-1]), output_node_id)
if stride == 1 and in_planes != out_planes:
shortcut_node_id = node_id
shortcut_node_id = graph.add_layer(self.conv(in_planes,
out_planes,
kernel_size=1,
stride=1,
padding=0), shortcut_node_id)
shortcut_node_id = graph.add_layer(self.batch_norm(graph.node_list[shortcut_node_id].shape[-1]), shortcut_node_id)
output_node_id = graph.add_layer(StubAdd(), (output_node_id, shortcut_node_id))
return output_node_id
