def generate(self, model_len=None, model_width=None):
"""Generates a CNN.
Args:
model_len: An integer. Number of convolutional layers.
model_width: An integer. Number of filters for the convolutional layers.
Returns:
An instance of the class Graph. Represents the neural architecture graph of the generated model.
"""
if model_len is None:
model_len = Constant.MODEL_LEN
if model_width is None:
model_width = Constant.MODEL_WIDTH
pooling_len = int(model_len / 4)
graph = Graph(self.input_shape, False)
temp_input_channel = self.input_shape[-1]
output_node_id = 0
stride = 1
for i in range(model_len):
output_node_id = graph.add_layer(StubReLU(), 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(
self.conv(temp_input_channel,
model_width,
kernel_size=3,
stride=stride),
output_node_id,
)
temp_input_channel = model_width
if pooling_len == 0 or ((i + 1) % pooling_len == 0
and i != model_len - 1):
output_node_id = graph.add_layer(self.pooling(),
output_node_id)
output_node_id = graph.add_layer(self.global_avg_pooling(),
output_node_id)
output_node_id = graph.add_layer(
self.dropout(Constant.CONV_DROPOUT_RATE), output_node_id)
output_node_id = graph.add_layer(
StubDense(graph.node_list[output_node_id].shape[0], model_width),
output_node_id,
)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
graph.add_layer(StubDense(model_width, self.n_output_node),
output_node_id)
return graph
def generate(self, model_len=None, model_width=None):
"""Generates a Multi-Layer Perceptron.
Args:
model_len: An integer. Number of hidden layers.
model_width: An integer or a list of integers of length `model_len`. If it is a list, it represents the
number of nodes in each hidden layer. If it is an integer, all hidden layers have nodes equal to this
value.
Returns:
An instance of the class Graph. Represents the neural architecture graph of the generated model.
"""
if model_len is None:
model_len = Constant.MODEL_LEN
if model_width is None:
model_width = Constant.MODEL_WIDTH
if isinstance(model_width, list) and not len(model_width) == model_len:
raise ValueError(
"The length of 'model_width' does not match 'model_len'")
elif isinstance(model_width, int):
model_width = [model_width] * model_len
graph = Graph(self.input_shape, False)
output_node_id = 0
n_nodes_prev_layer = self.input_shape[0]
for width in model_width:
output_node_id = graph.add_layer(
StubDense(n_nodes_prev_layer, width), output_node_id)
output_node_id = graph.add_layer(
StubDropout1d(Constant.MLP_DROPOUT_RATE), output_node_id)
output_node_id = graph.add_layer(StubReLU(), output_node_id)
n_nodes_prev_layer = width
graph.add_layer(StubDense(n_nodes_prev_layer, self.n_output_node),
output_node_id)
return graph
def deeper_conv_block(conv_layer, kernel_size, weighted=True):
'''deeper conv layer.
'''
n_dim = get_n_dim(conv_layer)
filter_shape = (kernel_size, ) * 2
n_filters = conv_layer.filters
weight = np.zeros((n_filters, n_filters) + filter_shape)
center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
for i in range(n_filters):
filter_weight = np.zeros((n_filters, ) + filter_shape)
index = (i, ) + center
filter_weight[index] = 1
weight[i, ...] = filter_weight
bias = np.zeros(n_filters)
new_conv_layer = get_conv_class(n_dim)(conv_layer.filters,
n_filters,
kernel_size=kernel_size)
bn = get_batch_norm_class(n_dim)(n_filters)
if weighted:
new_conv_layer.set_weights(
(add_noise(weight,
np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
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])),
]
bn.set_weights(new_weights)
return [StubReLU(), new_conv_layer, bn]
def to_deeper_graph2(graph):
''' deeper graph
'''
weighted_layer_ids = graph.deep_layer_ids2()
if len(weighted_layer_ids) >= Constant.MAX_LAYERS:
return None
deeper_layer_ids = sample(weighted_layer_ids, 1)#选一层
for layer_id in deeper_layer_ids:
layer = graph.layer_list[layer_id]
input_shape = layer.output.shape
layer_class = get_conv_class(graph.n_dim)
new_layer = layer_class(input_shape[-1], input_shape[-1], 3, stride=1)
output_id = graph.to_deeper_model(layer_id, new_layer)
layer_id2 = graph.get_layers_id(output_id)
layer2 = graph.layer_list[layer_id2]
input_shape2 = layer2.output.shape
layer_class = get_batch_norm_class(graph.n_dim)
new_layer2 = layer_class(input_shape2[-1])
output_id2=graph.to_deeper_model(layer_id2, new_layer2)
layer_id3 = graph.get_layers_id(output_id2)
graph.to_deeper_model(layer_id3, StubReLU())
return graph
def dense_to_deeper_block(dense_layer, weighted=True):
'''deeper dense layer.
'''
units = dense_layer.units
weight = np.eye(units)
bias = np.zeros(units)
new_dense_layer = StubDense(units, units)
if weighted:
new_dense_layer.set_weights(
(add_noise(weight,
np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
return [StubReLU(), new_dense_layer]
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 generate(self, model_len=None, model_width=None):
"""Generates a CNN.
Args:
model_len: An integer. Number of convolutional layers.
model_width: An integer. Number of filters for the convolutional layers.
Returns:
An instance of the class Graph. Represents the neural architecture graph of the generated model.
"""
if model_len is None:
model_len = Constant.MODEL_LEN
if model_width is None:
model_width = Constant.MODEL_WIDTH
pooling_len = int(model_len / 4)
graph = Graph(self.input_shape, False)
temp_input_channel = self.input_shape[-1]
output_node_id = 0
stride = 1
###
###
###conv_1 :7 2
output_node_id = graph.add_layer(
self.conv(temp_input_channel, 64, kernel_size=7, stride=2),
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.pooling(kernel_size=3, stride=2),
output_node_id)
###
###
###conv_2
output_node_id1 = graph.add_layer(
self.conv(64, 64, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 64, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 256, kernel_size=1, stride=1), output_node_id1)
output_node_id00 = graph.add_layer(
self.conv(64, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id00 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id00].shape[-1]),
output_node_id00)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(256, 64, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 64, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 256, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(256, 64, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 64, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(64, 256, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
###
###
###conv_3
output_node_id1 = graph.add_layer(
self.conv(256, 128, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 128, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 512, kernel_size=1, stride=1), output_node_id1)
output_node_id00 = graph.add_layer(
self.conv(256, 512, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id00 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id00].shape[-1]),
output_node_id00)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(512, 128, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 128, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 512, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(512, 128, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 128, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 512, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(512, 128, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 128, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(128, 512, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
###
###conv_4
output_node_id1 = graph.add_layer(
self.conv(512, 256, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1), output_node_id1)
output_node_id00 = graph.add_layer(
self.conv(512, 1024, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id00 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id00].shape[-1]),
output_node_id00)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(1024, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(1024, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1), output_node_id1)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(1024, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(1024, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(1024, 256, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 256, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(256, 1024, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
###
###conv_5
output_node_id1 = graph.add_layer(
self.conv(1024, 512, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 512, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 2048, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id00 = graph.add_layer(
self.conv(1024, 2048, kernel_size=1, stride=2), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id00 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id00].shape[-1]),
output_node_id00)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(2048, 512, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 512, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 2048, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id00 = output_node_id
output_node_id1 = graph.add_layer(
self.conv(2048, 512, kernel_size=1, stride=1), output_node_id)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 512, kernel_size=3, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id1 = graph.add_layer(StubReLU(), output_node_id1)
output_node_id1 = graph.add_layer(
self.conv(512, 2048, kernel_size=1, stride=1),
output_node_id1,
)
output_node_id1 = graph.add_layer(
self.batch_norm(graph.node_list[output_node_id1].shape[-1]),
output_node_id1)
output_node_id = graph.add_layer(StubAdd(),
[output_node_id1, output_node_id00])
output_node_id = graph.add_layer(StubReLU(), output_node_id)
###
output_node_id = graph.add_layer(self.global_avg_pooling(),
output_node_id)
# output_node_id = graph.add_layer(
# self.dropout(Constant.CONV_DROPOUT_RATE), output_node_id
# )
# output_node_id = graph.add_layer(
# StubDense(graph.node_list[output_node_id].shape[0], model_width),
# output_node_id
# )
# output_node_id = graph.add_layer(StubReLU(), output_node_id)
graph.add_layer(StubDense(2048, self.n_output_node), output_node_id)
return graph