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test_graph.py
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test_graph.py
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from autokeras.nn.generator import CnnGenerator, ResNetGenerator
from autokeras.nn.graph import *
from tests.common import get_conv_data, get_add_skip_model, get_conv_dense_model, get_pooling_model, \
get_concat_skip_model
def test_conv_wider_stub():
graph = get_add_skip_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_wider_model(7, 3)
assert graph.n_layers == layer_num
def test_conv_wider():
graph = get_concat_skip_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_wider_model(4, 3)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-1
def test_dense_wider_stub():
graph = get_add_skip_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_wider_model(26, 3)
assert graph.n_layers == layer_num
def test_dense_wider():
graph = get_add_skip_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_wider_model(26, 3)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-4
def test_skip_add_over_pooling_stub():
graph = get_pooling_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_add_skip_model(1, 8)
assert graph.n_layers == layer_num + 4
def test_skip_add_over_pooling():
graph = get_pooling_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_add_skip_model(1, 8)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-4
def test_skip_concat_over_pooling_stub():
graph = get_pooling_model()
graph.weighted = False
layer_num = graph.n_layers
graph.to_concat_skip_model(1, 11)
assert graph.n_layers == layer_num + 4
def test_skip_concat_over_pooling():
graph = get_pooling_model()
model = graph.produce_model()
graph = deepcopy(graph)
graph.to_concat_skip_model(4, 8)
graph.to_concat_skip_model(4, 8)
new_model = graph.produce_model()
input_data = torch.Tensor(get_conv_data())
model.eval()
new_model.eval()
output1 = model(input_data)
output2 = new_model(input_data)
assert (output1 - output2).abs().sum() < 1e-4
def test_extract_descriptor_add():
descriptor = get_add_skip_model().extract_descriptor()
assert len(descriptor.layers) == 24
assert descriptor.skip_connections == [(6, 10, NetworkDescriptor.ADD_CONNECT),
(10, 14, NetworkDescriptor.ADD_CONNECT)]
def test_extract_descriptor_concat():
descriptor = get_concat_skip_model().extract_descriptor()
assert len(descriptor.layers) == 32
assert descriptor.skip_connections == [(6, 10, NetworkDescriptor.CONCAT_CONNECT),
(13, 17, NetworkDescriptor.CONCAT_CONNECT)]
def test_deep_layer_ids():
graph = get_conv_dense_model()
assert len(graph.deep_layer_ids()) == 13
def test_wide_layer_ids():
graph = get_conv_dense_model()
assert len(graph.wide_layer_ids()) == 2
def test_skip_connection_layer_ids():
graph = get_conv_dense_model()
assert len(graph.skip_connection_layer_ids()) == 12
def test_wider_dense():
graph = CnnGenerator(10, (32, 32, 3)).generate()
graph.produce_model().set_weight_to_graph()
history = [('to_wider_model', 14, 64)]
for args in history:
getattr(graph, args[0])(*list(args[1:]))
graph.produce_model()
assert graph.layer_list[14].output.shape[-1] == 128
def test_node_consistency():
graph = CnnGenerator(10, (32, 32, 3)).generate()
assert graph.layer_list[6].output.shape == (16, 16, 64)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
graph.to_wider_model(6, 64)
assert graph.layer_list[6].output.shape == (16, 16, 128)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
def test_produce_keras_model():
import keras
for graph in [get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()]:
model = graph.produce_keras_model()
assert isinstance(model, keras.models.Model)
def test_keras_model():
for graph in [get_conv_dense_model(),
get_add_skip_model(),
get_pooling_model(),
get_concat_skip_model()]:
keras_model = KerasModel(graph)
keras_model.set_weight_to_graph()
assert isinstance(keras_model, KerasModel)
def test_graph_size():
graph = CnnGenerator(10, (32, 32, 3)).generate()
assert graph.size() == 7254
def test_long_transform():
graph = ResNetGenerator(10, (28, 28, 1)).generate()
graph.to_deeper_model(16, StubReLU())
graph.to_deeper_model(16, StubReLU())
graph.to_add_skip_model(13, 47)
model = graph.produce_model()
model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def test_long_transform2():
graph = CnnGenerator(10, (28, 28, 1)).generate()
graph.to_add_skip_model(2, 3)
graph.to_concat_skip_model(2, 3)
model = graph.produce_model()
model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def test_long_transform4():
graph = ResNetGenerator(10, (28, 28, 1)).generate()
graph.to_concat_skip_model(57, 68)
model = graph.produce_model()
model(torch.Tensor(np.random.random((10, 1, 28, 28))))
def test_long_transform5():
graph = ResNetGenerator(10, (28, 28, 1)).generate()
graph.to_concat_skip_model(19, 60)
graph.to_wider_model(52, 256)
model = graph.produce_model()
model(torch.Tensor(np.random.random((10, 1, 28, 28))))