def test_generate_densenet_model_dropout():
architecture = generate_densenet_2d_architecture(n_upsampling=4,
n_filter=48,
stride=2,
dropout_rate=0.1)
input_params = {'shape': [128, 128, 3]}
model = load_architecture(architecture, input_params)
model.summary()
def test_generate_densenet_model_batchnorm():
architecture = generate_densenet_2d_architecture(n_upsampling=4,
n_filter=48,
stride=2,
batchnorm=True)
input_params = {'shape': [128, 128, 3]}
model = load_architecture(architecture, input_params)
model.summary()
def test_generate_densenet_model_resize():
architecture = generate_densenet_2d_architecture(n_upsampling=4,
n_filter=48,
stride=2)
input_params = {'shape': [129, 129, 3]}
model = load_architecture(architecture, input_params)
model.summary()
assert np.all(model.input_shape == (None, 129, 129, 3))
assert np.all(model.output_shape == (None, 129, 129, 1))
def test_generate_densenet_model():
architecture = generate_densenet_2d_architecture(n_upsampling=4,
n_filter=48,
stride=2)
input_params = {'shape': [128, 128, 3]}
model = load_architecture(architecture, input_params)
model.summary()
expected_model = load_architecture(
load_json_config('tests/json/densenet_architecture.json'),
input_params)
assert check_same_models(model, expected_model)
def test_create_densenet_json():
unet = generate_densenet_2d_architecture()
generate_densenet_2d_json(FILE_NAME)
actual = load_json_config(FILE_NAME)
assert np.all(unet == actual)