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(5, 64)
assert graph.layer_list[5].output.shape == (16, 16, 128)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
graph.to_conv_deeper_model(5, 3)
assert graph.layer_list[19].output.shape == (16, 16, 128)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
graph.to_add_skip_model(5, 18)
assert graph.layer_list[23].output.shape == (16, 16, 128)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
graph.to_concat_skip_model(5, 18)
assert graph.layer_list[25].output.shape == (16, 16, 256)
for layer in graph.layer_list:
assert layer.output.shape == layer.output_shape
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_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))))