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_long_transform():
graph = CnnGenerator(10, (32, 32, 3)).generate()
history = [('to_wider_model', 1, 256), ('to_conv_deeper_model', 1, 3),
('to_concat_skip_model', 5, 9)]
for args in history:
getattr(graph, args[0])(*list(args[1:]))
graph.produce_model()
assert legal_graph(graph)
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 legal_graph(graph)
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_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))))