def test_custom_quantizable_subgraph_patterns(_case_config):
model = test_models.SENet18()
input_shape = (1, 3, 32, 32)
config = get_basic_quantization_config(_case_config.quant_type, input_sample_size=input_shape)
config["compression"].update({"quantize_outputs": False,
"quantizable_subgraph_patterns": [["sigmoid", "__mul__"],
["__iadd__", "batch_norm"]]})
compressed_model, _ = create_compressed_model_and_algo_for_test(model, config)
check_model_graph(compressed_model, 'senet_custom_patterns.dot', _case_config.graph_dir)
def test_custom_quantizable_subgraph_patterns(_quantize_config):
net = test_models.SENet18()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 32, 32)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, [ModelInputInfo(input_shape), ],
quantize_outputs=False,
quantizable_subgraph_patterns=(("sigmoid", "__mul__"),
("__iadd__", "batch_norm")))
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(ctx.graph, 'senet_custom_patterns.dot', _quantize_config.graph_dir)