def test_output_quantization(_case_config):
model = test_models.UNet()
input_shape = (1, 3, 360, 480)
config = get_basic_quantization_config(_case_config.quant_type, input_sample_size=input_shape)
config["compression"].update({"quantize_outputs": True})
compressed_model, _ = create_compressed_model_and_algo_for_test(model, config)
check_model_graph(compressed_model, 'unet_qoutput.dot', _case_config.graph_dir)
def test_output_quantization(_quantize_config):
net = test_models.UNet()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 360, 480)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, [ModelInputInfo(input_shape), ],
quantize_outputs=True)
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(ctx.graph, 'unet_qoutput.dot', _quantize_config.graph_dir)
def test_output_quantization(_quantize_config):
net = test_models.UNet()
ctx = reset_context('orig')
ctx = reset_context('quantized_graphs')
input_shape = (1, 3, 360, 480)
qnet = QuantizedNetwork(net, _quantize_config.quantizer, input_shape,
dummy_forward_fn=create_dummy_forward_fn(input_shape),
quantize_outputs=True)
_ = qnet(torch.zeros(*input_shape))
_ = qnet(torch.zeros(*input_shape))
check_graph(to_networkx(ctx), 'unet_qoutput.dot', _quantize_config.graph_dir)
def test_output_quantization(_case_config):
# TODO: Add support "quantize_outputs" option in propagation mode.
pytest.skip()
model = test_models.UNet()
input_shape = [1, 3, 360, 480]
config = get_basic_quantization_config(_case_config.quant_type,
input_sample_sizes=input_shape)
config["compression"].update({"quantize_outputs": True})
compressed_model, _ = create_compressed_model_and_algo_for_test(
model, config)
check_model_graph(compressed_model, 'unet_qoutput.dot',
_case_config.graph_dir)