def compare_and_validate_results(float_model, q_model, module_swap_list, data):
ob_dict = compare_model_stub(float_model, q_model, module_swap_list, data)
self.assertEqual(len(ob_dict), 1)
for k, v in ob_dict.items():
self.assertTrue(len(v["float"]) == len(v["quantized"]))
for i, val in enumerate(v["quantized"]):
self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
def test_compare_model_stub_functional_static(self):
r"""Compare the output of static quantized functional layer and its float shadow module"""
qengine = torch.backends.quantized.engine
model = ModelWithFunctionals().eval()
model.qconfig = torch.ao.quantization.get_default_qconfig("fbgemm")
q_model = prepare(model, inplace=False)
q_model(self.img_data_2d[0][0])
q_model = convert(q_model)
module_swap_list = [nnq.FloatFunctional]
ob_dict = compare_model_stub(
model, q_model, module_swap_list, self.img_data_2d[0][0]
)
self.assertEqual(len(ob_dict), 6)
self.assertTrue(isinstance(q_model.mycat, Shadow))
self.assertTrue(isinstance(q_model.myadd, Shadow))
self.assertTrue(isinstance(q_model.mymul, Shadow))
self.assertTrue(isinstance(q_model.myadd_relu, Shadow))
self.assertTrue(isinstance(q_model.my_scalar_add, Shadow))
self.assertTrue(isinstance(q_model.my_scalar_mul, Shadow))
for k, v in ob_dict.items():
self.assertTrue(len(v["float"]) == len(v["quantized"]))
for i, val in enumerate(v["quantized"]):
self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
def test_compare_model_stub_submodule_static(self):
r"""Compare the output of static quantized submodule and its float shadow module"""
qengine = torch.backends.quantized.engine
model = ModelWithSubModules().eval()
q_model = quantize(model, test_only_eval_fn, [self.img_data_2d])
module_swap_list = [SubModule, nn.Conv2d]
ob_dict = compare_model_stub(model, q_model, module_swap_list,
self.img_data_2d[0][0])
# Since conv is not quantized, we do not insert a shadow module
# mod1 contains a linear that is quantized, so we insert a shadow module
self.assertTrue(isinstance(q_model.mod1, Shadow))
self.assertFalse(isinstance(q_model.conv, Shadow))
