def test_compare_model_outputs_conv_static_fx(self):
r"""Compare the output of conv layer in static quantized model and corresponding
output of conv layer in float model
"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
model_list = [ConvModel(), ConvBnReLUModel()]
for float_model in model_list:
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
# Run calibration
test_only_eval_fn(prepared_model, self.img_data_2d)
q_model = convert_fx(prepared_model)
expected_act_compare_dict_keys = {"x.stats", "conv.stats"}
self.compare_and_validate_model_outputs_results_fx(
prepared_float_model,
q_model,
expected_act_compare_dict_keys,
self.img_data_2d[0][0],
)
def test_compare_model_stub_conv_static_fx(self):
r"""Compare the output of static quantized conv layer and its float shadow module"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
model_list = [ConvModel(), ConvBnReLUModel()]
for float_model in model_list:
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
# Run calibration
test_only_eval_fn(prepared_model, self.img_data_2d)
q_model = convert_fx(prepared_model)
module_swap_list = [nn.Conv2d, nni.modules.fused.ConvReLU2d]
expected_ob_dict_keys = {"conv.stats"}
self.compare_and_validate_model_stub_results_fx(
prepared_float_model,
q_model,
module_swap_list,
expected_ob_dict_keys,
self.img_data_2d[0][0],
)
def test_compare_weights_conv(self):
test_cases = (
(ConvModel(), ),
(ConvBnModel(), ),
(ConvBnReLUModel(), ),
)
for m, in test_cases:
m.eval()
self._test_extract_weights(m, results_len=1)
def test_compare_shadow_activations_conv(self):
test_cases = (
(ConvModel(), ),
(ConvBnModel(), ),
(ConvBnReLUModel(), ),
)
for m, in test_cases:
m.eval()
res = self._test_match_shadow_activations(
m, (torch.randn(1, 3, 4, 4), ), results_len=1)
def test_compare_weights_conv_static_fx(self):
r"""Compare the weights of float and static quantized conv layer"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
model_list = [ConvModel(), ConvBnModel(), ConvBnReLUModel()]
for float_model in model_list:
float_model.eval()
fused = fuse_fx(float_model)
prepared_model = prepare_fx(float_model, qconfig_dict)
# Run calibration
test_only_eval_fn(prepared_model, self.img_data_2d)
q_model = convert_fx(prepared_model)
expected_weight_dict_keys = {"conv.weight"}
self.compare_and_validate_model_weights_results_fx(
fused, q_model, expected_weight_dict_keys)