def create_dataloader(wrap_dataloader,
config,
num_samples=1) -> DataLoader:
data_loader = create_mock_dataloader(config, num_samples)
if wrap_dataloader:
data_loader = DefaultInitializingDataLoader(data_loader)
return data_loader
def test_hawq_manual_configs(manual_config_params):
config_name, bit_stats = manual_config_params
config = NNCFConfig.from_json(str(EXAMPLES_DIR.joinpath('classification', 'configs', 'quantization') / config_name))
config['quantizer_setup_type'] = 'pattern_based'
config = register_default_init_args(config, train_loader=create_mock_dataloader(config), criterion=None)
model = load_model(config['model'], pretrained=False)
model.eval()
_, compression_ctrl = create_compressed_model_and_algo_for_test(model, config)
table = compression_ctrl.non_stable_metric_collectors[0].get_bits_stat()
# pylint: disable=protected-access
assert table._rows == bit_stats
def test_hawq_manual_configs(manual_config_params, hw_config):
config_name, bit_stats = manual_config_params
config = NNCFConfig.from_json(str(EXAMPLES_DIR.joinpath('classification', 'configs', 'quantization') / config_name))
config = register_default_init_args(config, criterion=None, train_loader=create_mock_dataloader(config))
if hw_config:
config['hw_config'] = hw_config.value
model = load_model(config['model'], pretrained=False)
model.eval()
_, compression_ctrl = create_compressed_model_and_algo_for_test(model, config)
table = compression_ctrl.get_bit_stats()
# pylint: disable=protected-access
assert table._rows == bit_stats
def create_test_quantization_env() -> QuantizationEnv:
model = BasicConvTestModel()
nncf_network = NNCFNetwork(model,
input_infos=[ModelInputInfo([1, 1, 4, 4])])
hw_config_type = HWConfigType.VPU
hw_config_path = HWConfig.get_path_to_hw_config(hw_config_type)
hw_config = HWConfig.from_json(hw_config_path)
setup = PropagationBasedQuantizerSetupGenerator(
NNCFConfig(), nncf_network, hw_config=hw_config).generate_setup()
experimental_builder = ExperimentalQuantizationBuilder(setup, {})
experimental_builder.apply_to(nncf_network)
# pylint:disable=line-too-long
experimental_ctrl = nncf_network.commit_compression_changes(
) # type: ExperimentalQuantizationController
data_loader = create_mock_dataloader({
"sample_size": [1, 1, 4, 4],
})
constraints = HardwareQuantizationConstraints()
for qid in experimental_ctrl.all_quantizations:
qconf_constraint_list = []
qconf = experimental_ctrl.all_quantizations[qid].get_current_config()
bit_set = [8, 4, 2] if 'conv' in str(qid) else [8, 4]
for bits in bit_set:
adj_qconf = deepcopy(qconf)
adj_qconf.bits = bits
qconf_constraint_list.append(adj_qconf)
constraints.add(qid, qconf_constraint_list)
return QuantizationEnv(nncf_network,
experimental_ctrl,
constraints,
data_loader,
lambda *x: 0,
hw_config_type=HWConfigType.VPU,
params=QuantizationEnvParams(
compression_ratio=0.15,
eval_subset_ratio=1.0,
skip_constraint=False,
finetune=False,
bits=[2, 4, 8],
dump_init_precision_data=False))
def create_regular_dataloader():
return create_mock_dataloader(config=get_empty_config(),
num_samples=N_SAMPLE)