def test_json_against_nncf_config_schema(config_test_struct):
config_path, should_pass = config_test_struct
if should_pass:
_ = NNCFConfig.from_json(str(config_path))
else:
with pytest.raises(jsonschema.ValidationError):
_ = NNCFConfig.from_json(str(config_path))
def test_model_can_be_loaded_with_resume(_params):
p = _params
sample_config_path = p['sample_config_path']
checkpoint_path = p['checkpoint_path']
config = SampleConfig.from_json(str(sample_config_path))
nncf_config = NNCFConfig.from_json(str(sample_config_path))
config.execution_mode = p['execution_mode']
config.current_gpu = 0
config.device = get_device(config)
config.distributed = config.execution_mode in (ExecutionMode.DISTRIBUTED, ExecutionMode.MULTIPROCESSING_DISTRIBUTED)
if config.distributed:
config.dist_url = "tcp://127.0.0.1:9898"
config.dist_backend = "nccl"
config.rank = 0
config.world_size = 1
configure_distributed(config)
model_name = config['model']
model = load_model(model_name,
pretrained=False,
num_classes=config.get('num_classes', 1000),
model_params=config.get('model_params'))
model.to(config.device)
model, compression_ctrl = create_compressed_model_and_algo_for_test(model, nncf_config)
model, _ = prepare_model_for_execution(model, config)
if config.distributed:
compression_ctrl.distributed()
checkpoint = torch.load(checkpoint_path, map_location='cpu')
load_state(model, checkpoint['state_dict'], is_resume=True)
def main():
model_bin, model_xml = get_ir_paths(args.model, args.bin)
config = NNCFConfig.from_json(args.config)
input_infos_list = create_input_infos(config)
image_size = input_infos_list[0].shape[-1]
size = int(image_size / 0.875)
print('IE version: {}'.format(get_version()))
# NOTE: importing torch after loading IE to plugin to avoid issue with built-in MKLDNN of PyTorch
plugin = IEPlugin(device='CPU', plugin_dirs=args.cpu_plugin_dir)
plugin.add_cpu_extension(
os.path.join(args.cpu_plugin_dir, "libcpu_extension.so"))
net = IENetwork(model=model_xml, weights=model_bin)
exec_net = getExecNet(plugin, net)
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
val_loader = DataLoader(datasets.ImageFolder(
args.data,
transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])),
batch_size=1,
shuffle=False,
num_workers=4,
pin_memory=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
config['log_dir'] = args.output_dir
infer_fn = partial(infer_ie_model, net=net)
validate_general(val_loader, exec_net, infer_fn)
validate_torch_model(os.path.join(args.output_dir, "PTH"),
config=config,
num_layers=args.num_layers,
dump=args.dump,
val_loader=val_loader,
cuda=args.cuda)
def q_dq_config(config):
nncf_config = NNCFConfig.from_json(config)
if "compression" in nncf_config:
compression_config = nncf_config["compression"]
quantization_config = None
if isinstance(compression_config, list):
matches = []
for subconfig in compression_config:
if subconfig["algorithm"] == "quantization":
matches.append(subconfig)
if matches:
assert len(matches) == 1
quantization_config = matches[0]
else:
if compression_config["algorithm"] == "quantization":
quantization_config = compression_config
if quantization_config is not None:
quantization_config["export_to_onnx_standard_ops"] = True
return nncf_config
