def create_model(config):
input_info_list = create_input_infos(config)
image_size = input_info_list[0].shape[-1]
ssd_net = build_ssd(config.model, config.ssd_params, image_size, config.num_classes, config)
compression_algo, ssd_net = create_compressed_model(ssd_net, config)
ssd_net = compression_algo.model
weights = config.get('weights')
if weights:
sd = torch.load(weights, map_location='cpu')
load_state(ssd_net, sd)
ssd_net.train()
model, _ = prepare_model_for_execution(ssd_net, config)
return compression_algo, model
def create_model(config):
ssd_net = build_ssd(config.model, config.ssd_params,
config.input_sample_size[-1], config.num_classes,
config)
ssd_net.to(config.device)
compression_algo = create_compression_algorithm(ssd_net, config)
ssd_net = compression_algo.model
weights = config.get('weights')
if weights:
sd = torch.load(weights, map_location='cpu')
load_state(ssd_net, sd)
ssd_net.train()
model, _ = prepare_model_for_execution(ssd_net, config)
return compression_algo, model
def create_model(config: SampleConfig, resuming_model_sd: dict = None):
input_info_list = create_input_infos(config.nncf_config)
image_size = input_info_list[0].shape[-1]
ssd_net = build_ssd(config.model, config.ssd_params, image_size, config.num_classes, config)
weights = config.get('weights')
if weights:
sd = torch.load(weights, map_location='cpu')
load_state(ssd_net, sd)
ssd_net.to(config.device)
compression_ctrl, compressed_model = create_compressed_model(ssd_net, config.nncf_config, resuming_model_sd)
compressed_model, _ = prepare_model_for_execution(compressed_model, config)
compressed_model.train()
return compression_ctrl, compressed_model