def main(): parser = argparse.ArgumentParser() parser.add_argument('--config', default=None, type=str) parser.add_argument('--info', default=None, type=str) args = parser.parse_args() config = load_decoder_config(args.config) if args.info: config["experiment_name"] += args.info pprint.pprint(config) ######################### # Context Setting # Get context. from nnabla.ext_utils import get_extension_context logger.info(f'Running in {config["context"]}.') ctx = get_extension_context(config["context"], device_id=config["device_id"]) nn.set_default_context(ctx) ######################### # Data Loading logger.info('Initialing Datasource') train_iterator = data.celebv_data_iterator( dataset_mode="decoder", celeb_name=config["trg_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode=config["mode"], batch_size=config["train"]["batch_size"], shuffle=config["train"]["shuffle"], with_memory_cache=config["train"]["with_memory_cache"], with_file_cache=config["train"]["with_file_cache"], ) monitor = nm.Monitor( os.path.join(config["logdir"], "decoder", config["trg_celeb_name"], config["experiment_name"])) # Optimizer solver_netG = S.Adam(alpha=config["train"]["lr"], beta1=config["train"]["beta1"]) solver_netD = S.Adam(alpha=config["train"]["lr"], beta1=config["train"]["beta1"]) # Network netG = models.netG_decoder netD = models.netD_decoder train(config, netG, netD, solver_netG, solver_netD, train_iterator, monitor)
def main(): parser = argparse.ArgumentParser() parser.add_argument('config', default=None, type=str) parser.add_argument('--param-file', default=None, type=str) parser.add_argument('--num-test', '-n', default=None, type=int) args = parser.parse_args() param_file = args.param_file config = load_decoder_config(args.config) config["num_test"] = args.num_test ######################### # Context Setting # Get context. from nnabla.ext_utils import get_extension_context logger.info(f'Running in {config["context"]}.') ctx = get_extension_context(config["context"], device_id=config["device_id"]) nn.set_default_context(ctx) ######################### # Data Loading logger.info('Initialing Datasource') train_iterator = data.celebv_data_iterator( dataset_mode="decoder", celeb_name=config["trg_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode="test", batch_size=config["test"]["batch_size"], shuffle=False, with_memory_cache=config["test"]["with_memory_cache"], with_file_cache=config["test"]["with_file_cache"], ) monitor = nm.Monitor( os.path.join(config["test"]["logdir"], "decoder", config["trg_celeb_name"], config["experiment_name"])) # Network netG = models.netG_decoder if not param_file: param_file = sorted(glob.glob( os.path.join(config["logdir"], "decoder", config["trg_celeb_name"], config["experiment_name"], "netG_*")), key=os.path.getmtime)[-1] test(config, netG, train_iterator, monitor, param_file)
def main(): parser = argparse.ArgumentParser() parser.add_argument('config', default=None, type=str) parser.add_argument('--param-file', default=None, type=str) parser.add_argument('--num-test', '-n', default=None, type=int) args = parser.parse_args() param_file = args.param_file config = load_transformer_config(args.config) config["num_test"] = args.num_test ######################### # Context Setting # Get context. from nnabla.ext_utils import get_extension_context logger.info(f'Running in {config["context"]}.') ctx = get_extension_context(config["context"], device_id=config["device_id"]) nn.set_default_context(ctx) ######################### # Data Loading logger.info('Initializing Datasource') train_iterator_src = data.celebv_data_iterator( dataset_mode="transformer", celeb_name=config["src_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode="test", batch_size=config["test"]["batch_size"], shuffle=False, with_memory_cache=config["test"]["with_memory_cache"], with_file_cache=config["test"]["with_file_cache"], resize_size=config["preprocess"]["resize_size"], line_thickness=config["preprocess"]["line_thickness"], gaussian_kernel=config["preprocess"]["gaussian_kernel"], gaussian_sigma=config["preprocess"]["gaussian_sigma"]) train_iterator_trg = data.celebv_data_iterator( dataset_mode="transformer", celeb_name=config["trg_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode="test", batch_size=config["test"]["batch_size"], shuffle=False, with_memory_cache=config["test"]["with_memory_cache"], with_file_cache=config["test"]["with_file_cache"], resize_size=config["preprocess"]["resize_size"], line_thickness=config["preprocess"]["line_thickness"], gaussian_kernel=config["preprocess"]["gaussian_kernel"], gaussian_sigma=config["preprocess"]["gaussian_sigma"]) train_iterators = (train_iterator_src, train_iterator_trg) # monitor monitor = nm.Monitor( os.path.join(config["test"]["logdir"], "transformer", f'{config["src_celeb_name"]}2{config["trg_celeb_name"]}', config["experiment_name"])) # Network netG = { 'netG_A2B': models.netG_transformer, 'netG_B2A': models.netG_transformer } if not param_file: param_file_A2B = sorted(glob.glob( os.path.join( config["logdir"], "transformer", f'{config["src_celeb_name"]}2{config["trg_celeb_name"]}', config["experiment_name"], "netG_transformer_A2B_*")), key=os.path.getmtime)[-1] else: param_file_A2B = param_file test_transformer(config, netG, train_iterators, monitor, param_file_A2B)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--encoder-config', default=None, type=str) parser.add_argument('--transformer-config', default=None, type=str) parser.add_argument('--decoder-config', default=None, type=str) parser.add_argument('--src-celeb-name', default=None, type=str) parser.add_argument('--trg-celeb-name', default=None, type=str) parser.add_argument('--encoder-param-file', default=None, type=str) parser.add_argument('--transformer-param-file', default=None, type=str) parser.add_argument('--decoder-param-file', default=None, type=str) parser.add_argument('--info', default=None, type=str) args = parser.parse_args() encoder_param_file = args.encoder_param_file transformer_param_file = args.transformer_param_file decoder_param_file = args.decoder_param_file encoder_config = load_encoder_config(args.encoder_config) transformer_config = load_transformer_config(args.transformer_config) decoder_config = load_decoder_config(args.decoder_config) src_celeb_name = args.src_celeb_name trg_celeb_name = args.trg_celeb_name assert trg_celeb_name == transformer_config[ "trg_celeb_name"], f"not trained on {trg_celeb_name}." if args.info: decoder_config["experiment_name"] += args.info ######################### # Context Setting # Get context. from nnabla.ext_utils import get_extension_context logger.info(f'Running in {decoder_config["context"]}.') ctx = get_extension_context(decoder_config["context"], device_id=decoder_config["device_id"]) nn.set_default_context(ctx) ######################### # Data Loading logger.info('Initialing Datasource') test_iterator = data.celebv_data_iterator( dataset_mode="decoder", celeb_name=src_celeb_name, data_dir=decoder_config["train_dir"], ref_dir=decoder_config["ref_dir"], mode="test", batch_size=1, shuffle=False, with_memory_cache=decoder_config["test"]["with_memory_cache"], with_file_cache=decoder_config["test"]["with_file_cache"], ) # Encoder encoder_netG = models.stacked_hourglass_net if not encoder_param_file: encoder_param_file = sorted(glob.glob( os.path.join(encoder_config["logdir"], encoder_config["dataset_mode"], encoder_config["experiment_name"], "model", "model_epoch-*")), key=os.path.getmtime)[-1] # Transformer transformer_netG = models.netG_transformer if not transformer_param_file: transformer_param_file = sorted(glob.glob( os.path.join( transformer_config["logdir"], transformer_config["dataset_mode"], f'{transformer_config["src_celeb_name"]}2{transformer_config["trg_celeb_name"]}', transformer_config["experiment_name"], "netG_transformer_A2B_*")), key=os.path.getmtime)[-1] # Decoder decoder_netG = models.netG_decoder if not decoder_param_file: decoder_param_file = sorted(glob.glob( os.path.join(decoder_config["logdir"], decoder_config["dataset_mode"], decoder_config["trg_celeb_name"], decoder_config["experiment_name"], "netG_*")), key=os.path.getmtime)[-1] monitor = nm.Monitor( os.path.join("reenactment_result", f'{src_celeb_name}2{decoder_config["trg_celeb_name"]}', decoder_config["experiment_name"])) test(encoder_config, transformer_config, decoder_config, encoder_netG, transformer_netG, decoder_netG, src_celeb_name, trg_celeb_name, test_iterator, monitor, encoder_param_file, transformer_param_file, decoder_param_file)
def main(): parser = argparse.ArgumentParser() parser.add_argument('--config', default=None, type=str) parser.add_argument('--info', default=None, type=str) args = parser.parse_args() config = load_transformer_config(args.config) if args.info: config["experiment_name"] += args.info pprint.pprint(config) ######################### # Context Setting # Get context. from nnabla.ext_utils import get_extension_context logger.info(f'Running in {config["context"]}.') ctx = get_extension_context(config["context"], device_id=config["device_id"]) nn.set_default_context(ctx) ######################### # Data Loading logger.info('Initialing Datasource') train_iterator_src = data.celebv_data_iterator( dataset_mode="transformer", celeb_name=config["src_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode=config["mode"], batch_size=config["train"]["batch_size"], shuffle=config["train"]["shuffle"], with_memory_cache=config["train"]["with_memory_cache"], with_file_cache=config["train"]["with_file_cache"], resize_size=config["preprocess"]["resize_size"], line_thickness=config["preprocess"]["line_thickness"], gaussian_kernel=config["preprocess"]["gaussian_kernel"], gaussian_sigma=config["preprocess"]["gaussian_sigma"]) train_iterator_trg = data.celebv_data_iterator( dataset_mode="transformer", celeb_name=config["trg_celeb_name"], data_dir=config["train_dir"], ref_dir=config["ref_dir"], mode=config["mode"], batch_size=config["train"]["batch_size"], shuffle=config["train"]["shuffle"], with_memory_cache=config["train"]["with_memory_cache"], with_file_cache=config["train"]["with_file_cache"], resize_size=config["preprocess"]["resize_size"], line_thickness=config["preprocess"]["line_thickness"], gaussian_kernel=config["preprocess"]["gaussian_kernel"], gaussian_sigma=config["preprocess"]["gaussian_sigma"]) train_iterators = (train_iterator_src, train_iterator_trg) # monitor monitor = nm.Monitor( os.path.join(config["logdir"], "transformer", f'{config["src_celeb_name"]}2{config["trg_celeb_name"]}', config["experiment_name"])) # Network netG = { 'netG_A2B': models.netG_transformer, 'netG_B2A': models.netG_transformer } netD = { 'netD_A': models.netD_transformer, 'netD_B': models.netD_transformer } # Optimizer solver_netG = { 'netG_A2B': S.Adam(alpha=config["train"]["lr"], beta1=config["train"]["beta1"], beta2=config["train"]["beta2"]), 'netG_B2A': S.Adam(alpha=config["train"]["lr"], beta1=config["train"]["beta1"], beta2=config["train"]["beta2"]) } solver_netD = { 'netD_A': S.Adam(alpha=0.5 * config["train"]["lr"], beta1=config["train"]["beta1"], beta2=config["train"]["beta2"]), 'netD_B': S.Adam(alpha=0.5 * config["train"]["lr"], beta1=config["train"]["beta1"], beta2=config["train"]["beta2"]) } train_transformer(config, netG, netD, solver_netG, solver_netD, train_iterators, monitor)