Python train.Train() Examples

The following are 5 code examples of train.Train(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module train , or try the search function .
Example #1
Source File: main.py    From MobileNet-V2 with Apache License 2.0 5 votes vote down vote up 
def main():
    # Parse the JSON arguments
    config_args = parse_args()

    # Create the experiment directories
    _, config_args.summary_dir, config_args.checkpoint_dir = create_experiment_dirs(
        config_args.experiment_dir)

    model = MobileNetV2(config_args)

    if config_args.cuda:
        model.cuda()
        cudnn.enabled = True
        cudnn.benchmark = True

    print("Loading Data...")
    data = CIFAR10Data(config_args)
    print("Data loaded successfully\n")

    trainer = Train(model, data.trainloader, data.testloader, config_args)

    if config_args.to_train:
        try:
            print("Training...")
            trainer.train()
            print("Training Finished\n")
        except KeyboardInterrupt:
            pass

    if config_args.to_test:
        print("Testing...")
        trainer.test(data.testloader)
        print("Testing Finished\n")
Example #2
Source File: main.py    From MobileNet with Apache License 2.0 4 votes vote down vote up 
def main():
    # Parse the JSON arguments
    try:
        config_args = parse_args()
    except:
        print("Add a config file using \'--config file_name.json\'")
        exit(1)

    # Create the experiment directories
    _, config_args.summary_dir, config_args.checkpoint_dir = create_experiment_dirs(config_args.experiment_dir)

    # Reset the default Tensorflow graph
    tf.reset_default_graph()

    # Tensorflow specific configuration
    config = tf.ConfigProto(allow_soft_placement=True)
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)

    # Data loading
    data = DataLoader(config_args.batch_size, config_args.shuffle)
    print("Loading Data...")
    config_args.img_height, config_args.img_width, config_args.num_channels, \
    config_args.train_data_size, config_args.test_data_size = data.load_data()
    print("Data loaded\n\n")

    # Model creation
    print("Building the model...")
    model = MobileNet(config_args)
    print("Model is built successfully\n\n")

    # Summarizer creation
    summarizer = Summarizer(sess, config_args.summary_dir)
    # Train class
    trainer = Train(sess, model, data, summarizer)

    if config_args.to_train:
        try:
            print("Training...")
            trainer.train()
            print("Training Finished\n\n")
        except KeyboardInterrupt:
            trainer.save_model()

    if config_args.to_test:
        print("Final test!")
        trainer.test('val')
        print("Testing Finished\n\n")
Example #3
Source File: eval.py    From CROWN-IBP with BSD 2-Clause "Simplified" License 4 votes vote down vote up 
def main(args):
    config = load_config(args)
    global_eval_config = config["eval_params"]
    models, model_names = config_modelloader(config, load_pretrain = True)

    robust_errs = []
    errs = []

    for model, model_id, model_config in zip(models, model_names, config["models"]):
        # make a copy of global training config, and update per-model config
        eval_config = copy.deepcopy(global_eval_config)
        if "eval_params" in model_config:
            eval_config.update(model_config["eval_params"])

        model = BoundSequential.convert(model, eval_config["method_params"]["bound_opts"]) 
        model = model.cuda()
        # read training parameters from config file
        method = eval_config["method"]
        verbose = eval_config["verbose"]
        eps = eval_config["epsilon"]
        # parameters specific to a training method
        method_param = eval_config["method_params"]
        norm = float(eval_config["norm"])
        train_data, test_data = config_dataloader(config, **eval_config["loader_params"])

        model_name = get_path(config, model_id, "model", load = False)
        print(model_name)
        model_log = get_path(config, model_id, "eval_log")
        logger = Logger(open(model_log, "w"))
        logger.log("evaluation configurations:", eval_config)
            
        logger.log("Evaluating...")
        with torch.no_grad():
            # evaluate
            robust_err, err = Train(model, 0, test_data, EpsilonScheduler("linear", 0, 0, eps, eps, 1), eps, norm, logger, verbose, False, None, method, **method_param)
        robust_errs.append(robust_err)
        errs.append(err)

    print('model robust errors (for robustly trained models, not valid for naturally trained models):')
    print(robust_errs)
    robust_errs = np.array(robust_errs)
    print('min: {:.4f}, max: {:.4f}, median: {:.4f}, mean: {:.4f}'.format(np.min(robust_errs), np.max(robust_errs), np.median(robust_errs), np.mean(robust_errs)))
    print('clean errors for models with min, max and median robust errors')
    i_min = np.argmin(robust_errs)
    i_max = np.argmax(robust_errs)
    i_median = np.argsort(robust_errs)[len(robust_errs) // 2]
    print('for min: {:.4f}, for max: {:.4f}, for median: {:.4f}'.format(errs[i_min], errs[i_max], errs[i_median]))
    print('model clean errors:')
    print(errs)
    print('min: {:.4f}, max: {:.4f}, median: {:.4f}, mean: {:.4f}'.format(np.min(errs), np.max(errs), np.median(errs), np.mean(errs)))
Example #4
Source File: main.py    From face-antispoofing-using-mobileNet with Apache License 2.0 4 votes vote down vote up 
def main():
    # Parse the JSON arguments
    try:
        config_args = parse_args()
    except:
        print("Add a config file using \'--config file_name.json\'")
        exit(1)

    # Create the experiment directories
    _, config_args.summary_dir, config_args.checkpoint_dir = create_experiment_dirs(config_args.experiment_dir)

    # Reset the default Tensorflow graph
    tf.reset_default_graph()

    # Tensorflow specific configuration
    config = tf.ConfigProto(allow_soft_placement=True)
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)

    # Data loading
    data = DataLoader(config_args.batch_size, config_args.shuffle)
    print("Loading Data...")
    config_args.img_height, config_args.img_width, config_args.num_channels, \
    config_args.train_data_size, config_args.test_data_size = data.load_data()
    print("Data loaded\n\n")

    # Model creation
    print("Building the model...")
    model = MobileNet(config_args)
    print("Model is built successfully\n\n")

    # Summarizer creation
    summarizer = Summarizer(sess, config_args.summary_dir)
    # Train class
    trainer = Train(sess, model, data, summarizer)

    if config_args.to_train:
        try:
            print("Training...")
            trainer.train()
            print("Training Finished\n\n")
        except KeyboardInterrupt:
            trainer.save_model()

    if config_args.to_test:
        print("Final test!")
        trainer.test('val')
        print("Testing Finished\n\n")
Example #5
Source File: main.py    From ShuffleNet with Apache License 2.0 4 votes vote down vote up 
def main():
    # Parse the JSON arguments
    config_args = parse_args()

    # Create the experiment directories
    _, config_args.summary_dir, config_args.checkpoint_dir = create_experiment_dirs(config_args.experiment_dir)

    # Reset the default Tensorflow graph
    tf.reset_default_graph()

    # Tensorflow specific configuration
    config = tf.ConfigProto(allow_soft_placement=True)
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)

    # Data loading
    # The batch size is equal to 1 when testing to simulate the real experiment.
    data_batch_size = config_args.batch_size if config_args.train_or_test == "train" else 1
    data = DataLoader(data_batch_size, config_args.shuffle)
    print("Loading Data...")
    config_args.img_height, config_args.img_width, config_args.num_channels, \
    config_args.train_data_size, config_args.test_data_size = data.load_data()
    print("Data loaded\n\n")

    # Model creation
    print("Building the model...")
    model = ShuffleNet(config_args)
    print("Model is built successfully\n\n")

    # Parameters visualization
    show_parameters()

    # Summarizer creation
    summarizer = Summarizer(sess, config_args.summary_dir)
    # Train class
    trainer = Train(sess, model, data, summarizer)

    if config_args.train_or_test == 'train':
        try:
            # print("FLOPs for batch size = " + str(config_args.batch_size) + "\n")
            # calculate_flops()
            print("Training...")
            trainer.train()
            print("Training Finished\n\n")
        except KeyboardInterrupt:
            trainer.save_model()

    elif config_args.train_or_test == 'test':
        # print("FLOPs for single inference \n")
        # calculate_flops()
        # This can be 'val' or 'test' or even 'train' according to the needs.
        print("Testing...")
        trainer.test('val')
        print("Testing Finished\n\n")

    else:
        raise ValueError("Train or Test options only are allowed")