Python log.warning方法代码示例

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--checkpoint_path', type=str)
    parser.add_argument('--train_dir', type=str)
    parser.add_argument('--dataset', type=str, default='ImageNet', choices=['ImageNet'])
    parser.add_argument('--data_id', nargs='*', default=None)
    config = parser.parse_args()

    if config.dataset == 'ImageNet':
        import datasets.ImageNet as dataset
    else:
        raise ValueError(config.dataset)

    _, dataset = dataset.create_default_splits(ratio=0.999)

    image, _, label, _ = dataset.get_data(dataset.ids[0], dataset.ids[0])
    config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])

    evaler = Evaler(config, dataset)

    log.warning("dataset: %s", dataset)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():

    config, model, dataset_train, dataset_test = argparser(is_train=False)

    evaler = Evaler(config, model, dataset_test)

    log.warning("dataset: %s", config.dataset)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():

    config, model, dataset_train, dataset_test = argparser(is_train=True)

    trainer = Trainer(config, model, dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate_g: %f, learning_rate_d: %f",
                config.dataset, config.learning_rate_g, config.learning_rate_d)
    trainer.train() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=256)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint_path', type=str, default=None)
    parser.add_argument('--train_dir', type=str)
    parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10'])
    parser.add_argument('--reconstruct', action='store_true', default=False)
    parser.add_argument('--generate', action='store_true', default=False)
    parser.add_argument('--interpolate', action='store_true', default=False)
    parser.add_argument('--data_id', nargs='*', default=None)
    config = parser.parse_args()

    if config.dataset == 'MNIST':
        import datasets.mnist as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    config.conv_info = dataset.get_conv_info()
    config.deconv_info = dataset.get_deconv_info()
    dataset_train, dataset_test = dataset.create_default_splits()

    m, l = dataset_train.get_data(dataset_train.ids[0])
    config.data_info = np.concatenate([np.asarray(m.shape), np.asarray(l.shape)])

    evaler = Evaler(config, dataset_test, dataset_train)

    log.warning("dataset: %s", config.dataset)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str, default=None)
    parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10'])
    parser.add_argument('--learning_rate', type=float, default=1e-4)
    parser.add_argument('--alpha', type=float, default=1.0)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    parser.add_argument('--dump_result', action='store_true', default=False)
    config = parser.parse_args()

    if config.dataset == 'MNIST':
        import datasets.mnist as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    config.conv_info = dataset.get_conv_info()
    config.deconv_info = dataset.get_deconv_info()
    dataset_train, dataset_test = dataset.create_default_splits()

    m, l = dataset_train.get_data(dataset_train.ids[0])
    config.data_info = np.concatenate([np.asarray(m.shape), np.asarray(l.shape)])

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f",
                config.dataset, config.learning_rate)
    trainer.train(dataset_train) 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str, default=None)
    parser.add_argument('--dataset', type=str, default='ImageNet', choices=['ImageNet'])
    parser.add_argument('--learning_rate', type=float, default=1e-4)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    config = parser.parse_args()

    if config.dataset == 'ImageNet':
        import datasets.ImageNet as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    dataset_train, dataset_test = dataset.create_default_splits()

    image, _, label, _ = dataset_train.get_data(dataset_train.ids[0], dataset_train.ids[0])
    config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f",
                config.dataset, config.learning_rate)
    trainer.train(dataset_train) 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str)
    parser.add_argument('--dataset', type=str, default='ImageNet', choices=['ImageNet'])
    parser.add_argument('--learning_rate', type=float, default=1e-4)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    config = parser.parse_args()

    if config.dataset == 'ImageNet':
        import datasets.ImageNet as dataset
    else:
        raise ValueError(config.dataset)

    if not config.checkpoint:
        raise ValueError('Please specify a valid checkpoint: {}'.format(config.checkpoint))

    dataset_train, dataset_test = dataset.create_default_splits()

    image, _, label, _ = dataset_train.get_data(dataset_train.ids[0], dataset_train.ids[0])
    config.data_info = np.concatenate([np.asarray(image.shape), np.asarray(label.shape)])

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f",
                config.dataset, config.learning_rate)
    trainer.train(dataset_train) 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():

    config, model, _, dataset_test = argparser(is_train=False)

    evaler = Evaler(config, model, dataset_test)

    log.warning("dataset: %s", config.dataset)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    
    config, model, dataset_train, dataset_test = argparser(is_train=False)

    trainer = Trainer(config, model, dataset_train, dataset_test)

    log.warning("dataset: %s", config.dataset)
    trainer.train() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=64)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str, default=None)
    parser.add_argument('--dataset', type=str, default='MNIST', choices=['MNIST', 'SVHN', 'CIFAR10'])
    parser.add_argument('--learning_rate', type=float, default=1e-4)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    parser.add_argument('--activation', type=str, default='selu', choices=['relu', 'lrelu', 'selu'])
    config = parser.parse_args()

    if config.dataset == 'MNIST':
        import  datasets.mnist as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    config.data_info = dataset.get_data_info()
    config.conv_info = dataset.get_conv_info()
    config.visualize_shape = dataset.get_vis_info()
    dataset_train, dataset_test = dataset.create_default_splits()

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
    trainer.train() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=50)
    parser.add_argument('--model', type=str, default='conv', choices=['rn', 'baseline'])
    parser.add_argument('--checkpoint_path', type=str)
    parser.add_argument('--train_dir', type=str)
    parser.add_argument('--dataset_path', type=str, default='Sort-of-CLEVR_default')
    parser.add_argument('--data_id', nargs='*', default=None)
    config = parser.parse_args()

    path = os.path.join('./datasets', config.dataset_path)

    if check_data_path(path):
        import sort_of_clevr as dataset
    else:
        raise ValueError(path)

    config.data_info = dataset.get_data_info()
    config.conv_info = dataset.get_conv_info()
    dataset_train, dataset_test = dataset.create_default_splits(path)

    evaler = Evaler(config, dataset_test)

    log.warning("dataset: %s", config.dataset_path)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--model', type=str, default='rn', choices=['rn', 'baseline'])
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str, default=None)
    parser.add_argument('--dataset_path', type=str, default='Sort-of-CLEVR_default')
    parser.add_argument('--learning_rate', type=float, default=2.5e-4)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    config = parser.parse_args()

    path = os.path.join('./datasets', config.dataset_path)

    if check_data_path(path):
        import sort_of_clevr as dataset
    else:
        raise ValueError(path)

    config.data_info = dataset.get_data_info()
    config.conv_info = dataset.get_conv_info()
    dataset_train, dataset_test = dataset.create_default_splits(path)

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f",
                config.dataset_path, config.learning_rate)
    trainer.train() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=64)
    parser.add_argument('--output_file', type=str, default=None)
    parser.add_argument('--checkpoint_path', type=str)
    parser.add_argument('--train_dir', type=str)
    parser.add_argument('--dataset', type=str, default='CIFAR10', choices=['MNIST', 'Fashion', 'SVHN', 'CIFAR10'])
    parser.add_argument('--max_steps', type=int, default=1)
    config = parser.parse_args()

    if config.dataset == 'mnist':
        import datasets.mnist as dataset
    elif config.dataset == 'Fashion':
        import datasets.fashion_mnist as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    config.data_info = dataset.get_data_info()
    config.conv_info = dataset.get_conv_info()
    config.deconv_info = dataset.get_deconv_info()
    dataset_train, dataset_test = dataset.create_default_splits()

    evaler = Evaler(config, dataset_test)

    log.warning("dataset: %s", config.dataset)
    evaler.eval_run() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def main():
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch_size', type=int, default=64)
    parser.add_argument('--prefix', type=str, default='default')
    parser.add_argument('--checkpoint', type=str, default=None)
    parser.add_argument('--dataset', type=str, default='CIFAR10',
                        choices=['MNIST', 'Fashion', 'SVHN', 'CIFAR10'])
    parser.add_argument('--learning_rate', type=float, default=1e-4)
    parser.add_argument('--update_rate', type=int, default=5)
    parser.add_argument('--lr_weight_decay', action='store_true', default=False)
    config = parser.parse_args()

    if config.dataset == 'MNIST':
        import datasets.mnist as dataset
    elif config.dataset == 'Fashion':
        import datasets.fashion_mnist as dataset
    elif config.dataset == 'SVHN':
        import datasets.svhn as dataset
    elif config.dataset == 'CIFAR10':
        import datasets.cifar10 as dataset
    else:
        raise ValueError(config.dataset)

    config.data_info = dataset.get_data_info()
    config.conv_info = dataset.get_conv_info()
    config.deconv_info = dataset.get_deconv_info()
    dataset_train, dataset_test = dataset.create_default_splits()

    trainer = Trainer(config,
                      dataset_train, dataset_test)

    log.warning("dataset: %s, learning_rate: %f", config.dataset, config.learning_rate)
    trainer.train() 

# 需要导入模块: from util import log [as 别名]
# 或者: from util.log import warning [as 别名]
def eval_run(self):
        # load checkpoint
        if self.checkpoint_path:
            self.saver.restore(self.session, self.checkpoint_path)
            log.info("Loaded from checkpoint!")

        log.infov("Start Inference and Evaluation")

        log.info("# of testing examples = %d", len(self.dataset))
        length_dataset = len(self.dataset)

        max_steps = int(length_dataset / self.batch_size) + 1

        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(self.session,
                                               coord=coord, start=True)

        evaler = EvalManager()

        if not (self.config.interpolate or self.config.generate or self.config.reconstruct):
            raise ValueError('Please specify at least one task by indicating' +
                             '--reconstruct, --generate, or --interpolate.')
            return

        if self.config.reconstruct:
            try:
                for s in xrange(max_steps):
                    step, loss, step_time, batch_chunk, prediction_pred, prediction_gt = \
                        self.run_single_step(self.batch)
                    self.log_step_message(s, loss, step_time)
                    evaler.add_batch(batch_chunk['id'], prediction_pred, prediction_gt)

            except Exception as e:
                coord.request_stop(e)

            evaler.report()
            log.warning('Completed reconstruction.')

        if self.config.generate:
            x = self.generator(self.batch_size)
            img = self.image_grid(x)
            imageio.imwrite('generate_{}.png'.format(self.config.prefix), img)
            log.warning('Completed generation. Generated samples are save' +
                        'as generate_{}.png'.format(self.config.prefix))

        if self.config.interpolate:
            x = self.interpolator(self.dataset_train, self.batch_size)
            img = self.image_grid(x)
            imageio.imwrite('interpolate_{}.png'.format(self.config.prefix), img)
            log.warning('Completed interpolation. Interpolated samples are save' +
                        'as interpolate_{}.png'.format(self.config.prefix))

        coord.request_stop()
        try:
            coord.join(threads, stop_grace_period_secs=3)
        except RuntimeError as e:
            log.warn(str(e))

        log.infov("Completed evaluation.")