train_tf.py

import argparse
import numpy as np
import random

from tensorpack.input_source import QueueInput
from tensorpack.utils import logger
from tensorpack.utils.gpu import get_num_gpu
from tensorpack import ModelSaver, ScheduledHyperParamSetter, EstimatedTimeLeft, ClassificationError, InferenceRunner,\
    DataParallelInferenceRunner, TrainConfig, SyncMultiGPUTrainerParameterServer, launch_train_with_config

from common.logger_utils import initialize_logging
from tensorflow_.utils_tp import prepare_tf_context, prepare_model, get_data


def parse_args():
    parser = argparse.ArgumentParser(
        description='Train a model for image classification (TensorFlow/TensorPack)',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        '--data-dir',
        type=str,
        default='../imgclsmob_data/imagenet',
        help='training and validation pictures to use.')

    parser.add_argument(
        '--model',
        type=str,
        required=True,
        help='type of model to use. see model_provider for options.')
    parser.add_argument(
        '--use-pretrained',
        action='store_true',
        help='enable using pretrained model from gluon.')
    parser.add_argument(
        '--resume',
        type=str,
        default='',
        help='resume from previously saved parameters if not None')
    # parser.add_argument(
    #     '--resume-state',
    #     type=str,
    #     default='',
    #     help='resume from previously saved optimizer state if not None')

    parser.add_argument(
        '--input-size',
        type=int,
        default=224,
        help='size of the input for model. default is 224')
    parser.add_argument(
        '--resize-inv-factor',
        type=float,
        default=0.875,
        help='inverted ratio for input image crop. default is 0.875')

    parser.add_argument(
        '--num-gpus',
        type=int,
        default=0,
        help='number of gpus to use.')
    parser.add_argument(
        '-j',
        '--num-data-workers',
        dest='num_workers',
        default=4,
        type=int,
        help='number of preprocessing workers')

    parser.add_argument(
        '--batch-size',
        type=int,
        default=512,
        help='training batch size per device (CPU/GPU).')
    parser.add_argument(
        '--num-epochs',
        type=int,
        default=120,
        help='number of training epochs.')
    parser.add_argument(
        '--start-epoch',
        type=int,
        default=1,
        help='starting epoch for resuming, default is 1 for new training')
    parser.add_argument(
        '--attempt',
        type=int,
        default=1,
        help='current number of training')

    parser.add_argument(
        '--optimizer-name',
        type=str,
        default='nag',
        help='optimizer name')
    parser.add_argument(
        '--lr',
        type=float,
        default=0.1,
        help='learning rate. default is 0.1')
    parser.add_argument(
        '--momentum',
        type=float,
        default=0.9,
        help='momentum value for optimizer; default is 0.9')
    parser.add_argument(
        '--wd',
        type=float,
        default=0.0001,
        help='weight decay rate. default is 0.0001.')

    parser.add_argument(
        '--log-interval',
        type=int,
        default=50,
        help='number of batches to wait before logging.')
    parser.add_argument(
        '--save-interval',
        type=int,
        default=4,
        help='saving parameters epoch interval, best model will always be saved')
    parser.add_argument(
        '--save-dir',
        type=str,
        default='',
        help='directory of saved models and log-files')
    parser.add_argument(
        '--logging-file-name',
        type=str,
        default='train.log',
        help='filename of training log')

    parser.add_argument(
        '--seed',
        type=int,
        default=-1,
        help='Random seed to be fixed')
    parser.add_argument(
        '--log-packages',
        type=str,
        default='tensorflow-gpu',
        help='list of python packages for logging')
    parser.add_argument(
        '--log-pip-packages',
        type=str,
        default='tensorflow-gpu, tensorpack',
        help='list of pip packages for logging')
    args = parser.parse_args()
    return args


def init_rand(seed):
    if seed <= 0:
        seed = np.random.randint(10000)
    random.seed(seed)
    np.random.seed(seed)
    return seed


def train_net(net,
              session_init,
              batch_size,
              num_epochs,
              train_dataflow,
              val_dataflow):

    num_towers = max(get_num_gpu(), 1)
    batch_per_tower = batch_size // num_towers
    logger.info("Running on {} towers. Batch size per tower: {}".format(num_towers, batch_per_tower))

    num_training_samples = 1281167
    step_size = num_training_samples // batch_size
    max_iter = (num_epochs - 1) * step_size
    callbacks = [
        ModelSaver(),
        ScheduledHyperParamSetter(
            'learning_rate',
            [(0, 0.5), (max_iter, 0)],
            interp='linear',
            step_based=True),
        EstimatedTimeLeft()]

    infs = [ClassificationError('wrong-top1', 'val-error-top1'),
            ClassificationError('wrong-top5', 'val-error-top5')]
    if num_towers == 1:
        # single-GPU inference with queue prefetch
        callbacks.append(InferenceRunner(
            input=QueueInput(val_dataflow),
            infs=infs))
    else:
        # multi-GPU inference (with mandatory queue prefetch)
        callbacks.append(DataParallelInferenceRunner(
            input=val_dataflow,
            infs=infs,
            gpus=list(range(num_towers))))

    config = TrainConfig(
        dataflow=train_dataflow,
        model=net,
        callbacks=callbacks,
        session_init=session_init,
        steps_per_epoch=step_size,
        max_epoch=num_epochs)

    launch_train_with_config(
        config=config,
        trainer=SyncMultiGPUTrainerParameterServer(num_towers))


def main():
    args = parse_args()
    args.seed = init_rand(seed=args.seed)

    _, log_file_exist = initialize_logging(
        logging_dir_path=args.save_dir,
        logging_file_name=args.logging_file_name,
        script_args=args,
        log_packages=args.log_packages,
        log_pip_packages=args.log_pip_packages)
    logger.set_logger_dir(args.save_dir)

    batch_size = prepare_tf_context(
        num_gpus=args.num_gpus,
        batch_size=args.batch_size)

    net, inputs_desc = prepare_model(
        model_name=args.model,
        use_pretrained=args.use_pretrained,
        pretrained_model_file_path=args.resume.strip())

    train_dataflow = get_data(
        is_train=True,
        batch_size=batch_size,
        data_dir_path=args.data_dir,
        input_image_size=net.image_size,
        resize_inv_factor=args.resize_inv_factor)
    val_dataflow = get_data(
        is_train=False,
        batch_size=batch_size,
        data_dir_path=args.data_dir,
        input_image_size=net.image_size,
        resize_inv_factor=args.resize_inv_factor)

    train_net(
        net=net,
        session_init=inputs_desc,
        batch_size=batch_size,
        num_epochs=args.num_epochs,
        train_dataflow=train_dataflow,
        val_dataflow=val_dataflow)


if __name__ == '__main__':
    main()