eval_tf.py

import argparse
import tqdm
import time
import logging

from tensorpack.predict import PredictConfig, FeedfreePredictor
from tensorpack.utils.stats import RatioCounter
from tensorpack.input_source import QueueInput, StagingInput

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


def parse_args():
    parser = argparse.ArgumentParser(description='Evaluate 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 vision_model 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(
        '--calc-flops',
        dest='calc_flops',
        action='store_true',
        help='calculate FLOPs')

    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(
        '--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(
        '--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 test(net,
         session_init,
         val_dataflow,
         do_calc_flops=False,
         extended_log=False):

    pred_config = PredictConfig(
        model=net,
        session_init=session_init,
        input_names=['input', 'label'],
        output_names=['wrong-top1', 'wrong-top5']
    )
    err_top1 = RatioCounter()
    err_top5 = RatioCounter()

    tic = time.time()
    pred = FeedfreePredictor(pred_config, StagingInput(QueueInput(val_dataflow), device='/gpu:0'))

    # import tensorflow as tf
    # summ_writer = tf.summary.FileWriter("/home/semery/projects/imgclsmob_data/gl-squeezenet_v1_1/", pred._sess.graph)

    for _ in tqdm.trange(val_dataflow.size()):
        err_top1_val, err_top5_val = pred()
        batch_size = err_top1_val.shape[0]
        err_top1.feed(err_top1_val.sum(), batch_size)
        err_top5.feed(err_top5_val.sum(), batch_size)
        # print("err_top1_val={}".format(err_top1_val.sum() / batch_size))
        # print("err_top5_val={}".format(err_top5_val.sum() / batch_size))

    err_top1_val = err_top1.ratio
    err_top5_val = err_top5.ratio

    if extended_log:
        logging.info('Test: err-top1={top1:.4f} ({top1})\terr-top5={top5:.4f} ({top5})'.format(
            top1=err_top1_val, top5=err_top5_val))
    else:
        logging.info('Test: err-top1={top1:.4f}\terr-top5={top5:.4f}'.format(
            top1=err_top1_val, top5=err_top5_val))
    logging.info('Time cost: {:.4f} sec'.format(
        time.time() - tic))

    if do_calc_flops:
        calc_flops(model=net)


def main():
    args = parse_args()

    _, 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)

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

    classes = 1000
    net, inputs_desc = prepare_model(
        model_name=args.model,
        classes=classes,
        use_pretrained=args.use_pretrained,
        pretrained_model_file_path=args.resume.strip())

    val_dataflow = get_data(
        is_train=False,
        batch_size=batch_size,
        data_dir_path=args.data_dir)

    assert (args.use_pretrained or args.resume.strip())
    test(
        net=net,
        session_init=inputs_desc,
        val_dataflow=val_dataflow,
        do_calc_flops=args.calc_flops,
        extended_log=True)


if __name__ == '__main__':
    main()