main.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import division
from __future__ import print_function

import os
import argparse
import numpy as np

from paddle import fluid
from paddle.fluid.dygraph.parallel import ParallelEnv

from paddle.incubate.hapi.model import Model, Input, set_device
from paddle.incubate.hapi.loss import CrossEntropy
from paddle.incubate.hapi.metrics import Accuracy
from paddle.incubate.hapi.vision.transforms import Compose

from modeling import tsm_resnet50
from check import check_gpu, check_version
from kinetics_dataset import KineticsDataset
from transforms import *
from utils import print_arguments


def make_optimizer(step_per_epoch, parameter_list=None):
    boundaries = [e * step_per_epoch for e in [40, 60]]
    values = [FLAGS.lr * (0.1**i) for i in range(len(boundaries) + 1)]

    learning_rate = fluid.layers.piecewise_decay(
        boundaries=boundaries, values=values)
    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        regularization=fluid.regularizer.L2Decay(1e-4),
        momentum=0.9,
        parameter_list=parameter_list)

    return optimizer


def main():
    device = set_device(FLAGS.device)
    fluid.enable_dygraph(device) if FLAGS.dynamic else None

    train_transform = Compose([
        GroupScale(), GroupMultiScaleCrop(), GroupRandomCrop(),
        GroupRandomFlip(), NormalizeImage()
    ])
    train_dataset = KineticsDataset(
        file_list=os.path.join(FLAGS.data, 'train_10.list'),
        pickle_dir=os.path.join(FLAGS.data, 'train_10'),
        label_list=os.path.join(FLAGS.data, 'label_list'),
        transform=train_transform)
    val_transform = Compose(
        [GroupScale(), GroupCenterCrop(), NormalizeImage()])
    val_dataset = KineticsDataset(
        file_list=os.path.join(FLAGS.data, 'val_10.list'),
        pickle_dir=os.path.join(FLAGS.data, 'val_10'),
        label_list=os.path.join(FLAGS.data, 'label_list'),
        mode='val',
        transform=val_transform)

    pretrained = FLAGS.eval_only and FLAGS.weights is None
    model = tsm_resnet50(
        num_classes=train_dataset.num_classes, pretrained=pretrained)

    step_per_epoch = int(len(train_dataset) / FLAGS.batch_size \
                         / ParallelEnv().nranks)
    optim = make_optimizer(step_per_epoch, model.parameters())

    inputs = [Input([None, 8, 3, 224, 224], 'float32', name='image')]
    labels = [Input([None, 1], 'int64', name='label')]

    model.prepare(
        optim,
        CrossEntropy(),
        metrics=Accuracy(topk=(1, 5)),
        inputs=inputs,
        labels=labels,
        device=FLAGS.device)

    if FLAGS.eval_only:
        if FLAGS.weights is not None:
            model.load(FLAGS.weights, reset_optimizer=True)

        model.evaluate(
            val_dataset,
            batch_size=FLAGS.batch_size,
            num_workers=FLAGS.num_workers)
        return

    if FLAGS.resume is not None:
        model.load(FLAGS.resume)

    model.fit(train_data=train_dataset,
              eval_data=val_dataset,
              epochs=FLAGS.epoch,
              batch_size=FLAGS.batch_size,
              save_dir=FLAGS.save_dir or 'tsm_checkpoint',
              num_workers=FLAGS.num_workers,
              drop_last=True,
              shuffle=True)


if __name__ == '__main__':
    parser = argparse.ArgumentParser("CNN training on TSM")
    parser.add_argument(
        "--data",
        type=str,
        default='dataset/kinetics',
        help="path to dataset root directory")
    parser.add_argument(
        "--device", type=str, default='gpu', help="device to use, gpu or cpu")
    parser.add_argument(
        "-d", "--dynamic", action='store_true', help="enable dygraph mode")
    parser.add_argument(
        "--eval_only", action='store_true', help="run evaluation only")
    parser.add_argument(
        "-e", "--epoch", default=70, type=int, help="number of epoch")
    parser.add_argument(
        "-j", "--num_workers", default=4, type=int, help="read worker number")
    parser.add_argument(
        '--lr',
        '--learning-rate',
        default=1e-2,
        type=float,
        metavar='LR',
        help='initial learning rate')
    parser.add_argument(
        "-b", "--batch_size", default=16, type=int, help="batch size")
    parser.add_argument(
        "-r",
        "--resume",
        default=None,
        type=str,
        help="checkpoint path to resume")
    parser.add_argument(
        "-w",
        "--weights",
        default=None,
        type=str,
        help="weights path for evaluation")
    parser.add_argument(
        "-s",
        "--save_dir",
        default=None,
        type=str,
        help="directory path for checkpoint saving, default ./yolo_checkpoint")
    FLAGS = parser.parse_args()
    print_arguments(FLAGS)

    check_gpu(str.lower(FLAGS.device) == 'gpu')
    check_version()
    main()