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 argparse
import contextlib
import os

import time
import math
import numpy as np

import paddle.fluid as fluid
from paddle.fluid.dygraph.parallel import ParallelEnv
from paddle.io import BatchSampler, DataLoader

from hapi.model import Input, set_device
from hapi.loss import CrossEntropy
from hapi.distributed import DistributedBatchSampler
from hapi.metrics import Accuracy
import hapi.vision.models as models

from imagenet_dataset import ImageNetDataset


def make_optimizer(step_per_epoch, parameter_list=None):
    base_lr = FLAGS.lr
    lr_scheduler = FLAGS.lr_scheduler
    momentum = FLAGS.momentum
    weight_decay = FLAGS.weight_decay

    if lr_scheduler == 'piecewise':
        milestones = FLAGS.milestones
        boundaries = [step_per_epoch * e for e in milestones]
        values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
        learning_rate = fluid.layers.piecewise_decay(
            boundaries=boundaries, values=values)
    elif lr_scheduler == 'cosine':
        learning_rate = fluid.layers.cosine_decay(base_lr, step_per_epoch,
                                                  FLAGS.epoch)
    else:
        raise ValueError(
            "Expected lr_scheduler in ['piecewise', 'cosine'], but got {}".
            format(lr_scheduler))

    learning_rate = fluid.layers.linear_lr_warmup(
        learning_rate=learning_rate,
        warmup_steps=5 * step_per_epoch,
        start_lr=0.,
        end_lr=base_lr)

    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        momentum=momentum,
        regularization=fluid.regularizer.L2Decay(weight_decay),
        parameter_list=parameter_list)

    return optimizer


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

    model_list = [x for x in models.__dict__["__all__"]]
    assert FLAGS.arch in model_list, "Expected FLAGS.arch in {}, but received {}".format(
        model_list, FLAGS.arch)
    model = models.__dict__[FLAGS.arch](pretrained=FLAGS.eval_only and
                                        not FLAGS.resume)

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

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

    train_dataset = ImageNetDataset(
        os.path.join(FLAGS.data, 'train'),
        mode='train',
        image_size=FLAGS.image_size,
        resize_short_size=FLAGS.resize_short_size)

    val_dataset = ImageNetDataset(
        os.path.join(FLAGS.data, 'val'),
        mode='val',
        image_size=FLAGS.image_size,
        resize_short_size=FLAGS.resize_short_size)

    optim = make_optimizer(
        np.ceil(
            len(train_dataset) * 1. / FLAGS.batch_size / ParallelEnv().nranks),
        parameter_list=model.parameters())

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

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

    output_dir = os.path.join(FLAGS.output_dir, FLAGS.arch,
                              time.strftime('%Y-%m-%d-%H-%M',
                                            time.localtime()))
    if ParallelEnv().local_rank == 0 and not os.path.exists(output_dir):
        os.makedirs(output_dir)

    model.fit(train_dataset,
              val_dataset,
              batch_size=FLAGS.batch_size,
              epochs=FLAGS.epoch,
              save_dir=output_dir,
              num_workers=FLAGS.num_workers)


if __name__ == '__main__':
    parser = argparse.ArgumentParser("Resnet Training on ImageNet")
    parser.add_argument(
        'data',
        metavar='DIR',
        help='path to dataset '
        '(should have subdirectories named "train" and "val"')
    parser.add_argument(
        "--arch", type=str, default='resnet50', help="model name")
    parser.add_argument(
        "--device", type=str, default='gpu', help="device to run, cpu or gpu")
    parser.add_argument(
        "-d", "--dynamic", action='store_true', help="enable dygraph mode")
    parser.add_argument(
        "-e", "--epoch", default=90, type=int, help="number of epoch")
    parser.add_argument(
        '--lr',
        '--learning-rate',
        default=0.1,
        type=float,
        metavar='LR',
        help='initial learning rate')
    parser.add_argument(
        "-b", "--batch-size", default=64, type=int, help="batch size")
    parser.add_argument(
        "-n", "--num-workers", default=4, type=int, help="dataloader workers")
    parser.add_argument(
        "--output-dir", type=str, default='output', help="save dir")
    parser.add_argument(
        "-r",
        "--resume",
        default=None,
        type=str,
        help="checkpoint path to resume")
    parser.add_argument(
        "--eval-only", action='store_true', help="only evaluate the model")
    parser.add_argument(
        "--lr-scheduler",
        default='piecewise',
        type=str,
        help="learning rate scheduler")
    parser.add_argument(
        "--milestones",
        nargs='+',
        type=int,
        default=[30, 60, 80],
        help="piecewise decay milestones")
    parser.add_argument(
        "--weight-decay", default=1e-4, type=float, help="weight decay")
    parser.add_argument("--momentum", default=0.9, type=float, help="momentum")
    parser.add_argument(
        "--image-size", default=224, type=int, help="intput image size")
    parser.add_argument(
        "--resize-short-size",
        default=256,
        type=int,
        help="short size of keeping ratio resize")
    FLAGS = parser.parse_args()
    assert FLAGS.data, "error: must provide data path"
    main()