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 numpy as np

from paddle import fluid
from paddle.fluid.optimizer import Momentum
from paddle.io import DataLoader

from paddle.incubate.hapi.model import Model, Input, set_device
from paddle.incubate.hapi.distributed import DistributedBatchSampler
from paddle.incubate.hapi.vision.transforms import Compose, BatchCompose

from modeling import yolov3_darknet53, YoloLoss
from coco import COCODataset
from coco_metric import COCOMetric
from transforms import *
from utils import print_arguments

NUM_MAX_BOXES = 50


def make_optimizer(step_per_epoch, parameter_list=None):
    base_lr = FLAGS.lr
    warm_up_iter = 1000
    momentum = 0.9
    weight_decay = 5e-4
    boundaries = [step_per_epoch * e for e in [200, 250]]
    values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
    learning_rate = fluid.layers.piecewise_decay(
        boundaries=boundaries, values=values)
    learning_rate = fluid.layers.linear_lr_warmup(
        learning_rate=learning_rate,
        warmup_steps=warm_up_iter,
        start_lr=0.0,
        end_lr=base_lr)
    optimizer = fluid.optimizer.Momentum(
        learning_rate=learning_rate,
        regularization=fluid.regularizer.L2Decay(weight_decay),
        momentum=momentum,
        parameter_list=parameter_list)
    return optimizer


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

    inputs = [
        Input(
            [None, 1], 'int64', name='img_id'), Input(
                [None, 2], 'int32', name='img_shape'), Input(
                    [None, 3, None, None], 'float32', name='image')
    ]

    labels = [
        Input(
            [None, NUM_MAX_BOXES, 4], 'float32', name='gt_bbox'), Input(
                [None, NUM_MAX_BOXES], 'int32', name='gt_label'), Input(
                    [None, NUM_MAX_BOXES], 'float32', name='gt_score')
    ]

    if not FLAGS.eval_only:  # training mode
        train_transform = Compose([
            ColorDistort(), RandomExpand(), RandomCrop(), RandomFlip(),
            NormalizeBox(), PadBox(), BboxXYXY2XYWH()
        ])

        train_collate_fn = BatchCompose([RandomShape(), NormalizeImage()])
        dataset = COCODataset(
            dataset_dir=FLAGS.data,
            anno_path='annotations/instances_train2017.json',
            image_dir='train2017',
            with_background=False,
            mixup=True,
            transform=train_transform)
        batch_sampler = DistributedBatchSampler(
            dataset, batch_size=FLAGS.batch_size, shuffle=True, drop_last=True)
        loader = DataLoader(
            dataset,
            batch_sampler=batch_sampler,
            places=device,
            num_workers=FLAGS.num_workers,
            return_list=True,
            collate_fn=train_collate_fn)
    else:  # evaluation mode
        eval_transform = Compose([
            ResizeImage(target_size=608), NormalizeBox(), PadBox(),
            BboxXYXY2XYWH()
        ])

        eval_collate_fn = BatchCompose([NormalizeImage()])
        dataset = COCODataset(
            dataset_dir=FLAGS.data,
            anno_path='annotations/instances_val2017.json',
            image_dir='val2017',
            with_background=False,
            transform=eval_transform)
        # batch_size can only be 1 in evaluation for YOLOv3
        # prediction bbox is a LoDTensor
        batch_sampler = DistributedBatchSampler(
            dataset, batch_size=1, shuffle=False, drop_last=False)
        loader = DataLoader(
            dataset,
            batch_sampler=batch_sampler,
            places=device,
            num_workers=FLAGS.num_workers,
            return_list=True,
            collate_fn=eval_collate_fn)

    pretrained = FLAGS.eval_only and FLAGS.weights is None
    model = yolov3_darknet53(
        num_classes=dataset.num_classes,
        model_mode='eval' if FLAGS.eval_only else 'train',
        pretrained=pretrained)

    if FLAGS.pretrain_weights and not FLAGS.eval_only:
        model.load(
            FLAGS.pretrain_weights, skip_mismatch=True, reset_optimizer=True)

    optim = make_optimizer(
        len(batch_sampler), parameter_list=model.parameters())

    model.prepare(
        optim,
        YoloLoss(num_classes=dataset.num_classes),
        inputs=inputs,
        labels=labels,
        device=FLAGS.device)

    # NOTE: we implement COCO metric of YOLOv3 model here, separately
    # from 'prepare' and 'fit' framework for follwing reason:
    # 1. YOLOv3 network structure is different between 'train' and
    # 'eval' mode, in 'eval' mode, output prediction bbox is not the
    # feature map used for YoloLoss calculating
    # 2. COCO metric behavior is also different from defined Metric
    # for COCO metric should not perform accumulate in each iteration
    # but only accumulate at the end of an epoch
    if FLAGS.eval_only:
        if FLAGS.weights is not None:
            model.load(FLAGS.weights, reset_optimizer=True)
        preds = model.predict(loader, stack_outputs=False)
        _, _, _, img_ids, bboxes = preds

        anno_path = os.path.join(FLAGS.data,
                                 'annotations/instances_val2017.json')
        coco_metric = COCOMetric(anno_path=anno_path, with_background=False)
        for img_id, bbox in zip(img_ids, bboxes):
            coco_metric.update(img_id, bbox)
        coco_metric.accumulate()
        coco_metric.reset()
        return

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

    save_dir = FLAGS.save_dir or 'yolo_checkpoint'

    model.fit(train_data=loader,
              epochs=FLAGS.epoch - FLAGS.no_mixup_epoch,
              save_dir=os.path.join(save_dir, "mixup"),
              save_freq=10)

    # do not use image mixup transfrom in the last FLAGS.no_mixup_epoch epoches
    dataset.mixup = False
    model.fit(train_data=loader,
              epochs=FLAGS.no_mixup_epoch,
              save_dir=os.path.join(save_dir, "no_mixup"),
              save_freq=5)


if __name__ == '__main__':
    parser = argparse.ArgumentParser("Yolov3 Training on VOC")
    parser.add_argument(
        "--data",
        type=str,
        default='dataset/voc',
        help="path to dataset 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=300, type=int, help="number of epoch")
    parser.add_argument(
        "--no_mixup_epoch",
        default=30,
        type=int,
        help="number of the last N epoch without image mixup")
    parser.add_argument(
        '--lr',
        '--learning-rate',
        default=0.001,
        type=float,
        metavar='LR',
        help='initial learning rate')
    parser.add_argument(
        "-b", "--batch_size", default=16, type=int, help="batch size")
    parser.add_argument(
        "-j",
        "--num_workers",
        default=4,
        type=int,
        help="reader worker number")
    parser.add_argument(
        "-p",
        "--pretrain_weights",
        default=None,
        type=str,
        help="path to pretrained weights")
    parser.add_argument(
        "-r", "--resume", default=None, type=str, help="path to model weights")
    parser.add_argument(
        "-w",
        "--weights",
        default=None,
        type=str,
        help="path to weights 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)
    assert FLAGS.data, "error: must provide data path"
    main()