# 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

import paddle
from paddle import fluid
from paddle.fluid.optimizer import Momentum
from paddle.io import DataLoader, DistributedBatchSampler
from paddle.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 = paddle.set_device(FLAGS.device)
    paddle.disable_static(device) if FLAGS.dynamic else None

    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,
        num_max_boxes=NUM_MAX_BOXES,
        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(
        optimizer=optim, loss=YoloLoss(num_classes=dataset.num_classes))

    # 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()