import argparse
import cv2
import os
import numpy as np
import torch
from torch.nn import DataParallel
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import transforms

from datasets.coco import CocoTrainDataset
from datasets.transformations import ConvertKeypoints, Scale, Rotate, CropPad, Flip
from modules.get_parameters import get_parameters_conv, get_parameters_bn, get_parameters_conv_depthwise
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.loss import l2_loss
from modules.load_state import load_state, load_from_mobilenet
from val import evaluate
img=cv2.imread('C:\\Users\\92800\\Desktop\\3.jpg')
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)  # To prevent freeze of DataLoader

# 起码得有一行注释吧
# 起码得再来一行注释吧
def train(prepared_train_labels, train_images_folder, num_refinement_stages, base_lr, batch_size, batches_per_iter,
          num_workers, checkpoint_path, weights_only, from_mobilenet, checkpoints_folder, log_after,
          val_labels, val_images_folder, val_output_name, checkpoint_after, val_after):
    net = PoseEstimationWithMobileNet(num_refinement_stages)

    stride = 8
    sigma = 7
    path_thickness = 1
    dataset = CocoTrainDataset(prepared_train_labels, train_images_folder,
                               stride, sigma, path_thickness,
                               transform=transforms.Compose([
                                   ConvertKeypoints(),
                                   Scale(),
                                   Rotate(pad=(128, 128, 128)),
                                   CropPad(pad=(128, 128, 128)),
                                   Flip()]))
    train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

    optimizer = optim.Adam([
        {'params': get_parameters_conv(net.model, 'weight')},
        {'params': get_parameters_conv_depthwise(net.model, 'weight'), 'weight_decay': 0},
        {'params': get_parameters_bn(net.model, 'weight'), 'weight_decay': 0},
        {'params': get_parameters_bn(net.model, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
        {'params': get_parameters_conv(net.cpm, 'weight'), 'lr': base_lr},
        {'params': get_parameters_conv(net.cpm, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
        {'params': get_parameters_conv_depthwise(net.cpm, 'weight'), 'weight_decay': 0},
        {'params': get_parameters_conv(net.initial_stage, 'weight'), 'lr': base_lr},
        {'params': get_parameters_conv(net.initial_stage, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
        {'params': get_parameters_conv(net.refinement_stages, 'weight'), 'lr': base_lr * 4},
        {'params': get_parameters_conv(net.refinement_stages, 'bias'), 'lr': base_lr * 8, 'weight_decay': 0},
        {'params': get_parameters_bn(net.refinement_stages, 'weight'), 'weight_decay': 0},
        {'params': get_parameters_bn(net.refinement_stages, 'bias'), 'lr': base_lr * 2, 'weight_decay': 0},
    ], lr=base_lr, weight_decay=5e-4)

    num_iter = 0
    current_epoch = 0
    drop_after_epoch = [100, 200, 260]
    scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=drop_after_epoch, gamma=0.333)
    if checkpoint_path:
        checkpoint = torch.load(checkpoint_path)

        if from_mobilenet:
            load_from_mobilenet(net, checkpoint)
        else:
            load_state(net, checkpoint)
            if not weights_only:
                optimizer.load_state_dict(checkpoint['optimizer'])
                scheduler.load_state_dict(checkpoint['scheduler'])
                num_iter = checkpoint['iter']
                current_epoch = checkpoint['current_epoch']

    net = DataParallel(net).cuda()
    net.train()
    for epochId in range(current_epoch, 280):
        scheduler.step()
        total_losses = [0, 0] * (num_refinement_stages + 1)  # heatmaps loss, paf loss per stage
        batch_per_iter_idx = 0
        for batch_data in train_loader:
            if batch_per_iter_idx == 0:
                optimizer.zero_grad()

            images = batch_data['image'].cuda()
            keypoint_masks = batch_data['keypoint_mask'].cuda()
            paf_masks = batch_data['paf_mask'].cuda()
            keypoint_maps = batch_data['keypoint_maps'].cuda()
            paf_maps = batch_data['paf_maps'].cuda()

            stages_output = net(images)

            losses = []
            for loss_idx in range(len(total_losses) // 2):
                losses.append(l2_loss(stages_output[loss_idx * 2], keypoint_maps, keypoint_masks, images.shape[0]))
                losses.append(l2_loss(stages_output[loss_idx * 2 + 1], paf_maps, paf_masks, images.shape[0]))
                total_losses[loss_idx * 2] += losses[-2].item() / batches_per_iter
                total_losses[loss_idx * 2 + 1] += losses[-1].item() / batches_per_iter

            loss = losses[0]
            for loss_idx in range(1, len(losses)):
                loss += losses[loss_idx]
            loss /= batches_per_iter
            loss.backward()
            batch_per_iter_idx += 1
            if batch_per_iter_idx == batches_per_iter:
                optimizer.step()
                batch_per_iter_idx = 0
                num_iter += 1
            else:
                continue

            if num_iter % log_after == 0:
                print('Iter: {}'.format(num_iter))
                for loss_idx in range(len(total_losses) // 2):
                    print('\n'.join(['stage{}_pafs_loss:     {}', 'stage{}_heatmaps_loss: {}']).format(
                        loss_idx + 1, total_losses[loss_idx * 2 + 1] / log_after,
                        loss_idx + 1, total_losses[loss_idx * 2] / log_after))
                for loss_idx in range(len(total_losses)):
                    total_losses[loss_idx] = 0
            if num_iter % checkpoint_after == 0:
                snapshot_name = '{}/checkpoint_iter_{}.pth'.format(checkpoints_folder, num_iter)
                torch.save({'state_dict': net.module.state_dict(),
                            'optimizer': optimizer.state_dict(),
                            'scheduler': scheduler.state_dict(),
                            'iter': num_iter,
                            'current_epoch': epochId},
                           snapshot_name)
            if num_iter % val_after == 0:
                print('Validation...')
                evaluate(val_labels, val_output_name, val_images_folder, net)
                net.train()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--prepared-train-labels', type=str, required=True,
                        help='path to the file with prepared annotations')
    parser.add_argument('--train-images-folder', type=str, required=True, help='path to COCO train images folder')
    parser.add_argument('--num-refinement-stages', type=int, default=1, help='number of refinement stages')
    parser.add_argument('--base-lr', type=float, default=4e-5, help='initial learning rate')
    parser.add_argument('--batch-size', type=int, default=80, help='batch size')
    parser.add_argument('--batches-per-iter', type=int, default=1, help='number of batches to accumulate gradient from')
    parser.add_argument('--num-workers', type=int, default=8, help='number of workers')
    parser.add_argument('--checkpoint-path', type=str, required=True, help='path to the checkpoint to continue training from')
    parser.add_argument('--from-mobilenet', action='store_true',
                        help='load weights from mobilenet feature extractor')
    parser.add_argument('--weights-only', action='store_true',
                        help='just initialize layers with pre-trained weights and start training from the beginning')
    parser.add_argument('--experiment-name', type=str, default='default',
                        help='experiment name to create folder for checkpoints')
    parser.add_argument('--log-after', type=int, default=100, help='number of iterations to print train loss')

    parser.add_argument('--val-labels', type=str, required=True, help='path to json with keypoints val labels')
    parser.add_argument('--val-images-folder', type=str, required=True, help='path to COCO val images folder')
    parser.add_argument('--val-output-name', type=str, default='detections.json',
                        help='name of output json file with detected keypoints')
    parser.add_argument('--checkpoint-after', type=int, default=5000,
                        help='number of iterations to save checkpoint')
    parser.add_argument('--val-after', type=int, default=5000,
                        help='number of iterations to run validation')
    args = parser.parse_args()

    checkpoints_folder = '{}_checkpoints'.format(args.experiment_name)
    if not os.path.exists(checkpoints_folder):
        os.makedirs(checkpoints_folder)

    train(args.prepared_train_labels, args.train_images_folder, args.num_refinement_stages, args.base_lr, args.batch_size,
          args.batches_per_iter, args.num_workers, args.checkpoint_path, args.weights_only, args.from_mobilenet,
          checkpoints_folder, args.log_after, args.val_labels, args.val_images_folder, args.val_output_name,
          args.checkpoint_after, args.val_after)