import argparse

import yaml

from models.yolo import Model
from utils.utils import *

wdir = 'weights' + os.sep  # weights dir
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'

# Hyperparameters
hyp = {'lr0': 0.001,  # initial learning rate (SGD=1E-2, Adam=1E-3)
       'momentum': 0.937,  # SGD momentum
       'weight_decay': 5e-4,  # optimizer weight decay
       'giou': 0.05,  # giou loss gain
       'cls': 0.58,  # cls loss gain
       'cls_pw': 1.0,  # cls BCELoss positive_weight
       'obj': 1.0,  # obj loss gain (*=img_size/320 if img_size != 320)
       'obj_pw': 1.0,  # obj BCELoss positive_weight
       'iou_t': 0.20,  # iou training threshold
       'anchor_t': 4.0,  # anchor-multiple threshold
       'fl_gamma': 0.0,  # focal loss gamma (efficientDet default is gamma=1.5)
       'hsv_h': 0.014,  # image HSV-Hue augmentation (fraction)
       'hsv_s': 0.68,  # image HSV-Saturation augmentation (fraction)
       'hsv_v': 0.36,  # image HSV-Value augmentation (fraction)
       'degrees': 0.0,  # image rotation (+/- deg)
       'translate': 0.0,  # image translation (+/- fraction)
       'scale': 0.5,  # image scale (+/- gain)
       'shear': 0.0}  # image shear (+/- deg)
print(hyp)

# Overwrite hyp with hyp*.txt (optional)
f = glob.glob('hyp*.txt')
if f:
    print('Using %s' % f[0])
    for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
        hyp[k] = v

# Print focal loss if gamma > 0
if hyp['fl_gamma']:
    print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])


def train(hyp):
    epochs = opt.epochs  # 300
    batch_size = opt.batch_size  # 64
    weights = opt.weights  # initial training weights

    # Configure
    # 这里还不能使用，因为torch.manual_seed方法没有找到替代
    init_seeds(1)
    with open(opt.data) as f:
        data_dict = yaml.load(f, Loader=yaml.FullLoader)
    train_path = data_dict['train']
    test_path = data_dict['val']
    nc = 1 if opt.single_cls else int(data_dict['nc'])

    # Remove previous results
    for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
        os.remove(f)

    # Create model
    model = Model(opt.cfg).to(device)




if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--epochs', type=int, default=100)
    parser.add_argument('--batch-size', type=int, default=20)
    parser.add_argument('--cfg', type=str, default='./models/score/yolov5x.yaml', help='*.cfg path')
    parser.add_argument('--data', type=str, default='./data/score.yaml', help='*.data path')
    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
    parser.add_argument('--rect', action='store_true', help='rectangular training')
    parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
    parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
    parser.add_argument('--notest', action='store_true', help='only test final epoch')
    parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
    parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
    parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
    parser.add_argument('--weights', type=str, default='./weights/yolov5x.pt', help='initial weights path')
    parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--adam', action='store_true', help='use adam optimizer')
    parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%')
    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
    opt = parser.parse_args()
    opt.weights = last if opt.resume else opt.weights
    opt.cfg = glob.glob('./**/' + opt.cfg, recursive=True)[0]  # find file
    opt.data = glob.glob('./**/' + opt.data, recursive=True)[0]  # find file
    print(opt)