コード例 #1
0
        train_ds,
        epochs=cfg.train.epochs,
        batch_size=cfg.train.batch_size,
        verbose=cfg.train.verbose,
        validation_data=None if not cfg.train.val else val_ds,
        validation_steps=val_steps[cfg.data.dataset] //
        strategy.num_replicas_in_sync,
        steps_per_epoch=spe)

    yolo.save_weights(weights_path='./models/{}/weights'.format(
        cfg.model.name),
                      weights_type=cfg.train.save_weights_type)

    pickle.dump(hist.history,
                open('./models/{}/history.pkl'.format(cfg.model.name), 'wb'))
    return yolo


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--cfg', default='default')
    args = parser.parse_args()

    cfg = CN(new_allowed=True)
    cfg.merge_from_file(osp.join('configs', args.cfg + '.yaml'))
    cfg.model.name = args.cfg

    tpu, strategy = detect_hardware(tpu_name=None)
    yolo = train(cfg, strategy)
    predict(yolo, cfg, dataset=cfg.data.dataset, split='val')
コード例 #2
0
import numpy as np
from lr_schedules import WarmupCosineDecay, WarmupPiecewise
import os.path as osp
from utils import detect_hardware
from dataset.dataloader import load_representative_tfds, visualize, prediction_tf_lite, prediction_examples
from dataset.coco import cn as cfg
import argparse
from lr_schedules import WarmupCosineDecay
import math

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--cfg', required=True)
    args = parser.parse_args()

    tpu, strategy = detect_hardware(None)
    if tpu:
        cfg.TRAIN.ACCELERATOR = args.tpu
    else:
        cfg.TRAIN.ACCELERATOR = 'GPU/CPU'
    cfg.merge_from_file('configs/' + args.cfg)
    cfg.MODEL.NAME = args.cfg.split('.yaml')[0]

    if cfg.DATASET.OUTPUT_SHAPE[-1] == 17:
        cfg.DATASET.KP_FLIP = cfg.DATASET.KP_FLIP[:17]

    cfg.MODEL.TFRECORDS = 'data/tfrecords_foot/val'
    train_ds = load_representative_tfds(cfg)

    def representative_dataset():
        for img in train_ds:
コード例 #3
0
        AP_large = cocoEval.stats[5]
        return mAP, AP_50, AP_75, AP_small, AP_medium, AP_large  # AP


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--tpu', default=None)
    parser.add_argument('-c', '--cfg', required=True)  # yaml
    parser.add_argument('--det', type=int, default=-1)
    parser.add_argument('--ckpt', default=None)
    parser.add_argument('--split', default='val')
    args = parser.parse_args()

    from dataset.coco import cn as cfg
    cfg.merge_from_file('configs/' + args.cfg)
    cfg.MODEL.NAME = args.cfg.split('.')[0]

    if cfg.DATASET.OUTPUT_SHAPE[-1] == 17:
        cfg.DATASET.KP_FLIP = cfg.DATASET.KP_FLIP[:17]

    if args.ckpt:
        cfg.MODEL.NAME += '_{}'.format(args.ckpt)
    if args.det >= 0:
        cfg.VAL.DET = bool(args.det)
    tpu, strategy = detect_hardware(args.tpu)

    if args.split == 'val':
        AP, _ = validate(strategy, cfg, split='val')
        print('AP: {:.5f}'.format(AP))
    else:
        validate(strategy, cfg, split='test')