convert_weight.py

#! /usr/bin/env python
# coding=utf-8
#================================================================
#   Copyright (C) 2018 * Ltd. All rights reserved.
#
#   Editor      : VIM
#   File name   : convert_weight.py
#   Author      : YunYang1994
#   Created date: 2018-11-27 12:37:22
#   Description :
#
#================================================================

import os
import sys
import wget
import time
import argparse
import tensorflow as tf
from core import yolov3, utils


class parser(argparse.ArgumentParser):

    def __init__(self,description):
        super(parser, self).__init__(description)

        self.add_argument(
            "--ckpt_file", "-cf", default='./checkpoint/yolov3.ckpt', type=str,
            help="[default: %(default)s] The checkpoint file ...",
            metavar="<CF>",
        )

        self.add_argument(
            "--num_classes", "-nc", default=80, type=int,
            help="[default: %(default)s] The number of classes ...",
            metavar="<NC>",
        )

        self.add_argument(
            "--anchors_path", "-ap", default="./data/raccoon_anchors_wzp.txt", type=str,
            help="[default: %(default)s] The path of anchors ...",
            metavar="<AP>",
        )

        self.add_argument(
            "--weights_path", "-wp", default='./checkpoint/yolov3.weights', type=str,
            help="[default: %(default)s] Download binary file with desired weights",
            metavar="<WP>",
        )

        self.add_argument(
            "--convert", "-cv", action='store_true',
            help="[default: %(default)s] Downloading yolov3 weights and convert them",
        )

        self.add_argument(
            "--freeze", "-fz", action='store_true',
            help="[default: %(default)s] freeze the yolov3 graph to pb ...",
        )

        self.add_argument(
            "--image_h", "-ih", default=416, type=int,
            help="[default: %(default)s] The height of image, 416 or 608",
            metavar="<IH>",
        )

        self.add_argument(
            "--image_w", "-iw", default=416, type=int,
            help="[default: %(default)s] The width of image, 416 or 608",
            metavar="<IW>",
        )

        self.add_argument(
            "--iou_threshold", "-it", default=0.5, type=float,
            help="[default: %(default)s] The iou_threshold for gpu nms",
            metavar="<IT>",
        )

        self.add_argument(
            "--score_threshold", "-st", default=0.5, type=float,
            help="[default: %(default)s] The score_threshold for gpu nms",
            metavar="<ST>",
        )


def main(argv):

    flags = parser(description="freeze yolov3 graph from checkpoint file").parse_args()
    # print("=> the input image size is [%d, %d]" %(flags.image_h, flags.image_w))
    anchors_path ="./data/raccoon_anchors_wzp.txt"
    image_h = 416;image_w = 416
    num_classes = 1
    score_threshold = 0.5
    iou_threshold=0.5
    ckpt_file = './model/yolov3.ckpt-1000'

    anchors = utils.get_anchors(anchors_path,image_h, image_w)
    model = yolov3.yolov3(num_classes, anchors)

    with tf.Graph().as_default() as graph:
        sess = tf.Session(graph=graph)
        inputs = tf.placeholder(tf.float32, [1, image_h, image_w, 3]) # placeholder for detector inputs
        print("=>", inputs)

        with tf.variable_scope('yolov3'):
            feature_map = model.forward(inputs, is_training=False)

        boxes, confs, probs = model.predict(feature_map)
        scores = confs * probs
        print("=>", boxes.name[:-2], scores.name[:-2])
        cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
        boxes, scores, labels = utils.gpu_nms(boxes, scores, num_classes,
                                              score_thresh=score_threshold,
                                              iou_thresh=iou_threshold)
        print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
        gpu_out_node_names = [boxes.name[:-2], scores.name[:-2], labels.name[:-2]]
        feature_map_1, feature_map_2, feature_map_3 = feature_map
        saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))

        if flags.convert:
            if not os.path.exists(flags.weights_path):
                url = 'https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3.weights'
                for i in range(3):
                    time.sleep(1)
                    print("=> %s does not exists ! " %flags.weights_path)
                print("=> It will take a while to download it from %s" %url)
                print('=> Downloading yolov3 weights ... ')
                wget.download(url, flags.weights_path)

            load_ops = utils.load_weights(tf.global_variables(scope='yolov3'), flags.weights_path)
            sess.run(load_ops)
            save_path = saver.save(sess, save_path=flags.ckpt_file)
            print('=> model saved in path: {}'.format(save_path))

        if True:
            saver.restore(sess, ckpt_file)
            print('=> checkpoint file restored from ', ckpt_file)
            utils.freeze_graph(sess, './model/yolov3_cpu_nms.pb', cpu_out_node_names)
            utils.freeze_graph(sess, './model/yolov3_gpu_nms.pb', gpu_out_node_names)


if __name__ == "__main__":
    main(sys.argv)
    # python convert_weight.py -cf ./checkpoint/yolov3.ckpt -nc 1 -ap ./data/raccoon_anchors_wzp.txt --freeze