convert_weight.py

#! /usr/bin/env python
# coding=utf-8

import argparse
import tensorflow as tf
from core.yolov3 import YOLOV3
from core.config import cfg
parser = argparse.ArgumentParser()
parser.add_argument("--train_from_coco", action='store_true')
flag = parser.parse_args()

org_weights_path = cfg.YOLO.ORIGINAL_WEIGHT
cur_weights_path = cfg.YOLO.DEMO_WEIGHT
preserve_cur_names = ['conv_sbbox', 'conv_mbbox', 'conv_lbbox']
preserve_org_names = ['Conv_6', 'Conv_14', 'Conv_22']


org_weights_mess = []
tf.Graph().as_default()
load = tf.train.import_meta_graph(org_weights_path + '.meta')
with tf.Session() as sess:
    load.restore(sess, org_weights_path)
    for var in tf.global_variables():
        var_name = var.op.name
        var_name_mess = str(var_name).split('/')
        var_shape = var.shape
        if flag.train_from_coco:
            if (var_name_mess[-1] not in ['weights', 'gamma', 'beta', 'moving_mean', 'moving_variance']) or \
                    (var_name_mess[1] == 'yolo-v3' and (var_name_mess[-2] in preserve_org_names)): continue
        org_weights_mess.append([var_name, var_shape])
        print("=> " + str(var_name).ljust(50), var_shape)
print()
tf.reset_default_graph()

cur_weights_mess = []
tf.Graph().as_default()
with tf.name_scope('input'):
    # 608 can be changed to 416
    input_data = tf.placeholder(dtype=tf.float32, shape=(1, 608, 608, 3), name='input_data')
    training = tf.placeholder(dtype=tf.bool, name='trainable')
model = YOLOV3(input_data, training)
for var in tf.global_variables():
    var_name = var.op.name
    var_name_mess = str(var_name).split('/')
    var_shape = var.shape
    print(var_name_mess[0])
    if flag.train_from_coco:
        if var_name_mess[0] in preserve_cur_names: continue
    cur_weights_mess.append([var_name, var_shape])
    print("=> " + str(var_name).ljust(50), var_shape)

org_weights_num = len(org_weights_mess)
cur_weights_num = len(cur_weights_mess)
if cur_weights_num != org_weights_num:
    raise RuntimeError

print('=> Number of weights that will rename:\t%d' % cur_weights_num)
cur_to_org_dict = {}
for index in range(org_weights_num):
    org_name, org_shape = org_weights_mess[index]
    cur_name, cur_shape = cur_weights_mess[index]
    if cur_shape != org_shape:
        print(org_weights_mess[index])
        print(cur_weights_mess[index])
        raise RuntimeError
    cur_to_org_dict[cur_name] = org_name
    print("=> " + str(cur_name).ljust(50) + ' : ' + org_name)

with tf.name_scope('load_save'):
    name_to_var_dict = {var.op.name: var for var in tf.global_variables()}
    restore_dict = {cur_to_org_dict[cur_name]: name_to_var_dict[cur_name] for cur_name in cur_to_org_dict}
    load = tf.train.Saver(restore_dict)
    save = tf.train.Saver(tf.global_variables())
    for var in tf.global_variables():
        print("=> " + var.op.name)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print('=> Restoring weights from:\t %s' % org_weights_path)
    load.restore(sess, org_weights_path)
    save.save(sess, cur_weights_path)
    
    
tf.reset_default_graph()