def convert_weight(model_path, output_dir, size=608):
save_path = os.path.join(output_dir, 'YOLO_v4_' + str(size) + '.ckpt')
class_num = 80
yolo = YOLO()
with tf.Session() as sess:
tf_input = tf.placeholder(tf.float32, [1, size, size, 3])
feature = yolo.forward(tf_input, class_num, isTrain=False)
saver = tf.train.Saver(var_list=tf.global_variables())
load_ops = load_weights(tf.global_variables(), model_path)
sess.run(load_ops)
saver.save(sess, save_path=save_path)
print('YOLO v4 weights have been transformed to {}'.format(save_path))
import os
import sys
import tensorflow as tf
import numpy as np
from model import yolov3
from utils.misc_utils import parse_anchors, load_weights
num_class = 80
img_size = 416
weight_path = './data/darknet_weights/yolov3.weights'
save_path = './data/darknet_weights/yolov3.ckpt'
anchors = parse_anchors('./data/yolo_anchors.txt')
model = yolov3(80, anchors)
with tf.Session() as sess:
inputs = tf.placeholder(tf.float32, [1, img_size, img_size, 3])
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs)
saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
load_ops = load_weights(tf.global_variables(scope='yolov3'), weight_path)
sess.run(load_ops)
saver.save(sess, save_path=save_path)
print('TensorFlow model checkpoint has been saved to {}'.format(save_path))
import os
import sys
import tensorflow as tf
import numpy as np
import config
from src.YOLO import YOLO
from utils.misc_utils import load_weights
weight_path = './yolo_weights/yolov4.weights'
save_path = './yolo_weights/yolov4.ckpt'
#anchors = [10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326] #for yolov4-416
anchors = [12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401] # for yolov4.weights
class_num = 80 # for yolov4.weights
yolo = YOLO(class_num, anchors,width=608, height=608)
with tf.Session() as sess:
inputs = tf.placeholder(tf.float32, [1, 608, 608, 3])
feature = yolo.forward(inputs, isTrain=False)
saver = tf.train.Saver(var_list=tf.global_variables())
load_ops = load_weights(tf.global_variables(), weight_path)
sess.run(load_ops)
saver.save(sess, save_path=save_path)
print('TensorFlow model checkpoint has been saved to {}'.format(save_path))
img_size = 512
weight_path = '../data/darknet_weights/yolov3.weights'
save_path = './data/darknet_weights/yolov3.ckpt'
anchors = parse_anchors('../data/yolo_anchors.txt')
class_num = 5
model = yolov3(class_num, anchors)
with tf.Session() as sess:
inputs_ir = tf.placeholder(tf.float32, [1, img_size, img_size, 3])
inputs_co = tf.placeholder(tf.float32, [1, img_size, img_size, 3])
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs_ir, inputs_co)
saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
varlist_dark_ir = tf.global_variables(scope='yolov3/darknet53_body_ir')
varlist_dark_co = tf.global_variables(scope='yolov3/darknet53_body_co')
varlist_head = tf.global_variables(scope='yolov3/yolov3_head')
var_list_dark_head = varlist_dark_ir + varlist_head
init = tf.global_variables_initializer()
sess.run(init)
load_ops = load_weights(varlist_dark_co, weight_path)
sess.run(load_ops)
load_ops2 = load_weights(varlist_dark_ir, weight_path)
sess.run(load_ops2)
saver.save(sess, save_path=save_path)
print('TensorFlow model checkpoint has been saved to {}'.format(save_path))
