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 = utils.get_anchors(flags.anchors_path, flags.image_h,
flags.image_w)
model = yolov3.yolov3(flags.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, flags.image_h, flags.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,
flags.num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.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 flags.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, './checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess, './checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
def convert_weights(self):
print(f"=> the input image size is [{self.img_h}, {self.img_w}]")
anchors = utils.get_anchors(self.anchors_path, self.img_h, self.img_w)
model = yolov3.yolov3(self.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, self.img_h, self.img_w, 1
]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs,
n_filters_dn=self.n_filters_dn,
n_strides_dn=self.n_strides_dn,
n_ksizes_dn=self.n_ksizes_dn,
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,
self.num_classes)
print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
gpu_out_node_names = [
boxes.name[:-2], scores.name[:-2], labels.name[:-2]
]
saver = tf.train.Saver(var_list=tf.global_variables(
scope='yolov3'))
if self.convert:
load_ops = utils.load_weights(
tf.global_variables(scope='yolov3'), self.weights_dir)
sess.run(load_ops)
save_path = saver.save(sess, save_path=self.checkpoint_dir)
print(f'=> model saved in path: {save_path}')
if self.freeze:
ckpt_idx = self.checkpoint_dir + '-' + str(
self.checkpoint_step)
try:
saver.restore(sess, ckpt_idx)
except:
print(
f"Error: you tried to restore a checkpoint ({self.checkpoint_dir}) that doesn't exist."
)
print(
"Please clear the network and retrain, or load a different checkpoint by changing the steps parameter."
)
print('=> checkpoint file restored from ', ckpt_idx)
utils.freeze_graph(sess,
'../../data/checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess,
'../../data/checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
def main(argv=None):
input_data = tf.placeholder(dtype=tf.float32,
shape=(None, 608, 608, 3),
name='inputs')
model = YOLOV3(input_data, trainable=False, tiny=FLAGS.tiny)
load_ops = load_weights(tf.global_variables(), FLAGS.weights_file)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
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 = utils.get_anchors(flags.anchors_path, flags.image_h,
flags.image_w)
# print(anchors)
# exit()
model = YOLOv3.yolov3(flags.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, flags.image_h, flags.image_w, 3
]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(
inputs, is_training=False) # 返回3个尺度的feature_map
# 获取网络给出绝对boxes(左上角,右下角)信息, 未经过最大抑制去除多余boxes
boxes, confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes.name[:-2], scores.name[:-2])
# cpu 运行是恢复模型所需要的网络节点的名字
cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
print('cpu_out_node_names: ', cpu_out_node_names)
boxes, scores, labels = utils.gpu_nms(
boxes,
scores,
flags.num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.iou_threshold)
print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
# gpu 运行是恢复模型所需要的网络节点的名字 , 直接运算得出最终结果
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.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, '../checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess, '../checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
def main(argv):
flags = parser(
description="freeze yolov3 graph from checkpoint file").parse_args()
classes = utils.read_coco_names("./data/coco.names")
num_classes = len(classes)
SIZE = flags.image_size
print("=> the input image size is [%d, %d]" % (SIZE, SIZE))
model = yolov3.yolov3(num_classes)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(
tf.float32, [1, SIZE, SIZE, 3]) # placeholder for detector 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, scores)
boxes, scores, labels = utils.gpu_nms(
boxes,
scores,
num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.iou_threshold)
print("=>", boxes, scores, labels)
feature_map_1, feature_map_2, feature_map_3 = feature_map
print("=>", feature_map_1, feature_map_2, feature_map_3)
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 flags.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, './checkpoint/yolov3_cpu_nms.pb',
["concat_9", "mul_6"])
utils.freeze_graph(sess, './checkpoint/yolov3_gpu_nms.pb',
["concat_10", "concat_11", "concat_12"])
utils.freeze_graph(sess, './checkpoint/yolov3_feature.pb', [
"yolov3/yolo-v3/feature_map_1",
"yolov3/yolo-v3/feature_map_2",
"yolov3/yolo-v3/feature_map_3",
])
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Loading pre_trained weights
print("Loading weights ...")
sess.run(
utils.load_weights(tf.global_variables(scope='yolov3'), cfg.weights_file))
saver = tf.train.Saver()
if FLAGS.convert:
if not os.path.exists(cfg.weights_file):
url = 'https://pjreddie.com/media/files/yolov3.weights'
if not os.path.exists("./checkpoint/yolov3.weights"):
print("{} does not exists ! ".format(cfg.weights_file))
print("=> It will take a while to download it from {}".format(url))
print('=> Downloading yolov3 weights ... ')
wget.download(url, cfg.weights_file)
sess.run(
utils.load_weights(tf.global_variables(scope='yolov3'),
cfg.weights_file))
saver.save(sess, save_path=FLAGS.ckpt_file)
print('ckpt model havs been saved in path: {}'.format(FLAGS.ckpt_file))
if FLAGS.freeze:
saver.restore(sess, FLAGS.ckpt_file)
print('=> checkpoint file restored from ', FLAGS.ckpt_file)
utils.freeze_graph(sess, './checkpoint/yolov3_prediction.pb',
["concat_10", "concat_11", "concat_12"])
