def main(argv=None):
if FLAGS.tiny:
model = yolo_v3_tiny.yolo_v3_tiny
else:
model = yolo_v3.yolo_v3
config = configparser.ConfigParser(strict=False)
config.read(FLAGS.model_config)
classes = load_coco_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [
None,
config.getint("net", "height"),
config.getint("net", "width"), 3
], "inputs")
with tf.variable_scope('detector'):
detections = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
boxes = detections_boxes(detections)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
def main(argv=None):
if FLAGS.tiny:
model = yolov3_tiny_3l.yolo_v3_tiny
elif FLAGS.dense:
model = dense_yolov3_v1.dense_yolo_v3
else:
model = yolo_v3.yolo_v3
classes = load_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3],
"inputs")
with tf.variable_scope('detector'):
detections = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
boxes = detections_boxes(detections)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
writer = tf.summary.FileWriter("logs/", sess.graph)
def main(argv=None):
if FLAGS.tiny:
model = yolo_v3_tiny.yolo_v3_tiny
print ('doing tiny')
else:
model = yolo_v3.yolo_v3
classes = load_coco_names(FLAGS.class_names)
print ('num classes',len(classes))
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3], "inputs")
with tf.variable_scope('detector'):
detections = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'), FLAGS.weights_file)
#detect_1.shape = (?, 507, 85)
#detect_2.shape = (?, 2028, 85)
#detect_3.shape = (?, 8112, 85)
#detections.shape = (?, 10647, 85)
#detections = Tensor("detector/yolo-v3/detections:0", shape=(?, 10647, 85), dtype=float32)
print("detections.shape =", detections.shape)
print(detections)
print(detections.name)
# Sets the output nodes in the current session
boxes = detections_boxes(detections)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph, FLAGS.tiny)
def main(argv=None):
if FLAGS.tiny:
model = yolo_v3_tiny.yolo_v3_tiny
# model = yolov3_tiny_tflite.yolo_v3_tiny
elif FLAGS.spp:
model = yolo_v3.yolo_v3_spp
else:
model = yolo_v3.yolo_v3
classes = load_coco_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [1, FLAGS.size, FLAGS.size, 3],
"inputs")
with tf.variable_scope('detector'):
# detect_1,detect_2 = model(inputs, len(classes), data_format=FLAGS.data_format)
detection = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
# detect_1,detect_2 = detections_boxes(detect_1,detect_2)
detection = detections_boxes(detection)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
def main(argv=None):
if FLAGS.tiny:
model = yolo_v3_tiny.yolo_v3_tiny
elif FLAGS.spp:
model = yolo_v3.yolo_v3_spp
else:
model = yolo_v3.yolo_v3
classes = load_coco_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3],
"inputs")
with tf.variable_scope('detector'):
detections = model(
inputs, len(classes), data_format=FLAGS.data_format
) # 得到yolov3整体模型（包含模型输出(?, 10647, (num_classes + 5))）
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
boxes = detections_boxes(
detections) # 1，将整体输出分解为box结果与概率数值结果；2、将结果名称定义为output_boxes再放入graph中
with tf.Session() as sess:
sess.run(load_ops)
reader = pywrap_tensorflow.NewCheckpointReader(checkpoint_path)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in var_to_shape_map:
print("tensor_name: ", key)
freeze_graph(sess, FLAGS.output_graph)
def main(argv=None):
model = yolo_v3.yolo_v3
classes = load_coco_names(FLAGS.class_names)
# placeholder for detector inputs
inputs = tf.placeholder(tf.float32, [None, FLAGS.size, FLAGS.size, 3],
"inputs")
with tf.variable_scope('detector'):
detections = model(inputs, len(classes), data_format=FLAGS.data_format)
load_ops = load_weights(tf.global_variables(scope='detector'),
FLAGS.weights_file)
# Sets the output nodes in the current session
boxes = detections_boxes(detections)
with tf.Session() as sess:
sess.run(load_ops)
freeze_graph(sess, FLAGS.output_graph)
tf.float32,
[1, image_h, image_w, 3]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3-tiny'):
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=0.5,
iou_thresh=0.5)
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-tiny'))
saver.restore(sess, ckpt_file)
print('=> checkpoint file restored from ', ckpt_file)
utils.freeze_graph(sess, './pb/yolov3_cpu_nms_tiny_mix_v2_low_q_5k.pb',
cpu_out_node_names)
utils.freeze_graph(sess, './pb/yolov3_gpu_nms_tiny_mix_v2_low_q_5k.pb',
gpu_out_node_names)
