from modelv3 import yolov3
from utils.data_aug import letterbox_resize
from utils.misc_utils import parse_anchors, read_class_names
from utils.nms_utils import gpu_nms
from utils.plot_utils import get_color_table, plot_one_box
config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(allow_growth=True))
anchor_path = "./data/yolo_anchors.txt"
class_name_path = "./data/coco.names"
anchors = parse_anchors(anchor_path)
classes = read_class_names(class_name_path)
class_num = len(classes)
color_table = get_color_table(class_num)
img_size = [416, 416]
yolo_model = yolov3(class_num, anchors)
yolo_model.set_img_size(np.asarray(img_size))
sess = tf.Session(config=config)
with gfile.FastGFile("./pb_model/frozen_model_v3.pb",'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sess.graph.as_default()
tf.import_graph_def(graph_def, name='')
sess.run(tf.global_variables_initializer())
input = sess.graph.get_tensor_by_name('image:0')
feature_map_1 = sess.graph.get_tensor_by_name('yolov3/yolov3_head/feature_map_1:0')
feature_map_2 = sess.graph.get_tensor_by_name('yolov3/yolov3_head/feature_map_2:0')
feature_map_3 = sess.graph.get_tensor_by_name('yolov3/yolov3_head/feature_map_3:0')
pred_boxes, pred_confs, pred_probs = yolo_model.predict([feature_map_1, feature_map_2, feature_map_3])
pred_scores = pred_confs * pred_probs
import tensorflow as tf
from modelv3 import yolov3
from utils.misc_utils import parse_anchors, load_weights
num_class = 80
img_size = 416
weight_path = './data/darknet_weights_v3/yolov3.weights'
save_path = './data/darknet_weights_v3/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 tensorflow as tf
from modelv3 import yolov3
# params
restore_path = './data/darknet_weights_v3/'
class_num = 80
save_dir = './ckpt_without_optimizer/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
image = tf.placeholder(tf.float32, [1, 416, 416, 3])
yolo_model = yolov3(class_num, None)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(image)
saver_to_restore = tf.train.Saver()
saver_to_save = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ckpt = tf.compat.v1.train.get_checkpoint_state(restore_path)
if ckpt and ckpt.model_checkpoint_path:
saver_to_restore.restore(sess, ckpt.model_checkpoint_path)
saver_to_save.save(
sess, save_dir + ckpt.model_checkpoint_path.split('/')[-1])
print("done")
else:
print("message:can not fint ckpt model")
image_ids, image, y_true_13, y_true_26, y_true_52 = iterator.get_next()
y_true = [y_true_13, y_true_26, y_true_52]
# tf.data pipeline will lose the data `static` shape, so we need to set it manually
image_ids.set_shape([None])
image.set_shape([None, None, None, 3])
for y in y_true:
y.set_shape([None, None, None, None, None])
##################
# Model definition
##################
yolo_model = yolov3(args.class_num,
args.anchors,
args.use_label_smooth,
args.use_focal_loss,
args.batch_norm_decay,
args.weight_decay,
use_static_shape=False)
with tf.variable_scope('yolov3'):
pred_feature_maps = yolo_model.forward(image, is_training=is_training)
loss = yolo_model.compute_loss(pred_feature_maps, y_true)
y_pred = yolo_model.predict(pred_feature_maps)
l2_loss = tf.losses.get_regularization_loss()
# setting restore parts and vars to update
saver_to_restore = tf.train.Saver(
var_list=tf.contrib.framework.get_variables_to_restore(
include=args.restore_include, exclude=args.restore_exclude))
update_vars = tf.contrib.framework.get_variables_to_restore(