def create_model(input_shape, anchors, num_classes, load_pretrained=False, freeze_body=2,
# weights_path='f:/1_code/python/keras-YOLOv3-mobilenet/voc07/model_data/yolo_weights.h5'):
weights_path='f:/1_code/python/keras-YOLOv3-mobilenet/coco2017/model_data/yolo_weights.h5'):
'''create the training model'''
K.clear_session() # get a new session
image_input = Input(shape=(None, None, 3))
h, w = input_shape
num_anchors = len(anchors)
y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \
num_anchors//3, num_classes+5)) for l in range(3)]
model_body = yolo_body(image_input, num_anchors//3, num_classes)
print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))
if load_pretrained:
model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
print('Load weights {}.'.format(weights_path))
if freeze_body in [1, 2]:
# Freeze darknet53 body or freeze all but 3 output layers.
num = (185, len(model_body.layers)-3)[freeze_body-1]
for i in range(num): model_body.layers[i].trainable = False
print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))
print(model_body.output)
model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',
arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(
[*model_body.output, *y_true])
model = Model([model_body.input, *y_true], model_loss)
return model
def generate(self):
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
print(model_path)
# Load model, or construct model and load weights.
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
is_tiny_version = num_anchors == 6 # default setting
try:
self.yolo_model = load_model(model_path, compile=False)
except:
# self.yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
# if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
self.yolo_model = yolo_body(Input(shape=(416, 416, 3)),
num_classes)
self.yolo_model.load_weights(
self.model_path) # make sure model, anchors and classes match
else:
assert self.yolo_model.layers[-1].output_shape[-1] == \
num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
#self.yolo_model = yolo_body(Input(shape=(416, 416, 3)))
#self.yolo_model.load_weights(self.model_path) # make sure model, anchors and classes match
print('{} model, 3 anchors, and {} classes loaded.'.format(
model_path, num_classes))
from keras.models import Model
#self.yolo_model.save("yolo_final.h5")
# Generate colors for drawing bounding boxes.
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101) # Fixed seed for consistent colors across runs.
np.random.shuffle(
self.colors) # Shuffle colors to decorrelate adjacent classes.
np.random.seed(None) # Reset seed to default.
# Generate output tensor targets for filtered bounding boxes.
self.input_image_shape = K.placeholder(shape=(2, ))
if self.gpu_num >= 2:
self.yolo_model = multi_gpu_model(self.yolo_model,
gpus=self.gpu_num)
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
len(self.class_names),
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
def generate(self):
self.score = 0.05
model_path = os.path.expanduser(self.model_path)
assert model_path.endswith(
'.h5'), 'Keras model or weights must be a .h5 file.'
# 计算anchor数量
num_anchors = len(self.anchors)
num_classes = len(self.class_names)
# 载入模型，如果原来的模型里已经包括了模型结构则直接载入。
# 否则先构建模型再载入
try:
self.yolo_model = load_model(model_path, compile=False)
except:
self.yolo_model = yolo_body(Input(shape=(None, None, 3)),
num_anchors // 3, num_classes)
self.yolo_model.load_weights(self.model_path)
else:
assert self.yolo_model.layers[-1].output_shape[-1] == \
num_anchors/len(self.yolo_model.output) * (num_classes + 5), \
'Mismatch between model and given anchor and class sizes'
print('{} model, anchors, and classes loaded.'.format(model_path))
# 画框设置不同的颜色
hsv_tuples = [(x / len(self.class_names), 1., 1.)
for x in range(len(self.class_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
# 打乱颜色
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
self.input_image_shape = K.placeholder(shape=(2, ))
boxes, scores, classes = yolo_eval(self.yolo_model.output,
self.anchors,
num_classes,
self.input_image_shape,
score_threshold=self.score,
iou_threshold=self.iou)
return boxes, scores, classes
from keras.utils.generic_utils import CustomObjectScope
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0" #(or "1" or "2")
# model_path = '/opt/zhongls/object_detect/keras-YOLOv3-mobilenet-master/logs/carMobilenet/001_Mobilenet_finetune/ep120-loss7.479-val_loss6.658.h5'
model_path = '/opt/zhongls/object_detect/keras-YOLOv3-mobilenet-master/logs/carMobilenet/001_Mobilenet_finetune_03/ep456-loss4.194-val_loss3.792.h5'
num_anchors = 9 #len(anchor)
num_classes = 2 #类别数，替换成自己的类别数
is_tiny_version = num_anchors == 6 # default setting
try:
yolo_model = load_model(model_path, compile=False)
except:
yolo_model = tiny_yolo_body(Input(shape=(None,None,3)), num_anchors//2, num_classes) \
if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
yolo_model.load_weights(
model_path) # make sure model, anchors and classes match
model = yolo_model
export_path = "model/card/2"
if os.path.isdir(export_path):
shutil.rmtree(export_path)
builder = saved_model.builder.SavedModelBuilder(export_path)
signature = predict_signature_def(inputs={'images': model.input},
outputs={
'output0': model.output[0],
'output1': model.output[1],
