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],