def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5'): '''create the training model, for Tiny YOLOv3''' 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}[l], w//{0:32, 1:16}[l], \ num_anchors//2, num_classes+5)) for l in range(2)] model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes) print('Create Tiny 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 the darknet body or freeze all but 2 output layers. num = (20, len(model_body.layers)-2)[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))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model
def generate(self): '''to generate the bounding boxes''' model_path = os.path.expanduser(self.model_path) assert model_path.endswith( '.h5'), 'Keras model or weights must be a .h5 file.' # 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.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' print('{} model, anchors, and classes loaded.'.format(model_path)) h5_to_pb(self.yolo_model, 'pb', 'MobileV2_0.5_224_shortLayer.pb') # 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 gpu_num >= 2: self.yolo_model = multi_gpu_model(self.yolo_model, gpus=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) # default arg # self.yolo_model->'model_data/yolo.h5' # self.anchors->'model_data/yolo_anchors.txt'-> 9 scales for anchors return boxes, scores, classes
import keras 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],