compiled_model = core.compile_model(model=model, device_name="AUTO") #您可以限定设备使用AUTO Plugin: compiled_model = core.compile_model(model=model, device_name="AUTO:GPU,CPU") #您也可以剔除使用AUTO Plugin的设备: compiled_model = core.compile_model(model=model, device_name="AUTO:-CPU")...
publicRTDETRPredictor(stringmodel_path,stringlabel_path,stringdevice_name ="CPU",boolpostprcoess =true){ INFO("Model path: "+ model_path); INFO("Device name: "+ device_name); core =newCore(); model = core.read_model(model_path); pritf_model_info(model); compiled_model = core.compi...
model = ie_core.read_model(model=model_path) compiled_model = ie_core.compile_model(model=model, device_name=device_name) 「(2) 预处理图片数据」这一步主要对读取的本地图片数据进行处理,在此处我们定义了一个RtdetrProcess Class专门用于处理RT-DETR模型的输入输出数据,代码实现如下所示: image = cv...
在 OpenVINO™ 中使用 AUTO Plugin 的方法也十分简单,只需要在 compile_model() 函数中,指定 device_name=“AUTO:CPU,GPU” 就可以实现了,例如下方所示是 Python 语言的使用方法: 优化策略五: 权衡是否使用 NNCF 量化的低精度模型 由于FP32 全精度模型需要运算量相对比较大,很多开发者会将模型量化成低精度模型...
compiled_model=core.compile_model(model=model, device_name="AUTO:-CPU") AUTO Plugin 内置有三个模式可供选择: 1.THROUGHPUT 默认模式。该模式优先考虑高吞吐量,在延迟和功率之间进行平衡,最适合于涉及多个任务的推理,例如推理视频源或大量图像。注:此模式只会对CPU与GPU进行调用。若该模式下调用GPU进行推理,...
compiled_model = ie.compile_model(model=model, device_name="CPU") output_layer = compiled_model.output(0) means = np.zeros((224,224,3), dtype=np.float32) means[: ,:] = (0.485,0.456,0.406) dev = np.zeros((224,224,3), dtype=np.float32) ...
compiled_model = core.compile_model(model, device_name=device.value) input_key = compiled_model.input(0) output_key = compiled_model.output(0) network_input_shape = input_key.shape counter = 0 for idx, i in enumerate(X): i = np.expand_dims(i, axis=0) ...
compile_model(model=model, device_name="CPU") input_layer = compiled_model.input(0) output_layer = compiled_model.output(0) image_filename = "assets/demoimage.jpg" image = cv2.imread(image_filename) print(f"image shape {image.shape}") N, C, H, W = input_layer.shape resized_image...
1. from openvino.runtime import Core, Model 2. 3. core = Core() 4. ov_model = core.read_model(model_path) 5. device = "CPU" # GPU 6. if device != "CPU": 7. ov_model.reshape({0: [1, 3, 640, 640]}) 8. compiled_model = core.compile_model(ov_model, device) ...