config={"PERFORMANCE_HINT":"THROUGHPUT"} compiled_model=core.compile_model(model, "GPU", config) 无论是通过设置BATCH:GPU,还是选择”THROUGHPUT”的推理模式,推理的batch size值都会自动进行选取。选取的方式是查询当前设备的ov::optimal_batch_size属性并且通过模型拓扑结构的输入端获取batch size的值作为模型推...
config **=** {\"PERFORMANCE_HINT\"**:** \"THROUGHPUT\"} compiled_model **=** core.compile_model(model, \"GPU\", config) 1. 2. 无论是通过设置BATCH:GPU,还是选择"THROUGHPUT"的推理模式,推理的batch size值都会自动进行选取。选取的方式是查询当前设备的 ov::optimal_batch_size (https://doc...
config = {"PERFORMANCE_HINT": "THROUGHPUT"} compiled_model = core.compile_model(model, "GPU", config) 向右滑动查看完整代码 无论是通过设置 BATCH:GPU,还是选择”THROUGHPUT”的推理模式,推理的 batch size 值都会自动进行选取。选取的方式是查询当前设备的 ov::optimal_batch_size 属性并且通过模型拓扑结构...
model = ppp.build() compiled_model = ie.compile_model(model,device_name="CPU",config={'PERFORMANCE_HINT': 'LATENCY'}) return compiled_model,input_blob,out_blob # 模型推理 def identify(self,embed): input_data = torch.from_numpy(embed).float().unsqueeze(0) res = self.model.infer_new_...
自动批处理无需开发者手动指定。当compile_model()方法的config参数设置为{“PERFORMANCE_HINT”: ”THROUGHPUT”}时,OpenVINOTM Runtime会自动启动自动批处理执行,如图1-3所示,让开发人员以最少的编码工作即可享受计算设备利用率和吞吐量的提高。 图1-3 自动启动自动批处理执行 ...
compiled_model = ie.compile_model(model=model, device_name="CPU") input_layer = compiled_model.input(0) output_layer = compiled_model.output(0) print("model input info: {}".format(input_layer)) print("model output info: {}".format(output_layer)) ...
unet_model = core.compile_model(UNET_OV_PATH, device.value) ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {} vae_decoder = core.compile_model(VAE_DECODER_OV_PATH, device.value, ov_config) 左滑查看更多 ...
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) ...
--input_model <path_to_frozen.pb> --transformations_config <path_to_subgraph_replacement_configuration_file.json> --tensorflow_object_detection_api_pipeline_config <path_topipeline.config> --input_shape --reverse_input_channels(将rgb通道反序转换为bgr方便opencv后续操作) 版本信息要求 tensorflow:requir...
super().compile_model(model, device_name, {} if config is None else config), RuntimeError: Failed to allocate graph: MYRIAD device is not opened. openvino@4582fb921620:/opt/intel/openvino_2022.2.0.7713$ ls ` In which directory should we create myriad.conf file?