def init_predictor(args): if args.model_dir is not "": config = Config(args.model_dir) else: config = Config(args.model_file, args.params_file) config.enable_memory_optim() if args.tune: config.collect_shape_range_info(shape_file) if args.use_gpu: config.enable_use_gpu(1000, 0) if args.use_trt: # using dynamic shpae mode, the max_batch_size will be ignored. config.enable_tensorrt_engine(workspace_size=1 << 30, max_batch_size=1, min_subgraph_size=5, precision_mode=PrecisionType.Float32, use_static=False, use_calib_mode=False) if args.tuned_dynamic_shape: config.enable_tuned_tensorrt_dynamic_shape(shape_file, True) else: # If not specific mkldnn, you can set the blas thread. # The thread num should not be greater than the number of cores in the CPU. config.set_cpu_math_library_num_threads(4) config.enable_mkldnn() predictor = create_predictor(config) return predictor
def load_config(self, modelpath, use_gpu, gpu_id, use_mkldnn, cpu_threads): ''' load the model config modelpath: inference model path use_gpu: use gpu or not use_mkldnn: use mkldnn or not ''' # 对运行位置进行配置 if use_gpu: try: int(os.environ.get('CUDA_VISIBLE_DEVICES')) except Exception: print( '''Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU. Now switch to CPU to continue...''') use_gpu = False if os.path.isdir(modelpath): if os.path.exists(os.path.join(modelpath, "__params__")): # __model__ + __params__ model = os.path.join(modelpath, "__model__") params = os.path.join(modelpath, "__params__") config = Config(model, params) elif os.path.exists(os.path.join(modelpath, "params")): # model + params model = os.path.join(modelpath, "model") params = os.path.join(modelpath, "params") config = Config(model, params) elif os.path.exists(os.path.join(modelpath, "__model__")): # __model__ + others config = Config(modelpath) else: raise Exception( "Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath)) elif os.path.exists(modelpath+".pdmodel"): # *.pdmodel + *.pdiparams model = modelpath+".pdmodel" params = modelpath+".pdiparams" config = Config(model, params) else: raise Exception( "Error! Can\'t find the model in: %s. Please check your model path." % os.path.abspath(modelpath)) # 设置参数 if use_gpu: config.enable_use_gpu(100, gpu_id) else: config.disable_gpu() config.set_cpu_math_library_num_threads(cpu_threads) if use_mkldnn: config.enable_mkldnn() config.disable_glog_info() # 返回配置 return config
def create_predictors(args): config_ul_0 = Config(args.model_file_0, args.params_file_0) config_ul_1 = Config(args.model_file_1, args.params_file_1) config_ul_2 = Config(args.model_file_2, args.params_file_2) config_ul_3 = Config(args.model_file_3, args.params_file_3) config_wash = Config(args.wash_model_file, args.wash_params_file) ultra_model_0 = create_paddle_predictor(args, config_ul_0) ultra_model_1 = create_paddle_predictor(args, config_ul_1) ultra_model_2 = create_paddle_predictor(args, config_ul_2) ultra_model_3 = create_paddle_predictor(args, config_ul_3) wash_model = create_paddle_predictor(args, config_wash) return ultra_model_0, ultra_model_1, ultra_model_2, ultra_model_3, wash_model
def init_predictor(args): if args.model_dir: config = Config(args.model_dir) else: config = Config(args.model_file, args.params_file) if args.use_gpu: config.enable_use_gpu(1000, 0) else: config.disable_gpu() print(config) # config.delete('repeated_fc_relu_fuse_pass') predictor = create_predictor(config) return predictor
def __init__(self, cfg, name='PPTSM'): name = name.upper() self.name = name model_file = cfg[name]['model_file'] params_file = cfg[name]['params_file'] gpu_mem = cfg[name]['gpu_mem'] device_id = cfg[name]['device_id'] # model init config = Config(model_file, params_file) config.enable_use_gpu(gpu_mem, device_id) config.switch_ir_optim(True) # default true config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) self.predictor = create_predictor(config) input_names = self.predictor.get_input_names() self.input_tensor = self.predictor.get_input_handle(input_names[0]) output_names = self.predictor.get_output_names() print("output_names = ", output_names) #self.output_tensor = self.predictor.get_output_handle(output_names[1]) self.output_tensor = self.predictor.get_output_handle(output_names[0])
def init_predictor(args): if args.model_dir is not "": config = Config(args.model_dir) else: config = Config(args.model_file, args.params_file) config.enable_memory_optim() if args.use_gpu: config.enable_use_gpu(1000, 0) else: # If not specific mkldnn, you can set the blas thread. # The thread num should not be greater than the number of cores in the CPU. config.set_cpu_math_library_num_threads(4) predictor = create_predictor(config) return predictor
def load_model(self, model_dir, use_gpu=False, enable_mkldnn=False, cpu_threads=1): model = os.path.join(model_dir, '__model__') params = os.path.join(model_dir, '__params__') config = Config(model, params) # 设置参数 if use_gpu: config.enable_use_gpu(100, 0) else: config.disable_gpu() config.set_cpu_math_library_num_threads(cpu_threads) if enable_mkldnn: config.enable_mkldnn() config.set_mkldnn_cache_capacity(10) config.disable_glog_info() config.switch_ir_optim(True) config.enable_memory_optim() config.switch_use_feed_fetch_ops(False) config.switch_specify_input_names(True) # 通过参数加载模型预测器 predictor = create_predictor(config) # 获取模型的输入输出 input_names = predictor.get_input_names() output_names = predictor.get_output_names() input_handle = predictor.get_input_handle(input_names[0]) output_handle = predictor.get_output_handle(output_names[0]) return predictor, input_handle, output_handle
def test_static_save_and_run_inference_predictor(self): paddle.enable_static() np_data = np.random.random((1, 1, 28, 28)).astype("float32") np_label = np.random.random((1, 1)).astype("int64") path_prefix = "custom_op_inference/custom_relu" from paddle.inference import Config from paddle.inference import create_predictor for device in self.devices: predict = custom_relu_static_inference( self.custom_ops[0], device, np_data, np_label, path_prefix) # load inference model config = Config(path_prefix + ".pdmodel", path_prefix + ".pdiparams") predictor = create_predictor(config) input_tensor = predictor.get_input_handle(predictor.get_input_names( )[0]) input_tensor.reshape(np_data.shape) input_tensor.copy_from_cpu(np_data.copy()) predictor.run() output_tensor = predictor.get_output_handle( predictor.get_output_names()[0]) predict_infer = output_tensor.copy_to_cpu() self.assertTrue( np.isclose( predict, predict_infer, rtol=5e-5).any(), "custom op predict: {},\n custom op infer predict: {}".format( predict, predict_infer)) paddle.disable_static()
def create_paddle_predictor(self, args, inference_model_dir=None): if inference_model_dir is None: inference_model_dir = args.inference_model_dir params_file = os.path.join(inference_model_dir, "inference.pdiparams") model_file = os.path.join(inference_model_dir, "inference.pdmodel") config = Config(model_file, params_file) if args.use_gpu: config.enable_use_gpu(args.gpu_mem, 0) else: config.disable_gpu() if args.enable_mkldnn: # cache 10 different shapes for mkldnn to avoid memory leak config.set_mkldnn_cache_capacity(10) config.enable_mkldnn() config.set_cpu_math_library_num_threads(args.cpu_num_threads) if args.enable_profile: config.enable_profile() config.disable_glog_info() config.switch_ir_optim(args.ir_optim) # default true if args.use_tensorrt: config.enable_tensorrt_engine( precision_mode=Config.Precision.Half if args.use_fp16 else Config.Precision.Float32, max_batch_size=args.batch_size, min_subgraph_size=30) config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) return predictor, config
def __init__(self, cfg, name='BMN'): name = name.upper() self.name = name model_file = cfg[name]['model_file'] params_file = cfg[name]['params_file'] gpu_mem = cfg[name]['gpu_mem'] device_id = cfg[name]['device_id'] self.nms_thread = cfg[name]['nms_thread'] self.min_pred_score = cfg[name]['score_thread'] self.min_frame_thread = cfg['COMMON']['fps'] # model init config = Config(model_file, params_file) config.enable_use_gpu(gpu_mem, device_id) config.switch_ir_optim(True) # default true config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) self.predictor = create_predictor(config) input_names = self.predictor.get_input_names() self.input_tensor = self.predictor.get_input_handle(input_names[0]) output_names = self.predictor.get_output_names() self.output1_tensor = self.predictor.get_output_handle(output_names[0]) self.output2_tensor = self.predictor.get_output_handle(output_names[1]) self.output3_tensor = self.predictor.get_output_handle(output_names[2])
def create_paddle_predictor(args): config = Config(args.model_file, args.params_file) if args.use_gpu: config.enable_use_gpu(args.gpu_mem, 0) else: config.disable_gpu() if args.enable_mkldnn: # cache 10 different shapes for mkldnn to avoid memory leak config.set_mkldnn_cache_capacity(10) config.enable_mkldnn() #config.disable_glog_info() config.switch_ir_optim(args.ir_optim) # default true if args.use_tensorrt: config.enable_tensorrt_engine( precision_mode=Config.Precision.Half if args.use_fp16 else Config.Precision.Float32, max_batch_size=args.batch_size) config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) return predictor
def __init__(self, cfg, name='ACTION'): name = name.upper() self.name = name model_file = cfg[name]['model_file'] params_file = cfg[name]['params_file'] gpu_mem = cfg[name]['gpu_mem'] device_id = cfg[name]['device_id'] self.topk = cfg[name]['topk'] self.frame_offset = cfg[name]['nms_offset'] self.nms_thread = cfg[name]['nms_thread'] self.cls_thread = cfg[name]['classify_score_thread'] self.iou_thread = cfg[name]['iou_score_thread'] self.label_map_file = cfg['COMMON']['label_dic'] self.fps = cfg['COMMON']['fps'] self.nms_id = 5 # model init config = Config(model_file, params_file) config.enable_use_gpu(gpu_mem, device_id) config.switch_ir_optim(True) # default true config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) self.predictor = create_predictor(config) input_names = self.predictor.get_input_names() self.input1_tensor = self.predictor.get_input_handle(input_names[0]) #self.input2_tensor = self.predictor.get_input_handle(input_names[1]) output_names = self.predictor.get_output_names() self.output1_tensor = self.predictor.get_output_handle(output_names[0]) self.output2_tensor = self.predictor.get_output_handle(output_names[1])
def load_predictor(self, model_file, params_file): config = Config(model_file, params_file) if self.predictor_config["use_gpu"]: config.enable_use_gpu(200, 0) config.switch_ir_optim(True) else: config.disable_gpu() config.set_cpu_math_library_num_threads( self.predictor_config["cpu_threads"]) if self.predictor_config["enable_mkldnn"]: try: # cache 10 different shapes for mkldnn to avoid memory leak config.set_mkldnn_cache_capacity(10) config.enable_mkldnn() except Exception as e: logging.error( "The current environment does not support `mkldnn`, so disable mkldnn." ) config.disable_glog_info() config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) input_names = predictor.get_input_names() output_names = predictor.get_output_names() return predictor, input_names, output_names
def set_config(args): config = Config(args.model_file, args.params_file) config.enable_lite_engine(PrecisionType.Float32, True) # use lite xpu subgraph config.enable_xpu(10 * 1024 * 1024) # use lite cuda subgraph # config.enable_use_gpu(100, 0) config.set_cpu_math_library_num_threads(args.math_thread_num) return config
def init_predictor(args): if args.model_dir is not "": config = Config(args.model_dir) else: config = Config(args.model_file, args.params_file) config.enable_memory_optim() config.enable_use_gpu(100, 0) # using dynamic shpae mode, the max_batch_size will be ignored. config.enable_tensorrt_engine(workspace_size=1 << 30, max_batch_size=1, min_subgraph_size=5, precision_mode=PrecisionType.Float32, use_static=False, use_calib_mode=False) head_number = 12 names = [ "placeholder_0", "placeholder_1", "placeholder_2", "stack_0.tmp_0" ] min_input_shape = [1, 1, 1] max_input_shape = [100, 128, 1] opt_input_shape = [10, 60, 1] config.set_trt_dynamic_shape_info( { names[0]: min_input_shape, names[1]: min_input_shape, names[2]: min_input_shape, names[3]: [1, head_number, 1, 1] }, { names[0]: max_input_shape, names[1]: max_input_shape, names[2]: max_input_shape, names[3]: [100, head_number, 128, 128] }, { names[0]: opt_input_shape, names[1]: opt_input_shape, names[2]: opt_input_shape, names[3]: [10, head_number, 60, 60] }) predictor = create_predictor(config) return predictor
def load_predictor(model_dir, run_mode='fluid', batch_size=1, use_gpu=False, min_subgraph_size=3): """set AnalysisConfig, generate AnalysisPredictor Args: model_dir (str): root path of __model__ and __params__ use_gpu (bool): whether use gpu Returns: predictor (PaddlePredictor): AnalysisPredictor Raises: ValueError: predict by TensorRT need use_gpu == True. """ if not use_gpu and not run_mode == 'fluid': raise ValueError( "Predict by TensorRT mode: {}, expect use_gpu==True, but use_gpu == {}" .format(run_mode, use_gpu)) if run_mode == 'trt_int8': raise ValueError("TensorRT int8 mode is not supported now, " "please use trt_fp32 or trt_fp16 instead.") config = Config(os.path.join(model_dir, 'model.pdmodel'), os.path.join(model_dir, 'model.pdiparams')) precision_map = { 'trt_int8': Config.Precision.Int8, 'trt_fp32': Config.Precision.Float32, 'trt_fp16': Config.Precision.Half } if use_gpu: # initial GPU memory(M), device ID config.enable_use_gpu(200, 0) # optimize graph and fuse op # FIXME(dkp): ir optimize may prune variable inside graph # and incur error in Paddle 2.0, e.g. in SSDLite # FCOS model, set as False currently and should # be set as True after switch_ir_optim fixed config.switch_ir_optim(False) else: config.disable_gpu() if run_mode in precision_map.keys(): config.enable_tensorrt_engine(workspace_size=1 << 10, max_batch_size=batch_size, min_subgraph_size=min_subgraph_size, precision_mode=precision_map[run_mode], use_static=False, use_calib_mode=False) # disable print log when predict config.disable_glog_info() # enable shared memory config.enable_memory_optim() # disable feed, fetch OP, needed by zero_copy_run config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) return predictor
def init_predictor(args): config = Config() if args.model_dir == "": config.set_model(args.model_file, args.params_file) else: config.set_model(args.model_dir) #config.disable_glog_info() config.enable_use_gpu(1000, 3) predictor = create_predictor(config) return predictor
def init_predictor(args): if args.model_dir: config = Config(args.model_dir) else: config = Config(args.model_file, args.params_file) if args.use_gpu: config.enable_use_gpu(1000, 0) if args.enable_tensorRT: config.enable_tensorrt_engine( max_batch_size=args.batchsize, min_subgraph_size=1, precision_mode=paddle.inference.PrecisionType.Float32) else: config.disable_gpu() config.delete_pass("repeated_fc_relu_fuse_pass") config.set_cpu_math_library_num_threads(args.cpu_threads) if args.enable_mkldnn: config.enable_mkldnn() predictor = create_predictor(config) return predictor, config
def _set_config(self): """ predictor config setting. """ # create default cpu predictor cpu_config = Config(self.default_pretrained_model_path) cpu_config.disable_glog_info() cpu_config.disable_gpu() self.cpu_predictor = create_predictor(cpu_config) # create predictors using various types of devices # npu npu_id = self._get_device_id("FLAGS_selected_npus") if npu_id != -1: # use npu npu_config = Config(self.default_pretrained_model_path) npu_config.disable_glog_info() npu_config.enable_npu(device_id=npu_id) self.npu_predictor = create_predictor(npu_config) # gpu gpu_id = self._get_device_id("CUDA_VISIBLE_DEVICES") if gpu_id != -1: # use gpu gpu_config = Config(self.default_pretrained_model_path) gpu_config.disable_glog_info() gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=gpu_id) self.gpu_predictor = create_predictor(gpu_config) # xpu xpu_id = self._get_device_id("XPU_VISIBLE_DEVICES") if xpu_id != -1: # use xpu xpu_config = Config(self.default_pretrained_model_path) xpu_config.disable_glog_info() xpu_config.enable_xpu(100) self.xpu_predictor = create_predictor(xpu_config)
def load_config(self, modelpath, use_gpu, use_mkldnn, combined): ''' load the model config modelpath: inference model path use_gpu: use gpu or not use_mkldnn: use mkldnn or not combined: inference model format is combined or not ''' # 对运行位置进行配置 if use_gpu: try: int(os.environ.get('CUDA_VISIBLE_DEVICES')) except Exception: print( 'Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU.' ) use_gpu = False # 加载模型参数 if combined: model = os.path.join(modelpath, "__model__") params = os.path.join(modelpath, "__params__") config = Config(model, params) else: config = Config(modelpath) # 设置参数 if use_gpu: config.enable_use_gpu(100, 0) else: config.disable_gpu() if use_mkldnn: config.enable_mkldnn() # 返回配置 return config
def load_model(self, modelpath, use_gpu, use_mkldnn, combined): # 对运行位置进行配置 if use_gpu: try: int(os.environ.get('CUDA_VISIBLE_DEVICES')) except Exception: print( 'Error! Unable to use GPU. Please set the environment variables "CUDA_VISIBLE_DEVICES=GPU_id" to use GPU.' ) use_gpu = False # 加载模型参数 if combined: model = os.path.join(modelpath, "__model__") params = os.path.join(modelpath, "__params__") config = Config(model, params) else: config = Config(modelpath) # 设置参数 if use_gpu: config.enable_use_gpu(100, 0) else: config.disable_gpu() if use_mkldnn: config.enable_mkldnn() config.disable_glog_info() config.switch_ir_optim(True) config.enable_memory_optim() config.switch_use_feed_fetch_ops(False) config.switch_specify_input_names(True) # 通过参数加载模型预测器 predictor = create_predictor(config) # 返回预测器 return predictor
def init_predictor(args): if args.model_dir: has_model = 0 pdmodel_name = 0 pdiparams_name = 0 for file_name in os.listdir(args.model_dir): if re.search("__model__", file_name): has_model = 1 if file_name.endswith(".pdmodel"): pdmodel_name = os.path.join(args.model_dir, file_name) if file_name.endswith(".pdiparams"): pdiparams_name = os.path.join(args.model_dir, file_name) if has_model == 1: config = Config(args.model_dir) elif pdmodel_name and pdiparams_name: config = Config(pdmodel_name, pdiparams_name) else: raise ValueError( "config setting error, please check your model path") else: config = Config(args.model_file, args.params_file) if args.use_gpu: config.enable_use_gpu(1000, 0) if args.enable_tensorRT: config.enable_tensorrt_engine( max_batch_size=args.batchsize, min_subgraph_size=9, precision_mode=paddle.inference.PrecisionType.Float32) else: config.disable_gpu() config.set_cpu_math_library_num_threads(args.cpu_threads) if args.enable_mkldnn: config.enable_mkldnn() config.delete_pass("scale_matmul_fuse_pass") predictor = create_predictor(config) return predictor, config
def init_predictor(args): config = Config(os.path.join(args.model_dir, "inference.pdmodel"), os.path.join(args.model_dir, "inference.pdiparams")) config.enable_memory_optim() if args.use_gpu: config.enable_use_gpu(1000, 0) else: # If not specific mkldnn, you can set the blas thread. # The thread num should not be greater than the number of cores in the CPU. config.set_cpu_math_library_num_threads(4) predictor = create_predictor(config) return predictor
def __init__(self, model_file, params_file, use_mkldnn=True, use_gpu=False, device_id=0): config = Config(model_file, params_file) config.enable_memory_optim() if use_gpu: print("ENABLE_GPU") config.enable_use_gpu(100, device_id) if use_mkldnn: config.enable_mkldnn() self.predictor = create_predictor(config)
def get_config(self, model, params, tuned=False): config = Config() config.set_model_buffer(model, len(model), params, len(params)) config.enable_use_gpu(100, 0) config.set_optim_cache_dir('tuned_test') if tuned: config.collect_shape_range_info('shape_range.pbtxt') else: config.enable_tensorrt_engine( workspace_size=1024, max_batch_size=1, min_subgraph_size=0, precision_mode=paddle.inference.PrecisionType.Float32, use_static=True, use_calib_mode=False) config.enable_tuned_tensorrt_dynamic_shape('shape_range.pbtxt', True) return config
def init_predictor(self, cfg): model_dir = cfg['model_dir'] params_file = cfg['params_file'] use_gpu = cfg['use_gpu'] config = Config(model_dir, params_file) # config.enable_memory_optim() if use_gpu: config.enable_use_gpu(1000, 0) else: # If not specific mkldnn, you can set the blas thread. # The thread num should not be greater than the number of cores in the CPU. config.set_cpu_math_library_num_threads(4) config.enable_mkldnn() config.disable_glog_info() predictor = create_predictor(config) return predictor
def preprocess(): #for i in range(1000): # _preprocess() #return 1 #feed = _preprocess() import numpy as np from paddle.inference import Config from paddle.inference import create_predictor config = Config("serving_server/__model__", "serving_server/__params__") config.disable_gpu() config.switch_use_feed_fetch_ops(False) config.switch_specify_input_names(True) predictor = create_predictor(config) for i in range(10): feed = _preprocess() # input 0 im shape input_names = predictor.get_input_names() input_handle = predictor.get_input_handle(input_names[0]) input_t = feed["im_shape"] input_handle.reshape(input_t.shape) input_handle.copy_from_cpu(input_t) # input 1 image input_names = predictor.get_input_names() input_handle = predictor.get_input_handle(input_names[1]) input_t = feed["image"] input_handle.reshape(input_t.shape) input_handle.copy_from_cpu(input_t) # input 2 scale factor input_names = predictor.get_input_names() input_handle = predictor.get_input_handle(input_names[2]) input_t = feed["scale_factor"] input_handle.reshape(input_t.shape) input_handle.copy_from_cpu(input_t) predictor.run() output_names = predictor.get_output_names() output_handle = predictor.get_output_handle(output_names[0]) output_data = output_handle.copy_to_cpu()
def create_paddle_predictor(args): config = Config(args.model_file, args.params_file) if args.use_gpu: config.enable_use_gpu(args.gpu_mem, 0) else: config.disable_gpu() if args.use_mkldnn: config.enable_mkldnn() config.set_cpu_math_library_num_threads(args.cpu_num_threads) config.set_mkldnn_cache_capacity(100) config.disable_glog_info() config.switch_ir_optim(args.ir_optim) # default true config.enable_memory_optim() # use zero copy config.switch_use_feed_fetch_ops(False) predictor = create_predictor(config) return predictor
def init_model(self): from paddle.inference import Config from paddle.inference import PrecisionType from paddle.inference import create_predictor precision_mode = PrecisionType.Float32 use_calib_mode = False if self.param_type == "fp16": precision_mode = PrecisionType.Half elif self.param_type == "int8": precision_mode = PrecisionType.Int8 use_calib_mode = True mode_path = os.path.join(self.model_dir,"yolov3/__model__") param_path = os.path.join(self.model_dir,"yolov3/__params__") config = Config(mode_path, param_path) config.enable_use_gpu(100, 0) config.switch_ir_optim(True) size = (self.batch_size * 101) << 20 config.enable_tensorrt_engine( workspace_size= size, max_batch_size=self.batch_size, min_subgraph_size=3, precision_mode=precision_mode, use_static=False, use_calib_mode=use_calib_mode) if not self.debug: config.disable_glog_info() else: config.enable_profile() config.enable_memory_optim() config.switch_use_feed_fetch_ops(False) config.enable_mkldnn() #exit(1) self.predictor = create_predictor(config)
def main(): args = parse_args() # 配置 config = Config(args.model_file, args.params_file) config.disable_gpu() config.switch_use_feed_fetch_ops(False) config.switch_specify_input_names(True) # 创建paddlePredictor predictor = create_predictor(config) # 获取输入 val_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transforms.ToTensor()) (image, label) = val_dataset[np.random.randint(10000)] # fake_input = np.random.randn(1, 1, 28, 28).astype("float32") # image = np.asndarray(image).astype("float32") # print(image.shape) image = image.numpy().reshape([1, 1, 28, 28]) # print(image.shape) # print(fake_input.shape) input_names = predictor.get_input_names() input_handle = predictor.get_input_handle(input_names[0]) input_handle.reshape([1, 1, 28, 28]) input_handle.copy_from_cpu(image) # 运行predictor predictor.run() # 获取输出 output_names = predictor.get_output_names() output_handle = predictor.get_output_handle(output_names[0]) output = output_handle.copy_to_cpu() print("True label: ", label.item()) print("Prediction: ", np.argmax(output))