input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32) interpreter.set_tensor(input_details[0]['index'], input_data) # Tead the ECG data interpreter.set_tensor(input_details[0]['index'],ecg_signals) # Predict interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) print(outpu...
start = time.time() interpreter = tf.lite.Interpreter(model_path="/home/alcht0/share/project/tensorflow-v1.12.0/converted_model.tflite") interpreter.allocate_tensors() input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() interpreter.set_tensor(input_...
input_details = interpreter.get_input_details() # print(input_details) output_details = interpreter.get_output_details() # print(output_details) #模型预测 interpreter.set_tensor(input_details[0]['index'], image)#传入的数据必须为ndarray类型 interpreter.invoke() output_data = interpreter.get_tens...
interpreter= tf.contrib.lite.Interpreter(model_path="tflite_model/eval_graph.tflite") interpreter.allocate_tensors() input_details=interpreter.get_input_details() output_details=interpreter.get_output_details() start_time=time.time() predictions=[]forimageinimages: interpreter.set_tensor(input_detai...
output_data= interpreter.get_tensor(output_details[0]['index']) end=time.time()print("1st", str(end - start)) 首先根据tflite文件生成解析器,然后用allocate_tensors()分配内存。将输入通过set_tensor传入,然后调用invoke()来真正运行。最后得到输出。
问TensorFlowLite错误interpreter.set_tensor()EN本文介绍了TensorFlow Lite的架构设计、功能特性、开发工具包...
astype('uint8')# 类型也要满足要求 interpreter.set_tensor(input_details[0]['index'],new_img) # 注意注意,我要调用模型了 interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) print("test_tflite finish!") intput_tflite_file = "uint8.tflite" test_tflite...
如果用户未禁用，现在优化 parallel_batch 现在成为默认值，这样可以并行复制批处理元素；添加了 TensorSliceDataset，用于识别和处理文件输入。tf.lite：为 Java API 的序列化添加 GPU 委托支持，当 OpenCL 可用时，这将初始化时间提高了 90%；弃用 Interpreter::SetNumThreads，支持 InterpreterBuilder::SetNumThreads...
调用interpreter.invoke() 来推理, def classify_image(interpreter, image, top_k=1): """Returns a sorted array of classification results.""" set_input_tensor(interpreter, image) interpreter.invoke() output_details = interpreter.get_output_details()[0] output = np.squeeze(interpreter.get_tensor(...
The public constantstflite::Interpreter:kTensorsReservedCapacityandtflite::Interpreter:kTensorsCapacityHeadroomare now const references, rather thanconstexprcompile-time constants. (This is to enable better API compatibility for TFLite in Play services while preserving the implementation flexibility to chan...