INPUT_SIZE = 160 if __name__ == '__main__': # Create RKNN object rknn = RKNN(verbose=True, verbose_file='./test2.log') #load rknn model print('--> Loading rknn model') rknn.load_rknn('./facenet_Reshape_1.rknn') print('done') #set inputs img1 = np.load(...
input = sess.graph.get_tensor_by_name("x:0") output = sess.graph.get_tensor_by_name("y_conv:0") y_conv_2 = sess.run(output, feed_dict={input:reshape_test[0:length]}) y_2 = mnist.test.labels[0:length] print("first image:",y_conv_2[0]) correct_prediction_2 = tf.equal(...
示例代码如下: rknn_sdk_version version; ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &version, sizeof(rknn_sdk_version)); printf("sdk api version: %s\n", version.api_version); printf("driver version: %s\n", version.drv_version); 3.2.3.4 rknn_inputs_set 通过 rknn_inputs_set ...
Wxh = np.random.randn(hidden_size, vocab_size)*0.01 # input to hidden Whh = np.random.randn(hidden_size, hidden_size)*0.01 # hidden to hidden Why = np.random.randn(vocab_size, hidden_size)*0.01 # hidden to output bh = np.zeros((hidden_size, 1)) # hidden bias by = np.zeros(...
rknn_input inputs[]:输入数据数组,数组每个元素是 rknn_input 结构体对象。 返回值 int 错误码(见rknn 返回值错误码)。 示例代码如下: rknn_input inputs[1]; memset(inputs, 0, sizeof(inputs)); inputs[0].index = 0; inputs[0].type = RKNN_TENSOR_UINT8; inputs[0].size = img_width*img...
在初始化步骤中,总线上的主机通过拉低单总线至少480μs来产生复位脉冲。然后总线主机释放总线并进入接收模式。 当总线释放后,5kΩ的上拉电阻把单总线上的电平拉回高电平。当DS18B20检测到上升沿后等待15到60us,然后以拉低总线60-240us的方式发出存在脉冲。
[0].type = RKNN_TENSOR_UINT8; inputs[0].size = img_width*img_height*img_channels; inputs[0].pass_through = FALSE; inputs[0].fmt = RKNN_TENSOR_NHWC; inputs[0].buf = in_data; ret = rknn_inputs_set(ctx, 1, inputs); For more detailed usage, see the step 4 of the [RKNN...
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Before performing the bind operation, set s32RecvPicNum to 0 to // make the encoding enter the pause state. VENC_RECV_PIC_PARAM_S stRecvParam; stRecvParam.s32RecvPicNum = 0; RK_MPI_VENC_StartRecvFrame(0, &stRecvParam); MPP_CHN_S stSrcChn; stSrcChn.enModId = RK_ID_VI; stSrc...
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # Inference outputs = rknn.inference(inputs=[img]) # post process input0_data = outputs[0] input1_data = outputs[1] input2_data = outputs[2] input0_data = input0_data.reshape([3, -1] + list(input0_data.shape[-2:])) ...