| | |
| | | void Detecter::setOutput(int type) |
| | | { |
| | | m_OutputTensors.clear(); |
| | | printf("0-0-0-0-0-0------------------%d",type); |
| | | if(type==2) |
| | | for (int i = 0; i < 2; ++i) |
| | | { |
| | | |
| | | TensorInfo outputTensor; |
| | | outputTensor.numClasses = CLASS_BUM; |
| | | outputTensor.blobName = "yolo_" + std::to_string(i); |
| | |
| | | { |
| | | TensorInfo outputTensor; |
| | | outputTensor.numClasses = CLASS_BUM; |
| | | outputTensor.blobName = "yolo_" + to_string(i); |
| | | // if (i==0) |
| | | // { |
| | | // outputTensor.blobName = "139_convolutional_reshape_2"; |
| | | // }else if (i==1) |
| | | // { |
| | | // outputTensor.blobName = "150_convolutional_reshape_2"; |
| | | // }else if (i==2) |
| | | // { |
| | | // outputTensor.blobName = "161_convolutional_reshape_2"; |
| | | // } |
| | | outputTensor.blobName = "yolo_" + std::to_string(i); |
| | | outputTensor.gridSize = (m_InputH / 32) * pow(2, 2-i); |
| | | outputTensor.grid_h = (m_InputH / 32) * pow(2, 2-i); |
| | | outputTensor.grid_w = (m_InputW / 32) * pow(2, 2-i); |