pBuff = mInputData.back().ptr<void*>(0);
size_t sizeBuff = mInputData.back().elemSize() * mInputData.back().rows * mInputData.back().cols;
LOGI("elemSize: %zd, rows: %d, cols: %d", mInputData.back().elemSize(), mInputData.back().rows, mInputData.back().cols );
- inference_engine_tensor_buffer buffer = { pBuff, TENSOR_DATA_TYPE_FLOAT32, sizeBuff, 1 };
+ inference_engine_tensor_buffer buffer = { pBuff, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, sizeBuff, 1 };
buffers.push_back(buffer);
}
for (iter = mOutputBlobs.begin(); iter != mOutputBlobs.end(); ++iter) {
pBuff = (*iter).ptr<void*>(0);
size_t sizeBuff = (*iter).total() * (*iter).elemSize();
- inference_engine_tensor_buffer buffer = { pBuff, TENSOR_DATA_TYPE_FLOAT32, sizeBuff, 1};
+ inference_engine_tensor_buffer buffer = { pBuff, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, sizeBuff, 1};
buffers.push_back(buffer);
}
if (buffers.empty()) {
LOGI("buff empty");
- inference_engine_tensor_buffer buffer = { nullptr, TENSOR_DATA_TYPE_FLOAT32, 0, 1};
+ inference_engine_tensor_buffer buffer = { nullptr, INFERENCE_TENSOR_DATA_TYPE_FLOAT32, 0, 1};
buffers.push_back(buffer);
}
lInputShape,
lOutputShape);
inference_engine_tensor_info tensor_info;
- tensor_info.data_type =TENSOR_DATA_TYPE_FLOAT32;
- tensor_info.shape_type = TENSOR_SHAPE_NCHW;
+ tensor_info.data_type =INFERENCE_TENSOR_DATA_TYPE_FLOAT32;
+ tensor_info.shape_type = INFERENCE_TENSOR_SHAPE_NCHW;
// lOutputShape may have multiple tensors
// even though the output layer's name is only one
LOGI("size of OutputShape: %d", lOutputShape.size());