Added ResetVariableTensor #3298
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| TfLiteStatus MicroInterpreterGraph::ResetVariableTensor(int tensor_index, | ||
| int subgraph_index) { | ||
| const SubGraph* subgraph = (*subgraphs_)[subgraph_index]; | ||
| auto* tensor = subgraph->tensors()->Get(tensor_index); | ||
| if (tensor->is_variable()) { | ||
| size_t buffer_size; | ||
| TF_LITE_ENSURE_STATUS(TfLiteEvalTensorByteLength( | ||
| &subgraph_allocations_[subgraph_index].tensors[tensor_index], | ||
| &buffer_size)); | ||
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| int value = 0; | ||
| if (tensor->type() == tflite::TensorType_INT8) { | ||
| value = tensor->quantization()->zero_point()->Get(0); | ||
| } | ||
| memset(subgraph_allocations_[subgraph_index].tensors[tensor_index].data.raw, | ||
| value, buffer_size); | ||
| } | ||
| return kTfLiteOk; | ||
| } | ||
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Since this only works for TensorFlow 1.0 variable type tensors, the default return value should be failure. Also TensorType_INT8 should not be used for quantization checks. Instead the existence of the quantization field in the schema should be used. Multi-channel quantization checks also need to be here, or alternatively fail this method.
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Since this only works for TensorFlow 1.0 variable type tensors, the default return value should be failure.
Done.
Also TensorType_INT8 should not be used for quantization checks. Instead the existence of the quantization field in the schema should be used. Multi-channel quantization checks also need to be here, or alternatively fail this method.
This reset logic is identical to the existing ResetVariableTensors. We can improve it in a separate PR if this is needed.
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@veblush This will not work for RESOURCE variable tensors (TensorFlow 2.0) |
Noted. If we need a way to reset RESOURCE variable, then we can add a new api for that. |
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Brainstroming.
BUG=N/a