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[bugfix] fix coverity issues #2722

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merged 1 commit into from
Aug 30, 2024
Merged

[bugfix] fix coverity issues #2722

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16 changes: 2 additions & 14 deletions nntrainer/tensor/short_tensor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -244,6 +244,7 @@ void ShortTensor::copyData(const Tensor &from) {
switch (from.getDataType()) {
case ml::train::TensorDim::DataType::UINT16:
copy(from.getData());
break;
default:
throw std::invalid_argument("Error: Unsupported data type");
break;
Expand Down Expand Up @@ -278,20 +279,7 @@ std::vector<unsigned int> ShortTensor::argmax() const {
return result;
}

float ShortTensor::max_abs() const {
const uint16_t *data = (uint16_t *)getData();
unsigned int idx;

uint16_t max_val = data[0];
for (unsigned int i = 1; i < size(); i += 1) {
uint16_t cur_val = (data[i] >= 0) ? data[i] : -1 * data[i];
if (cur_val > max_val) {
max_val = cur_val;
}
}

return max_val;
}
float ShortTensor::max_abs() const { return maxValue(); }

float ShortTensor::maxValue() const {
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@skykongkong8 skykongkong8 Aug 29, 2024 •
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And just curious, if this tensor is containing 'unsigned' integer, why should it be max_"abs" ? it is already absolute.

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sorry I didn't quite understand. do you mean why max_abs() should be used?

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Yes. I think can't see any difference between maxValue and maxAbsoluteValue for this tensor

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You're right! There's no difference between max_abs() and maxValue() for this tensor class. That's why max_abs() is using maxValue(). The reason why there are max_abs() and maxValue() is to be compatible with the TensorBase and Tensor class. While max_abs() is currently a pure virtual function, it will get a compile error without implementing max_abs() in the ShortTensor class.

const uint16_t *data = (uint16_t *)getData();
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