using Type = int32_t;
};
-// Modifies a std::vector in-place using a specified bias
+// Modifies a std::vector in-place using a specified bias.
template<typename T, typename B>
void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
const std::vector<B>& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)
BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()),
"Invalid type and parameter combination.");
- // Note we need to dequantize and re-quantize the image value and the bias
+ // Note we need to dequantize and re-quantize the image value and the bias.
for (uint32_t i = 0; i < bias.size(); ++i)
{
float dBias = SelectiveDequantize(bias[i], bScale, bOffset);
bool biasEnabled = bias.size() > 0;
- // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches)
+ // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
BOOST_ASSERT(inputNum == 1);
BOOST_ASSERT(outputNum == 1);
- // If a bias is used, its size must equal the number of output channels
+ // If a bias is used, its size must equal the number of output channels.
BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
- // Note these tensors will use two (identical) batches
+ // Note these tensors will use two (identical) batches.
armnn::TensorInfo inputTensorInfo({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth},
armnn::GetDataType<T>());
LayerTestResult<T, 4> ret(outputTensorInfo);
- // Construct input data - Two batches of the same input image
+ // Construct input data - two batches of the same input image.
std::vector<T> inputImage;
inputImage.assign(input.data(), input.data() + 1*inputChannels*inputHeight*inputWidth);
std::vector<T> inputData;
std::vector<T> outputImage;
outputImage.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
- // Apply bias to output image if enabled
+ // Apply bias to output image if it is enabled.
if(biasEnabled)
{
std::vector<T> biasV;
outputWidth, outputHeight);
}
- // Construct expected output data - two identical images
+ // Construct expected output data - two identical images.
std::vector<T> outputData;
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
- // todo: nontrivial padding and strides
+ // Todo: nontrivial padding and strides.
uint32_t strideX = 1;
uint32_t strideY = 1;
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
- data.m_Bias = &biasTensor; // still set this whether or not bias is enabled - can be a source of bugs
+ data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
- // If a bias is used, its size must equal the number of output channels
+ // If a bias is used, its size must equal the number of output channels.
bool biasEnabled = bias.size() > 0;
BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
- // create the tensors
+ // Creates the tensors.
armnn::TensorInfo inputTensorInfo({inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({outputNum, outputChannels, outputHeight, outputWidth},
armnn::GetDataType<T>());
biasDesc.SetQuantizationOffset(0);
}
- // Construct the input data
+ // Construct the input data.
std::vector<T> inputData;
inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);
auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
- // Construct the output data, with bias applied, as appropriate
+ // Construct the output data, with bias applied, as appropriate.
std::vector<T> outputData;
outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
if (biasEnabled)
armnn::DepthwiseConvolution2dQueueDescriptor data;
data.m_Weight = &weightsTensor;
- data.m_Bias = &biasTensor; // still set this whether or not bias is enabled - can be a source of bugs
+ data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
data.m_Parameters.m_StrideX = strideX;
data.m_Parameters.m_StrideY = strideY;
data.m_Parameters.m_PadLeft = padLeft;
-1.f, 0.f, -1.f,
})));
- // manually calculated
+ // Manually calculated.
std::vector<T> outputImage(
QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset(),
{0.f, 0.f})
);
- // Optionally apply bias to output image
+ // Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
- data.m_Bias = &biasTensor; // still set this whether or not bias is enabled
+ data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
data.m_Parameters.m_StrideX = 1;
data.m_Parameters.m_StrideY = 1;
data.m_Parameters.m_PadLeft = 0;
0, 0, 0
})));
- // manually calculated
+ // Manually calculated.
std::vector<T> outputImage = std::vector<T>(
QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f, 3.5f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
}));
- // Optionally apply bias to output image
+ // Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
data.m_Weight = &weightsTensor;
- data.m_Bias = &biasTensor; // still set this whether or not bias is enabled
+ data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
data.m_Parameters.m_StrideX = 2;
data.m_Parameters.m_StrideY = 1;
data.m_Parameters.m_PadLeft = 0;
{
using B = typename FullyConnectedBiasTypeForInputType<T>::Type;
- // until we have a specialist 1D convolution layer, we can fake one using
+ // Until we have a specialist 1D convolution layer, we can fake one using
// 2D convolution with the final dimension set to 1.
// I don't anticipate this being particularly slow, given that convolution is implemented
// as a matrix multiplication, at which point dimension doesn't matter.
unsigned int batchSize = 1;
unsigned int inputChannels = 2;
unsigned int outputChannels = 3;
- unsigned int inputSize = 5; // the 1D size (could view as 'width' or 'height')
+ unsigned int inputSize = 5; // The 1D size (could view as 'width' or 'height').
unsigned int kernelSize = 3;
unsigned int padSize = 2;
unsigned int stride = 1;
- unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride
+ unsigned int outputSize = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride.
armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType<T>());
armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType<T>());
2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f
}));
- // Optionally apply bias to output image
+ // Optionally apply bias to output image.
if(biasEnabled)
{
ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),
workloadFactory.Finalize();
workload->Execute();
- // output
+ // Output
LayerTestResult<T,4> ret(outputInfo);
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
ret.outputExpected = MakeTensor<T, 4>(outputInfo, outputData);
projects / platform / upstream / armnn.git / blobdiff