IVGCVSW-7785 Extend support for 3D tensors BATCH_TO_SPACE and SPACE_TO_BATCH in CpuRef
* Both layers were assuming 4D tensors, now 3D is supported too.
* Remove some unnecessary includes
* Add Unit Tests
Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: I7bdd11e4936a27cd97ec65fd915e6ccaa1494cff
diff --git a/src/backends/backendsCommon/WorkloadData.cpp b/src/backends/backendsCommon/WorkloadData.cpp
index 6a5963d..d4ae08d 100644
--- a/src/backends/backendsCommon/WorkloadData.cpp
+++ b/src/backends/backendsCommon/WorkloadData.cpp
@@ -1815,47 +1815,66 @@
const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0];
const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];
- ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, "input");
- ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output");
-
- if (m_Parameters.m_BlockShape.size() != 2)
- {
- throw InvalidArgumentException(descriptorName + ": Block Shape must contain 2 spatial dimensions.");
- }
-
if (m_Parameters.m_BlockShape.size() != m_Parameters.m_PadList.size())
{
throw InvalidArgumentException(descriptorName + ": Pad List must contain the same number of "
"dimensions as Block Shape.");
}
- const TensorShape& inputShape = inputTensorInfo.GetShape();
-
- std::pair<unsigned int, unsigned int> heightPad = m_Parameters.m_PadList[0];
- std::pair<unsigned int, unsigned int> widthPad = m_Parameters.m_PadList[1];
-
- DataLayoutIndexed dimensionIndices(m_Parameters.m_DataLayout);
-
- const unsigned int inputWidth = inputShape[dimensionIndices.GetWidthIndex()] +
- widthPad.first + widthPad.second;
- const unsigned int inputHeight = inputShape[dimensionIndices.GetHeightIndex()] +
- heightPad.first + heightPad.second;
-
- const unsigned int numInputElements = inputShape[0] * inputHeight * inputWidth *
- inputShape[dimensionIndices.GetChannelsIndex()];
- const unsigned int numOutputElements = outputTensorInfo.GetNumElements();
-
- if (numOutputElements != numInputElements)
+ if (m_Parameters.m_BlockShape.size() == 2)
{
- throw InvalidArgumentException(descriptorName + ": Input tensor has " +
- to_string(numInputElements) + " after padding but output tensor has " +
- to_string(numOutputElements) + " elements.");
+ ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, "input");
+ ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output");
+ }
+ else if (m_Parameters.m_BlockShape.size() == 1)
+ {
+ ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 3, "input");
+ ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 3, "output");
+ }
+ else
+ {
+ throw InvalidArgumentException(descriptorName + ": Invalid Block and Crops size.");
}
- if (inputHeight % m_Parameters.m_BlockShape[0] != 0 || inputWidth % m_Parameters.m_BlockShape[1] != 0)
+ // Check input + padding and output have the same number of elements
+ DataLayoutIndexed dimensionIndices(m_Parameters.m_DataLayout);
+ const unsigned int inputHeight = inputTensorInfo.GetShape()[dimensionIndices.GetHeightIndex()] +
+ m_Parameters.m_PadList[0].first + m_Parameters.m_PadList[0].second;
+ const unsigned int inputWidth = (inputTensorInfo.GetNumDimensions() == 3) ? 1 :
+ inputTensorInfo.GetShape()[dimensionIndices.GetWidthIndex()] +
+ m_Parameters.m_PadList[1].first + m_Parameters.m_PadList[1].second;
+
+ const int channelsIndex_int = (m_Parameters.m_DataLayout == DataLayout::NCHW) ? 1 : -1;
+ const unsigned int channelsIndex = channelsIndex_int < 0 ?
+ static_cast<unsigned int>(channelsIndex_int) + inputTensorInfo.GetNumDimensions()
+ : static_cast<unsigned int>(channelsIndex_int);
+
+ const unsigned int numInputElements = inputTensorInfo.GetShape()[0] *
+ inputHeight *
+ inputWidth *
+ inputTensorInfo.GetShape()[channelsIndex];
+
+ if (outputTensorInfo.GetNumElements() != numInputElements)
{
- throw InvalidArgumentException(descriptorName + ": Input shape after padding must be "
- "divisible by Block Shape in all spatial dimensions");
+ throw InvalidArgumentException(descriptorName + ": Input tensor has " +
+ to_string(numInputElements) + " after padding but output tensor has " +
+ to_string(outputTensorInfo.GetNumElements()) + " elements.");
+ }
+
+ // In a 4D tensor, there will be 2 spatialDimensions (H and W), and the for loop will run twice.
+ // In a 3D tensor, there will be 1 spatialDimensions, and the for loop will run once.
+ unsigned int firstSpatialDimension = m_Parameters.m_DataLayout == DataLayout::NCHW ? 2 : 1;
+ for (unsigned int i = 0; i < m_Parameters.m_BlockShape.size(); ++i)
+ {
+ unsigned int spatialDimension = firstSpatialDimension + i;
+ auto inputSize = inputTensorInfo.GetShape()[spatialDimension] +
+ m_Parameters.m_PadList[i].first +
+ m_Parameters.m_PadList[i].second;
+ if (inputSize % m_Parameters.m_BlockShape[i] != 0)
+ {
+ throw InvalidArgumentException(descriptorName + ": Input dimension size after padding must be "
+ "divisible by Block Shape in dimension: " + to_string(spatialDimension) + ".");
+ }
}
std::vector<DataType> supportedTypes =
@@ -2472,6 +2491,42 @@
const TensorInfo& inputTensorInfo = workloadInfo.m_InputTensorInfos[0];
const TensorInfo& outputTensorInfo = workloadInfo.m_OutputTensorInfos[0];
+ if (m_Parameters.m_BlockShape.size() != m_Parameters.m_Crops.size())
+ {
+ throw InvalidArgumentException(descriptorName + ": Crops must contain the same number of "
+ "dimensions as Block Shape.");
+ }
+
+ if (m_Parameters.m_BlockShape.size() == 2)
+ {
+ ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 4, "input");
+ ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 4, "output");
+ }
+ else if (m_Parameters.m_BlockShape.size() == 1)
+ {
+ ValidateTensorNumDimensions(inputTensorInfo, descriptorName, 3, "input");
+ ValidateTensorNumDimensions(outputTensorInfo, descriptorName, 3, "output");
+ }
+ else
+ {
+ throw InvalidArgumentException(descriptorName + ": Invalid Block and Crops size.");
+ }
+
+ // In a 4D tensor, there will be 2 spatialDimensions (H and W), and the for loop will run twice.
+ // In a 3D tensor, there will be 1 spatialDimensions, and the for loop will run once.
+ unsigned int firstSpatialDimension = m_Parameters.m_DataLayout == DataLayout::NCHW ? 2 : 1;
+ for (unsigned int i = 0; i < m_Parameters.m_BlockShape.size(); ++i)
+ {
+ unsigned int spatialDimension = firstSpatialDimension + i;
+ unsigned int cropSize = m_Parameters.m_Crops[i].first + m_Parameters.m_Crops[i].second;
+ unsigned int outputSize = inputTensorInfo.GetShape()[spatialDimension] * m_Parameters.m_BlockShape[i];
+ if (cropSize > outputSize)
+ {
+ throw InvalidArgumentException(descriptorName + ": CropSize must be less than or equal to the uncropped"
+ "outputSize in dimension: " + to_string(spatialDimension) + ".");
+ }
+ }
+
std::vector<DataType> supportedTypes =
{
DataType::BFloat16,