Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "TensorUtils.hpp" |
Jim Flynn | f92dfce | 2019-05-02 11:33:25 +0100 | [diff] [blame] | 7 | #include <backendsCommon/ITensorHandle.hpp> |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 8 | |
Narumol Prangnawarat | 0280785 | 2019-09-11 16:43:09 +0100 | [diff] [blame] | 9 | #include <boost/assert.hpp> |
| 10 | #include <boost/format.hpp> |
| 11 | #include <boost/numeric/conversion/cast.hpp> |
| 12 | |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 13 | namespace armnnUtils |
| 14 | { |
| 15 | |
| 16 | armnn::TensorShape GetTensorShape(unsigned int numberOfBatches, |
| 17 | unsigned int numberOfChannels, |
| 18 | unsigned int height, |
| 19 | unsigned int width, |
| 20 | const armnn::DataLayout dataLayout) |
| 21 | { |
| 22 | switch (dataLayout) |
| 23 | { |
| 24 | case armnn::DataLayout::NCHW: |
| 25 | return armnn::TensorShape({numberOfBatches, numberOfChannels, height, width}); |
| 26 | case armnn::DataLayout::NHWC: |
| 27 | return armnn::TensorShape({numberOfBatches, height, width, numberOfChannels}); |
| 28 | default: |
| 29 | throw armnn::InvalidArgumentException("Unknown data layout [" |
| 30 | + std::to_string(static_cast<int>(dataLayout)) + |
| 31 | "]", CHECK_LOCATION()); |
| 32 | } |
| 33 | } |
| 34 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 35 | armnn::TensorInfo GetTensorInfo(unsigned int numberOfBatches, |
| 36 | unsigned int numberOfChannels, |
| 37 | unsigned int height, |
| 38 | unsigned int width, |
| 39 | const armnn::DataLayout dataLayout, |
| 40 | const armnn::DataType dataType) |
| 41 | { |
| 42 | switch (dataLayout) |
| 43 | { |
| 44 | case armnn::DataLayout::NCHW: |
| 45 | return armnn::TensorInfo({numberOfBatches, numberOfChannels, height, width}, dataType); |
| 46 | case armnn::DataLayout::NHWC: |
| 47 | return armnn::TensorInfo({numberOfBatches, height, width, numberOfChannels}, dataType); |
| 48 | default: |
| 49 | throw armnn::InvalidArgumentException("Unknown data layout [" |
| 50 | + std::to_string(static_cast<int>(dataLayout)) + |
| 51 | "]", CHECK_LOCATION()); |
| 52 | } |
Nina Drozd | d41b259 | 2018-11-19 13:03:36 +0000 | [diff] [blame] | 53 | } |
| 54 | |
Jim Flynn | f92dfce | 2019-05-02 11:33:25 +0100 | [diff] [blame] | 55 | std::pair<float, float> FindMinMax(armnn::ITensorHandle* tensorHandle) |
| 56 | { |
| 57 | auto tensor_data = static_cast<const float *>(tensorHandle->Map(true)); |
| 58 | auto tensor_size = tensorHandle->GetShape().GetNumElements(); |
| 59 | |
| 60 | // Set min/max initially to first value in tensor |
| 61 | float min = tensor_data[0]; |
| 62 | float max = tensor_data[0]; |
| 63 | |
| 64 | // Loop over rest of tensor and update min/max if necessary |
| 65 | for (unsigned int val = 1; val < tensor_size; val++) |
| 66 | { |
| 67 | if (tensor_data[val] < min) |
| 68 | { |
| 69 | min = tensor_data[val]; |
| 70 | } |
| 71 | else if (tensor_data[val] > max) |
| 72 | { |
| 73 | max = tensor_data[val]; |
| 74 | } |
| 75 | } |
| 76 | |
| 77 | tensorHandle->Unmap(); |
| 78 | |
| 79 | return std::make_pair(min, max); |
| 80 | } |
| 81 | |
Narumol Prangnawarat | 0280785 | 2019-09-11 16:43:09 +0100 | [diff] [blame] | 82 | armnn::TensorShape ExpandDims(const armnn::TensorShape& tensorShape, int axis) |
| 83 | { |
| 84 | unsigned int outputDim = tensorShape.GetNumDimensions() + 1; |
| 85 | |
| 86 | if (axis < -boost::numeric_cast<int>(outputDim) || axis > boost::numeric_cast<int>(tensorShape.GetNumDimensions())) |
| 87 | { |
| 88 | throw armnn::InvalidArgumentException( |
| 89 | boost::str(boost::format("Invalid expansion axis %1% for %2%D input tensor. %3%") % |
| 90 | axis % |
| 91 | tensorShape.GetNumDimensions() % |
| 92 | CHECK_LOCATION().AsString())); |
| 93 | } |
| 94 | |
| 95 | if (axis < 0) |
| 96 | { |
| 97 | axis = boost::numeric_cast<int>(outputDim) + axis; |
| 98 | } |
| 99 | |
| 100 | std::vector<unsigned int> outputShape; |
| 101 | for (unsigned int i = 0; i < tensorShape.GetNumDimensions(); ++i) |
| 102 | { |
| 103 | outputShape.push_back(tensorShape[i]); |
| 104 | } |
| 105 | outputShape.insert(outputShape.begin() + axis, 1); |
| 106 | |
| 107 | return armnn::TensorShape(outputDim, outputShape.data()); |
| 108 | } |
| 109 | |
Narumol Prangnawarat | 4dc64a6 | 2019-09-16 17:00:22 +0100 | [diff] [blame^] | 110 | unsigned int GetNumElementsBetween(const armnn::TensorShape& shape, |
| 111 | const unsigned int firstAxisInclusive, |
| 112 | const unsigned int lastAxisExclusive) |
| 113 | { |
| 114 | BOOST_ASSERT(0 <= firstAxisInclusive); |
| 115 | BOOST_ASSERT(firstAxisInclusive <= lastAxisExclusive); |
| 116 | BOOST_ASSERT(lastAxisExclusive <= shape.GetNumDimensions()); |
| 117 | unsigned int count = 1; |
| 118 | for (unsigned int i = firstAxisInclusive; i < lastAxisExclusive; i++) |
| 119 | { |
| 120 | count *= shape[i]; |
| 121 | } |
| 122 | return count; |
| 123 | } |
| 124 | |
| 125 | unsigned int GetUnsignedAxis(const unsigned int inputDimension, const int axis) |
| 126 | { |
| 127 | BOOST_ASSERT_MSG(axis < boost::numeric_cast<int>(inputDimension), |
| 128 | "Required axis index greater than number of dimensions."); |
| 129 | BOOST_ASSERT_MSG(axis >= -boost::numeric_cast<int>(inputDimension), |
| 130 | "Required axis index lower than negative of the number of dimensions"); |
| 131 | |
| 132 | unsigned int uAxis = axis < 0 ? |
| 133 | inputDimension - boost::numeric_cast<unsigned int>(abs(axis)) |
| 134 | : boost::numeric_cast<unsigned int>(axis); |
| 135 | return uAxis; |
| 136 | } |
| 137 | |
Nattapat Chaimanowong | 649dd95 | 2019-01-22 16:10:44 +0000 | [diff] [blame] | 138 | } |