/*
* Copyright (C) 2017 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "OperationsUtils"
#include "OperationsUtils.h"
#include "Operations.h"
#include "Utils.h"
#include <cmath>
namespace android {
namespace nn {
namespace {
bool validateOperandTypes(const std::vector<OperandType>& expectedTypes, const char* tag,
uint32_t operandCount,
std::function<OperandType(uint32_t)> getOperandType) {
NN_RET_CHECK_EQ(operandCount, expectedTypes.size());
for (uint32_t i = 0; i < operandCount; ++i) {
OperandType type = getOperandType(i);
NN_RET_CHECK(type == expectedTypes[i])
<< "Invalid " << tag << " tensor type " << toString(type) << " for " << tag << " "
<< i << ", expected " << toString(expectedTypes[i]);
}
return true;
}
} // namespace
bool validateInputTypes(const IOperationValidationContext* context,
const std::vector<OperandType>& expectedTypes) {
return validateOperandTypes(expectedTypes, "input", context->getNumInputs(),
[context](uint32_t index) { return context->getInputType(index); });
}
bool validateOutputTypes(const IOperationValidationContext* context,
const std::vector<OperandType>& expectedTypes) {
return validateOperandTypes(
expectedTypes, "output", context->getNumOutputs(),
[context](uint32_t index) { return context->getOutputType(index); });
}
bool validateHalVersion(const IOperationValidationContext* context,
HalVersion minSupportedHalVersion) {
if (context->getHalVersion() < minSupportedHalVersion) {
NN_RET_CHECK_FAIL() << "The given inputs and outputs are only supported in "
<< toString(minSupportedHalVersion) << " and later (validating using "
<< toString(context->getHalVersion()) << ")";
}
return true;
}
bool SameShape(const Shape& in1, const Shape& in2) {
if (in1.type != in2.type || in1.dimensions.size() != in2.dimensions.size()) {
return false;
}
for (size_t i = 0; i < in1.dimensions.size(); i++) {
if (in1.dimensions[i] != in2.dimensions[i]) {
return false;
}
}
return true;
}
bool SetShape(const Shape& in, Shape* out) {
if (in.type != out->type) {
return false;
}
out->dimensions = in.dimensions;
return true;
}
bool combineDimensions(const std::vector<uint32_t>& lhs, const std::vector<uint32_t>& rhs,
std::vector<uint32_t>* combined) {
if (rhs.empty()) {
*combined = lhs;
return true;
}
if (lhs.empty()) {
*combined = rhs;
return true;
}
NN_RET_CHECK_EQ(lhs.size(), rhs.size()) << "incompatible ranks";
combined->resize(lhs.size());
for (uint32_t i = 0; i < lhs.size(); i++) {
if (lhs[i] == 0) {
(*combined)[i] = rhs[i];
continue;
}
if (rhs[i] == 0) {
(*combined)[i] = lhs[i];
continue;
}
NN_RET_CHECK_EQ(lhs[i], rhs[i]) << "incompatible dimension: " << i;
(*combined)[i] = lhs[i];
}
return true;
}
uint32_t getNumberOfElements(const Shape& shape) {
uint32_t count = 1;
for (size_t i = 0; i < shape.dimensions.size(); i++) {
count *= shape.dimensions[i];
}
return count;
}
uint32_t getNumberOfElements(const Shape& shape,
size_t firstAxisInclusive,
size_t lastAxisExclusive) {
nnAssert(0 <= firstAxisInclusive);
nnAssert(firstAxisInclusive <= lastAxisExclusive);
nnAssert(lastAxisExclusive <= shape.dimensions.size());
uint32_t count = 1;
for (size_t i = firstAxisInclusive; i < lastAxisExclusive; i++) {
count *= shape.dimensions[i];
}
return count;
}
uint32_t getNumberOfDimensions(const Shape& shape) {
return shape.dimensions.size();
}
uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx) {
nnAssert(0 <= dimensionIdx && dimensionIdx < shape.dimensions.size());
return shape.dimensions[dimensionIdx];
}
bool handleNegativeAxis(int32_t numberOfDimensions, int32_t* axis) {
NN_CHECK(-numberOfDimensions <= *axis && *axis < numberOfDimensions);
if (*axis < 0) {
*axis += numberOfDimensions;
}
return true;
}
bool QuantizeMultiplier(double double_multiplier, int32_t* quantized_multiplier, int* shift) {
if (double_multiplier == 0.) {
*quantized_multiplier = 0;
*shift = 0;
return true;
}
const double q = std::frexp(double_multiplier, shift);
auto q_fixed = static_cast<int64_t>(std::round(q * (1ll << 31)));
NN_RET_CHECK(q_fixed <= (1ll << 31));
if (q_fixed == (1ll << 31)) {
q_fixed /= 2;
++*shift;
}
NN_RET_CHECK_LE(q_fixed, std::numeric_limits<int32_t>::max());
*quantized_multiplier = static_cast<int32_t>(q_fixed);
return true;
}
bool QuantizeMultiplierSmallerThanOne(double double_multiplier,
int32_t* quantized_multiplier,
int32_t* right_shift) {
NN_OPS_CHECK(double_multiplier >= 0.);
NN_OPS_CHECK(double_multiplier < 1.);
if (double_multiplier == 0.) {
*quantized_multiplier = 0;
*right_shift = 0;
return true;
}
NN_OPS_CHECK(double_multiplier > 0.);
const double q = std::frexp(double_multiplier, right_shift);
*right_shift *= -1;
int64_t q_fixed = static_cast<int64_t>(std::round(q * (1LL << 31)));
NN_OPS_CHECK(q_fixed <= (1LL << 31));
if (q_fixed == (1LL << 31)) {
q_fixed /= 2;
--*right_shift;
}
NN_OPS_CHECK(*right_shift >= 0);
NN_OPS_CHECK(q_fixed <= std::numeric_limits<int32_t>::max());
*quantized_multiplier = static_cast<int32_t>(q_fixed);
return true;
}
bool QuantizeMultiplierGreaterThanOne(double double_multiplier,
int32_t* quantized_multiplier,
int* left_shift) {
NN_OPS_CHECK(double_multiplier > 1.);
const double q = std::frexp(double_multiplier, left_shift);
int64_t q_fixed = static_cast<int64_t>(std::round(q * (1LL << 31)));
NN_OPS_CHECK(q_fixed <= (1LL << 31));
if (q_fixed == (1LL << 31)) {
q_fixed /= 2;
++*left_shift;
}
NN_OPS_CHECK(*left_shift >= 0);
NN_OPS_CHECK(q_fixed <= std::numeric_limits<int32_t>::max());
*quantized_multiplier = static_cast<int32_t>(q_fixed);
return true;
}
bool GetQuantizedConvolutionMultipler(const Shape& inputShape, const Shape& filterShape,
const Shape& biasShape, const Shape& outputShape,
double* multiplier) {
// Upcast bias and input_product to double
const double input_product_scale = inputShape.scale * filterShape.scale;
const double bias_scale = biasShape.scale;
// The following conditions must be guaranteed by the training pipeline.
NN_OPS_CHECK(std::abs(input_product_scale - bias_scale) <=
1e-6 * std::min(input_product_scale, bias_scale));
NN_OPS_CHECK(input_product_scale >= 0);
*multiplier = input_product_scale / outputShape.scale;
return true;
}
void CalculateActivationRangeUint8(int32_t activation,
const Shape& outputShape,
int32_t* act_min,
int32_t* act_max) {
const int32_t qmin = std::numeric_limits<uint8_t>::min();
const int32_t qmax = std::numeric_limits<uint8_t>::max();
const auto scale = outputShape.scale;
const auto zero_point = outputShape.offset;
auto quantize = [scale, zero_point](float f) {
return zero_point + static_cast<int32_t>(std::round(f / scale));
};
if (activation == kActivationRelu) {
*act_min = std::max(qmin, quantize(0.0));
*act_max = qmax;
} else if (activation == kActivationRelu6) {
*act_min = std::max(qmin, quantize(0.0));
*act_max = std::min(qmax, quantize(6.0));
} else if (activation == kActivationRelu1) {
*act_min = std::max(qmin, quantize(-1.0));
*act_max = std::min(qmax, quantize(1.0));
} else if (activation == kActivationNone){
*act_min = qmin;
*act_max = qmax;
} else {
LOG(ERROR) << "Unsupported fused activation function.";
}
}
void CalculateActivationRangeFloat(int32_t activation,
float* activation_min,
float* activation_max) {
if (activation == kActivationRelu) {
*activation_min = 0.f;
*activation_max = std::numeric_limits<float>::max();
} else if (activation == kActivationRelu6) {
*activation_min = 0.f;
*activation_max = 6.f;
} else if (activation == kActivationRelu1) {
*activation_min = -1.f;
*activation_max = 1.f;
} else if (activation == kActivationNone){
*activation_min = std::numeric_limits<float>::lowest();
*activation_max = std::numeric_limits<float>::max();
} else {
LOG(ERROR) << "Unsupported fused activation function.";
}
}
int32_t CalculateInputRadius(int input_integer_bits, int input_left_shift) {
const double max_input_rescaled = 1.0 * ((1 << input_integer_bits) - 1) *
(1LL << (31 - input_integer_bits)) /
(1LL << input_left_shift);
// Tighten bound using floor. Suppose that we could use the exact value.
// After scaling the difference, the result would be at the maximum. Thus we
// must ensure that our value has lower magnitude.
return static_cast<int32_t>(std::floor(max_input_rescaled));
}
void calculateExplicitPaddingImpl(int32_t in_size, int32_t stride, int32_t dilation_factor,
int32_t filter_size, int32_t padding_implicit,
bool isTransposeConv, int32_t* padding_head,
int32_t* padding_tail) {
*padding_head = 0;
*padding_tail = 0;
int32_t effective_filter_size = (filter_size - 1) * dilation_factor + 1;
if (padding_implicit == kPaddingSame) {
int32_t out_size = (in_size + stride - 1) / stride;
int32_t tmp = (out_size - 1) * stride + effective_filter_size;
if (tmp > in_size) {
*padding_head = (tmp - in_size) / 2;
*padding_tail = (tmp - in_size) - *padding_head;
}
// For transpose conv, make padding tail fit tightly to the end of the last stride.
if (isTransposeConv) {
*padding_tail = (tmp - in_size) - *padding_head;
}
}
}
bool calculateBroadcastedShape(const Shape& in1, const Shape& in2, Shape* out) {
NN_RET_CHECK(in1.type == in2.type);
uint32_t numberOfDims1 = getNumberOfDimensions(in1);
uint32_t numberOfDims2 = getNumberOfDimensions(in2);
uint32_t maxDims = std::max(numberOfDims1, numberOfDims2);
out->dimensions = std::vector<uint32_t>(maxDims);
for (uint32_t i = 1; i <= maxDims; i++) {
uint32_t dim1 = 1;
if (i <= numberOfDims1) {
dim1 = getSizeOfDimension(in1, numberOfDims1 - i);
}
uint32_t dim2 = 1;
if (i <= numberOfDims2) {
dim2 = getSizeOfDimension(in2, numberOfDims2 - i);
}
if (dim1 != dim2 && dim1 != 1 && dim2 != 1) {
LOG(ERROR) << "Dimensions mismatch for broadcast:\n"
<< "First tensor: dimension " << numberOfDims1 - i << " of size " << dim1
<< "\nSecond tensor: dimension " << numberOfDims2 - i << "of size " << dim2;
return false;
}
out->dimensions[maxDims - i] = (dim1 == 1) ? dim2 : dim1;
}
return true;
}
uint8_t requantize(uint8_t value, const Shape& oldShape, const Shape& newShape) {
double doubleValue = (value - oldShape.offset) * oldShape.scale;
double doubleRet = doubleValue / newShape.scale + newShape.offset;
if (doubleRet < 0) return 0;
if (doubleRet > 255) return 255;
return static_cast<uint8_t>(std::round(doubleRet));
}
bool floorPrepare(const Shape& input, Shape* output) {
return SetShape(input, output);
}
bool depthwiseConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
int32_t padding_left, int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
int32_t depth_multiplier, int32_t dilation_width_factor,
int32_t dilation_height_factor, Shape* output) {
if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
NN_OPS_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM);
} else {
NN_OPS_CHECK(input.type == filter.type);
}
if (input.type == OperandType::TENSOR_QUANT8_ASYMM) {
NN_OPS_CHECK(bias.type == OperandType::TENSOR_INT32);
} else {
NN_OPS_CHECK(input.type == bias.type);
}
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
NN_OPS_CHECK(getNumberOfDimensions(filter) == 4);
NN_OPS_CHECK(getNumberOfDimensions(bias) == 1);
NN_OPS_CHECK(getSizeOfDimension(filter, 3) == getSizeOfDimension(bias, 0));
uint32_t channels_out = getSizeOfDimension(filter, 3);
uint32_t channels_in = getSizeOfDimension(input, 3);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t filterWidth = getSizeOfDimension(filter, 2);
uint32_t filterHeight = getSizeOfDimension(filter, 1);
uint32_t batches = getSizeOfDimension(input, 0);
NN_OPS_CHECK(depth_multiplier * channels_in == channels_out);
int32_t effectiveFilterWidth = (filterWidth - 1) * dilation_width_factor + 1;
int32_t effectiveFilterHeight = (filterHeight - 1) * dilation_height_factor + 1;
NN_RET_CHECK_GT(effectiveFilterWidth, padding_left);
NN_RET_CHECK_GT(effectiveFilterWidth, padding_right);
NN_RET_CHECK_GT(effectiveFilterHeight, padding_top);
NN_RET_CHECK_GT(effectiveFilterHeight, padding_bottom);
uint32_t outWidth = computeOutSize(width, filterWidth, stride_width, dilation_width_factor,
padding_left, padding_right);
uint32_t outHeight = computeOutSize(height, filterHeight, stride_height, dilation_height_factor,
padding_top, padding_bottom);
output->type = input.type;
output->dimensions = {batches, outHeight, outWidth, channels_out};
return true;
}
bool genericActivationPrepare(const Shape& input,
Shape* output) {
NN_OPS_CHECK(getNumberOfDimensions(input) <= 4);
return SetShape(input, output);
}
bool genericNormalizationPrepare(const Shape& input, Shape* output) {
return SetShape(input, output);
}
bool reshapePrepare(const Shape& input,
const int32_t* targetDims,
const int32_t targetDimsSize,
Shape* output) {
// Reshape allows one of the targetDims components to have the
// special -1 value, meaning it will be calculated automatically based on the
// input. Here we calculate what that dimension should be so that the number
// of output elements in the same as the number of input elements.
int32_t numInputElements = (int32_t) getNumberOfElements(input);
std::vector<uint32_t> outDims(targetDimsSize);
int32_t numOutputElements = 1;
int32_t strechDim = -1;
for (int32_t i = 0; i < targetDimsSize; ++i) {
int32_t value = targetDims[i];
if (value == -1) {
NN_OPS_CHECK(strechDim == -1);
strechDim = i;
} else {
numOutputElements *= value;
outDims[i] = (uint32_t)value;
}
}
if (strechDim != -1) {
int32_t strechValue = numInputElements / numOutputElements;
outDims[strechDim] = (uint32_t) strechValue;
numOutputElements *= strechValue;
}
NN_OPS_CHECK(numInputElements == numOutputElements);
output->type = input.type;
output->dimensions = outDims;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool depthToSpacePrepare(const Shape& input,
int32_t blockSize,
Shape* output) {
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
NN_OPS_CHECK(blockSize > 0);
uint32_t batches = getSizeOfDimension(input, 0);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t channels = getSizeOfDimension(input, 3);
NN_OPS_CHECK(channels % (blockSize * blockSize) == 0);
output->type = input.type;
output->dimensions = {batches,
height * blockSize,
width * blockSize,
channels / (blockSize * blockSize)};
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool spaceToDepthPrepare(const Shape& input,
int32_t blockSize,
Shape* output) {
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
NN_OPS_CHECK(blockSize > 0);
uint32_t batches = getSizeOfDimension(input, 0);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t channels = getSizeOfDimension(input, 3);
NN_OPS_CHECK(height % blockSize == 0);
NN_OPS_CHECK(width % blockSize == 0);
output->type = input.type;
output->dimensions = {batches,
height / blockSize,
width / blockSize,
channels * (blockSize * blockSize)};
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool embeddingLookupPrepare(const Shape &valueShape,
const Shape &lookupShape,
Shape *outputShape) {
NN_OPS_CHECK(getNumberOfDimensions(valueShape) >= 2);
NN_OPS_CHECK(getNumberOfDimensions(lookupShape) == 1);
const uint32_t rows = getSizeOfDimension(valueShape, 0);
const uint32_t columns = getSizeOfDimension(valueShape, 1);
const uint32_t lookups = getSizeOfDimension(lookupShape, 0);
outputShape->type = valueShape.type;
outputShape->dimensions = { lookups, columns };
for (uint32_t i = 2; i < getNumberOfDimensions(valueShape); i++) {
outputShape->dimensions.push_back(getSizeOfDimension(valueShape, i));
}
outputShape->offset = valueShape.offset;
outputShape->scale = valueShape.scale;
return true;
}
bool hashtableLookupPrepare(const Shape &lookupShape,
const Shape &keyShape,
const Shape &valueShape,
Shape *outputShape,
Shape *hitShape) {
NN_OPS_CHECK(getNumberOfDimensions(lookupShape) == 1);
NN_OPS_CHECK(getNumberOfDimensions(keyShape) == 1);
NN_OPS_CHECK(getNumberOfDimensions(valueShape) >= 1);
const uint32_t lookups = getSizeOfDimension(lookupShape, 0);
const uint32_t keys = getSizeOfDimension(keyShape, 0);
const uint32_t rows = getSizeOfDimension(valueShape, 0);
outputShape->type = valueShape.type;
outputShape->dimensions = { lookups };
for (uint32_t i = 1; i < getNumberOfDimensions(valueShape); i++) {
outputShape->dimensions.push_back(getSizeOfDimension(valueShape, i));
}
outputShape->offset = valueShape.offset;
outputShape->scale = valueShape.scale;
hitShape->type = OperandType::TENSOR_QUANT8_ASYMM;
hitShape->dimensions = { lookups };
hitShape->offset = 0;
hitShape->scale = 1.f;
return true;
}
bool padPrepare(const Shape& input,
const int32_t* paddingsData,
const Shape& paddingsShape,
Shape* output) {
uint32_t numInputDims = getNumberOfDimensions(input);
// paddings need to be provided as a 2-D int32 tensor.
NN_OPS_CHECK(paddingsShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(paddingsShape) == 2);
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 0) == numInputDims);
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 1) == 2);
std::vector<uint32_t> outDims(numInputDims);
for (uint32_t i = 0; i < numInputDims; ++i) {
int32_t beforePadding = *paddingsData++;
int32_t afterPadding = *paddingsData++;
// Pad value has to be greater than equal to 0.
NN_OPS_CHECK(beforePadding >= 0 && afterPadding >= 0);
outDims[i] = beforePadding + getSizeOfDimension(input, i) + afterPadding;
}
output->type = input.type;
output->dimensions = outDims;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool batchToSpacePrepare(const Shape& input,
const int32_t* blockSizeData,
const Shape& blockSizeShape,
Shape* output) {
// Only 4D NHWC tensors are supported.
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
// blockSize need to be provided as a 1-D int32 tensor.
NN_OPS_CHECK(blockSizeShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(blockSizeShape) == 1);
// Only applies to spatial dimensions.
NN_OPS_CHECK(getSizeOfDimension(blockSizeShape, 0) == 2);
uint32_t batches = getSizeOfDimension(input, 0);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t channels = getSizeOfDimension(input, 3);
NN_OPS_CHECK(batches % (blockSizeData[0] * blockSizeData[1]) == 0);
output->type = input.type;
output->dimensions = {batches / (blockSizeData[0] * blockSizeData[1]),
height * blockSizeData[0],
width * blockSizeData[1],
channels};
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool spaceToBatchPrepare(const Shape& input,
const int32_t* blockSizeData,
const Shape& blockSizeShape,
const int32_t* paddingsData,
const Shape& paddingsShape,
Shape* output) {
// Only 4D NHWC tensors are supported.
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
// blockSize need to be provided as a 1-D int32 tensor.
NN_OPS_CHECK(blockSizeShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(blockSizeShape) == 1);
// Only applies to spatial dimensions.
NN_OPS_CHECK(getSizeOfDimension(blockSizeShape, 0) == 2);
// paddings need to be provided as a 2-D int32 tensor.
NN_OPS_CHECK(paddingsShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(paddingsShape) == 2);
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 0) == 2);
NN_OPS_CHECK(getSizeOfDimension(paddingsShape, 1) == 2);
uint32_t batches = getSizeOfDimension(input, 0);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t channels = getSizeOfDimension(input, 3);
uint32_t paddedHeight = paddingsData[0] + height + paddingsData[1];
uint32_t paddedWidth = paddingsData[2] + width + paddingsData[3];
NN_OPS_CHECK(paddedHeight % blockSizeData[0] == 0);
NN_OPS_CHECK(paddedWidth % blockSizeData[1] == 0);
output->type = input.type;
output->dimensions = {batches * (blockSizeData[0] * blockSizeData[1]),
paddedHeight / blockSizeData[0],
paddedWidth / blockSizeData[1],
channels};
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool squeezePrepare(const Shape& input,
const int32_t* squeezeDims,
const Shape& squeezeDimsShape,
Shape* output) {
int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(input));
// squeezeDims need to be provided as a 1-D int32 tensor.
NN_OPS_CHECK(squeezeDimsShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(squeezeDimsShape) == 1);
int32_t squeezeDimsSize = static_cast<int32_t>(getSizeOfDimension(squeezeDimsShape, 0));
std::vector<bool> shouldSqueeze(numInputDims, false);
int32_t numDimsSqueezed = 0;
if (squeezeDimsSize == 0) {
// If squeezeDimsSize is 0, all dims with value 1 will be squeezed.
for (int32_t idx = 0; idx < numInputDims; ++idx) {
if (getSizeOfDimension(input, idx) == 1) {
shouldSqueeze[idx] = true;
++numDimsSqueezed;
}
}
} else {
for (int32_t idx = 0; idx < squeezeDimsSize; ++idx) {
int32_t current = squeezeDims[idx] < 0 ? squeezeDims[idx] + numInputDims
: squeezeDims[idx];
NN_OPS_CHECK(current >= 0 && current < numInputDims &&
getSizeOfDimension(input, current) == 1);
if (!shouldSqueeze[current]) ++numDimsSqueezed;
shouldSqueeze[current] = true;
}
}
// Sets output dimensions.
std::vector<uint32_t> outDims(numInputDims - numDimsSqueezed);
for (int32_t inIdx = 0, outIdx = 0; inIdx < numInputDims; ++inIdx) {
if (!shouldSqueeze[inIdx]) {
outDims[outIdx++] = getSizeOfDimension(input, inIdx);
}
}
output->type = input.type;
output->dimensions = outDims;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool meanPrepare(const Shape& input,
const int32_t* axisData,
const Shape& axisShape,
bool keepDims,
Shape* output) {
// perm need to be provided as a 1-D int32 tensor.
NN_OPS_CHECK(axisShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(getNumberOfDimensions(axisShape) == 1);
int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(input));
int32_t axisSize = static_cast<int32_t>(getSizeOfDimension(axisShape, 0));
// Determines size of output tensor.
if (keepDims) {
std::vector<uint32_t> outDims(numInputDims);
for (int32_t idx = 0; idx < numInputDims; ++idx) {
bool isAxis = false;
for (int32_t axisIdx = 0; axisIdx < axisSize; ++axisIdx) {
if (axisData[axisIdx] == idx || axisData[axisIdx] + numInputDims == idx) {
isAxis = true;
break;
}
}
if (isAxis) {
outDims[idx] = 1;
} else {
outDims[idx] = getSizeOfDimension(input, idx);
}
}
output->dimensions = outDims;
} else {
// Calculates size of reducing axis.
int32_t numReduceAxis = axisSize;
for (int32_t i = 0; i < axisSize; ++i) {
int32_t current = axisData[i];
if (current < 0) {
current += numInputDims;
}
NN_OPS_CHECK(current >= 0 && current < numInputDims);
for (int32_t j = 0; j < i; ++j) {
int32_t previous = axisData[j];
if (previous < 0) {
previous += numInputDims;
}
if (current == previous) {
--numReduceAxis;
break;
}
}
}
// Determines output dimensions.
std::vector<uint32_t> outDims(numInputDims - numReduceAxis);
int32_t numSkipAxis = 0;
for (int32_t idx = 0; idx < numInputDims; ++idx) {
bool isAxis = false;
for (int32_t axisIdx = 0; axisIdx < axisSize; ++axisIdx) {
if (axisData[axisIdx] == idx || axisData[axisIdx] + numInputDims == idx) {
++numSkipAxis;
isAxis = true;
break;
}
}
if (!isAxis) {
outDims[idx - numSkipAxis] = getSizeOfDimension(input, idx);
}
}
output->dimensions = outDims;
}
output->type = input.type;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool stridedSlicePrepare(const Shape& input,
const int32_t* beginData, const Shape& beginShape,
const int32_t* endData, const Shape& endShape,
const int32_t* stridesData, const Shape& stridesShape,
int32_t beginMask, int32_t endMask, int32_t shrinkAxisMask,
Shape* output) {
uint32_t numInputDims = getNumberOfDimensions(input);
// StridedSlice op only supports 1D-4D input arrays.
NN_OPS_CHECK(numInputDims <= 4);
NN_OPS_CHECK(getNumberOfDimensions(beginShape) == 1);
NN_OPS_CHECK(getNumberOfDimensions(endShape) == 1);
NN_OPS_CHECK(getNumberOfDimensions(stridesShape) == 1);
NN_OPS_CHECK(getSizeOfDimension(beginShape, 0) == numInputDims);
NN_OPS_CHECK(getSizeOfDimension(endShape, 0) == numInputDims);
NN_OPS_CHECK(getSizeOfDimension(stridesShape, 0) == numInputDims);
NN_OPS_CHECK(beginShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(endShape.type == OperandType::TENSOR_INT32);
NN_OPS_CHECK(stridesShape.type == OperandType::TENSOR_INT32);
// Determine size of output tensor and map indices
std::vector<uint32_t> outDims;
for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); idx++) {
int32_t dim = static_cast<int32_t>(getSizeOfDimension(input, idx));
int32_t stride = stridesData[idx];
// stride value has to be non-zero
NN_OPS_CHECK(stride != 0);
bool positiveStride = stride > 0;
int32_t begin = beginMask & (1 << idx)
? positiveStride ? 0 : dim - 1
: ClampedIndex(beginData[idx], dim, positiveStride);
int32_t end = endMask & (1 << idx)
? positiveStride ? dim : -1
: ClampedIndex(endData[idx], dim, positiveStride);
// This is valid for both positive and negative strides
int32_t outDim = ceil((end - begin) / static_cast<float>(stride));
outDim = outDim < 0 ? 0 : static_cast<uint32_t>(outDim);
if (!(shrinkAxisMask & (1 << idx))) {
outDims.push_back(outDim);
} else {
if (outDim != 1) {
LOG(ERROR) << "Outdim " << idx << " is " << outDim << ", expected 1";
NN_OPS_CHECK(outDim == 1);
}
}
}
output->type = input.type;
output->dimensions = outDims;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
bool argMinMaxPrepare(const Shape& input, int32_t axis, Shape* output) {
NN_CHECK(handleNegativeAxis(input, &axis));
output->type = OperandType::TENSOR_INT32;
// Copy the input dimensions, omitting the axis dimension.
output->dimensions.clear();
output->dimensions.reserve(getNumberOfDimensions(input) - 1);
output->dimensions.insert(output->dimensions.end(),
input.dimensions.begin(),
input.dimensions.begin() + axis);
output->dimensions.insert(output->dimensions.end(),
input.dimensions.begin() + axis + 1,
input.dimensions.end());
return true;
}
bool splitPrepare(const Shape& input, int32_t axis, int32_t numOutputs,
std::vector<Shape>* output) {
NN_CHECK(handleNegativeAxis(input, &axis));
const int32_t sizeOfAxisToSplit = input.dimensions[axis];
NN_OPS_CHECK(sizeOfAxisToSplit % numOutputs == 0);
const int32_t sliceSize = sizeOfAxisToSplit / numOutputs;
for (int i = 0; i < numOutputs; ++i) {
output->at(i).type = input.type;
output->at(i).dimensions = input.dimensions;
output->at(i).dimensions[axis] = sliceSize;
output->at(i).offset = input.offset;
output->at(i).scale = input.scale;
}
return true;
}
bool groupedConvPrepare(const Shape& input, const Shape& filter, const Shape& bias,
int32_t padding_left, int32_t padding_right, int32_t padding_top,
int32_t padding_bottom, int32_t stride_width, int32_t stride_height,
int32_t numGroups, Shape* output) {
if (filter.type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL) {
NN_OPS_CHECK(input.type == OperandType::TENSOR_QUANT8_ASYMM);
} else {
NN_OPS_CHECK(input.type == filter.type);
}
if (input.type == OperandType::TENSOR_QUANT8_ASYMM) {
NN_OPS_CHECK(bias.type == OperandType::TENSOR_INT32);
} else {
NN_OPS_CHECK(input.type == bias.type);
}
NN_OPS_CHECK(getNumberOfDimensions(input) == 4);
NN_OPS_CHECK(getNumberOfDimensions(filter) == 4);
NN_OPS_CHECK(getNumberOfDimensions(bias) == 1);
NN_OPS_CHECK(getSizeOfDimension(filter, 0) == getSizeOfDimension(bias, 0));
NN_OPS_CHECK(getSizeOfDimension(filter, 3) * numGroups == getSizeOfDimension(input, 3));
NN_OPS_CHECK(getSizeOfDimension(filter, 0) % numGroups == 0);
uint32_t channels_out = getSizeOfDimension(filter, 0);
uint32_t width = getSizeOfDimension(input, 2);
uint32_t height = getSizeOfDimension(input, 1);
uint32_t filterWidth = getSizeOfDimension(filter, 2);
uint32_t filterHeight = getSizeOfDimension(filter, 1);
uint32_t batches = getSizeOfDimension(input, 0);
NN_RET_CHECK_GT(static_cast<int32_t>(filterWidth), padding_left);
NN_RET_CHECK_GT(static_cast<int32_t>(filterWidth), padding_right);
NN_RET_CHECK_GT(static_cast<int32_t>(filterHeight), padding_top);
NN_RET_CHECK_GT(static_cast<int32_t>(filterHeight), padding_bottom);
uint32_t outWidth =
computeOutSize(width, filterWidth, stride_width, padding_left, padding_right);
uint32_t outHeight =
computeOutSize(height, filterHeight, stride_height, padding_top, padding_bottom);
output->type = input.type;
output->dimensions = {batches, outHeight, outWidth, channels_out};
return true;
}
} // namespace nn
} // namespace android