| /* |
| * 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 || in.dimensions.size() != out->dimensions.size()) { |
| return false; |
| } |
| out->dimensions = in.dimensions; |
| 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 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, |
| float* multiplier) { |
| const float input_product_scale = inputShape.scale * filterShape.scale; |
| const float bias_scale = biasShape.scale; |
| const float output_scale = outputShape.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); |
| NN_OPS_CHECK(input_product_scale < output_scale); |
| *multiplier = input_product_scale / output_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)); |
| } |
| |
| bool calculateBroadcastedShape(const Shape& in1, const Shape& in2, Shape* out) { |
| 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] = std::max(dim1, dim2); |
| } |
| return true; |
| } |
| |
| uint8_t requantize(uint8_t value, const Shape& oldShape, const Shape& newShape) { |
| double doubleValue = (value - oldShape.offset) * oldShape.scale; |
| return static_cast<uint8_t>(doubleValue / newShape.scale + newShape.offset); |
| } |
| |
| bool addMulPrepare(const Shape& in1, const Shape& in2, Shape* out) { |
| NN_OPS_CHECK(getNumberOfDimensions(in1) <= 4 && getNumberOfDimensions(in2) <= 4); |
| NN_OPS_CHECK(in1.type == in2.type); |
| if (SameShape(in1, in2)) { |
| return SetShape(in1, out); |
| } |
| return calculateBroadcastedShape(in1, in2, out); |
| } |
| |
| bool floorPrepare(const Shape& input, Shape* output) { |
| return SetShape(input, output); |
| } |
| |
| bool dequantizePrepare(const Shape& input, Shape* output) { |
| if (input.type != OperandType::TENSOR_QUANT8_ASYMM || |
| (output->type != OperandType::TENSOR_FLOAT16 && |
| output->type != OperandType::TENSOR_FLOAT32)) { |
| LOG(ERROR) << "bad input / output operand type."; |
| return false; |
| } |
| if (input.dimensions.size() != output->dimensions.size()) { |
| LOG(ERROR) << "input and output tensors don't have the same rank."; |
| return false; |
| } |
| output->dimensions = input.dimensions; |
| return true; |
| } |
| |
| bool quantizePrepare(const Shape& input, Shape* output) { |
| if (input.type != OperandType::TENSOR_FLOAT32) { |
| LOG(ERROR) << "QUANTIZE input must be TENSOR_FLOAT32"; |
| return false; |
| } |
| if (output->type != OperandType::TENSOR_QUANT8_ASYMM) { |
| LOG(ERROR) << "QUANTIZE output must be TENSOR_QUANT8_ASYMM"; |
| return false; |
| } |
| if (input.dimensions.size() != output->dimensions.size()) { |
| LOG(ERROR) << "QUANTIZE input and output tensors must have the same rank"; |
| return false; |
| } |
| output->dimensions = input.dimensions; |
| return true; |
| } |
| |
| bool convPrepare(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, |
| 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) == getSizeOfDimension(input, 3)); |
| |
| 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); |
| |
| 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; |
| } |
| |
| 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, |
| 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 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); |
| |
| 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; |
| } |
| |
| |
| bool genericPoolingPrepare(const Shape& input, |
| 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 filter_width, int32_t filter_height, |
| Shape* output) { |
| NN_OPS_CHECK(getNumberOfDimensions(input) == 4); |
| |
| uint32_t batches = getSizeOfDimension(input, 0); |
| uint32_t width = getSizeOfDimension(input, 2); |
| uint32_t height = getSizeOfDimension(input, 1); |
| uint32_t channels_out = getSizeOfDimension(input, 3); |
| |
| uint32_t outWidth = computeOutSize(width, filter_width, stride_width, |
| padding_left, padding_right); |
| uint32_t outHeight = computeOutSize(height, filter_height, stride_height, |
| 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 fullyConnectedPrepare(const Shape& input, |
| const Shape& weights, |
| const Shape& bias, |
| Shape* output) { |
| // Check all the parameters of tensor match within themselves and match the |
| // input configuration. |
| NN_OPS_CHECK(input.type == weights.type); |
| if (input.type == OperandType::TENSOR_QUANT8_ASYMM) { |
| NN_OPS_CHECK(bias.type == OperandType::TENSOR_INT32); |
| } else { |
| NN_OPS_CHECK(input.type == bias.type); |
| } |
| // The Tensorflow fully connected layer specification says that input should |
| // be of at least rank 2, so we check. Tflite doesn't check. |
| NN_OPS_CHECK(getNumberOfDimensions(input) >= 2); |
| NN_OPS_CHECK(getNumberOfDimensions(weights) == 2); |
| uint32_t input_n_elements = getNumberOfElements(input); |
| uint32_t num_units = getSizeOfDimension(weights, 0); |
| uint32_t input_size = getSizeOfDimension(weights, 1); |
| uint32_t batch_size = input_n_elements / input_size; |
| |
| NN_OPS_CHECK(getSizeOfDimension(bias, 0) == num_units); |
| NN_OPS_CHECK(input_size * batch_size == input_n_elements); |
| |
| output->type = input.type; |
| output->dimensions = {batch_size, num_units}; |
| |
| return true; |
| } |
| |
| bool concatenationPrepare(const std::vector<Shape>& inputShapes, int32_t axis, Shape* output) { |
| int num_inputs = inputShapes.size(); |
| OperandType input_type = inputShapes[0].type; |
| uint32_t num_dimensions = getNumberOfDimensions(inputShapes[0]); |
| |
| NN_RET_CHECK(axis >= 0); |
| NN_RET_CHECK(axis < (int32_t)num_dimensions); |
| |
| int sumAxis = getSizeOfDimension(inputShapes[0], axis); |
| for (int i = 1; i < num_inputs; ++i) { |
| NN_RET_CHECK(getNumberOfDimensions(inputShapes[i]) == num_dimensions); |
| NN_RET_CHECK(inputShapes[i].type == inputShapes[0].type); |
| for (int d = 0; d < (int32_t)num_dimensions; ++d) { |
| if (d == axis) { |
| sumAxis += getSizeOfDimension(inputShapes[i], axis); |
| } else { |
| NN_RET_CHECK_EQ(getSizeOfDimension(inputShapes[0], d), |
| getSizeOfDimension(inputShapes[i], d)); |
| } |
| } |
| } |
| |
| output->type = input_type; |
| output->dimensions = inputShapes[0].dimensions; |
| output->dimensions[axis] = sumAxis; |
| |
| return true; |
| } |
| |
| 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 resizeBilinearPrepare(const Shape& input, |
| int32_t width, |
| int32_t height, |
| Shape* output) { |
| NN_OPS_CHECK(getNumberOfDimensions(input) == 4); |
| uint32_t batches = getSizeOfDimension(input, 0); |
| uint32_t channels = getSizeOfDimension(input, 3); |
| |
| output->type = input.type; |
| output->dimensions = {batches, (uint32_t)height, (uint32_t)width, channels}; |
| |
| 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) { |
| // Currently only 4D tensors are supported. |
| uint32_t numInputDims = getNumberOfDimensions(input); |
| NN_OPS_CHECK(numInputDims == 4); |
| |
| // 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 transposePrepare(const Shape& input, |
| const int32_t* permData, |
| const Shape& permShape, |
| Shape* output) { |
| uint32_t numInputDims = getNumberOfDimensions(input); |
| |
| // permData can be NO_VALUE representing a regular 2D matrix transpose |
| if (permData == nullptr) { |
| NN_OPS_CHECK(numInputDims == 2); |
| output->type = input.type; |
| output->dimensions = {getSizeOfDimension(input, 1), getSizeOfDimension(input, 0)}; |
| output->offset = input.offset; |
| output->scale = input.scale; |
| return true; |
| } |
| |
| // Transpose op only supports 1D-4D input arrays. |
| NN_OPS_CHECK(numInputDims <= 4); |
| |
| // perm need to be provided as a 1-D int32 tensor. |
| NN_OPS_CHECK(permShape.type == OperandType::TENSOR_INT32); |
| NN_OPS_CHECK(getNumberOfDimensions(permShape) == 1); |
| NN_OPS_CHECK(numInputDims == getSizeOfDimension(permShape, 0)); |
| |
| std::vector<uint32_t> outDims(numInputDims); |
| for (int32_t idx = 0; idx < static_cast<int32_t>(numInputDims); ++idx) { |
| NN_OPS_CHECK(permData[idx] >= 0 && permData[idx] < static_cast<int32_t>(numInputDims)); |
| outDims[idx] = getSizeOfDimension(input, permData[idx]); |
| } |
| |
| 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) { |
| 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); |
| |
| 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; |
| } |
| |
| bool transposeConvPrepare(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, |
| Shape* output) { |
| 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)); |
| |
| 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); |
| |
| uint32_t outWidth = computeOutSizeTransposeConv(width, filterWidth, stride_width, padding_left, |
| padding_right); |
| uint32_t outHeight = computeOutSizeTransposeConv(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 |
