| /* | |
| * 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 { | |
| 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 getNumberOfDimensions(const Shape& shape) { | |
| return shape.dimensions.size(); | |
| } | |
| uint32_t getSizeOfDimension(const Shape& shape, uint32_t dimensionIdx) { | |
| if (dimensionIdx >= shape.dimensions.size()) { | |
| // TODO, log the error | |
| return 0; | |
| } | |
| return shape.dimensions[dimensionIdx]; | |
| } | |
| 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 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); | |
| } else { | |
| // BroadcastAdd needed | |
| 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 BroadcastAdd"; | |
| return false; | |
| } | |
| out->dimensions[maxDims - i] = std::max(dim1, dim2); | |
| } | |
| } | |
| return true; | |
| } | |
| 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_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 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) { | |
| 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) { | |
| 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); | |
| } | |
| NN_OPS_CHECK(getNumberOfDimensions(input) >= 2); | |
| uint32_t input_size = getNumberOfElements(input); | |
| uint32_t num_units = getSizeOfDimension(weights, 0); | |
| uint32_t batch_size = input_size / getSizeOfDimension(weights, 1); | |
| NN_OPS_CHECK(getSizeOfDimension(bias, 0) == num_units); | |
| NN_OPS_CHECK(getSizeOfDimension(weights, 1) * batch_size == input_size); | |
| NN_OPS_CHECK(getNumberOfDimensions(weights) == 2); | |
| 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_OPS_CHECK(axis >= 0); | |
| NN_OPS_CHECK(axis < (int32_t)num_dimensions); | |
| int sumAxis = getSizeOfDimension(inputShapes[0], axis); | |
| for (int i = 1; i < num_inputs; ++i) { | |
| NN_OPS_CHECK(getNumberOfDimensions(inputShapes[i]) == num_dimensions); | |
| NN_OPS_CHECK(inputShapes[i].type == inputShapes[0].type); | |
| if (input_type == OperandType::TENSOR_QUANT8_ASYMM) { | |
| NN_OPS_CHECK(inputShapes[0].offset == inputShapes[i].offset); | |
| NN_OPS_CHECK(inputShapes[0].scale == inputShapes[i].scale); | |
| } | |
| for (int d = 0; d < (int32_t)num_dimensions; ++d) { | |
| if (d == axis) { | |
| sumAxis += getSizeOfDimension(inputShapes[i], axis); | |
| } else { | |
| NN_OPS_CHECK(getSizeOfDimension(inputShapes[0], d) == | |
| getSizeOfDimension(inputShapes[i], d)); | |
| } | |
| } | |
| } | |
| output->type = input_type; | |
| output->dimensions = inputShapes[0].dimensions; | |
| output->dimensions[axis] = sumAxis; | |
| if (input_type == OperandType::TENSOR_QUANT8_ASYMM) { | |
| NN_OPS_CHECK(inputShapes[0].offset == output->offset); | |
| NN_OPS_CHECK(inputShapes[0].scale == output->scale); | |
| } | |
| return true; | |
| } | |
| bool genericNormalizationPrepare(const Shape& input, Shape* output) { | |
| NN_OPS_CHECK(getNumberOfDimensions(input) == 4); | |
| 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); | |
| // 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, int32_t beginMask, | |
| const int32_t* endData, const Shape& endShape, int32_t endMask, | |
| const int32_t* stridesData, const Shape& stridesShape, | |
| 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(numInputDims); | |
| for (int32_t idx = static_cast<int32_t>(numInputDims) - 1; idx >= 0; --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)); | |
| outDims[idx] = outDim < 0 ? 0 : static_cast<uint32_t>(outDim); | |
| } | |
| output->type = input.type; | |
| output->dimensions = outDims; | |
| output->offset = input.offset; | |
| output->scale = input.scale; | |
| return true; | |
| } | |
| } // namespace nn | |
| } // namespace android |