| /* |
| * 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 "android.hardware.neuralnetworks@1.0-impl-hvx" |
| |
| #include "HexagonModel.h" |
| #include "HexagonOperations.h" |
| #include "OperationsUtils.h" |
| |
| namespace android { |
| namespace hardware { |
| namespace neuralnetworks { |
| namespace V1_0 { |
| namespace implementation { |
| namespace hexagon { |
| |
| using android::nn::Shape; |
| |
| namespace { |
| |
| bool addMul(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model, OperationType op) { |
| HEXAGON_SOFT_ASSERT_EQ(3, ins.size(), "Need 3 inputs for " << toString(op)); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << toString(op)); |
| |
| // get output size |
| const Shape in1Shape = model->getShape(ins[0]); |
| const Shape in2Shape = model->getShape(ins[1]); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(addMulPrepare(in1Shape, in2Shape, &outShape), "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool add(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { |
| return addMul(ins, outs, model, OperationType::ADD); |
| } |
| |
| bool mul(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model) { |
| return addMul(ins, outs, model, OperationType::MUL); |
| } |
| |
| bool pool(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, HexagonModel* model, |
| OperationType op) { |
| HEXAGON_SOFT_ASSERT(ins.size() == 10 || ins.size() == 7, |
| "Need 7 or 10 inputs for " << toString(op)); |
| |
| // get parameters |
| const Shape inShape = model->getShape(ins[0]); |
| |
| // setup parameters |
| 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; |
| |
| // get parameters |
| if (ins.size() == 10) { |
| padding_left = model->getScalar<int32_t>(ins[1]); |
| padding_right = model->getScalar<int32_t>(ins[2]); |
| padding_top = model->getScalar<int32_t>(ins[3]); |
| padding_bottom = model->getScalar<int32_t>(ins[4]); |
| stride_width = model->getScalar<int32_t>(ins[5]); |
| stride_height = model->getScalar<int32_t>(ins[6]); |
| filter_width = model->getScalar<int32_t>(ins[7]); |
| filter_height = model->getScalar<int32_t>(ins[8]); |
| |
| HEXAGON_SOFT_ASSERT_NE(getPadding(inShape.dimensions[2], inShape.dimensions[1], |
| stride_width, stride_height, filter_width, filter_height, |
| padding_left, padding_right, padding_top, padding_bottom), |
| NN_PAD_NA, "Unknown padding"); |
| } else { |
| const int32_t padding_implicit = model->getScalar<int32_t>(ins[1]); |
| stride_width = model->getScalar<int32_t>(ins[2]); |
| stride_height = model->getScalar<int32_t>(ins[3]); |
| filter_width = model->getScalar<int32_t>(ins[4]); |
| filter_height = model->getScalar<int32_t>(ins[5]); |
| |
| nn::calculateExplicitPadding(inShape.dimensions[2], stride_width, filter_width, |
| padding_implicit, &padding_left, &padding_right); |
| nn::calculateExplicitPadding(inShape.dimensions[1], stride_height, filter_height, |
| padding_implicit, &padding_top, &padding_bottom); |
| } |
| |
| // get output size |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT( |
| genericPoolingPrepare(inShape, padding_left, padding_right, padding_top, padding_bottom, |
| stride_width, stride_height, filter_width, filter_height, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool average_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return pool(ins, outs, model, OperationType::AVERAGE_POOL_2D); |
| } |
| |
| bool l2_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return pool(ins, outs, model, OperationType::L2_POOL_2D); |
| } |
| |
| bool max_pool_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return pool(ins, outs, model, OperationType::MAX_POOL_2D); |
| } |
| |
| bool concatenation(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::CONCATENATION); |
| HEXAGON_SOFT_ASSERT_LE(3, ins.size(), "Need at least 3 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| const size_t numInputTensors = ins.size() - 1; |
| |
| const int32_t axis = model->getScalar<int32_t>(ins[numInputTensors]); |
| |
| // get output size |
| std::vector<Shape> inShapes(numInputTensors); |
| for (size_t i = 0; i < numInputTensors; ++i) { |
| inShapes[i] = model->getShape(ins[i]); |
| } |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(concatenationPrepare(inShapes, axis, &outShape), "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::CONV_2D); |
| HEXAGON_SOFT_ASSERT(ins.size() == 10 || ins.size() == 7, "Need 7 or 10 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // setup shapes |
| const Shape inputShape = model->getShape(ins[0]); |
| const Shape filterShape = model->getShape(ins[1]); |
| const Shape biasShape = model->getShape(ins[2]); |
| |
| // setup parameters |
| int32_t padding_left; |
| int32_t padding_right; |
| int32_t padding_top; |
| int32_t padding_bottom; |
| int32_t stride_width; |
| int32_t stride_height; |
| |
| // get parameters |
| if (ins.size() == 10) { |
| padding_left = model->getScalar<int32_t>(ins[3]); |
| padding_right = model->getScalar<int32_t>(ins[4]); |
| padding_top = model->getScalar<int32_t>(ins[5]); |
| padding_bottom = model->getScalar<int32_t>(ins[6]); |
| stride_width = model->getScalar<int32_t>(ins[7]); |
| stride_height = model->getScalar<int32_t>(ins[8]); |
| |
| HEXAGON_SOFT_ASSERT_NE( |
| getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, |
| stride_height, filterShape.dimensions[2], filterShape.dimensions[1], |
| padding_left, padding_right, padding_top, padding_bottom), |
| NN_PAD_NA, "Unknown padding"); |
| } else { |
| const int32_t padding_implicit = model->getScalar<int32_t>(ins[3]); |
| stride_width = model->getScalar<int32_t>(ins[4]); |
| stride_height = model->getScalar<int32_t>(ins[5]); |
| |
| nn::calculateExplicitPadding(inputShape.dimensions[2], stride_width, |
| filterShape.dimensions[2], padding_implicit, &padding_left, |
| &padding_right); |
| nn::calculateExplicitPadding(inputShape.dimensions[1], stride_height, |
| filterShape.dimensions[1], padding_implicit, &padding_top, |
| &padding_bottom); |
| } |
| |
| // get output size |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT( |
| convPrepare(inputShape, filterShape, biasShape, padding_left, padding_right, padding_top, |
| padding_bottom, stride_width, stride_height, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| // enforce filter is a constant |
| HEXAGON_SOFT_ASSERT(model->isConstant(ins[1]), name << "requires filter to be constant data"); |
| |
| return true; |
| } |
| |
| bool depthwise_conv_2d(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::DEPTHWISE_CONV_2D); |
| HEXAGON_SOFT_ASSERT(ins.size() == 8 || ins.size() == 11, "Need 8 or 11 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // setup shapes |
| const Shape inputShape = model->getShape(ins[0]); |
| const Shape filterShape = model->getShape(ins[1]); |
| const Shape biasShape = model->getShape(ins[2]); |
| |
| // setup parameters |
| int32_t padding_left; |
| int32_t padding_right; |
| int32_t padding_top; |
| int32_t padding_bottom; |
| int32_t stride_width; |
| int32_t stride_height; |
| |
| // get parameters |
| if (ins.size() == 11) { |
| padding_left = model->getScalar<int32_t>(ins[3]); |
| padding_right = model->getScalar<int32_t>(ins[4]); |
| padding_top = model->getScalar<int32_t>(ins[5]); |
| padding_bottom = model->getScalar<int32_t>(ins[6]); |
| stride_width = model->getScalar<int32_t>(ins[7]); |
| stride_height = model->getScalar<int32_t>(ins[8]); |
| |
| HEXAGON_SOFT_ASSERT_NE( |
| getPadding(inputShape.dimensions[2], inputShape.dimensions[1], stride_width, |
| stride_height, filterShape.dimensions[2], filterShape.dimensions[1], |
| padding_left, padding_right, padding_top, padding_bottom), |
| NN_PAD_NA, "Unknown padding"); |
| |
| } else { |
| const int32_t padding_implicit = model->getScalar<int32_t>(ins[3]); |
| stride_width = model->getScalar<int32_t>(ins[4]); |
| stride_height = model->getScalar<int32_t>(ins[5]); |
| |
| nn::calculateExplicitPadding(inputShape.dimensions[2], stride_width, |
| filterShape.dimensions[2], padding_implicit, &padding_left, |
| &padding_right); |
| nn::calculateExplicitPadding(inputShape.dimensions[1], stride_height, |
| filterShape.dimensions[1], padding_implicit, &padding_top, |
| &padding_bottom); |
| } |
| |
| // get output size |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT( |
| depthwiseConvPrepare(inputShape, filterShape, biasShape, padding_left, padding_right, |
| padding_top, padding_bottom, stride_width, stride_height, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| // enforce filter is a constant |
| HEXAGON_SOFT_ASSERT(model->isConstant(ins[1]), name << " requires filter to be constant data"); |
| |
| return true; |
| } |
| |
| bool dequantize(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::DEQUANTIZE); |
| HEXAGON_SOFT_ASSERT_EQ(1, ins.size(), "Need 1 input for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // get output size |
| const Shape inputShape = model->getShape(ins[0]); |
| Shape outShape = model->getShape(outs[0]); |
| |
| HEXAGON_SOFT_ASSERT(dequantizePrepare(inputShape, &outShape), "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool fully_connected(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::FULLY_CONNECTED); |
| HEXAGON_SOFT_ASSERT_EQ(4, ins.size(), "Need 4 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // get output size |
| const Shape inputShape = model->getShape(ins[0]); |
| const Shape weightsShape = model->getShape(ins[1]); |
| const Shape biasShape = model->getShape(ins[2]); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(fullyConnectedPrepare(inputShape, weightsShape, biasShape, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| // enforce weight is a constant |
| HEXAGON_SOFT_ASSERT(model->isConstant(ins[1]), name << "requires weight to be constant data"); |
| |
| return true; |
| } |
| |
| bool local_response_normalization(const std::vector<uint32_t>& ins, |
| const std::vector<uint32_t>& outs, HexagonModel* model) { |
| std::string name = toString(OperationType::LOCAL_RESPONSE_NORMALIZATION); |
| HEXAGON_SOFT_ASSERT_EQ(5, ins.size(), "Need 5 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // get output size |
| const Shape inShape = model->getShape(ins[0]); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(genericNormalizationPrepare(inShape, &outShape), "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool activation(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model, uint32_t numInputs, OperationType op) { |
| HEXAGON_SOFT_ASSERT_EQ(numInputs, ins.size(), |
| "Need " << numInputs << " input for " << toString(op)); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << toString(op)); |
| |
| // get output size |
| const Shape inShape = model->getShape(ins[0]); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(genericActivationPrepare(inShape, &outShape), "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool logistic(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 1, OperationType::LOGISTIC); |
| } |
| |
| bool relu(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 1, OperationType::RELU); |
| } |
| |
| bool relu1(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 1, OperationType::RELU1); |
| } |
| |
| bool relu6(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 1, OperationType::RELU6); |
| } |
| |
| bool softmax(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 2, OperationType::SOFTMAX); |
| } |
| |
| bool tanh(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| return activation(ins, outs, model, 1, OperationType::TANH); |
| } |
| |
| bool reshape(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::RESHAPE); |
| HEXAGON_SOFT_ASSERT_EQ(2, ins.size(), "Need 2 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // get output size |
| const Shape inShape = model->getShape(ins[0]); |
| const Shape targetShape = model->getShape(ins[1]); |
| const int32_t* targetShapePtr = model->getPointer(ins[1]); |
| int32_t targetShapeNumElem = ::android::nn::getNumberOfElements(targetShape); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(targetShapePtr != nullptr, "pointer value is currently nullptr"); |
| |
| HEXAGON_SOFT_ASSERT(reshapePrepare(inShape, targetShapePtr, targetShapeNumElem, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| bool resize_bilinear(const std::vector<uint32_t>& ins, const std::vector<uint32_t>& outs, |
| HexagonModel* model) { |
| std::string name = toString(OperationType::RESIZE_BILINEAR); |
| HEXAGON_SOFT_ASSERT_EQ(3, ins.size(), "Need 3 inputs for " << name); |
| HEXAGON_SOFT_ASSERT_EQ(1, outs.size(), "Need 1 output for " << name); |
| |
| // get parameters |
| const int32_t width = model->getScalar<int32_t>(ins[1]); |
| const int32_t height = model->getScalar<int32_t>(ins[2]); |
| |
| // get output size |
| const Shape inShape = model->getShape(ins[0]); |
| Shape outShape = model->getShape(outs[0]); |
| HEXAGON_SOFT_ASSERT(resizeBilinearPrepare(inShape, width, height, &outShape), |
| "Error getting shape"); |
| HEXAGON_SOFT_ASSERT(model->setShape(outs[0], outShape), "Error setting shape"); |
| |
| return true; |
| } |
| |
| } // namespace |
| |
| OperationTable& getOperationCheckTable() { |
| static OperationTable table = { |
| // NOTE: the operations that are commented out via inline represent |
| // operations that are valid for the Android O NNAPI release, but are |
| // currently not implemented in HVX. |
| |
| // -------------------------- 32-BIT FLOAT ---------------------------- |
| // HVX is only performant when running on quantized values. Further, as |
| // an optimization, the current HVX driver will convert some floating |
| // point tensors into quantized values, perform the operation, and then |
| // convert them back to floating point. This results in a loss in |
| // precision causing some tests to fail. For these reasons, the FLOAT32 |
| // operations are being temporarily disabled. |
| /* |
| {{OperationType::ADD, OperandType::TENSOR_FLOAT32}, add}, |
| {{OperationType::AVERAGE_POOL_2D, OperandType::TENSOR_FLOAT32}, average_pool_2d}, |
| {{OperationType::CONCATENATION, OperandType::TENSOR_FLOAT32}, concatenation}, |
| {{OperationType::CONV_2D, OperandType::TENSOR_FLOAT32}, conv_2d}, |
| {{OperationType::DEPTHWISE_CONV_2D, OperandType::TENSOR_FLOAT32}, depthwise_conv_2d}, |
| //{{OperationType::DEPTH_TO_SPACE, OperandType::TENSOR_FLOAT32}, depth_to_space}, |
| //{{OperationType::EMBEDDING_LOOKUP, OperandType::TENSOR_FLOAT32}, embedding_lookup}, |
| //{{OperationType::FLOOR, OperandType::TENSOR_FLOAT32}, floor}, |
| {{OperationType::FULLY_CONNECTED, OperandType::TENSOR_FLOAT32}, fully_connected}, |
| //{{OperationType::HASHTABLE_LOOKUP, OperandType::TENSOR_FLOAT32}, hashtable_lookup}, |
| //{{OperationType::L2_NORMALIZATION, OperandType::TENSOR_FLOAT32}, l2_normalization}, |
| {{OperationType::L2_POOL_2D, OperandType::TENSOR_FLOAT32}, l2_pool_2d}, |
| {{OperationType::LOCAL_RESPONSE_NORMALIZATION, OperandType::TENSOR_FLOAT32}, |
| local_response_normalization}, |
| {{OperationType::LOGISTIC, OperandType::TENSOR_FLOAT32}, logistic}, |
| //{{OperationType::LSH_PROJECTION, OperandType::TENSOR_FLOAT32}, lsh_projection}, |
| //{{OperationType::LSTM, OperandType::TENSOR_FLOAT32}, lstm }, |
| {{OperationType::MAX_POOL_2D, OperandType::TENSOR_FLOAT32}, max_pool_2d}, |
| {{OperationType::MUL, OperandType::TENSOR_FLOAT32}, mul}, |
| {{OperationType::RELU, OperandType::TENSOR_FLOAT32}, relu}, |
| {{OperationType::RELU1, OperandType::TENSOR_FLOAT32}, relu1}, |
| {{OperationType::RELU6, OperandType::TENSOR_FLOAT32}, relu6}, |
| {{OperationType::RESHAPE, OperandType::TENSOR_FLOAT32}, reshape}, |
| {{OperationType::RESIZE_BILINEAR, OperandType::TENSOR_FLOAT32}, resize_bilinear}, |
| //{{OperationType::RNN, OperandType::TENSOR_FLOAT32}, rnn}, |
| {{OperationType::SOFTMAX, OperandType::TENSOR_FLOAT32}, softmax}, |
| //{{OperationType::SPACE_TO_DEPTH, OperandType::TENSOR_FLOAT32}, space_to_depth}, |
| //{{OperationType::SVDF, OperandType::TENSOR_FLOAT32}, svdf }, |
| {{OperationType::TANH, OperandType::TENSOR_FLOAT32}, tanh}, |
| */ |
| |
| // -------------------- QUANTIZED 8-BIT ASYMMETRICAL ------------------ |
| {{OperationType::ADD, OperandType::TENSOR_QUANT8_ASYMM}, add}, |
| {{OperationType::AVERAGE_POOL_2D, OperandType::TENSOR_QUANT8_ASYMM}, average_pool_2d}, |
| {{OperationType::CONCATENATION, OperandType::TENSOR_QUANT8_ASYMM}, concatenation}, |
| {{OperationType::CONV_2D, OperandType::TENSOR_QUANT8_ASYMM}, conv_2d}, |
| {{OperationType::DEPTHWISE_CONV_2D, OperandType::TENSOR_QUANT8_ASYMM}, depthwise_conv_2d}, |
| //{{OperationType::DEPTH_TO_SPACE, OperandType::TENSOR_QUANT8_ASYMM}, depth_to_space}, |
| {{OperationType::DEQUANTIZE, OperandType::TENSOR_QUANT8_ASYMM}, dequantize}, |
| //{{OperationType::EMBEDDING_LOOKUP, OperandType::TENSOR_QUANT8_ASYMM}, embedding_lookup}, |
| {{OperationType::FULLY_CONNECTED, OperandType::TENSOR_QUANT8_ASYMM}, fully_connected}, |
| //{{OperationType::HASHTABLE_LOOKUP, OperandType::TENSOR_QUANT8_ASYMM}, hashtable_lookup}, |
| {{OperationType::LOGISTIC, OperandType::TENSOR_QUANT8_ASYMM}, logistic}, |
| //{{OperationType::LSH_PROJECTION, OperandType::TENSOR_QUANT8_ASYMM}, lsh_projection}, |
| {{OperationType::MAX_POOL_2D, OperandType::TENSOR_QUANT8_ASYMM}, max_pool_2d}, |
| {{OperationType::MUL, OperandType::TENSOR_QUANT8_ASYMM}, mul}, |
| {{OperationType::RELU, OperandType::TENSOR_QUANT8_ASYMM}, relu}, |
| {{OperationType::RELU1, OperandType::TENSOR_QUANT8_ASYMM}, relu1}, |
| {{OperationType::RELU6, OperandType::TENSOR_QUANT8_ASYMM}, relu6}, |
| {{OperationType::RESHAPE, OperandType::TENSOR_QUANT8_ASYMM}, reshape}, |
| {{OperationType::SOFTMAX, OperandType::TENSOR_QUANT8_ASYMM}, softmax}, |
| //{{OperationType::SPACE_TO_DEPTH, OperandType::TENSOR_QUANT8_ASYMM}, space_to_depth}, |
| }; |
| |
| // The following functions are normally used by float32, but those |
| // operations have been temporarily disabled. Void explicitly marks them as |
| // unused, and prevents the compiler from throwing an error. |
| (void)l2_pool_2d; |
| (void)local_response_normalization; |
| (void)tanh; |
| (void)resize_bilinear; |
| |
| return table; |
| } |
| |
| } // namespace hexagon |
| } // namespace implementation |
| } // namespace V1_0 |
| } // namespace neuralnetworks |
| } // namespace hardware |
| } // namespace android |
