| /* |
| * Copyright (C) 2018 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. |
| */ |
| |
| #include "NeuralNetworksWrapper.h" |
| #include "NeuralNetworksOEM.h" |
| |
| #include <gtest/gtest.h> |
| |
| using namespace android::nn::wrapper; |
| |
| namespace { |
| |
| static const int32_t kAvailableOperandCodes[] = { |
| ANEURALNETWORKS_FLOAT32, |
| ANEURALNETWORKS_INT32, |
| ANEURALNETWORKS_UINT32, |
| ANEURALNETWORKS_TENSOR_FLOAT32, |
| ANEURALNETWORKS_TENSOR_INT32, |
| ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, |
| ANEURALNETWORKS_TENSOR_OEM_BYTE |
| }; |
| |
| class OperationTestBase { |
| public: |
| OperationTestBase(ANeuralNetworksOperationType opCode, |
| std::vector<ANeuralNetworksOperandType> validInputs, |
| std::vector<ANeuralNetworksOperandType> validOutputs) |
| : mOpCode(opCode), |
| mValidInputs(std::move(validInputs)), |
| mValidOutputs(std::move(validOutputs)) {} |
| |
| // Add each operand separately and add the operation using these operands. |
| // This function does not cover the cases that a operand used mutiple times. |
| int32_t addOperation(const std::vector<ANeuralNetworksOperandType>& inputs, |
| const std::vector<ANeuralNetworksOperandType>& outputs) { |
| ANeuralNetworksModel* model = nullptr; |
| ANeuralNetworksModel_create(&model); |
| |
| uint32_t opIdx = 0; |
| std::vector<uint32_t> inputIds; |
| std::vector<uint32_t> outputIds; |
| for (uint32_t i = 0; i < inputs.size(); i++) { |
| ANeuralNetworksModel_addOperand(model, &inputs[i]); |
| inputIds.push_back(opIdx++); |
| } |
| for (uint32_t i = 0; i < outputs.size(); i++) { |
| ANeuralNetworksModel_addOperand(model, &outputs[i]); |
| outputIds.push_back(opIdx++); |
| } |
| |
| int32_t result = ANeuralNetworksModel_addOperation(model, mOpCode, |
| static_cast<uint32_t>(inputIds.size()), |
| inputIds.data(), |
| static_cast<uint32_t>(outputIds.size()), |
| outputIds.data()); |
| ANeuralNetworksModel_free(model); |
| return result; |
| } |
| |
| bool testMutatingInputOperandCode() { |
| for (uint32_t i = 0; i < mValidInputs.size(); i++) { |
| // LSH_PROJECTION's second argument is allowed to have any type. |
| // This is the only operation that currently has a type that can be |
| // anything independent from any other type. Changing the operand |
| // type to any other type will result in a valid model for |
| // LSH_PROJECTION. If this is the case, skip the test. |
| if (mOpCode == ANEURALNETWORKS_LSH_PROJECTION && i == 1) { |
| continue; |
| } |
| ANeuralNetworksOperandType newType = mValidInputs[i]; |
| int32_t originalOperandCode = mValidInputs[i].type; |
| for (int32_t newOperandCode : kAvailableOperandCodes) { |
| if (newOperandCode == originalOperandCode) { |
| continue; |
| } |
| newType.type = newOperandCode; |
| std::vector<ANeuralNetworksOperandType> inputs = mValidInputs; |
| inputs[i] = newType; |
| int32_t result = addOperation(inputs, mValidOutputs); |
| if (ANEURALNETWORKS_NO_ERROR == result) { |
| return false; |
| } |
| } |
| } |
| return true; |
| } |
| |
| bool testMutatingOutputOperandCode() { |
| for (uint32_t i = 0; i < mValidOutputs.size(); i++) { |
| // LSH_PROJECTION's second argument is allowed to have any type. |
| // This is the only operation that currently has a type that can be |
| // anything independent from any other type. Changing the operand |
| // type to any other type will result in a valid model for |
| // LSH_PROJECTION. If this is the case, skip the test. |
| if (mOpCode == ANEURALNETWORKS_LSH_PROJECTION && i == 1) { |
| continue; |
| } |
| ANeuralNetworksOperandType newType = mValidOutputs[i]; |
| int32_t originalOperandCode = mValidOutputs[i].type; |
| for (int32_t newOperandCode : kAvailableOperandCodes) { |
| if (newOperandCode == originalOperandCode) { |
| continue; |
| } |
| newType.type = newOperandCode; |
| std::vector<ANeuralNetworksOperandType> outputs = mValidOutputs; |
| outputs[i] = newType; |
| int32_t result = addOperation(mValidInputs, outputs); |
| if (ANEURALNETWORKS_NO_ERROR == result) { |
| return false; |
| } |
| } |
| } |
| return true; |
| } |
| |
| bool testMutatingInputOperandCounts() { |
| std::vector<ANeuralNetworksOperandType> inputs = mValidInputs; |
| for (uint32_t i = 0; i < 5; i++) { |
| inputs.push_back(inputs[0]); |
| if (ANEURALNETWORKS_NO_ERROR == addOperation(inputs, mValidOutputs)) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| bool testMutatingOutputOperandCounts() { |
| std::vector<ANeuralNetworksOperandType> outputs = mValidOutputs; |
| for (int i = 0; i < 5; i++) { |
| outputs.push_back(outputs[0]); |
| if (ANEURALNETWORKS_NO_ERROR == addOperation(mValidInputs, outputs)) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| private: |
| ANeuralNetworksOperationType mOpCode; |
| // The dimensions in the ANeuralNetworksOperandType must outlive the test object. |
| std::vector<ANeuralNetworksOperandType> mValidInputs; |
| std::vector<ANeuralNetworksOperandType> mValidOutputs; |
| }; |
| |
| TEST(OperationValidationTest, DEQUANTIZE_float32) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 1.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType output = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| OperationTestBase dequantizeTest(ANEURALNETWORKS_DEQUANTIZE, {input}, {output}); |
| |
| EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(dequantizeTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(dequantizeTest.testMutatingOutputOperandCounts()); |
| } |
| |
| void simpleMathOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input1 = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input1.scale = 0.5f; |
| } |
| |
| ANeuralNetworksOperandType input2 = input1; |
| ANeuralNetworksOperandType output = input1; |
| ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| OperationTestBase simpleMathTest(operationCode, {input1, input2, activation}, {output}); |
| |
| EXPECT_TRUE(simpleMathTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(simpleMathTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(simpleMathTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(simpleMathTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, ADD_float32) { |
| simpleMathOpTest(ANEURALNETWORKS_ADD, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, MUL_float32) { |
| simpleMathOpTest(ANEURALNETWORKS_MUL, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, SUB_float32) { |
| simpleMathOpTest(ANEURALNETWORKS_SUB, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, DIV_float32) { |
| simpleMathOpTest(ANEURALNETWORKS_DIV, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, ADD_quant8) { |
| simpleMathOpTest(ANEURALNETWORKS_ADD, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, MUL_quant8) { |
| simpleMathOpTest(ANEURALNETWORKS_MUL, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void activationOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| |
| ANeuralNetworksOperandType output = input; |
| OperationTestBase activationTest(operationCode, {input}, {output}); |
| |
| EXPECT_TRUE(activationTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(activationTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(activationTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(activationTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, L2_NORMALIZATION_float32) { |
| activationOpTest(ANEURALNETWORKS_L2_NORMALIZATION, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, FLOOR_float32) { |
| activationOpTest(ANEURALNETWORKS_FLOOR, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, TANH_float32) { |
| activationOpTest(ANEURALNETWORKS_TANH, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, RELU_float32) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, RELU1_float32) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, RELU6_float32) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, LOGISTIC_float32) { |
| activationOpTest(ANEURALNETWORKS_LOGISTIC, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, RELU_quant8) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, RELU1_quant8) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, RELU6_quant8) { |
| activationOpTest(ANEURALNETWORKS_RELU, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, LOGISTIC_quant8) { |
| activationOpTest(ANEURALNETWORKS_LOGISTIC, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void softmaxOpTest(int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| |
| ANeuralNetworksOperandType output = input; |
| ANeuralNetworksOperandType beta = {.type = ANEURALNETWORKS_FLOAT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| OperationTestBase softmaxTest(ANEURALNETWORKS_SOFTMAX, {input, beta}, {output}); |
| |
| EXPECT_TRUE(softmaxTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(softmaxTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(softmaxTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(softmaxTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, SOFTMAX_float32) { |
| softmaxOpTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, SOFTMAX_quant8) { |
| softmaxOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void poolingOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 4, 4, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| ANeuralNetworksOperandType output = input; |
| |
| ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType padLeft = scalar; |
| ANeuralNetworksOperandType padRight = scalar; |
| ANeuralNetworksOperandType padTop = scalar; |
| ANeuralNetworksOperandType padBottom = scalar; |
| ANeuralNetworksOperandType strideWidth = scalar; |
| ANeuralNetworksOperandType strideHeight = scalar; |
| ANeuralNetworksOperandType filterWidth = scalar; |
| ANeuralNetworksOperandType filterHeight = scalar; |
| ANeuralNetworksOperandType activation = scalar; |
| |
| OperationTestBase explicitPoolingTest(operationCode, |
| {input, |
| padLeft, padRight, padTop, padBottom, |
| strideWidth, strideHeight, |
| filterWidth, filterHeight, |
| activation}, |
| {output}); |
| |
| EXPECT_TRUE(explicitPoolingTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(explicitPoolingTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(explicitPoolingTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(explicitPoolingTest.testMutatingOutputOperandCounts()); |
| |
| ANeuralNetworksOperandType padImplicit = scalar; |
| OperationTestBase implicitPoolingTest(operationCode, |
| {input, |
| padImplicit, |
| strideWidth, strideHeight, |
| filterWidth, filterHeight, |
| activation}, |
| {output}); |
| |
| EXPECT_TRUE(implicitPoolingTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(implicitPoolingTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(implicitPoolingTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(implicitPoolingTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, AVERAGE_POOL_2D_float32) { |
| poolingOpTest(ANEURALNETWORKS_AVERAGE_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, MAX_POOL_2D_float32) { |
| poolingOpTest(ANEURALNETWORKS_MAX_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, L2_POOL_2D_float32) { |
| poolingOpTest(ANEURALNETWORKS_L2_POOL_2D, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, AVERAGE_POOL_2D_quant8) { |
| poolingOpTest(ANEURALNETWORKS_AVERAGE_POOL_2D, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, MAX_POOL_2D_quant8) { |
| poolingOpTest(ANEURALNETWORKS_MAX_POOL_2D, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void spaceDepthOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| |
| ANeuralNetworksOperandType block_size = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType output = input; |
| OperationTestBase spaceDepthTest(operationCode, {input, block_size}, {output}); |
| |
| EXPECT_TRUE(spaceDepthTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(spaceDepthTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(spaceDepthTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(spaceDepthTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, SPACE_TO_DEPTH_float32) { |
| spaceDepthOpTest(ANEURALNETWORKS_SPACE_TO_DEPTH, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, DEPTH_TO_SPACE_float32) { |
| spaceDepthOpTest(ANEURALNETWORKS_DEPTH_TO_SPACE, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, SPACE_TO_DEPTH_quant8) { |
| spaceDepthOpTest(ANEURALNETWORKS_SPACE_TO_DEPTH, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, DEPTH_TO_SPACE_quant8) { |
| spaceDepthOpTest(ANEURALNETWORKS_DEPTH_TO_SPACE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void spaceBatchOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| |
| uint32_t blockDimensions[1] = {2}; |
| ANeuralNetworksOperandType blockShape = {.type = ANEURALNETWORKS_TENSOR_INT32, |
| .dimensionCount = 1, |
| .dimensions = blockDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType padding = blockShape; |
| ANeuralNetworksOperandType output = input; |
| if (operationCode == ANEURALNETWORKS_SPACE_TO_BATCH_ND) { |
| OperationTestBase spaceBatchTest(operationCode, {input, blockShape, padding}, {output}); |
| |
| EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCounts()); |
| } else { |
| OperationTestBase spaceBatchTest(operationCode, {input, blockShape}, {output}); |
| |
| EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(spaceBatchTest.testMutatingOutputOperandCounts()); |
| } |
| } |
| |
| TEST(OperationValidationTest, SPACE_TO_BATCH_ND_float32) { |
| spaceBatchOpTest(ANEURALNETWORKS_SPACE_TO_BATCH_ND, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, BATCH_TO_SPACE_ND_float32) { |
| spaceBatchOpTest(ANEURALNETWORKS_BATCH_TO_SPACE_ND, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, SPACE_TO_BATCH_ND_quant8) { |
| spaceBatchOpTest(ANEURALNETWORKS_SPACE_TO_BATCH_ND, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, BATCH_TO_SPACE_ND_quant8) { |
| spaceBatchOpTest(ANEURALNETWORKS_BATCH_TO_SPACE_ND, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void transposeAndSqueezeOpTest(ANeuralNetworksOperationType operationCode, int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 1.f / 256; |
| } |
| |
| uint32_t blockDimensions[1] = {4}; |
| ANeuralNetworksOperandType dims = {.type = ANEURALNETWORKS_TENSOR_INT32, |
| .dimensionCount = 1, |
| .dimensions = blockDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType output = input; |
| OperationTestBase transposeAndSqueezeTest(operationCode, {input, dims}, {output}); |
| |
| EXPECT_TRUE(transposeAndSqueezeTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(transposeAndSqueezeTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(transposeAndSqueezeTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(transposeAndSqueezeTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, TRANSPOSE_float32) { |
| transposeAndSqueezeOpTest(ANEURALNETWORKS_TRANSPOSE, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, SQUEEZE_float32) { |
| transposeAndSqueezeOpTest(ANEURALNETWORKS_SQUEEZE, ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, TRANSPOSE_quant8) { |
| transposeAndSqueezeOpTest(ANEURALNETWORKS_TRANSPOSE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, SQUEEZE_quant8) { |
| transposeAndSqueezeOpTest(ANEURALNETWORKS_SQUEEZE, ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void convOpTest(int32_t operandCode) { |
| uint32_t inputDimensions[4] = {2, 4, 4, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| |
| ANeuralNetworksOperandType filter = input; |
| ANeuralNetworksOperandType output = input; |
| |
| uint32_t biasDimensions[1] = {2}; |
| ANeuralNetworksOperandType bias = {.type = operandCode, |
| .dimensionCount = 1, |
| .dimensions = biasDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| bias.type = ANEURALNETWORKS_TENSOR_INT32; |
| bias.scale = 0.25f; |
| } |
| |
| ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType padLeft = scalar; |
| ANeuralNetworksOperandType padRight = scalar; |
| ANeuralNetworksOperandType padTop = scalar; |
| ANeuralNetworksOperandType padBottom = scalar; |
| ANeuralNetworksOperandType strideWidth = scalar; |
| ANeuralNetworksOperandType strideHeight = scalar; |
| ANeuralNetworksOperandType activation = scalar; |
| |
| OperationTestBase explicitConvTest(ANEURALNETWORKS_CONV_2D, |
| {input, filter, bias, |
| padLeft, padRight, padTop, padBottom, |
| strideWidth, strideHeight, |
| activation}, |
| {output}); |
| |
| EXPECT_TRUE(explicitConvTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(explicitConvTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(explicitConvTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(explicitConvTest.testMutatingOutputOperandCounts()); |
| |
| ANeuralNetworksOperandType padImplicit = scalar; |
| OperationTestBase implicitConvTest(ANEURALNETWORKS_CONV_2D, |
| {input, filter, bias, |
| padImplicit, |
| strideWidth, strideHeight, |
| activation}, |
| {output}); |
| |
| EXPECT_TRUE(implicitConvTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(implicitConvTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(implicitConvTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(implicitConvTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, CONV_2D_float32) { |
| convOpTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, CONV_2D_quant8) { |
| convOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void depthwiseConvOpTest(int32_t operandCode) { |
| uint32_t inputDimensions[4] = {1, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| |
| ANeuralNetworksOperandType filter = input; |
| ANeuralNetworksOperandType output = input; |
| |
| uint32_t biasDimensions[1] = {2}; |
| ANeuralNetworksOperandType bias = {.type = operandCode, |
| .dimensionCount = 1, |
| .dimensions = biasDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| bias.type = ANEURALNETWORKS_TENSOR_INT32; |
| bias.scale = 0.25f; |
| } |
| |
| ANeuralNetworksOperandType scalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType padLeft = scalar; |
| ANeuralNetworksOperandType padRight = scalar; |
| ANeuralNetworksOperandType padTop = scalar; |
| ANeuralNetworksOperandType padBottom = scalar; |
| ANeuralNetworksOperandType strideWidth = scalar; |
| ANeuralNetworksOperandType strideHeight = scalar; |
| ANeuralNetworksOperandType multiplier = scalar; |
| ANeuralNetworksOperandType activation = scalar; |
| |
| OperationTestBase explicitDepthwiseConvTest(ANEURALNETWORKS_DEPTHWISE_CONV_2D, |
| {input, filter, bias, |
| padLeft, padRight, padTop, padBottom, |
| strideWidth, strideHeight, |
| multiplier, activation}, |
| {output}); |
| |
| EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(explicitDepthwiseConvTest.testMutatingOutputOperandCounts()); |
| |
| ANeuralNetworksOperandType padImplicit = scalar; |
| OperationTestBase implicitDepthwiseConvTest(ANEURALNETWORKS_DEPTHWISE_CONV_2D, |
| {input, filter, bias, |
| padImplicit, |
| strideWidth, strideHeight, |
| multiplier, activation}, |
| {output}); |
| |
| EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(implicitDepthwiseConvTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, DEPTHWISE_CONV_2D_float32) { |
| depthwiseConvOpTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, DEPTHWISE_CONV_2D_quant8) { |
| depthwiseConvOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void fullyConnectedOpTest(int32_t operandCode) { |
| uint32_t inputDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 2, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| |
| ANeuralNetworksOperandType weights = input; |
| ANeuralNetworksOperandType output = input; |
| |
| uint32_t biasDimensions[1] = {5}; |
| ANeuralNetworksOperandType bias = {.type = operandCode, |
| .dimensionCount = 1, |
| .dimensions = biasDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| bias.type = ANEURALNETWORKS_TENSOR_INT32; |
| bias.scale = 0.25f; |
| } |
| |
| ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| OperationTestBase fullyConnectedTest(ANEURALNETWORKS_FULLY_CONNECTED, |
| {input, weights, bias, activation}, |
| {output}); |
| |
| EXPECT_TRUE(fullyConnectedTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(fullyConnectedTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(fullyConnectedTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(fullyConnectedTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, FULLY_CONNECTED_float32) { |
| fullyConnectedOpTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, FULLY_CONNECTED_quant8) { |
| fullyConnectedOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void concatenationTest(int32_t operandCode) { |
| uint32_t inputDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType input1 = {.type = operandCode, |
| .dimensionCount = 2, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input1.scale = 0.5f; |
| } |
| ANeuralNetworksOperandType input2 = input1; |
| ANeuralNetworksOperandType output = input1; |
| |
| ANeuralNetworksOperandType activation = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| OperationTestBase concat2Test(ANEURALNETWORKS_CONCATENATION, |
| {input1, input2, activation}, {output}); |
| |
| EXPECT_TRUE(concat2Test.testMutatingInputOperandCode()); |
| EXPECT_TRUE(concat2Test.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(concat2Test.testMutatingOutputOperandCounts()); |
| |
| OperationTestBase concat1Test(ANEURALNETWORKS_CONCATENATION, |
| {input1, activation}, {output}); |
| |
| EXPECT_TRUE(concat1Test.testMutatingInputOperandCode()); |
| EXPECT_TRUE(concat1Test.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(concat1Test.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, CONCATENATION_float32) { |
| concatenationTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, CONCATENATION_quant8) { |
| concatenationTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, RESIZE_BILINEAR_float32) { |
| uint32_t inputDimensions[4] = {2, 2, 2, 2}; |
| ANeuralNetworksOperandType input = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 4, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType height = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType width = height; |
| ANeuralNetworksOperandType output = input; |
| OperationTestBase resizeTest(ANEURALNETWORKS_RESIZE_BILINEAR, |
| {input, height, width}, {output}); |
| |
| EXPECT_TRUE(resizeTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(resizeTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(resizeTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(resizeTest.testMutatingOutputOperandCounts()); |
| } |
| |
| void embeddingLookupTest(int32_t operandCode) { |
| uint32_t lookupDimensions[1] = {5}; |
| ANeuralNetworksOperandType lookup = {.type = ANEURALNETWORKS_TENSOR_INT32, |
| .dimensionCount = 1, |
| .dimensions = lookupDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| uint32_t inputDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 2, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| ANeuralNetworksOperandType output = input; |
| |
| OperationTestBase embedLookupTest(ANEURALNETWORKS_EMBEDDING_LOOKUP, |
| {lookup, input}, {output}); |
| |
| EXPECT_TRUE(embedLookupTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(embedLookupTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(embedLookupTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(embedLookupTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, EMBEDDING_LOOKUP_float32) { |
| embeddingLookupTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, EMBEDDING_LOOKUP_quant8) { |
| embeddingLookupTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void hashtableLookupTest(int32_t operandCode) { |
| uint32_t lookupDimensions[1] = {5}; |
| ANeuralNetworksOperandType lookup = {.type = ANEURALNETWORKS_TENSOR_INT32, |
| .dimensionCount = 1, |
| .dimensions = lookupDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType keys = lookup; |
| |
| uint32_t valuesDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType values = {.type = operandCode, |
| .dimensionCount = 2, |
| .dimensions = valuesDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| values.scale = 0.5f; |
| } |
| ANeuralNetworksOperandType output = values; |
| |
| ANeuralNetworksOperandType hits = lookup; |
| hits.type = ANEURALNETWORKS_TENSOR_QUANT8_ASYMM; |
| hits.scale = 1.0f; |
| |
| OperationTestBase hashLookupTest(ANEURALNETWORKS_HASHTABLE_LOOKUP, |
| {lookup, keys, values}, {output, hits}); |
| |
| EXPECT_TRUE(hashLookupTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(hashLookupTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(hashLookupTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(hashLookupTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, HASHTABLE_LOOKUP_float32) { |
| hashtableLookupTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, HASHTABLE_LOOKUP_quant8) { |
| hashtableLookupTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| void lshProjectionTest(int32_t operandCode) { |
| uint32_t inputDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType hash = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 2, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType input = hash; |
| input.type = operandCode; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| |
| uint32_t weightDimensions[1] = {5}; |
| ANeuralNetworksOperandType weight = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 1, |
| .dimensions = weightDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType type = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType output = weight; |
| output.type = ANEURALNETWORKS_TENSOR_INT32; |
| |
| OperationTestBase lshProjTest(ANEURALNETWORKS_LSH_PROJECTION, |
| {hash, input, weight, type}, {output}); |
| |
| EXPECT_TRUE(lshProjTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(lshProjTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(lshProjTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(lshProjTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, LSH_PROJECTION_float32) { |
| lshProjectionTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, LSH_PROJECTION_quant8) { |
| lshProjectionTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| TEST(OperationValidationTest, LSTM_float32) { |
| uint32_t oneDimensional[1] = {5}; |
| uint32_t twoDimensional[2] = {5, 5}; |
| ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 1, |
| .dimensions = oneDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 2, |
| .dimensions = twoDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType floatScalar = {.type = ANEURALNETWORKS_FLOAT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType input = floatTensor2D; |
| ANeuralNetworksOperandType inputToInput = floatTensor2D; |
| ANeuralNetworksOperandType inputToForget = floatTensor2D; |
| ANeuralNetworksOperandType inputToCell = floatTensor2D; |
| ANeuralNetworksOperandType inputToOutput = floatTensor2D; |
| ANeuralNetworksOperandType recurrentToInput = floatTensor2D; |
| ANeuralNetworksOperandType recurrentToForget = floatTensor2D; |
| ANeuralNetworksOperandType recurrentToCell = floatTensor2D; |
| ANeuralNetworksOperandType recurrentToOutput = floatTensor2D; |
| ANeuralNetworksOperandType cellToInput = floatTensor1D; |
| ANeuralNetworksOperandType cellToForget = floatTensor1D; |
| ANeuralNetworksOperandType cellToOutput = floatTensor1D; |
| ANeuralNetworksOperandType inputGateBias = floatTensor1D; |
| ANeuralNetworksOperandType forgetGateBias = floatTensor1D; |
| ANeuralNetworksOperandType cellBias = floatTensor1D; |
| ANeuralNetworksOperandType outputGateBias = floatTensor1D; |
| ANeuralNetworksOperandType projWeights = floatTensor2D; |
| ANeuralNetworksOperandType projBias = floatTensor1D; |
| ANeuralNetworksOperandType outputStateIn = floatTensor2D; |
| ANeuralNetworksOperandType cellStateIn = floatTensor2D; |
| ANeuralNetworksOperandType activation = intScalar; |
| ANeuralNetworksOperandType clipCellState = floatScalar; |
| ANeuralNetworksOperandType clipProjLayer = floatScalar; |
| |
| ANeuralNetworksOperandType scratch = floatTensor2D; |
| ANeuralNetworksOperandType outputStateOut = floatTensor2D; |
| ANeuralNetworksOperandType cellStateOut = floatTensor2D; |
| ANeuralNetworksOperandType output = floatTensor2D; |
| |
| OperationTestBase lstmTest(ANEURALNETWORKS_LSTM, |
| {input, inputToInput, inputToForget, inputToCell, inputToOutput, recurrentToInput, |
| recurrentToForget, recurrentToCell, recurrentToOutput, cellToInput, cellToForget, |
| cellToOutput, inputGateBias, forgetGateBias, cellBias, outputGateBias, projWeights, |
| projBias, outputStateIn, cellStateIn, activation, clipCellState, clipProjLayer}, |
| {scratch, outputStateOut, cellStateOut, output}); |
| |
| EXPECT_TRUE(lstmTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(lstmTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(lstmTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(lstmTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, RNN_float32) { |
| uint32_t oneDimensional[1] = {5}; |
| uint32_t twoDimensional[2] = {5, 5}; |
| ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 1, |
| .dimensions = oneDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 2, |
| .dimensions = twoDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType input = floatTensor2D; |
| ANeuralNetworksOperandType weights = floatTensor2D; |
| ANeuralNetworksOperandType recurrentWeights = floatTensor2D; |
| ANeuralNetworksOperandType bias = floatTensor1D; |
| ANeuralNetworksOperandType hiddenStateIn = floatTensor2D; |
| ANeuralNetworksOperandType activation = intScalar; |
| |
| ANeuralNetworksOperandType hiddenStateOut = floatTensor2D; |
| ANeuralNetworksOperandType output = floatTensor2D; |
| |
| OperationTestBase rnnTest(ANEURALNETWORKS_RNN, |
| {input, weights, recurrentWeights, bias, hiddenStateIn, activation}, |
| {hiddenStateOut, output}); |
| |
| EXPECT_TRUE(rnnTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(rnnTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(rnnTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(rnnTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, SVDF_float32) { |
| uint32_t oneDimensional[1] = {5}; |
| uint32_t twoDimensional[2] = {5, 5}; |
| ANeuralNetworksOperandType floatTensor1D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 1, |
| .dimensions = oneDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType floatTensor2D = {.type = ANEURALNETWORKS_TENSOR_FLOAT32, |
| .dimensionCount = 2, |
| .dimensions = twoDimensional, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType intScalar = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType input = floatTensor2D; |
| ANeuralNetworksOperandType weightsFeature = floatTensor2D; |
| ANeuralNetworksOperandType weightsTime = floatTensor2D; |
| ANeuralNetworksOperandType bias = floatTensor1D; |
| ANeuralNetworksOperandType stateIn = floatTensor2D; |
| ANeuralNetworksOperandType rank = intScalar; |
| ANeuralNetworksOperandType activation = intScalar; |
| |
| ANeuralNetworksOperandType stateOut = floatTensor2D; |
| ANeuralNetworksOperandType output = floatTensor2D; |
| |
| OperationTestBase svdfTest(ANEURALNETWORKS_SVDF, |
| {input, weightsFeature, weightsTime, bias, stateIn, rank, activation}, |
| {stateOut, output}); |
| |
| EXPECT_TRUE(svdfTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(svdfTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(svdfTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(svdfTest.testMutatingOutputOperandCounts()); |
| } |
| |
| void stridedSliceOpTest(int32_t operandCode) { |
| uint32_t inputDimensions[2] = {5, 5}; |
| ANeuralNetworksOperandType input = {.type = operandCode, |
| .dimensionCount = 2, |
| .dimensions = inputDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| if (operandCode == ANEURALNETWORKS_TENSOR_QUANT8_ASYMM) { |
| input.scale = 0.5f; |
| } |
| ANeuralNetworksOperandType output = input; |
| |
| uint32_t beginsDimensions[1] = {2}; |
| ANeuralNetworksOperandType begins = {.type = ANEURALNETWORKS_TENSOR_INT32, |
| .dimensionCount = 1, |
| .dimensions = beginsDimensions, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| |
| ANeuralNetworksOperandType ends = begins; |
| ANeuralNetworksOperandType strides = begins; |
| |
| ANeuralNetworksOperandType beginMask = {.type = ANEURALNETWORKS_INT32, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0}; |
| ANeuralNetworksOperandType endMask = beginMask; |
| ANeuralNetworksOperandType shrinkAxisMask = beginMask; |
| |
| OperationTestBase stridedSliceTest(ANEURALNETWORKS_STRIDED_SLICE, |
| {input, begins, ends, strides, |
| beginMask, endMask, shrinkAxisMask}, |
| {output}); |
| |
| EXPECT_TRUE(stridedSliceTest.testMutatingInputOperandCode()); |
| EXPECT_TRUE(stridedSliceTest.testMutatingInputOperandCounts()); |
| EXPECT_TRUE(stridedSliceTest.testMutatingOutputOperandCode()); |
| EXPECT_TRUE(stridedSliceTest.testMutatingOutputOperandCounts()); |
| } |
| |
| TEST(OperationValidationTest, STRIDED_SLICE_float32) { |
| stridedSliceOpTest(ANEURALNETWORKS_TENSOR_FLOAT32); |
| } |
| |
| TEST(OperationValidationTest, STRIDED_SLICE_quant8) { |
| stridedSliceOpTest(ANEURALNETWORKS_TENSOR_QUANT8_ASYMM); |
| } |
| |
| } // end namespace |
