| /* |
| * 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 "NeuralNetworksOEM.h" |
| #include "NeuralNetworksWrapper.h" |
| #ifndef NNTEST_ONLY_PUBLIC_API |
| #include "Utils.h" |
| #endif |
| |
| #include <gtest/gtest.h> |
| |
| namespace { |
| |
| using namespace android::nn::wrapper; |
| |
| class OperandExtraParamsTest : public ::testing::Test { |
| protected: |
| virtual void SetUp() { |
| ::testing::Test::SetUp(); |
| ASSERT_EQ(ANeuralNetworksModel_create(&mModel), ANEURALNETWORKS_NO_ERROR); |
| } |
| virtual void TearDown() { |
| ANeuralNetworksModel_free(mModel); |
| ::testing::Test::TearDown(); |
| } |
| |
| static const uint32_t CHANNEL_DIM_SIZE = 4; |
| |
| ANeuralNetworksOperandType createOperandWithExt(int32_t dataType, |
| ANeuralNetworksOperandType::ExtraParams ext) { |
| static uint32_t dims[4] = {1, 2, 3, CHANNEL_DIM_SIZE}; |
| switch (dataType) { |
| case ANEURALNETWORKS_FLOAT32: |
| case ANEURALNETWORKS_FLOAT16: |
| case ANEURALNETWORKS_INT32: |
| case ANEURALNETWORKS_UINT32: |
| case ANEURALNETWORKS_BOOL: |
| case ANEURALNETWORKS_OEM_SCALAR: |
| return {.type = dataType, |
| .dimensionCount = 0, |
| .dimensions = nullptr, |
| .scale = 0.0f, |
| .zeroPoint = 0, |
| .extraParams = ext}; |
| case ANEURALNETWORKS_TENSOR_OEM_BYTE: |
| case ANEURALNETWORKS_TENSOR_FLOAT32: |
| case ANEURALNETWORKS_TENSOR_FLOAT16: |
| case ANEURALNETWORKS_TENSOR_INT32: |
| case ANEURALNETWORKS_TENSOR_BOOL8: |
| case ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL: |
| return {.type = dataType, |
| .dimensionCount = 4, |
| .dimensions = dims, |
| .scale = 0.0f, |
| .zeroPoint = 0, |
| .extraParams = ext}; |
| case ANEURALNETWORKS_TENSOR_QUANT8_ASYMM: |
| return {.type = dataType, |
| .dimensionCount = 4, |
| .dimensions = dims, |
| .scale = 1.0, |
| .zeroPoint = 128, |
| .extraParams = ext}; |
| case ANEURALNETWORKS_TENSOR_QUANT16_SYMM: |
| return {.type = dataType, |
| .dimensionCount = 4, |
| .dimensions = dims, |
| .scale = 1.0, |
| .zeroPoint = 0, |
| .extraParams = ext}; |
| default: |
| ADD_FAILURE(); |
| return {}; |
| } |
| } |
| |
| ANeuralNetworksOperandType::ExtraParams createExtNone() { return {.none = nullptr}; } |
| |
| ANeuralNetworksOperandType::ExtraParams createExtChannelQuant() { |
| static float scales[CHANNEL_DIM_SIZE] = {1.0, 2.0, 3.0, 4.0}; |
| return {.channelQuant = { |
| .scales = scales, |
| .scaleCount = CHANNEL_DIM_SIZE, |
| .channelDim = 3, |
| }}; |
| } |
| |
| void testAddingOperand(int32_t dataType, ANeuralNetworksOperandType::ExtraParams ext, |
| bool expectSuccess) { |
| ANeuralNetworksOperandType operandType = createOperandWithExt(dataType, ext); |
| if (expectSuccess) { |
| EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), |
| ANEURALNETWORKS_NO_ERROR); |
| } else { |
| EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), |
| ANEURALNETWORKS_BAD_DATA); |
| } |
| } |
| |
| ANeuralNetworksModel* mModel = nullptr; |
| }; |
| |
| const uint32_t kOperandCodeIgnoringExt[]{ |
| ANEURALNETWORKS_FLOAT32, |
| ANEURALNETWORKS_FLOAT16, |
| ANEURALNETWORKS_INT32, |
| ANEURALNETWORKS_UINT32, |
| ANEURALNETWORKS_BOOL, |
| ANEURALNETWORKS_OEM_SCALAR, |
| ANEURALNETWORKS_TENSOR_OEM_BYTE, |
| ANEURALNETWORKS_TENSOR_FLOAT32, |
| ANEURALNETWORKS_TENSOR_INT32, |
| ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, |
| ANEURALNETWORKS_TENSOR_QUANT16_SYMM, |
| ANEURALNETWORKS_TENSOR_FLOAT16, |
| ANEURALNETWORKS_TENSOR_BOOL8, |
| }; |
| |
| #ifndef NNTEST_ONLY_PUBLIC_API |
| // android::nn::k* consts are defined in private headers |
| static_assert(sizeof(kOperandCodeIgnoringExt) / sizeof(kOperandCodeIgnoringExt[0]) == |
| android::nn::kNumberOfDataTypes + android::nn::kNumberOfDataTypesOEM - 1, |
| "New type added, OperandExtraParamsTest needs an update"); |
| #endif |
| |
| const uint32_t kOperandCodeChannelQuant[]{ |
| ANEURALNETWORKS_TENSOR_QUANT8_SYMM_PER_CHANNEL, |
| }; |
| |
| TEST_F(OperandExtraParamsTest, TestIgnoring) { |
| // Test for operands that are expected to ignore extensions |
| for (uint32_t dataType : kOperandCodeIgnoringExt) { |
| testAddingOperand(dataType, createExtNone(), /*expectSuccess=*/true); |
| testAddingOperand(dataType, createExtChannelQuant(), /*expectSuccess=*/true); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuant) { |
| // Test for operands that are expected to see ChannelQuant extension |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| testAddingOperand(dataType, createExtNone(), /*expectSuccess=*/false); |
| testAddingOperand(dataType, createExtChannelQuant(), /*expectSuccess=*/true); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadDim) { |
| // Bad .channelDim value |
| static float scales[4] = {1.0, 2.0, 3.0, 4.0}; |
| ANeuralNetworksOperandType::ExtraParams ext{.channelQuant = { |
| .channelDim = 7, |
| .scales = scales, |
| .scaleCount = 4, |
| }}; |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| testAddingOperand(dataType, ext, /*expectSuccess=*/false); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadScalesCount) { |
| // Bad .scaleCount value |
| static float scales[4] = {1.0, 2.0, 3.0, 4.0}; |
| ANeuralNetworksOperandType::ExtraParams lowScaleCountExt{.channelQuant = { |
| .channelDim = 3, |
| .scales = scales, |
| .scaleCount = 3, |
| }}; |
| ANeuralNetworksOperandType::ExtraParams highScaleCountExt{.channelQuant = { |
| .channelDim = 3, |
| .scales = scales, |
| .scaleCount = 10, |
| }}; |
| |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| testAddingOperand(dataType, lowScaleCountExt, /*expectSuccess=*/false); |
| testAddingOperand(dataType, highScaleCountExt, /*expectSuccess=*/false); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesBadScalesNegative) { |
| // Bad .scales value |
| static float scales[4] = {1.0, 2.0, -3.0, 4.0}; |
| ANeuralNetworksOperandType::ExtraParams ext{.channelQuant = { |
| .channelDim = 3, |
| .scales = scales, |
| .scaleCount = 4, |
| }}; |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| testAddingOperand(dataType, ext, /*expectSuccess=*/false); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesNullScales) { |
| // .scales == nullptr value |
| ANeuralNetworksOperandType::ExtraParams ext{.channelQuant = { |
| .channelDim = 3, |
| .scales = nullptr, |
| .scaleCount = 4, |
| }}; |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| testAddingOperand(dataType, ext, /*expectSuccess=*/false); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesOperandScale) { |
| ANeuralNetworksOperandType::ExtraParams ext = createExtChannelQuant(); |
| |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| ANeuralNetworksOperandType operandType = createOperandWithExt(dataType, ext); |
| operandType.scale = 1.0f; |
| EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), ANEURALNETWORKS_BAD_DATA); |
| } |
| } |
| |
| TEST_F(OperandExtraParamsTest, TestChannelQuantValuesOperandZeroPoint) { |
| ANeuralNetworksOperandType::ExtraParams ext = createExtChannelQuant(); |
| |
| for (uint32_t dataType : kOperandCodeChannelQuant) { |
| ANeuralNetworksOperandType operandType = createOperandWithExt(dataType, ext); |
| operandType.zeroPoint = 1; |
| EXPECT_EQ(ANeuralNetworksModel_addOperand(mModel, &operandType), ANEURALNETWORKS_BAD_DATA); |
| } |
| } |
| |
| } // namespace |