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Diffstat (limited to 'contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp')
| -rw-r--r-- | contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp | 607 |
1 files changed, 0 insertions, 607 deletions
diff --git a/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp b/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp deleted file mode 100644 index 190be016e..000000000 --- a/contrib/labs/kerneltesting/conv2d/nnfw_conv2d_test.cpp +++ /dev/null @@ -1,607 +0,0 @@ -/* - * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved - * - * 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. - */ - -/* - * Copyright (c) 2018 ARM Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ - -#include <iostream> -#include <vector> -#include <cassert> - -#include <Eigen/Core> -#include <gemmlowp.h> - -#include "types.h" -#include "common.h" -#include "optimized_ops.h" -#include "OperationUtils.h" - -#include <arm_compute/graph.h> - -#include <arm_compute/runtime/CL/CLFunctions.h> -#include <arm_compute/runtime/CL/functions/CLConvolution.h> - -#include "io_accessor.h" -#include "misc/environment.h" - -static constexpr int kStaticBufferSize = 1605632; -static char static_scratch_buffer[kStaticBufferSize]; - -#define ANDROID_NN_CONV_PARAMETERS(Type) \ - uint32_t height = getSizeOfDimension(inputShape, 1); \ - uint32_t width = getSizeOfDimension(inputShape, 2); \ - uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \ - uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \ - uint32_t outHeight = getSizeOfDimension(outputShape, 1); \ - uint32_t outWidth = getSizeOfDimension(outputShape, 2); \ - uint32_t inDepth = getSizeOfDimension(inputShape, 3); \ - \ - uint32_t paddingHeight = (uint32_t)padding_top; \ - uint32_t paddingWidth = (uint32_t)padding_left; \ - \ - Dims<4> im2colDim; \ - im2colDim.sizes[3] = (int)getSizeOfDimension(outputShape, 0); \ - im2colDim.sizes[2] = (int)getSizeOfDimension(outputShape, 1); \ - im2colDim.sizes[1] = (int)getSizeOfDimension(outputShape, 2); \ - im2colDim.sizes[0] = (int)inDepth * filterHeight * filterWidth; \ - \ - im2colDim.strides[0] = 1; \ - for (int i=1; i<4; i++) { \ - im2colDim.strides[i] = im2colDim.strides[i-1] * im2colDim.sizes[i-1]; \ - } \ - \ - Type* im2colData = nullptr; \ - int im2colByteSize = sizeof(Type); \ - for (int i=0; i<4; i++) { \ - im2colByteSize *= im2colDim.sizes[i]; \ - } \ - if (im2colByteSize <= kStaticBufferSize) { \ - im2colData = reinterpret_cast<Type *>(static_scratch_buffer); \ - } else { \ - im2colData = new (std::nothrow) Type[im2colByteSize / sizeof(Type)]; \ - } - - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - 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 activation, - float* outputData, const Shape& outputShape) { - - ANDROID_NN_CONV_PARAMETERS(float) - - #define ANDROID_NN_CONV(activation) \ - Conv<FusedActivationFunctionType::activation>( \ - inputData, convertShapeToDims(inputShape), \ - filterData, convertShapeToDims(filterShape), \ - biasData, convertShapeToDims(biasShape), \ - stride_width, stride_height, paddingWidth, paddingHeight, \ - outputData, convertShapeToDims(outputShape), \ - im2colData, im2colDim) - - ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_CONV) - - #undef ANDROID_NN_CONV - - if (im2colByteSize > kStaticBufferSize) { - delete[] im2colData; - } - return true; -} - -//----------------------------------------------------------------------------- - -using arm_compute::DataType; -using arm_compute::graph::Target; -using arm_compute::graph::TensorDescriptor; -using arm_compute::TensorShape; -using arm_compute::graph::frontend::InputLayer; -using arm_compute::graph::frontend::OutputLayer; - -namespace acl_graph { - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - 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 activation, - float* outputData, const Shape& outputShape) -{ - // Try with simple build-run with ACL Layer - arm_compute::graph::frontend::Stream graph{0, "ACL_CONV2D_TEST"}; - - Target target_hint = nnfw::misc::get_env_int("NNFW_ACL_USENEON") - ? Target::NEON : Target::CL; - - // Not sure about which index is which value - uint32_t tsi_c = getSizeOfDimension(inputShape, 0); - uint32_t tsi_h = getSizeOfDimension(inputShape, 1); - uint32_t tsi_w = getSizeOfDimension(inputShape, 2); - uint32_t tsi_n = getSizeOfDimension(inputShape, 3); - - uint32_t tsk_h = getSizeOfDimension(filterShape, 1); - uint32_t tsk_w = getSizeOfDimension(filterShape, 2); - uint32_t tsk_n = getSizeOfDimension(filterShape, 3); - - graph << target_hint - << InputLayer(TensorDescriptor(TensorShape(tsi_w, tsi_h, tsi_c, tsi_n), DataType::F32), - std::unique_ptr<InputAccessor>(new InputAccessor(inputData, inputShape))) - << arm_compute::graph::frontend::ConvolutionLayer( - tsk_w, tsk_h, tsk_n, - std::unique_ptr<WeightAccessor>(new WeightAccessor(filterData, filterShape)), - std::unique_ptr<BiasAccessor>(new BiasAccessor(biasData, biasShape)), - arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom)) - ; - if (activation != static_cast<int32_t>(FusedActivationFunc::NONE)) { - arm_compute::ActivationLayerInfo::ActivationFunction actFunc = - arm_compute::ActivationLayerInfo::ActivationFunction::RELU; - - graph << arm_compute::graph::frontend::ActivationLayer(arm_compute::ActivationLayerInfo(actFunc)); - // Activation does not provide output Tensor and makes next layer fail to add to graph - // when it's the last(output) layer. To solve this, need to add a dummy layer. - uint32_t tso_c = getSizeOfDimension(outputShape, 0); - uint32_t tso_h = getSizeOfDimension(outputShape, 1); - uint32_t tso_w = getSizeOfDimension(outputShape, 2); - uint32_t tso_n = getSizeOfDimension(outputShape, 3); - graph << arm_compute::graph::frontend::ReshapeLayer(TensorShape(tso_w, tso_h, tso_c, tso_n)); - } - graph << OutputLayer(std::unique_ptr<OutputAccessor>(new OutputAccessor(outputData, outputShape))) - ; - - graph.run(); - - return true; -} - -} // namespace acl_graph - -//----------------------------------------------------------------------------- - -using arm_compute::TensorInfo; - -namespace acl_runtime { - -TensorShape calculate_convolution_layer_output_shape( - const arm_compute::TensorShape &input_shape, - const arm_compute::TensorShape &weights_shape, - const arm_compute::PadStrideInfo &conv_info) -{ - unsigned int output_width = 0; - unsigned int output_height = 0; - - // Get output width and height - std::tie(output_width, output_height) = - arm_compute::scaled_dimensions( - input_shape.x(), input_shape.y(), - weights_shape.x(), weights_shape.y(), - conv_info); - - // Create output shape - TensorShape output_shape = input_shape; - output_shape.set(0, output_width); - output_shape.set(1, output_height); - output_shape.set(2, weights_shape[3]); - - return output_shape; -} - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - 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 activation, - float* outputData, const Shape& outputShape) -{ - arm_compute::CLScheduler::get().default_init(); - - uint32_t tsi_c = getSizeOfDimension(inputShape, 0); - uint32_t tsi_h = getSizeOfDimension(inputShape, 1); - uint32_t tsi_w = getSizeOfDimension(inputShape, 2); - uint32_t tsi_n = getSizeOfDimension(inputShape, 3); - - uint32_t tsk_h = getSizeOfDimension(filterShape, 1); - uint32_t tsk_w = getSizeOfDimension(filterShape, 2); - uint32_t tsk_n = getSizeOfDimension(filterShape, 3); - - TensorShape input_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); - TensorShape filter_shape = TensorShape(tsi_w, tsi_h, tsi_c, tsi_n); - arm_compute::PadStrideInfo conv_info = - arm_compute::PadStrideInfo(stride_width, stride_height, padding_top, padding_bottom); - - TensorShape output_shape = calculate_convolution_layer_output_shape( - input_shape, filter_shape, conv_info); - - uint32_t tso_c = output_shape[0]; - uint32_t tso_w = output_shape[1]; - uint32_t tso_h = output_shape[2]; - uint32_t tso_n = output_shape[3]; - - arm_compute::CLTensor input, output, bias, filter; - - input.allocator()->init(TensorInfo(tsi_w, tsi_h, arm_compute::Format::F32)); - output.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); - bias.allocator()->init(TensorInfo(tso_w, tso_h, arm_compute::Format::F32)); - filter.allocator()->init(TensorInfo(tsk_w, tsk_h, arm_compute::Format::F32)); - - input.allocator()->allocate(); - output.allocator()->allocate(); - bias.allocator()->allocate(); - filter.allocator()->allocate(); - - input.map(); - InputAccessor ia(inputData, inputShape); - ia.access_tensor(input); - input.unmap(); - - bias.map(); - BiasAccessor ba(biasData, biasShape); - ba.access_tensor(bias); - bias.unmap(); - - filter.map(); - WeightAccessor fa(filterData, filterShape); - fa.access_tensor(filter); - filter.unmap(); - - arm_compute::CLConvolutionLayer conv_f; - conv_f.configure(&input, &filter, &bias, &output, conv_info); - - arm_compute::CLScheduler::get().sync(); - - conv_f.run(); - - output.map(); - OutputAccessor oa(outputData, outputShape); - oa.access_tensor(output); - output.unmap(); - - return true; -} - -} // namespace acl_runtime - -//----------------------------------------------------------------------------- - -enum COMPUTE_TYPE { - COMPUTE_DEFAULT = 0, - COMPUTE_ACLGRAPH, - COMPUTE_ACLRT -}; - -bool convFloat32(const float* inputData, const Shape& inputShape, - const float* filterData, const Shape& filterShape, - const float* biasData, const Shape& biasShape, - 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 activation, - float* outputData, const Shape& outputShape, - COMPUTE_TYPE compType) { - - switch (compType) - { - case COMPUTE_DEFAULT : - return convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - - case COMPUTE_ACLGRAPH : - return acl_graph::convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - - case COMPUTE_ACLRT : - return acl_runtime::convFloat32(inputData, inputShape, filterData, filterShape, - biasData, biasShape, padding_left, padding_right, - padding_top, padding_bottom, stride_width, stride_height, - activation, outputData, outputShape); - } - return false; -} - -//----------------------------------------------------------------------------- - -void dumpData(const char* name, const float* data, const Shape& shape) -{ - uint32_t height = getSizeOfDimension(shape, 1); - uint32_t width = getSizeOfDimension(shape, 2); - - std::cout << "---" << name << "---" << std::endl; - for (int h = 0; h < height; h++) { - std::cout << "H=" << h << " | "; - for (int w = 0; w < width; w++) { - std::cout << data[h * width + w] << ","; - } - std::cout << std::endl; - } -} - -void initData(float* outputData, int num, float value) -{ - for (int i = 0; i < num; i++) { - *(outputData + i) = value; - } -} - -void initDataSeq(float* outputData, int num, float value) -{ - for (int i = 0; i < num; i++) { - *(outputData + i) = value; - value += 1.0; - } -} - -// compareData -// return true if result == expected with the shape info, -// otherwise false -bool compareData(const float* result, const float* expected, const Shape& shape) -{ - NN_CHECK_EQ(shape.dimensions.size(), 4); - - uint32_t height = getSizeOfDimension(shape, 1); - uint32_t width = getSizeOfDimension(shape, 2); - uint32_t numitems = height * width; - for (int item = 0; item < numitems; item++) { - if (*(result + item) != *(expected + item)) { - LOG(ERROR) << "compareData failed: result " << *(result + item) - << ", expected " << *(expected + item) << std::endl; - return false; - } - } - return true; -} - -int test_3x3_1x1_one(COMPUTE_TYPE comptype) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 0; - int32_t padding_right = 0; - int32_t padding_top = 0; - int32_t padding_bottom = 0; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - comptype); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int test_3x3_3x3_one(void) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 1; - int32_t padding_right = 1; - int32_t padding_top = 1; - int32_t padding_bottom = 1; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initData(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initData(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initData(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - COMPUTE_ACLGRAPH); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int test_3x3_3x3_seq(void) -{ - float inputData[9]; - const Shape inputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float filterData[9]; - const Shape filterShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float biasData[1] = { 1.0 }; - const Shape biasShape = { OperandType::FLOAT32, {1,1,1,1}, 1.0, 0 }; - int32_t padding_left = 1; - int32_t padding_right = 1; - int32_t padding_top = 1; - int32_t padding_bottom = 1; - int32_t stride_width = 1; - int32_t stride_height = 1; - int32_t activation = static_cast<int32_t>(FusedActivationFunc::RELU); - float* outputData = new float[9]; - const Shape outputShape = { OperandType::FLOAT32, {1,3,3,1}, 1.0, 0 }; - float* expectData = new float[9]; - bool bret; - - initDataSeq(inputData, sizeof(inputData) / sizeof(inputData[0]), 1.0); - initDataSeq(filterData, sizeof(filterData) / sizeof(filterData[0]), 1.0); - initDataSeq(outputData, sizeof(outputData) / sizeof(outputData[0]), 0.0); - initData(expectData, sizeof(expectData) / sizeof(expectData[0]), 0.0); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - expectData, outputShape, - COMPUTE_DEFAULT); - - bret = convFloat32(inputData, inputShape, - filterData, filterShape, - biasData, biasShape, - padding_left, padding_right, - padding_top, padding_bottom, - stride_width, stride_height, - activation, - outputData, outputShape, - COMPUTE_ACLGRAPH); - - dumpData("Input ", inputData, inputShape); - dumpData("Filter ", filterData, filterShape); - dumpData("Bias ", biasData, biasShape); - dumpData("Output ", outputData, outputShape); - std::cout << std::endl; - - bret = compareData(outputData, expectData, outputShape); - - delete outputData; - delete expectData; - - if (!bret) - { - LOG(ERROR) << "TEST FAILED " << __FUNCTION__ << std::endl; - return -1; - } - return 0; -} - -int main(int argc, char* argv[]) -{ - int result; - - // input 3x3, output 1x1, all data 1.0 - result = test_3x3_1x1_one(COMPUTE_ACLGRAPH); - if (result) return result; - result = test_3x3_1x1_one(COMPUTE_ACLRT); - if (result) return result; - - // input 3x3, output 3x3, all data 1.0 - result = test_3x3_3x3_one(); - if (result) return result; - - result = test_3x3_3x3_seq(); - if (result) return result; - - return result; -} |