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Merge branch 'lp_normalization' into 'master'
add lpnorm、mvnorm op for caffe, enhance biasadd、reshape op See merge request !1224
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| // Copyright 2018 The MACE Authors. 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. | ||
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| #include <functional> | ||
| #include <memory> | ||
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| #include "mace/core/operator.h" | ||
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| #ifdef MACE_ENABLE_OPENCL | ||
| #include "mace/ops/opencl/image/lpnorm.h" | ||
| #endif // MACE_ENABLE_OPENCL | ||
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| /** | ||
| * LpNormOp is a Normalization OP which support L1 and L2, which is a custom op | ||
| * of caffe (not exist in official caffe), please reference: | ||
| * https://github.com/freesouls/caffe/blob/master/src/caffe/layers/normalization_layer.cpp #noqa | ||
| */ | ||
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| namespace mace { | ||
| namespace ops { | ||
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| template<DeviceType D, typename T> | ||
| class LpNormOp; | ||
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| template<> | ||
| class LpNormOp<DeviceType::CPU, float> : public Operation { | ||
| public: | ||
| explicit LpNormOp(OpConstructContext *context) | ||
| : Operation(context), | ||
| p_(Operation::GetOptionalArg<int>("p", 2)), | ||
| axis_(Operation::GetOptionalArg<int>("axis", -1)) {} | ||
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| MaceStatus Run(OpContext *context) override { | ||
| MACE_UNUSED(context); | ||
| const Tensor *input = this->Input(0); | ||
| Tensor *output = this->Output(0); | ||
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| if (axis_ < 0) { | ||
| axis_ += input->dim_size(); | ||
| } | ||
| MACE_CHECK(axis_ < input->dim_size() && axis_ >= 0, | ||
| "The axis_ must be small than dim size"); | ||
| const std::vector<index_t> &input_shape = input->shape(); | ||
| MACE_RETURN_IF_ERROR(output->Resize(input_shape)); | ||
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| Tensor::MappingGuard guard_input(input); | ||
| Tensor::MappingGuard guard_output(output); | ||
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| const auto *input_data = input->data<float>(); | ||
| auto *output_data = output->mutable_data<float>(); | ||
| utils::ThreadPool | ||
| &thread_pool = context->device()->cpu_runtime()->thread_pool(); | ||
| auto outer_loop = std::accumulate(input_shape.begin(), | ||
| input_shape.begin() + axis_, 1, | ||
| std::multiplies<index_t>()); | ||
| auto inner_loop = std::accumulate(input_shape.begin() + axis_, | ||
| input_shape.end(), 1, | ||
| std::multiplies<index_t>()); | ||
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| if (p_ == 1) { | ||
| thread_pool.Compute1D([=](index_t start, index_t end, index_t step) { | ||
| for (index_t i = start; i < end; i += step) { | ||
| output_data[i] = std::abs(input_data[i]); | ||
| } | ||
| }, 0, input->size(), 1); | ||
| } else if (p_ == 2) { | ||
| thread_pool.Compute1D([=](index_t start, index_t end, index_t step) { | ||
| for (index_t i = start; i < end; i += step) { | ||
| output_data[i] = input_data[i] * input_data[i]; | ||
| } | ||
| }, 0, input->size(), 1); | ||
| } else { | ||
| LOG(FATAL) << "LpNorm's p should be 1 or 2, current p is: " << p_; | ||
| } | ||
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| const float power = 1 / static_cast<float>(p_); | ||
| auto norm_buffer = context->device()->scratch_buffer(); | ||
| norm_buffer->Rewind(); | ||
| MACE_RETURN_IF_ERROR(norm_buffer->GrowSize(outer_loop * sizeof(float))); | ||
| float *norm_ptr = norm_buffer->mutable_data<float>(); | ||
| thread_pool.Compute1D([=](index_t start, index_t end, index_t step) { | ||
| for (index_t i = start; i < end; i += step) { | ||
| auto output_data_base = output_data + inner_loop * i; | ||
| norm_ptr[i] = std::accumulate(output_data_base, | ||
| output_data_base + inner_loop, 0.0f); | ||
| norm_ptr[i] = std::pow(norm_ptr[i], power); | ||
| norm_ptr[i] += 1e-6; | ||
| } | ||
| }, 0, outer_loop, 1); | ||
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| thread_pool.Compute2D([=](index_t start0, index_t end0, index_t step0, | ||
| index_t start1, index_t end1, index_t step1) { | ||
| for (index_t i = start0; i < end0; i += step0) { | ||
| const auto offset = i * inner_loop; | ||
| for (index_t j = start1; j < end1; j += step1) { | ||
| output_data[offset + j] = input_data[offset + j] / norm_ptr[i]; | ||
| } | ||
| } | ||
| }, 0, outer_loop, 1, 0, inner_loop, 1); | ||
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| return MaceStatus::MACE_SUCCESS; | ||
| } | ||
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| private: | ||
| int p_; | ||
| int axis_; | ||
| }; | ||
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| #ifdef MACE_ENABLE_OPENCL | ||
| template<> | ||
| class LpNormOp<DeviceType::GPU, float> : public Operation { | ||
| public: | ||
| explicit LpNormOp(OpConstructContext *context) | ||
| : Operation(context) { | ||
| const auto p = Operation::GetOptionalArg<int>("p", 2); | ||
| const auto axis = Operation::GetOptionalArg<int>("axis", -1); | ||
| if (context->GetOpMemoryType() == MemoryType::GPU_IMAGE) { | ||
| kernel_ = make_unique<opencl::image::LpNormKernel>(p, axis); | ||
| } else { | ||
| MACE_NOT_IMPLEMENTED; | ||
| } | ||
| } | ||
| MaceStatus Run(OpContext *context) override { | ||
| const Tensor *input = this->Input(INPUT); | ||
| Tensor *output = this->Output(OUTPUT); | ||
| MACE_RETURN_IF_ERROR(output->ResizeLike(input)); | ||
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| return kernel_->Compute(context, input, output); | ||
| } | ||
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| private: | ||
| std::unique_ptr<OpenCLLpNormKernel> kernel_; | ||
| MACE_OP_INPUT_TAGS(INPUT); | ||
| MACE_OP_OUTPUT_TAGS(OUTPUT); | ||
| }; | ||
| #endif // MACE_ENABLE_OPENCL | ||
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| void RegisterLpNorm(OpRegistryBase *op_registry) { | ||
| MACE_REGISTER_OP(op_registry, "LpNorm", LpNormOp, | ||
| DeviceType::CPU, float); | ||
| MACE_REGISTER_GPU_OP(op_registry, "LpNorm", LpNormOp); | ||
| } | ||
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| } // namespace ops | ||
| } // namespace mace |
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