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| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
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| * http://www.apache.org/licenses/LICENSE-2.0 |
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| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
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| * KIND, either express or implied. See the License for the |
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| ************************************************************/ |
| #ifndef SINGA_MODEL_LAYER_BATCHNORM_H |
| #define SINGA_MODEL_LAYER_BATCHNORM_H |
| #include "singa/model/layer.h" |
| #include "singa/core/common.h" |
| #include "singa/proto/core.pb.h" |
| #include <stack> |
| |
| namespace singa { |
| class BatchNorm : public Layer { |
| public: |
| /// \copydoc Layer::layer_type() |
| // const std::string layer_type() const override { return "BatchNorm"; } |
| |
| /// \copydoc Layer::Setup(const LayerConf&); |
| void Setup(const Shape& in_sample, const LayerConf& conf) override; |
| const Shape GetOutputSampleShape() const override { |
| CHECK(out_sample_shape_.size()) << "You may haven't call Setup()"; |
| return out_sample_shape_; |
| } |
| |
| const Tensor Forward(int flag, const Tensor& input) |
| override; |
| |
| /// \copydoc Layer::Backward(int, const Tensor&, const Tensor&); |
| const std::pair<Tensor, vector<Tensor>> Backward( |
| int flag, const Tensor& grad) override; |
| virtual const std::vector<Tensor> param_values() override { |
| return std::vector<Tensor> { bnScale_, bnBias_, runningMean_, |
| runningVariance_ }; |
| } |
| float factor() const { return factor_; } |
| const Tensor& bnScale() const { return bnScale_; } |
| const Tensor& bnBias() const { return bnBias_; } |
| const Tensor& runningMean() const { return runningMean_; } |
| const Tensor& runningVariance() const { return runningVariance_; } |
| size_t channels() const { return channels_; } |
| size_t height() const { return height_; } |
| size_t width() const { return width_; } |
| void set_bnScale(const Tensor& x) { |
| bnScale_.ResetLike(x); |
| bnScale_.CopyData(x); |
| } |
| void set_bnBias(const Tensor& x) { |
| bnBias_.ResetLike(x); |
| bnBias_.CopyData(x); |
| } |
| void set_runningMean(const Tensor& x) { |
| runningMean_.ResetLike(x); |
| runningMean_.CopyData(x); |
| } |
| void set_runningVariance(const Tensor& x) { |
| runningVariance_.ResetLike(x); |
| runningVariance_.CopyData(x); |
| } |
| virtual void ToDevice(std::shared_ptr<Device> device) override; |
| |
| protected: |
| float factor_; |
| size_t channels_, height_, width_; |
| bool is_2d_ = false; |
| Tensor bnScale_, bnBias_; |
| Tensor dbnScale_, dbnBias_; |
| Tensor runningMean_, runningVariance_; |
| // Store intermediate data, i.e., input tensor |
| std::stack<Tensor> buf_; |
| Shape out_sample_shape_; |
| }; // class batchnorm |
| } // namespace |
| |
| #endif // SINGA_MODEL_LAYER_BATCHNORM_H |