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// Copyright 2013 Yangqing Jia
#ifndef CAFFE_VISION_LAYERS_HPP_
#define CAFFE_VISION_LAYERS_HPP_
#include <leveldb/db.h>
#include <pthread.h>
#include <vector>
#include "caffe/layer.hpp"
namespace caffe {
// The neuron layer is a specific type of layers that just works on single
// celements.
template <typename Dtype>
class NeuronLayer : public Layer<Dtype> {
public:
explicit NeuronLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
};
template <typename Dtype>
class ReLULayer : public NeuronLayer<Dtype> {
public:
explicit ReLULayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param) {}
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
};
template <typename Dtype>
class DropoutLayer : public NeuronLayer<Dtype> {
public:
explicit DropoutLayer(const LayerParameter& param)
: NeuronLayer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
shared_ptr<SyncedMemory> rand_vec_;
float threshold_;
float scale_;
unsigned int uint_thres_;
};
template <typename Dtype>
class InnerProductLayer : public Layer<Dtype> {
public:
explicit InnerProductLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
int M_;
int K_;
int N_;
bool biasterm_;
shared_ptr<SyncedMemory> bias_multiplier_;
};
template <typename Dtype>
class PaddingLayer : public Layer<Dtype> {
public:
explicit PaddingLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
unsigned int PAD_;
int NUM_;
int CHANNEL_;
int HEIGHT_IN_;
int WIDTH_IN_;
int HEIGHT_OUT_;
int WIDTH_OUT_;
};
template <typename Dtype>
class LRNLayer : public Layer<Dtype> {
public:
explicit LRNLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
// scale_ stores the intermediate summing results
Blob<Dtype> scale_;
int size_;
int pre_pad_;
Dtype alpha_;
Dtype beta_;
int num_;
int channels_;
int height_;
int width_;
};
template <typename Dtype>
class Im2colLayer : public Layer<Dtype> {
public:
explicit Im2colLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
int KSIZE_;
int STRIDE_;
int CHANNELS_;
int HEIGHT_;
int WIDTH_;
};
template <typename Dtype>
class PoolingLayer : public Layer<Dtype> {
public:
explicit PoolingLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
int KSIZE_;
int STRIDE_;
int CHANNELS_;
int HEIGHT_;
int WIDTH_;
int POOLED_HEIGHT_;
int POOLED_WIDTH_;
};
template <typename Dtype>
class ConvolutionLayer : public Layer<Dtype> {
public:
explicit ConvolutionLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
Blob<Dtype> col_bob_;
int KSIZE_;
int STRIDE_;
int NUM_;
int CHANNELS_;
int HEIGHT_;
int WIDTH_;
int NUM_OUTPUT_;
int GROUP_;
Blob<Dtype> col_buffer_;
shared_ptr<SyncedMemory> bias_multiplier_;
bool biasterm_;
int M_;
int K_;
int N_;
};
// This function is used to create a pthread that prefetches the data.
template <typename Dtype>
void* DataLayerPrefetch(void* layer_pointer);
template <typename Dtype>
class DataLayer : public Layer<Dtype> {
// The function used to perform prefetching.
friend void* DataLayerPrefetch<Dtype>(void* layer_pointer);
public:
explicit DataLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
shared_ptr<leveldb::DB> db_;
shared_ptr<leveldb::Iterator> iter_;
int datum_channels_;
int datum_height_;
int datum_width_;
int datum_size_;
pthread_t thread_;
shared_ptr<Blob<Dtype> > prefetch_data_;
shared_ptr<Blob<Dtype> > prefetch_label_;
Blob<Dtype> data_mean_;
};
template <typename Dtype>
class SoftmaxLayer : public Layer<Dtype> {
public:
explicit SoftmaxLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
// sum_multiplier is just used to carry out sum using blas
Blob<Dtype> sum_multiplier_;
// scale is an intermediate blob to hold temporary results.
Blob<Dtype> scale_;
};
template <typename Dtype>
class MultinomialLogisticLossLayer : public Layer<Dtype> {
public:
explicit MultinomialLogisticLossLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
// The loss layer will do nothing during forward - all computation are
// carried out in the backward pass.
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) { return; }
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) { return; }
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
// virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
// const bool propagate_down, vector<Blob<Dtype>*>* bottom);
};
// SoftmaxWithLossLayer is a layer that implements softmax and then computes
// the loss - it is preferred over softmax + multinomiallogisticloss in the
// sense that during training, this will produce more numerically stable
// gradients. During testing this layer could be replaced by a softmax layer
// to generate probability outputs.
template <typename Dtype>
class SoftmaxWithLossLayer : public Layer<Dtype> {
public:
explicit SoftmaxWithLossLayer(const LayerParameter& param)
: Layer<Dtype>(param), softmax_layer_(new SoftmaxLayer<Dtype>(param)) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
shared_ptr<SoftmaxLayer<Dtype> > softmax_layer_;
// prob stores the output probability of the layer.
Blob<Dtype> prob_;
// Vector holders to call the underlying softmax layer forward and backward.
vector<Blob<Dtype>*> softmax_bottom_vec_;
vector<Blob<Dtype>*> softmax_top_vec_;
};
template <typename Dtype>
class EuclideanLossLayer : public Layer<Dtype> {
public:
explicit EuclideanLossLayer(const LayerParameter& param)
: Layer<Dtype>(param), difference_() {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
// The loss layer will do nothing during forward - all computation are
// carried out in the backward pass.
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) { return; }
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) { return; }
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom);
// virtual Dtype Backward_gpu(const vector<Blob<Dtype>*>& top,
// const bool propagate_down, vector<Blob<Dtype>*>* bottom);
Blob<Dtype> difference_;
};
template <typename Dtype>
class AccuracyLayer : public Layer<Dtype> {
public:
explicit AccuracyLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top);
// The accuracy layer should not be used to compute backward operations.
virtual Dtype Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
NOT_IMPLEMENTED;
return Dtype(0.);
}
};
} // namespace caffe
#endif // CAFFE_VISION_LAYERS_HPP_
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