Caffe
input_layer.hpp
1 #ifndef CAFFE_INPUT_LAYER_HPP_
2 #define CAFFE_INPUT_LAYER_HPP_
3 
4 #include <vector>
5 
6 #include "caffe/blob.hpp"
7 #include "caffe/layer.hpp"
8 #include "caffe/proto/caffe.pb.h"
9 
10 namespace caffe {
11 
18 template <typename Dtype>
19 class InputLayer : public Layer<Dtype> {
20  public:
21  explicit InputLayer(const LayerParameter& param)
22  : Layer<Dtype>(param) {}
23  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
24  const vector<Blob<Dtype>*>& top);
25  // Data layers should be shared by multiple solvers in parallel
26  virtual inline bool ShareInParallel() const { return true; }
27  // Data layers have no bottoms, so reshaping is trivial.
28  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
29  const vector<Blob<Dtype>*>& top) {}
30 
31  virtual inline const char* type() const { return "Input"; }
32  virtual inline int ExactNumBottomBlobs() const { return 0; }
33  virtual inline int MinTopBlobs() const { return 1; }
34 
35  protected:
36  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
37  const vector<Blob<Dtype>*>& top) {}
38  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
39  const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
40 };
41 
42 } // namespace caffe
43 
44 #endif // CAFFE_INPUT_LAYER_HPP_
An interface for the units of computation which can be composed into a Net.
Definition: layer.hpp:33
A layer factory that allows one to register layers. During runtime, registered layers can be called b...
Definition: blob.hpp:14
virtual int ExactNumBottomBlobs() const
Returns the exact number of bottom blobs required by the layer, or -1 if no exact number is required...
Definition: input_layer.hpp:32
virtual void LayerSetUp(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Does layer-specific setup: your layer should implement this function as well as Reshape.
Definition: input_layer.cpp:8
virtual bool ShareInParallel() const
Whether a layer should be shared by multiple nets during data parallelism. By default, all layers except for data layers should not be shared. data layers should be shared to ensure each worker solver access data sequentially during data parallelism.
Definition: input_layer.hpp:26
virtual void Forward_cpu(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Using the CPU device, compute the layer output.
Definition: input_layer.hpp:36
virtual int MinTopBlobs() const
Returns the minimum number of top blobs required by the layer, or -1 if no minimum number is required...
Definition: input_layer.hpp:33
Provides data to the Net by assigning tops directly.
Definition: input_layer.hpp:19
virtual const char * type() const
Returns the layer type.
Definition: input_layer.hpp:31
virtual void Backward_cpu(const vector< Blob< Dtype > *> &top, const vector< bool > &propagate_down, const vector< Blob< Dtype > *> &bottom)
Using the CPU device, compute the gradients for any parameters and for the bottom blobs if propagate_...
Definition: input_layer.hpp:38
virtual void Reshape(const vector< Blob< Dtype > *> &bottom, const vector< Blob< Dtype > *> &top)
Adjust the shapes of top blobs and internal buffers to accommodate the shapes of the bottom blobs...
Definition: input_layer.hpp:28
A wrapper around SyncedMemory holders serving as the basic computational unit through which Layers...
Definition: blob.hpp:24