layer.hpp

#ifndef CAFFE_LAYER_H_
#define CAFFE_LAYER_H_

#include <algorithm>
#include <string>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/device_alternate.hpp"

namespace caffe {

/**
 * @brief An interface for the units of computation which can be composed into a
 *        Net.
 *
 * Layer%s must implement a Forward function, in which they take their input
 * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
 * They may also implement a Backward function, in which they compute the error
 * gradients with respect to their input Blob%s, given the error gradients with
 * their output Blob%s.
 */
template <typename Dtype>
class Layer {
  public:
    /**
     * You should not implement your own constructor. Any set up code should go
     * to SetUp(), where the dimensions of the bottom blobs are provided to the
     * layer.
     */
    explicit Layer(const LayerParameter& param)
        : layer_param_(param) {
      // Set phase and copy blobs (if there are any).
      phase_ = param.phase();
      if (layer_param_.blobs_size() > 0) {
        blobs_.resize(layer_param_.blobs_size());
        for (int i = 0; i < layer_param_.blobs_size(); ++i) {
          blobs_[i].reset(new Blob<Dtype>());
          blobs_[i]->FromProto(layer_param_.blobs(i));
        }
      }
    }
    virtual ~Layer() {
    }

    /**
     * @brief Implements common layer setup functionality.
     *
     * @param bottom the preshaped input blobs
     * @param top
     *     the allocated but unshaped output blobs, to be shaped by Reshape
     *
     * Checks that the number of bottom and top blobs is correct.
     * Calls LayerSetUp to do special layer setup for individual layer types,
     * followed by Reshape to set up sizes of top blobs and internal buffers.
     * Sets up the loss weight multiplier blobs for any non-zero loss weights.
     * This method may not be overridden.
     */
    void SetUp(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
      CheckBlobCounts(bottom, top);
      LayerSetUp(bottom, top);
      Reshape(bottom, top);
      SetLossWeights(top);
    }

    /**
     * @brief Does layer-specific setup: your layer should implement this function
     *        as well as Reshape.
     *
     * @param bottom
     *     the preshaped input blobs, whose data fields store the input data for
     *     this layer
     * @param top
     *     the allocated but unshaped output blobs
     *
     * This method should do one-time layer specific setup. This includes reading
     * and processing relevent parameters from the <code>layer_param_</code>.
     * Setting up the shapes of top blobs and internal buffers should be done in
     * <code>Reshape</code>, which will be called before the forward pass to
     * adjust the top blob sizes.
     */
    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
    }

    /**
     * @brief Adjust the shapes of top blobs and internal buffers to accomodate
     *        the shapes of the bottom blobs.
     *
     * @param bottom the input blobs, with the requested input shapes
     * @param top the top blobs, which should be reshaped as needed
     *
     * This method should reshape top blobs as needed according to the shapes
     * of the bottom (input) blobs, as well as reshaping any internal buffers
     * and making any other necessary adjustments so that the layer can
     * accomodate the bottom blobs.
     */
    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) = 0;

    /**
     * @brief Given the bottom blobs, compute the top blobs and the loss.
     *
     * @param bottom
     *     the input blobs, whose data fields store the input data for this layer
     * @param top
     *     the preshaped output blobs, whose data fields will store this layers'
     *     outputs
     * \return The total loss from the layer.
     *
     * The Forward wrapper calls the relevant device wrapper function
     * (Forward_cpu or Forward_gpu) to compute the top blob values given the
     * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper
     * then computes and returns the loss.
     *
     * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
     */
    inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top);

    /**
     * @brief Given the top blob error gradients, compute the bottom blob error
     *        gradients.
     *
     * @param top
     *     the output blobs, whose diff fields store the gradient of the error
     *     with respect to themselves
     * @param propagate_down
     *     a vector with equal length to bottom, with each index indicating
     *     whether to propagate the error gradients down to the bottom blob at
     *     the corresponding index
     * @param bottom
     *     the input blobs, whose diff fields will store the gradient of the error
     *     with respect to themselves after Backward is run
     *
     * The Backward wrapper calls the relevant device wrapper function
     * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
     * top blob diffs.
     *
     * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
     */
    inline void Backward(const vector<Blob<Dtype>*>& top,
        const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

    /**
     * @brief Returns the vector of learnable parameter blobs.
     */
    vector<shared_ptr<Blob<Dtype> > >& blobs() {
      return blobs_;
    }

    /**
     * @brief Returns the layer parameter.
     */
    const LayerParameter& layer_param() const {
      return layer_param_;
    }

    /**
     * @brief Writes the layer parameter to a protocol buffer
     */
    virtual void ToProto(LayerParameter* param, bool write_diff = false);

    /**
     * @brief Returns the scalar loss associated with a top blob at a given index.
     */
    inline Dtype loss(const int top_index) const {
      return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
    }

    /**
     * @brief Sets the loss associated with a top blob at a given index.
     */
    inline void set_loss(const int top_index, const Dtype value) {
      if (loss_.size() <= top_index) {
        loss_.resize(top_index + 1, Dtype(0));
      }
      loss_[top_index] = value;
    }

    /**
     * @brief Returns the layer type.
     */
    virtual inline const char* type() const {
      return "";
    }

    /**
     * @brief Returns the exact number of bottom blobs required by the layer,
     *        or -1 if no exact number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some exact number of bottom blobs.
     */
    virtual inline int ExactNumBottomBlobs() const {
      return -1;
    }
    /**
     * @brief Returns the minimum number of bottom blobs required by the layer,
     *        or -1 if no minimum number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some minimum number of bottom blobs.
     */
    virtual inline int MinBottomBlobs() const {
      return -1;
    }
    /**
     * @brief Returns the maximum number of bottom blobs required by the layer,
     *        or -1 if no maximum number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some maximum number of bottom blobs.
     */
    virtual inline int MaxBottomBlobs() const {
      return -1;
    }
    /**
     * @brief Returns the exact number of top blobs required by the layer,
     *        or -1 if no exact number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some exact number of top blobs.
     */
    virtual inline int ExactNumTopBlobs() const {
      return -1;
    }
    /**
     * @brief Returns the minimum number of top blobs required by the layer,
     *        or -1 if no minimum number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some minimum number of top blobs.
     */
    virtual inline int MinTopBlobs() const {
      return -1;
    }
    /**
     * @brief Returns the maximum number of top blobs required by the layer,
     *        or -1 if no maximum number is required.
     *
     * This method should be overridden to return a non-negative value if your
     * layer expects some maximum number of top blobs.
     */
    virtual inline int MaxTopBlobs() const {
      return -1;
    }
    /**
     * @brief Returns true if the layer requires an equal number of bottom and
     *        top blobs.
     *
     * This method should be overridden to return true if your layer expects an
     * equal number of bottom and top blobs.
     */
    virtual inline bool EqualNumBottomTopBlobs() const {
      return false;
    }

    /**
     * @brief Return whether "anonymous" top blobs are created automatically
     *        by the layer.
     *
     * If this method returns true, Net::Init will create enough "anonymous" top
     * blobs to fulfill the requirement specified by ExactNumTopBlobs() or
     * MinTopBlobs().
     */
    virtual inline bool AutoTopBlobs() const {
      return false;
    }

    /**
     * @brief Return whether to allow force_backward for a given bottom blob
     *        index.
     *
     * If AllowForceBackward(i) == false, we will ignore the force_backward
     * setting and backpropagate to blob i only if it needs gradient information
     * (as is done when force_backward == false).
     */
    virtual inline bool AllowForceBackward(const int bottom_index) const {
      return true;
    }

    /**
     * @brief Specifies whether the layer should compute gradients w.r.t. a
     *        parameter at a particular index given by param_id.
     *
     * You can safely ignore false values and always compute gradients
     * for all parameters, but possibly with wasteful computation.
     */
    inline bool param_propagate_down(const int param_id) {
      return
          (param_propagate_down_.size() > param_id) ?
              param_propagate_down_[param_id] : false;
    }
    /**
     * @brief Sets whether the layer should compute gradients w.r.t. a
     *        parameter at a particular index given by param_id.
     */
    inline void set_param_propagate_down(const int param_id, const bool value) {
      if (param_propagate_down_.size() <= param_id) {
        param_propagate_down_.resize(param_id + 1, true);
      }
      param_propagate_down_[param_id] = value;
    }

  protected:
    /** The protobuf that stores the layer parameters */
    LayerParameter layer_param_;
    /** The phase: TRAIN or TEST */
    Phase phase_;
    /** The vector that stores the learnable parameters as a set of blobs. */
    vector<shared_ptr<Blob<Dtype> > > blobs_;
    /** Vector indicating whether to compute the diff of each param blob. */
    vector<bool> param_propagate_down_;

    /** The vector that indicates whether each top blob has a non-zero weight in
     *  the objective function. */
    vector<Dtype> loss_;

    /** @brief Using the CPU device, compute the layer output. */
    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) = 0;
    /**
     * @brief Using the GPU device, compute the layer output.
     *        Fall back to Forward_cpu() if unavailable.
     */
    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
      // LOG(WARNING) << "Using CPU code as backup.";
      return Forward_cpu(bottom, top);
    }

    /**
     * @brief Using the CPU device, compute the gradients for any parameters and
     *        for the bottom blobs if propagate_down is true.
     */
    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
        const vector<bool>& propagate_down,
        const vector<Blob<Dtype>*>& bottom) = 0;
    /**
     * @brief Using the GPU device, compute the gradients for any parameters and
     *        for the bottom blobs if propagate_down is true.
     *        Fall back to Backward_cpu() if unavailable.
     */
    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
        const vector<bool>& propagate_down,
        const vector<Blob<Dtype>*>& bottom) {
      // LOG(WARNING) << "Using CPU code as backup.";
      Backward_cpu(top, propagate_down, bottom);
    }

    /**
     * Called by the parent Layer's SetUp to check that the number of bottom
     * and top Blobs provided as input match the expected numbers specified by
     * the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
     */
    virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
      if (ExactNumBottomBlobs() >= 0) {
        CHECK_EQ(ExactNumBottomBlobs(), bottom.size()) << type()
            << " Layer takes " << ExactNumBottomBlobs()
            << " bottom blob(s) as input.";
      }
      if (MinBottomBlobs() >= 0) {
        CHECK_LE(MinBottomBlobs(), bottom.size()) << type()
            << " Layer takes at least " << MinBottomBlobs()
            << " bottom blob(s) as input.";
      }
      if (MaxBottomBlobs() >= 0) {
        CHECK_GE(MaxBottomBlobs(), bottom.size()) << type()
            << " Layer takes at most " << MaxBottomBlobs()
            << " bottom blob(s) as input.";
      }
      if (ExactNumTopBlobs() >= 0) {
        CHECK_EQ(ExactNumTopBlobs(), top.size()) << type() << " Layer produces "
            << ExactNumTopBlobs() << " top blob(s) as output.";
      }
      if (MinTopBlobs() >= 0) {
        CHECK_LE(MinTopBlobs(), top.size()) << type()
            << " Layer produces at least " << MinTopBlobs()
            << " top blob(s) as output.";
      }
      if (MaxTopBlobs() >= 0) {
        CHECK_GE(MaxTopBlobs(), top.size()) << type()
            << " Layer produces at most " << MaxTopBlobs()
            << " top blob(s) as output.";
      }
      if (EqualNumBottomTopBlobs()) {
        CHECK_EQ(bottom.size(), top.size()) << type()
            << " Layer produces one top blob as output for each "
            << "bottom blob input.";
      }
    }

    /**
     * Called by SetUp to initialize the weights associated with any top blobs in
     * the loss function. Store non-zero loss weights in the diff blob.
     */
    inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
      const int num_loss_weights = layer_param_.loss_weight_size();
      if (num_loss_weights) {
        CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
            "unspecified or specified once per top blob.";
        for (int top_id = 0; top_id < top.size(); ++top_id) {
          const Dtype loss_weight = layer_param_.loss_weight(top_id);
          if (loss_weight == Dtype(0)) {
            continue;
          }
          this->set_loss(top_id, loss_weight);
          const int count = top[top_id]->count();
          Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
          caffe_set(count, loss_weight, loss_multiplier);
        }
      }
    }

    DISABLE_COPY_AND_ASSIGN (Layer);
};
// class Layer

// Forward and backward wrappers. You should implement the cpu and
// gpu specific implementations instead, and should not change these
// functions.
template <typename Dtype>
inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  Dtype loss = 0;
  Reshape(bottom, top);
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Forward_cpu(bottom, top);
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) {
        continue;
      }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->cpu_data();
      const Dtype* loss_weights = top[top_id]->cpu_diff();
      loss += caffe_cpu_dot(count, data, loss_weights);
    }
    break;
  case Caffe::GPU:
    Forward_gpu(bottom, top);
#ifndef CPU_ONLY
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) {
        continue;
      }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->gpu_data();
      const Dtype* loss_weights = top[top_id]->gpu_diff();
      Dtype blob_loss = 0;
      caffe_gpu_dot(count, data, loss_weights, &blob_loss);
      loss += blob_loss;
    }
#endif
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
  return loss;
}

template <typename Dtype>
inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Backward_cpu(top, propagate_down, bottom);
    break;
  case Caffe::GPU:
    Backward_gpu(top, propagate_down, bottom);
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
}

// Serialize LayerParameter to protocol buffer
template <typename Dtype>
void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
  param->Clear();
  param->CopyFrom(layer_param_);
  param->clear_blobs();
  for (int i = 0; i < blobs_.size(); ++i) {
    blobs_[i]->ToProto(param->add_blobs(), write_diff);
  }
}

}  // namespace caffe

#endif  // CAFFE_LAYER_H_