| /** | |
| Forward declare boost::thread instead of including boost/thread.hpp | |
| to avoid a boost/NVCC issues (#1009, #1010) on OSX. | |
| */ | |
| namespace boost { class mutex; } | |
| 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 accommodate | |
| * 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 | |
| * accommodate 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); | |
| } | |
| } | |
| } | |
| private: | |
| 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); | |
| 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; | |
| } | |
| 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 | |