Modifications to Net to facilitate unrolled recurrent networks

include/caffe/net.hpp

@@ -84,6 +84,13 @@ class Net {
 
   /// @brief Updates the network weights based on the diff values computed.
   void Update();
+  /**
+   * @brief Shares weight data of owner blobs with shared blobs.
+   *
+   * Note: this is called by Net::Init, and thus should normally not be
+   * called manually.
+   */
+  void ShareWeightData();
 
   /**
    * @brief For an already initialized net, implicitly copies (i.e., using no
@@ -148,6 +155,9 @@ class Net {
     return param_names_index_;
   }
   inline const vector<int>& param_owners() const { return param_owners_; }
+  inline const vector<string>& param_display_names() const {
+    return param_display_names_;
+  }
   /// @brief Input and output blob numbers
   inline int num_inputs() const { return net_input_blobs_.size(); }
   inline int num_outputs() const { return net_output_blobs_.size(); }

src/caffe/net.cpp

@@ -206,6 +206,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
   GetLearningRateAndWeightDecay();
+  ShareWeightData();
   debug_info_ = param.debug_info();
   LOG(INFO) << "Network initialization done.";
   LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
@@ -444,8 +445,6 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
       // Strict dimension checking -- all dims must be the same.
       CHECK(this_blob->shape() == owner_blob->shape());
     }
-    layers_[layer_id]->blobs()[param_id]->ShareData(
-        *layers_[owner_layer_id]->blobs()[owner_param_id]);
   }
 }
 
@@ -749,42 +748,23 @@ void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
 
 template <typename Dtype>
 void Net<Dtype>::Update() {
-  // First, accumulate the diffs of any shared parameters into their owner's
-  // diff. (Assumes that the learning rate, weight decay, etc. have already been
-  // accounted for in the current diff.)
-  for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] < 0) { continue; }
-    if (debug_info_) { UpdateDebugInfo(i); }
-    const int count = params_[i]->count();
-    const Dtype* this_diff;
-    Dtype* owner_diff;
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      this_diff = params_[i]->cpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
-      caffe_add(count, this_diff, owner_diff, owner_diff);
-      break;
-#ifndef CPU_ONLY
-    case Caffe::GPU:
-      this_diff = params_[i]->gpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
-      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
-      break;
-#else
-      NO_GPU;
-#endif
-    default:
-      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
-    }
-  }
-  // Now, update the owned parameters.
+  // Update only the owned parameters.
   for (int i = 0; i < params_.size(); ++i) {
     if (param_owners_[i] >= 0) { continue; }
     if (debug_info_) { UpdateDebugInfo(i); }
     params_[i]->Update();
   }
 }
 
+template <typename Dtype>
+void Net<Dtype>::ShareWeightData() {
+  for (int i = 0; i < params_.size(); ++i) {
+    if (param_owners_[i] < 0) { continue; }
+    params_[i]->ShareData(*params_[param_owners_[i]]);
+    params_[i]->ShareDiff(*params_[param_owners_[i]]);
+  }
+}
+
 template <typename Dtype>
 bool Net<Dtype>::has_blob(const string& blob_name) const {
   return blob_names_index_.find(blob_name) != blob_names_index_.end();