add InputDataLayer for Net input

Create an input layer to replace oddball Net `input` fields.

close BVLC#1245

include/caffe/data_layers.hpp

@@ -256,6 +256,39 @@ class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
   int lines_id_;
 };
 
+/**
+ * @brief Provides data to the Net by assigning tops directly.
+ *
+ * This data layer is a container that merely holds the data assigned to it;
+ * forward, backward, and reshape are all no-ops.
+ */
+template <typename Dtype>
+class InputDataLayer : public Layer<Dtype> {
+ public:
+  explicit InputDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "InputData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+};
+
+
 /**
  * @brief Provides data to the Net from memory.
  *

src/caffe/layers/input_data_layer.cpp

@@ -0,0 +1,29 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/vision_layers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void InputDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
+  const InputDataParameter& param = this->layer_param_.input_data_param();
+  const int num_shape = param.shape_size();
+  CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top)
+      << "Must specify 'shape' once, once per top blob, or not at all: "
+      << num_top << " tops vs. " << num_shape << " shapes.";
+  if (num_shape > 0) {
+    for (int i = 0; i < num_top; ++i) {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
+  }
+}
+
+INSTANTIATE_CLASS(InputDataLayer);
+REGISTER_LAYER_CLASS(InputData);
+
+}  // namespace caffe

src/caffe/proto/caffe.proto

@@ -306,7 +306,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 139 (last added: tile_param)
+// LayerParameter next available layer-specific ID: 139 (last added: input_data_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -371,6 +371,7 @@ message LayerParameter {
   optional ImageDataParameter image_data_param = 115;
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
+  optional InputDataParameter input_data_param = 999; // TODO(shelhamer)
   optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
@@ -710,6 +711,14 @@ message InnerProductParameter {
   optional int32 axis = 5 [default = 1];
 }
 
+message InputDataParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
+
 // Message that stores parameters used by LogLayer
 message LogParameter {
   // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.