Merge pull request #2859 from philkr/image_data

ImageData layer default batch size of 1, and check for zero batch size

src/caffe/layers/image_data_layer.cpp

@@ -62,11 +62,13 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read an image, and use it to initialize the top blob.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
   // Use data_transformer to infer the expected blob shape from a cv_image.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
   this->transformed_data_.Reshape(top_shape);
   // Reshape prefetch_data and top[0] according to the batch_size.
   const int batch_size = this->layer_param_.image_data_param().batch_size();
+  CHECK_GT(batch_size, 0) << "Positive batch size required";
   top_shape[0] = batch_size;
   this->prefetch_data_.Reshape(top_shape);
   top[0]->ReshapeLike(this->prefetch_data_);
@@ -108,6 +110,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
   // on single input batches allows for inputs of varying dimension.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
       new_height, new_width, is_color);
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
   // Use data_transformer to infer the expected blob shape from a cv_img.
   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
   this->transformed_data_.Reshape(top_shape);

src/caffe/proto/caffe.proto

@@ -290,7 +290,7 @@ message LayerParameter {
 
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
-  
+
   // Specifies on which bottoms the backpropagation should be skipped.
   // The size must be either 0 or equal to the number of bottoms.
   repeated bool propagate_down = 11;
@@ -431,7 +431,7 @@ message ContrastiveLossParameter {
   // Hadsell paper. New models should probably use this version.
   // legacy_version = true uses (margin - d^2). This is kept to support /
   // reproduce existing models and results
-  optional bool legacy_version = 2 [default = false]; 
+  optional bool legacy_version = 2 [default = false];
 }
 
 message ConvolutionParameter {
@@ -579,7 +579,7 @@ message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
   // Specify the batch size.
-  optional uint32 batch_size = 4;
+  optional uint32 batch_size = 4 [default = 1];
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not