Fixed FlattenLayer Backward_cpu/gpu have no return value

BVLC · Jan 11, 2014 · 842a435 · 842a435
1 parent 1ff7241
commit 842a435
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Showing 3 changed files with 18 additions and 8 deletions.
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
@@ -43,6 +43,7 @@ Dtype FlattenLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
   caffe_copy(count_, top_diff, bottom_diff);
+  return Dtype(0);
 }
 
 
@@ -52,6 +53,7 @@ Dtype FlattenLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = (*bottom)[0]->mutable_gpu_diff();
   caffe_gpu_copy(count_, top_diff, bottom_diff);
+  return Dtype(0);
 }
 
 INSTANTIATE_CLASS(FlattenLayer);

diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
@@ -22,6 +22,7 @@ class FlattenLayerTest : public ::testing::Test {
   FlattenLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
@@ -72,6 +73,8 @@ TYPED_TEST(FlattenLayerTest, TestGPU) {
   for (int c = 0; c < 3 * 6 * 5; ++c) {
     EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
         this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
+    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
+        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
   }
 }
 

diff --git a/src/caffe/test/test_gradient_check_util.hpp b/src/caffe/test/test_gradient_check_util.hpp
@@ -82,11 +82,11 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>& layer,
     blobs_to_check.push_back(bottom[check_bottom]);
   }
   // go through the bottom and parameter blobs
-  // LOG(ERROR) << "Checking " << blobs_to_check.size() << " blobs.";
+//   LOG(ERROR) << "Checking " << blobs_to_check.size() << " blobs.";
   for (int blobid = 0; blobid < blobs_to_check.size(); ++blobid) {
     Blob<Dtype>* current_blob = blobs_to_check[blobid];
-    // LOG(ERROR) << "Blob " << blobid << ": checking " << current_blob->count()
-    //     << " parameters.";
+//     LOG(ERROR) << "Blob " << blobid << ": checking " << current_blob->count()
+//         << " parameters.";
     // go through the values
     for (int feat_id = 0; feat_id < current_blob->count(); ++feat_id) {
       // First, obtain the original data
@@ -96,25 +96,28 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>& layer,
       // Get any additional loss from the layer
       computed_objective += layer.Backward(top, true, &bottom);
       Dtype computed_gradient = current_blob->cpu_diff()[feat_id];
+
       // compute score by adding stepsize
       current_blob->mutable_cpu_data()[feat_id] += stepsize_;
       Caffe::set_random_seed(seed_);
       layer.Forward(bottom, &top);
       Dtype positive_objective = GetObjAndGradient(top, top_id, top_data_id);
       positive_objective += layer.Backward(top, true, &bottom);
+
       // compute score by subtracting stepsize
       current_blob->mutable_cpu_data()[feat_id] -= stepsize_ * 2;
       Caffe::set_random_seed(seed_);
       layer.Forward(bottom, &top);
       Dtype negative_objective = GetObjAndGradient(top, top_id, top_data_id);
       negative_objective += layer.Backward(top, true, &bottom);
+
       // Recover stepsize
       current_blob->mutable_cpu_data()[feat_id] += stepsize_;
       Dtype estimated_gradient = (positive_objective - negative_objective) /
           stepsize_ / 2.;
       Dtype feature = current_blob->cpu_data()[feat_id];
-      // LOG(ERROR) << "debug: " << current_blob->cpu_data()[feat_id] << " "
-      //     << current_blob->cpu_diff()[feat_id];
+//       LOG(ERROR) << "debug: " << current_blob->cpu_data()[feat_id] << " "
+//           << current_blob->cpu_diff()[feat_id];
       if (kink_ - kink_range_ > feature || feature > kink_ + kink_range_) {
         // We check relative accuracy, but for too small values, we threshold
         // the scale factor by 1.
@@ -126,10 +129,12 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>& layer,
         EXPECT_LT(computed_gradient, estimated_gradient + threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
           << top_id << "," << top_data_id << "," << blobid << "," << feat_id;
+//        LOG(ERROR) << "computed gradient: " << computed_gradient
+//           << " estimated_gradient: " << estimated_gradient
+//           << " positive_objective: " << positive_objective
+//           << " negative_objective: " << negative_objective;
       }
-      // LOG(ERROR) << "Feature: " << current_blob->cpu_data()[feat_id];
-      // LOG(ERROR) << "computed gradient: " << computed_gradient
-      //    << " estimated_gradient: " << estimated_gradient;
+      // LOG(ERROR) << "Feature: " << current_blob->cpu_data()[feat_id]
     }
   }
 }