separate CPU from GPU ConvolutionLayerTests

diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index 1f8b076..b08486e 100644 (file)
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -69,7 +69,7 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 1);
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
+TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolution) {
   // We will simply see if the convolution layer carries out averaging well.
   FillerParameter filler_param;
   filler_param.set_value(1.);
@@ -93,21 +93,39 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   // After the convolution, the output should all have output values 27.1
   const TypeParam* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_GE(top_data[i], 27.1 - 1e-4);
-    EXPECT_LE(top_data[i], 27.1 + 1e-4);
+    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
-  // Test GPU
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolution) {
+  // We will simply see if the convolution layer carries out averaging well.
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<TypeParam> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<TypeParam> > layer(
+      new ConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   Caffe::set_mode(Caffe::GPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 27.1
-  top_data = this->blob_top_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
   for (int i = 0; i < this->blob_top_->count(); ++i) {
-    EXPECT_GE(top_data[i], 27.1 - 1e-4);
-    EXPECT_LE(top_data[i], 27.1 + 1e-4);
+    EXPECT_NEAR(top_data[i], 27.1, 1e-4);
   }
 }
 
-TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
+TYPED_TEST(ConvolutionLayerTest, TestCPUSimpleConvolutionGroup) {
   // We will simply see if the convolution layer carries out averaging well.
   FillerParameter filler_param;
   filler_param.set_value(1.);
@@ -146,24 +164,54 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
           TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_GE(data, c * 9 + 0.1 - 1e-4);
-          EXPECT_LE(data, c * 9 + 0.1 + 1e-4);
+          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
+        }
+      }
+    }
+  }
+}
+
+
+TYPED_TEST(ConvolutionLayerTest, TestGPUSimpleConvolutionGroup) {
+  // We will simply see if the convolution layer carries out averaging well.
+  FillerParameter filler_param;
+  filler_param.set_value(1.);
+  ConstantFiller<TypeParam> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  TypeParam* bottom_data = this->blob_bottom_->mutable_cpu_data();
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          bottom_data[this->blob_bottom_->offset(n, c, h, w)] = c;
         }
       }
     }
   }
-  // Test GPU
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_kernel_size(3);
+  convolution_param->set_stride(2);
+  convolution_param->set_num_output(3);
+  convolution_param->set_group(3);
+  convolution_param->mutable_weight_filler()->set_type("constant");
+  convolution_param->mutable_weight_filler()->set_value(1);
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<TypeParam> > layer(
+      new ConvolutionLayer<TypeParam>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, &(this->blob_top_vec_));
   Caffe::set_mode(Caffe::GPU);
   layer->Forward(this->blob_bottom_vec_, &(this->blob_top_vec_));
   // After the convolution, the output should all have output values 9.1
-  top_data = this->blob_top_->cpu_data();
+  const TypeParam* top_data = this->blob_top_->cpu_data();
   for (int n = 0; n < this->blob_top_->num(); ++n) {
     for (int c = 0; c < this->blob_top_->channels(); ++c) {
       for (int h = 0; h < this->blob_top_->height(); ++h) {
         for (int w = 0; w < this->blob_top_->width(); ++w) {
           TypeParam data = top_data[this->blob_top_->offset(n, c, h, w)];
-          EXPECT_GE(data, c * 9 + 0.1 - 1e-4);
-          EXPECT_LE(data, c * 9 + 0.1 + 1e-4);
+          EXPECT_NEAR(data, c * 9 + 0.1, 1e-4);
         }
       }
     }