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Fix prior box generation in case of squared proposals.

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Fix batch norm in training phase.
This commit is contained in:
Dmitry Kurtaev
2018-03-22 13:56:07 +03:00
parent 0366c1b093
commit e8fe6ee4e3
4 changed files with 73 additions and 13 deletions
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modules/dnn/test/test_layers.cpp
+34
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@@ -252,6 +252,11 @@ TEST(Layer_Test_BatchNorm, Accuracy)
testLayerUsingCaffeModels("layer_batch_norm", DNN_TARGET_CPU, true);
}
TEST(Layer_Test_BatchNorm, local_stats)
{
testLayerUsingCaffeModels("layer_batch_norm_local_stats", DNN_TARGET_CPU, true, false);
}
TEST(Layer_Test_ReLU, Accuracy)
{
testLayerUsingCaffeModels("layer_relu");
@@ -831,4 +836,33 @@ TEST(Layer_Test_Average_pooling_kernel_area, Accuracy)
normAssert(out, blobFromImage(target));
}
// Test PriorBoxLayer in case of no aspect ratios (just squared proposals).
TEST(Layer_PriorBox, squares)
{
LayerParams lp;
lp.name = "testPriorBox";
lp.type = "PriorBox";
lp.set("min_size", 32);
lp.set("flip", true);
lp.set("clip", true);
float variance[] = {0.1f, 0.1f, 0.2f, 0.2f};
float aspectRatios[] = {1.0f}; // That should be ignored.
lp.set("variance", DictValue::arrayReal<float*>(&variance[0], 4));
lp.set("aspect_ratio", DictValue::arrayReal<float*>(&aspectRatios[0], 1));
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 1); // The second input is an input image. Shapes are used for boxes normalization.
Mat inp(1, 2, CV_32F);
randu(inp, -1, 1);
net.setInput(blobFromImage(inp));
Mat out = net.forward();
Mat target = (Mat_<float>(4, 4) << -7.75f, -15.5f, 8.25f, 16.5f,
-7.25f, -15.5f, 8.75f, 16.5f,
0.1f, 0.1f, 0.2f, 0.2f,
0.1f, 0.1f, 0.2f, 0.2f);
normAssert(out.reshape(1, 4), target);
}
}} // namespace