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Fix multiple inputs models from Caffe.

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Fixed Concat optimization.
This commit is contained in:
Dmitry Kurtaev
2017-11-02 16:21:06 +03:00
parent bc348eb8ab
commit 20a2dc6ac5
4 changed files with 97 additions and 4 deletions
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modules/dnn/test/test_caffe_importer.cpp
+27
View File
@@ -280,4 +280,31 @@ TEST(Reproducibility_DenseNet_121, Accuracy)
normAssert(out, ref);
}
TEST(Test_Caffe, multiple_inputs)
{
const string proto = findDataFile("dnn/layers/net_input.prototxt", false);
Net net = readNetFromCaffe(proto);
Mat first_image(10, 11, CV_32FC3);
Mat second_image(10, 11, CV_32FC3);
randu(first_image, -1, 1);
randu(second_image, -1, 1);
first_image = blobFromImage(first_image);
second_image = blobFromImage(second_image);
Mat first_image_blue_green = slice(first_image, Range::all(), Range(0, 2), Range::all(), Range::all());
Mat first_image_red = slice(first_image, Range::all(), Range(2, 3), Range::all(), Range::all());
Mat second_image_blue_green = slice(second_image, Range::all(), Range(0, 2), Range::all(), Range::all());
Mat second_image_red = slice(second_image, Range::all(), Range(2, 3), Range::all(), Range::all());
net.setInput(first_image_blue_green, "old_style_input_blue_green");
net.setInput(first_image_red, "different_name_for_red");
net.setInput(second_image_blue_green, "input_layer_blue_green");
net.setInput(second_image_red, "old_style_input_red");
Mat out = net.forward();
normAssert(out, first_image + second_image);
}
}
modules/dnn/test/test_layers.cpp
+42
View File
@@ -274,6 +274,48 @@ OCL_TEST(Layer_Test_Concat, Accuracy)
testLayerUsingCaffeModels("layer_concat", DNN_TARGET_OPENCL);
}
TEST(Layer_Test_Fused_Concat, Accuracy)
{
// Test case
// input
// |
// v
// some_layer
// | |
// v v
// concat
Net net;
int interLayer;
{
LayerParams lp;
lp.type = "AbsVal";
lp.name = "someLayer";
interLayer = net.addLayerToPrev(lp.name, lp.type, lp);
}
{
LayerParams lp;
lp.set("axis", 1);
lp.type = "Concat";
lp.name = "testConcat";
int id = net.addLayer(lp.name, lp.type, lp);
net.connect(interLayer, 0, id, 0);
net.connect(interLayer, 0, id, 1);
}
int shape[] = {1, 2, 3, 4};
Mat input(4, shape, CV_32F);
randu(input, 0.0f, 1.0f); // [0, 1] to make AbsVal an identity transformation.
net.setInput(input);
Mat out = net.forward();
normAssert(slice(out, Range::all(), Range(0, 2), Range::all(), Range::all()), input);
normAssert(slice(out, Range::all(), Range(2, 4), Range::all(), Range::all()), input);
//
testLayerUsingCaffeModels("layer_concat_optim", DNN_TARGET_CPU, true, false);
}
TEST(Layer_Test_Eltwise, Accuracy)
{
testLayerUsingCaffeModels("layer_eltwise");