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@ -1780,4 +1780,61 @@ TEST_P(Layer_Test_Slice, variable_input_shape) |
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Slice, dnnBackendsAndTargets()); |
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typedef testing::TestWithParam<tuple<Backend, Target> > Layer_Test_BatchNorm; |
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TEST_P(Layer_Test_BatchNorm, fusion) |
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{ |
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// This tests reinitializes network by forwarding different batch size input.
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// We check BatchNorm layer weights restoring after fusion.
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int backendId = get<0>(GetParam()); |
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int targetId = get<1>(GetParam()); |
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const int ch = 4; |
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Mat mean(1, ch, CV_32F), var(1, ch, CV_32F), weights(1, ch, CV_32F); |
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randu(mean, 0, 1); |
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randu(var, 0, 1); |
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randu(weights, 0, 1); |
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Net net; |
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{ |
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LayerParams lp; |
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lp.type = "BatchNorm"; |
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lp.name = "bn"; |
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lp.set("has_weight", false); |
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lp.set("has_bias", false); |
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lp.blobs.push_back(mean); |
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lp.blobs.push_back(var); |
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net.addLayerToPrev(lp.name, lp.type, lp); |
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} |
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{ |
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LayerParams lp; |
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lp.type = "Scale"; |
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lp.name = "scale"; |
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lp.set("has_bias", false); |
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lp.blobs.push_back(weights); |
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net.addLayerToPrev(lp.name, lp.type, lp); |
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} |
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Mat inp(4, 5, CV_32FC(ch)); |
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randu(inp, 0, 1); |
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net.setPreferableBackend(backendId); |
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net.setPreferableTarget(targetId); |
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net.setInput(blobFromImage(inp)); |
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Mat ref = net.forward(); |
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net.setInput(blobFromImages(std::vector<Mat>(2, inp))); |
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Mat out = net.forward(); |
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for (int i = 0; i < 2; ++i) |
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{ |
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std::vector<Range> ranges(4, Range::all()); |
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ranges[0].start = i; |
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ranges[0].end = i + 1; |
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normAssert(out(ranges), ref); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_BatchNorm, dnnBackendsAndTargets()); |
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}} // namespace
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