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7fb336322d
dnn: no layer norm fusion if axes.back() is not the axis of last dimension #24808 Merge with https://github.com/opencv/opencv_extra/pull/1137 Resolves https://github.com/opencv/opencv/issues/24797 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
147 lines
5.4 KiB
C++
147 lines
5.4 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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class Test_Graph_Simplifier : public ::testing::Test {
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public:
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bool required;
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Test_Graph_Simplifier() : required(true) {}
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void test_conformance(const std::string &basename, const std::string &expected_layer) {
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test(basename + std::string("/model"), std::vector<std::string>{expected_layer}, std::string("dnn/onnx/conformance/node/"));
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}
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void test(const std::string &basename, const std::string &expected_layer) {
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test(basename, std::vector<std::string>{expected_layer});
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}
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void test(const std::string &basename, const std::vector<std::string> &expected_layers, const std::string &model_path_prefix = std::string("dnn/onnx/models/")) {
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std::string model_path = findDataFile(model_path_prefix + basename + std::string(".onnx"), required);
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auto net = readNet(model_path);
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std::vector<std::string> layers;
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net.getLayerTypes(layers);
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// remove Const, Identity (output layer), __NetInputLayer__ (input layer)
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layers.erase(std::remove_if(layers.begin(), layers.end(), [] (const std::string l) { return l == "Const" || l == "Identity" || l == "__NetInputLayer__"; }), layers.end());
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EXPECT_EQ(layers, expected_layers);
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}
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};
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TEST_F(Test_Graph_Simplifier, GeluSubGraph) {
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test("gelu", "Gelu");
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test("bias_gelu", std::vector<std::string>{"Gelu", "NaryEltwise"});
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}
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TEST_F(Test_Graph_Simplifier, GeluApproximationSubGraph) {
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test("gelu_approximation", "GeluApproximation");
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}
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TEST_F(Test_Graph_Simplifier, LayerNormSubGraph) {
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test("layer_norm_expanded", "LayerNormalization");
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test("layer_norm_expanded_with_initializers", "LayerNormalization");
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}
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TEST_F(Test_Graph_Simplifier, LayerNormNoFusionSubGraph) {
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test("layer_norm_no_fusion", std::vector<std::string>{"NaryEltwise", "Reduce", "Sqrt"});
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}
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TEST_F(Test_Graph_Simplifier, ResizeSubgraph) {
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/* Test for 6 subgraphs:
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- GatherCastSubgraph
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- MulCastSubgraph
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- UpsampleSubgraph
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- ResizeSubgraph1
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- ResizeSubgraph2
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- ResizeSubgraph3
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*/
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test("upsample_unfused_torch1.2", std::vector<std::string>{"BatchNorm", "Resize"});
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test("resize_nearest_unfused_opset11_torch1.3", std::vector<std::string>{"BatchNorm", "Convolution", "Resize"});
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test("resize_nearest_unfused_opset11_torch1.4", std::vector<std::string>{"BatchNorm", "Convolution", "Resize"});
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test("upsample_unfused_opset9_torch1.4", std::vector<std::string>{"BatchNorm", "Convolution", "Resize"});
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test("two_resizes_with_shared_subgraphs", std::vector<std::string>{"NaryEltwise", "Resize"});
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}
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TEST_F(Test_Graph_Simplifier, SoftmaxSubgraph) {
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/* Test for 3 subgraphs
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- SoftMaxSubgraph
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- SoftMaxSubgraph2 (conformance)
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- LogSoftMaxSubgraph (conformance)
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*/
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test("softmax_unfused", "Softmax");
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test_conformance("test_softmax_example_expanded", "Softmax");
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test_conformance("test_softmax_axis_2_expanded", "Softmax");
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test_conformance("test_softmax_default_axis_expanded", "Softmax");
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test_conformance("test_softmax_axis_0_expanded", "Softmax");
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test_conformance("test_softmax_axis_1_expanded", "Softmax");
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test_conformance("test_softmax_large_number_expanded", "Softmax");
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test_conformance("test_softmax_negative_axis_expanded", "Softmax");
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test_conformance("test_logsoftmax_axis_2_expanded", "Softmax");
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test_conformance("test_logsoftmax_example_1_expanded", "Softmax");
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test_conformance("test_logsoftmax_negative_axis_expanded", "Softmax");
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test_conformance("test_logsoftmax_axis_0_expanded", "Softmax");
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test_conformance("test_logsoftmax_axis_1_expanded", "Softmax");
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test_conformance("test_logsoftmax_large_number_expanded", "Softmax");
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test_conformance("test_logsoftmax_default_axis_expanded", "Softmax");
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}
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TEST_F(Test_Graph_Simplifier, HardSwishSubgraph) {
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test_conformance("test_hardswish_expanded", "HardSwish");
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}
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TEST_F(Test_Graph_Simplifier, CeluSubgraph) {
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test_conformance("test_celu_expanded", "Celu");
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}
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TEST_F(Test_Graph_Simplifier, NormalizeSubgraph) {
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/* Test for 6 subgraphs
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- NormalizeSubgraph1
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- NormalizeSubgraph2
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- NormalizeSubgraph2_2
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- NormalizeSubgraph3
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- NormalizeSubgraph4
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- NormalizeSubgraph5
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*/
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test("reduceL2_subgraph_2", "Normalize");
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test("reduceL2_subgraph", "Normalize");
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test("normalize_fusion", "Normalize");
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}
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TEST_F(Test_Graph_Simplifier, BatchNormalizationSubgraph) {
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/* Test for 2 subgraphs
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- BatchNormalizationSubgraph1
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- BatchNormalizationSubgraph2
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*/
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test("frozenBatchNorm2d", "BatchNorm");
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test("batch_norm_subgraph", "BatchNorm");
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}
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TEST_F(Test_Graph_Simplifier, ExpandSubgraph) {
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test("expand_neg_batch", "Expand");
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}
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TEST_F(Test_Graph_Simplifier, MishSubgraph) {
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/* Test for 2 subgraphs
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- SoftplusSubgraph
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- MishSubgraph
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*/
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test("mish_no_softplus", "Mish");
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test("mish", "Mish");
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}
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TEST_F(Test_Graph_Simplifier, AttentionSubgraph) {
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/* Test for 2 subgraphs
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- AttentionSubgraph
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- AttentionSingleHeadSubgraph
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*/
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test("attention", "Attention");
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test("attention_single_head", "Attention");
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}
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}}
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