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Merge pull request #14627 from l-bat:demo_kinetics
* Support 3D ResNet-34-kinetics * Update sample * Remove preprocess * Change test * Fix sample
This commit is contained in:
Lubov Batanina
Alexander Alekhin
parent
8ea2f59d33
commit
f94253b82b
@ -352,7 +352,7 @@ TEST_P(Test_ONNX_nets, ResNet18v1)
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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// output range: [-16; 22], after Softmax [0, 0.51]
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// output range: [-16; 22], after Softmax [0, 0.51]
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testONNXModels("resnet18v1", pb, default_l1, default_lInf, true);
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testONNXModels("resnet18v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
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}
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}
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TEST_P(Test_ONNX_nets, ResNet50v1)
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TEST_P(Test_ONNX_nets, ResNet50v1)
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@ -360,7 +360,7 @@ TEST_P(Test_ONNX_nets, ResNet50v1)
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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// output range: [-67; 75], after Softmax [0, 0.98]
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// output range: [-67; 75], after Softmax [0, 0.98]
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testONNXModels("resnet50v1", pb, default_l1, default_lInf, true);
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testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
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}
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}
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TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
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TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
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@ -477,7 +477,7 @@ TEST_P(Test_ONNX_nets, DenseNet121)
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
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// output range: [-87; 138], after Softmax [0; 1]
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// output range: [-87; 138], after Softmax [0; 1]
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testONNXModels("densenet121", pb, default_l1, default_lInf, true);
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testONNXModels("densenet121", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
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}
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}
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TEST_P(Test_ONNX_nets, Inception_v1)
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TEST_P(Test_ONNX_nets, Inception_v1)
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@ -497,6 +497,61 @@ TEST_P(Test_ONNX_nets, Shufflenet)
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testONNXModels("shufflenet", pb);
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testONNXModels("shufflenet", pb);
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}
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}
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TEST_P(Test_ONNX_nets, Resnet34_kinetics)
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{
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if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
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throw SkipTestException("Only DLIE backend on CPU is supported");
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String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx");
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Mat image0 = imread(findDataFile("dnn/dog416.png"));
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Mat image1 = imread(findDataFile("dnn/street.png"));
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Mat ref0 = blobFromNPY(_tf("data/output_kinetics0.npy"));
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Mat ref1 = blobFromNPY(_tf("data/output_kinetics1.npy"));
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std::vector<Mat> images_0(16, image0);
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std::vector<Mat> images_1(16, image1);
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Mat blob0 = blobFromImages(images_0, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
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Mat blob1 = blobFromImages(images_1, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
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Net permute;
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LayerParams lp;
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int order[] = {1, 0, 2, 3};
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lp.set("order", DictValue::arrayInt<int*>(&order[0], 4));
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permute.addLayerToPrev("perm", "Permute", lp);
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permute.setInput(blob0);
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Mat input0 = permute.forward().clone();
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permute.setInput(blob1);
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Mat input1 = permute.forward().clone();
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int dims[] = {1, 3, 16, 112, 112};
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input0 = input0.reshape(0, 5, &dims[0]);
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input1 = input1.reshape(0, 5, &dims[0]);
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Net net = readNetFromONNX(onnxmodel);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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// output range [-5, 11]
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float l1 = 0.0013;
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float lInf = 0.009;
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checkBackend(&input0, &ref0);
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net.setInput(input0);
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Mat out = net.forward().clone();
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normAssert(ref0, out, "", l1, lInf);
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checkBackend(&input1, &ref1);
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net.setInput(input1);
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out = net.forward().clone();
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normAssert(ref1, out, "", l1, lInf);
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expectNoFallbacksFromIE(net);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
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INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
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}} // namespace
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}} // namespace
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400
samples/data/dnn/action_recongnition_kinetics.txt
Normal file
400
samples/data/dnn/action_recongnition_kinetics.txt
Normal file
@ -0,0 +1,400 @@
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abseiling
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air drumming
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answering questions
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applauding
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applying cream
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archery
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arm wrestling
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arranging flowers
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assembling computer
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auctioning
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baby waking up
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baking cookies
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balloon blowing
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bandaging
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barbequing
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bartending
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beatboxing
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bee keeping
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belly dancing
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bench pressing
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bending back
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bending metal
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biking through snow
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blasting sand
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blowing glass
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blowing leaves
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blowing nose
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blowing out candles
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bobsledding
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bookbinding
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bouncing on trampoline
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bowling
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braiding hair
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breading or breadcrumbing
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breakdancing
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brush painting
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brushing hair
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brushing teeth
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building cabinet
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building shed
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bungee jumping
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busking
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canoeing or kayaking
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capoeira
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carrying baby
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cartwheeling
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carving pumpkin
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catching fish
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catching or throwing baseball
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catching or throwing frisbee
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catching or throwing softball
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celebrating
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changing oil
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changing wheel
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checking tires
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cheerleading
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chopping wood
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clapping
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clay pottery making
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clean and jerk
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cleaning floor
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cleaning gutters
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cleaning pool
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cleaning shoes
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cleaning toilet
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cleaning windows
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climbing a rope
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climbing ladder
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climbing tree
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contact juggling
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cooking chicken
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cooking egg
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cooking on campfire
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cooking sausages
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counting money
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country line dancing
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cracking neck
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crawling baby
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crossing river
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crying
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curling hair
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cutting nails
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cutting pineapple
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cutting watermelon
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dancing ballet
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dancing charleston
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dancing gangnam style
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dancing macarena
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deadlifting
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decorating the christmas tree
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digging
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dining
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disc golfing
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diving cliff
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dodgeball
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doing aerobics
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doing laundry
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doing nails
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drawing
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dribbling basketball
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drinking
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drinking beer
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drinking shots
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driving car
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driving tractor
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drop kicking
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drumming fingers
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dunking basketball
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dying hair
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eating burger
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eating cake
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eating carrots
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eating chips
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eating doughnuts
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eating hotdog
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eating ice cream
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eating spaghetti
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eating watermelon
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egg hunting
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exercising arm
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exercising with an exercise ball
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extinguishing fire
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faceplanting
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feeding birds
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feeding fish
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feeding goats
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filling eyebrows
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finger snapping
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fixing hair
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flipping pancake
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flying kite
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folding clothes
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folding napkins
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folding paper
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front raises
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frying vegetables
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garbage collecting
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gargling
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getting a haircut
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getting a tattoo
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giving or receiving award
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golf chipping
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golf driving
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golf putting
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grinding meat
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grooming dog
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grooming horse
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gymnastics tumbling
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hammer throw
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headbanging
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headbutting
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high jump
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high kick
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hitting baseball
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hockey stop
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holding snake
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hopscotch
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hoverboarding
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hugging
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hula hooping
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hurdling
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hurling (sport)
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ice climbing
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ice fishing
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ice skating
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ironing
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javelin throw
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jetskiing
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jogging
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juggling balls
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juggling fire
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juggling soccer ball
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jumping into pool
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jumpstyle dancing
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kicking field goal
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kicking soccer ball
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kissing
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kitesurfing
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knitting
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krumping
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laughing
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laying bricks
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long jump
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lunge
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making a cake
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making a sandwich
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making bed
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making jewelry
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making pizza
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making snowman
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making sushi
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making tea
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marching
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massaging back
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massaging feet
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massaging legs
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massaging person's head
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milking cow
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mopping floor
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motorcycling
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moving furniture
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mowing lawn
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news anchoring
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opening bottle
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opening present
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paragliding
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parasailing
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parkour
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passing American football (in game)
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passing American football (not in game)
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peeling apples
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peeling potatoes
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petting animal (not cat)
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petting cat
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picking fruit
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planting trees
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plastering
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playing accordion
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playing badminton
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playing bagpipes
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playing basketball
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playing bass guitar
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playing cards
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playing cello
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playing chess
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playing clarinet
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playing controller
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playing cricket
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playing cymbals
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playing didgeridoo
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playing drums
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playing flute
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playing guitar
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playing harmonica
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playing harp
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playing ice hockey
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playing keyboard
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playing kickball
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playing monopoly
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playing organ
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playing paintball
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playing piano
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playing poker
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playing recorder
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playing saxophone
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playing squash or racquetball
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playing tennis
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playing trombone
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playing trumpet
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playing ukulele
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playing violin
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playing volleyball
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playing xylophone
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pole vault
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presenting weather forecast
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pull ups
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pumping fist
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pumping gas
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punching bag
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punching person (boxing)
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push up
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pushing car
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pushing cart
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pushing wheelchair
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reading book
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reading newspaper
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recording music
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riding a bike
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riding camel
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riding elephant
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riding mechanical bull
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riding mountain bike
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riding mule
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riding or walking with horse
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riding scooter
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riding unicycle
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ripping paper
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robot dancing
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rock climbing
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rock scissors paper
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roller skating
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running on treadmill
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sailing
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salsa dancing
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sanding floor
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scrambling eggs
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scuba diving
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setting table
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shaking hands
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shaking head
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sharpening knives
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sharpening pencil
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shaving head
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shaving legs
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shearing sheep
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shining shoes
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shooting basketball
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shooting goal (soccer)
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shot put
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shoveling snow
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shredding paper
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shuffling cards
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side kick
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sign language interpreting
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singing
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situp
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skateboarding
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ski jumping
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skiing (not slalom or crosscountry)
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skiing crosscountry
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skiing slalom
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skipping rope
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skydiving
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slacklining
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slapping
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sled dog racing
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smoking
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smoking hookah
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snatch weight lifting
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sneezing
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sniffing
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snorkeling
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snowboarding
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snowkiting
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snowmobiling
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somersaulting
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spinning poi
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spray painting
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spraying
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springboard diving
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squat
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sticking tongue out
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stomping grapes
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stretching arm
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stretching leg
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strumming guitar
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surfing crowd
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surfing water
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sweeping floor
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swimming backstroke
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swimming breast stroke
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swimming butterfly stroke
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|
swing dancing
|
||||||
|
swinging legs
|
||||||
|
swinging on something
|
||||||
|
sword fighting
|
||||||
|
tai chi
|
||||||
|
taking a shower
|
||||||
|
tango dancing
|
||||||
|
tap dancing
|
||||||
|
tapping guitar
|
||||||
|
tapping pen
|
||||||
|
tasting beer
|
||||||
|
tasting food
|
||||||
|
testifying
|
||||||
|
texting
|
||||||
|
throwing axe
|
||||||
|
throwing ball
|
||||||
|
throwing discus
|
||||||
|
tickling
|
||||||
|
tobogganing
|
||||||
|
tossing coin
|
||||||
|
tossing salad
|
||||||
|
training dog
|
||||||
|
trapezing
|
||||||
|
trimming or shaving beard
|
||||||
|
trimming trees
|
||||||
|
triple jump
|
||||||
|
tying bow tie
|
||||||
|
tying knot (not on a tie)
|
||||||
|
tying tie
|
||||||
|
unboxing
|
||||||
|
unloading truck
|
||||||
|
using computer
|
||||||
|
using remote controller (not gaming)
|
||||||
|
using segway
|
||||||
|
vault
|
||||||
|
waiting in line
|
||||||
|
walking the dog
|
||||||
|
washing dishes
|
||||||
|
washing feet
|
||||||
|
washing hair
|
||||||
|
washing hands
|
||||||
|
water skiing
|
||||||
|
water sliding
|
||||||
|
watering plants
|
||||||
|
waxing back
|
||||||
|
waxing chest
|
||||||
|
waxing eyebrows
|
||||||
|
waxing legs
|
||||||
|
weaving basket
|
||||||
|
welding
|
||||||
|
whistling
|
||||||
|
windsurfing
|
||||||
|
wrapping present
|
||||||
|
wrestling
|
||||||
|
writing
|
||||||
|
yawning
|
||||||
|
yoga
|
||||||
|
zumba
|
||||||
82
samples/dnn/action_recognition.py
Normal file
82
samples/dnn/action_recognition.py
Normal file
@ -0,0 +1,82 @@
|
|||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import cv2 as cv
|
||||||
|
import argparse
|
||||||
|
from common import findFile
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
|
||||||
|
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
|
||||||
|
parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
|
||||||
|
parser.add_argument('--model', required=True, help='Path to model.')
|
||||||
|
parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')
|
||||||
|
|
||||||
|
# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
|
||||||
|
# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
|
||||||
|
# change
|
||||||
|
# - def downsample_basic_block(x, planes, stride):
|
||||||
|
# - out = F.avg_pool3d(x, kernel_size=1, stride=stride)
|
||||||
|
# - zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
|
||||||
|
# - out.size(2), out.size(3),
|
||||||
|
# - out.size(4)).zero_()
|
||||||
|
# - if isinstance(out.data, torch.cuda.FloatTensor):
|
||||||
|
# - zero_pads = zero_pads.cuda()
|
||||||
|
# -
|
||||||
|
# - out = Variable(torch.cat([out.data, zero_pads], dim=1))
|
||||||
|
# - return out
|
||||||
|
|
||||||
|
# To
|
||||||
|
# + def downsample_basic_block(x, planes, stride):
|
||||||
|
# + out = F.avg_pool3d(x, kernel_size=1, stride=stride)
|
||||||
|
# + out = F.pad(out, (0, 0, 0, 0, 0, 0, 0, int(planes - out.size(1)), 0, 0), "constant", 0)
|
||||||
|
# + return out
|
||||||
|
|
||||||
|
# To ONNX export use torch.onnx.export(model, inputs, model_name)
|
||||||
|
|
||||||
|
def get_class_names(path):
|
||||||
|
class_names = []
|
||||||
|
with open(path) as f:
|
||||||
|
for row in f:
|
||||||
|
class_names.append(row[:-1])
|
||||||
|
return class_names
|
||||||
|
|
||||||
|
def classify_video(video_path, net_path):
|
||||||
|
SAMPLE_DURATION = 16
|
||||||
|
SAMPLE_SIZE = 112
|
||||||
|
mean = (114.7748, 107.7354, 99.4750)
|
||||||
|
class_names = get_class_names(args.classes)
|
||||||
|
|
||||||
|
net = cv.dnn.readNet(net_path)
|
||||||
|
net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
|
||||||
|
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
|
||||||
|
|
||||||
|
winName = 'Deep learning image classification in OpenCV'
|
||||||
|
cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
|
||||||
|
cap = cv.VideoCapture(video_path)
|
||||||
|
while cv.waitKey(1) < 0:
|
||||||
|
frames = []
|
||||||
|
for _ in range(SAMPLE_DURATION):
|
||||||
|
hasFrame, frame = cap.read()
|
||||||
|
if not hasFrame:
|
||||||
|
exit(0)
|
||||||
|
frames.append(frame)
|
||||||
|
|
||||||
|
inputs = cv.dnn.blobFromImages(frames, 1, (SAMPLE_SIZE, SAMPLE_SIZE), mean, True, crop=True)
|
||||||
|
inputs = np.transpose(inputs, (1, 0, 2, 3))
|
||||||
|
inputs = np.expand_dims(inputs, axis=0)
|
||||||
|
net.setInput(inputs)
|
||||||
|
outputs = net.forward()
|
||||||
|
class_pred = np.argmax(outputs)
|
||||||
|
label = class_names[class_pred]
|
||||||
|
|
||||||
|
for frame in frames:
|
||||||
|
labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
|
||||||
|
cv.rectangle(frame, (0, 10 - labelSize[1]),
|
||||||
|
(labelSize[0], 10 + baseLine), (255, 255, 255), cv.FILLED)
|
||||||
|
cv.putText(frame, label, (0, 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
|
||||||
|
cv.imshow(winName, frame)
|
||||||
|
if cv.waitKey(1) & 0xFF == ord('q'):
|
||||||
|
break
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
args, _ = parser.parse_known_args()
|
||||||
|
classify_video(args.input if args.input else 0, args.model)
|
||||||
Loading…
Reference in New Issue
Block a user