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Commit80998b3

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added opencl fp16 target for int8 tests
1 parent4298e27 commit80998b3

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4 files changed

+74
-27
lines changed

4 files changed

+74
-27
lines changed

‎modules/dnn/src/dnn.cpp‎

Lines changed: 5 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -2544,7 +2544,7 @@ struct Net::Impl : public detail::NetImplBase
25442544

25452545
CV_Assert(layerShapesIt != layersShapes.end());
25462546

2547-
if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_OPENCL_FP16)
2547+
if (preferableBackend == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_OPENCL_FP16 && ld.dtype == CV_32F)
25482548
ld.dtype = CV_16S;
25492549

25502550
std::vector<LayerPin> pinsForInternalBlobs;
@@ -3152,7 +3152,8 @@ struct Net::Impl : public detail::NetImplBase
31523152
Mat& inp = layers[0].outputBlobs[i];
31533153
CV_Assert(inp.total());
31543154
if (preferableBackend == DNN_BACKEND_OPENCV &&
3155-
preferableTarget == DNN_TARGET_OPENCL_FP16)
3155+
preferableTarget == DNN_TARGET_OPENCL_FP16 &&
3156+
layers[0].dtype == CV_32F)
31563157
{
31573158
layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S);
31583159
}
@@ -3611,7 +3612,8 @@ struct Net::Impl : public detail::NetImplBase
36113612
Mat& inp = layers[0].outputBlobs[i];
36123613
CV_Assert(inp.total());
36133614
if (preferableBackend == DNN_BACKEND_OPENCV &&
3614-
preferableTarget == DNN_TARGET_OPENCL_FP16)
3615+
preferableTarget == DNN_TARGET_OPENCL_FP16 &&
3616+
layers[0].dtype == CV_32F)
36153617
{
36163618
layers[0].outputBlobs[i].create(inp.dims, inp.size, CV_16S);
36173619
}

‎modules/dnn/src/int8layers/quantize_dequantize_layer.cpp‎

Lines changed: 45 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -42,15 +42,36 @@ class QuantizeLayerImpl CV_FINAL : public QuantizeLayer
4242
outputs_arr.getMatVector(outputs);
4343
}
4444

45+
#ifdef HAVE_OPENCL
46+
boolforward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
47+
{
48+
std::vector<UMat> inputs, outputs;
49+
inputs_.getUMatVector(inputs);
50+
outputs_.getUMatVector(outputs);
51+
52+
if (inputs_.depth() == CV_16S)
53+
{
54+
UMatinputFp32(shape(inputs[0]), CV_32F);
55+
convertFp16(inputs[0], inputFp32);
56+
inputFp32.copyTo(inputs[0]);
57+
}
58+
59+
inputs[0].convertTo(outputs[0], CV_8S,1.f/scale, zeropoint);
60+
returntrue;
61+
}
62+
#endif
63+
4564
voidforward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
4665
{
4766
CV_TRACE_FUNCTION();
4867
CV_TRACE_ARG_VALUE(name,"name", name.c_str());
4968

50-
std::vector<Mat> inputs, outputs, internals;
69+
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
70+
forward_ocl(inputs_arr, outputs_arr, internals_arr))
71+
72+
std::vector<Mat> inputs, outputs;
5173
inputs_arr.getMatVector(inputs);
5274
outputs_arr.getMatVector(outputs);
53-
internals_arr.getMatVector(internals);
5475

5576
inputs[0].convertTo(outputs[0], CV_8S,1.f/scale, zeropoint);
5677
}
@@ -88,15 +109,35 @@ class DequantizeLayerImpl CV_FINAL : public DequantizeLayer
88109
outputs_arr.getMatVector(outputs);
89110
}
90111

112+
#ifdef HAVE_OPENCL
113+
boolforward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
114+
{
115+
std::vector<UMat> inputs, outputs;
116+
inputs_.getUMatVector(inputs);
117+
outputs_.getUMatVector(outputs);
118+
119+
UMatoutputFp32(shape(outputs[0]), CV_32F);
120+
inputs[0].convertTo(outputFp32, CV_32F, scale, -(scale*zeropoint));
121+
122+
if (outputs_.depth() == CV_16S)
123+
convertFp16(outputFp32, outputs[0]);
124+
else
125+
outputFp32.copyTo(outputs[0]);
126+
returntrue;
127+
}
128+
#endif
129+
91130
voidforward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
92131
{
93132
CV_TRACE_FUNCTION();
94133
CV_TRACE_ARG_VALUE(name,"name", name.c_str());
95134

96-
std::vector<Mat> inputs, outputs, internals;
135+
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget),
136+
forward_ocl(inputs_arr, outputs_arr, internals_arr))
137+
138+
std::vector<Mat> inputs, outputs;
97139
inputs_arr.getMatVector(inputs);
98140
outputs_arr.getMatVector(outputs);
99-
internals_arr.getMatVector(internals);
100141

101142
inputs[0].convertTo(outputs[0], CV_32F, scale, -(scale*zeropoint));
102143
}

‎modules/dnn/src/int8layers/softmax_layer.cpp‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -47,7 +47,7 @@ class SoftMaxLayerInt8Impl CV_FINAL : public SoftmaxLayerInt8
4747
virtualbooltryFuse(Ptr<Layer>& top) CV_OVERRIDE
4848
{
4949
Ptr<DequantizeLayer> dequantize_layer = top.dynamicCast<DequantizeLayer>();
50-
return !dequantize_layer.empty();
50+
return !dequantize_layer.empty() && preferableTarget != DNN_TARGET_OPENCL_FP16;
5151
}
5252

5353
voidforward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE

‎modules/dnn/test/test_int8_layers.cpp‎

Lines changed: 23 additions & 19 deletions
Original file line numberDiff line numberDiff line change
@@ -341,10 +341,8 @@ TEST_P(Test_Int8_layers, Eltwise)
341341
testLayer("split_max","ONNX",0.004,0.012);
342342
}
343343

344-
// TODO : Only CPU backend has int8 implementation, so testing is done on OCV/CPU and OCV/OCL backend.
345-
// Need to test the behaviour of int8 layers on other backends.
346-
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, Combine(Values(DNN_BACKEND_OPENCV), Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)));
347-
344+
// TODO : Only CPU backend has int8 implementation. Need to test the behaviour of int8 layers on other backends.
345+
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_layers, Combine(Values(DNN_BACKEND_OPENCV), ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV))));
348346

349347
classTest_Int8_nets :publicDNNTestLayer
350348
{
@@ -399,18 +397,24 @@ class Test_Int8_nets : public DNNTestLayer
399397
baseNet.setPreferableBackend(backend);
400398
baseNet.setPreferableTarget(target);
401399

400+
Net qnet = baseNet.quantize(blob, CV_32F, CV_32F);
401+
qnet.setInput(blob);
402+
Mat out = qnet.forward();
403+
402404
if (useSoftmax)
403405
{
404406
LayerParams lp;
405-
baseNet.addLayerToPrev("softmaxLayer","Softmax", lp);
407+
Net netSoftmax;
408+
netSoftmax.addLayerToPrev("softmaxLayer","Softmax", lp);
409+
netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);
406410

407-
baseNet.setInput(blob);
408-
ref = baseNet.forward();
411+
netSoftmax.setInput(out);
412+
out = netSoftmax.forward();
413+
414+
netSoftmax.setInput(ref);
415+
ref = netSoftmax.forward();
409416
}
410417

411-
Net qnet = baseNet.quantize(blob, CV_32F, CV_32F);
412-
qnet.setInput(blob);
413-
Mat out = qnet.forward();
414418
normAssert(ref, out,"", l1, lInf);
415419
}
416420

@@ -547,7 +551,7 @@ TEST_P(Test_Int8_nets, AlexNet)
547551
Mat blob =blobFromImage(inp,1.0,Size(227,227),Scalar(),false);
548552
Mat ref =blobFromNPY(_tf("caffe_alexnet_prob.npy"));
549553

550-
float l1 =1e-5, lInf =0.0013;
554+
float l1 =1e-4, lInf =0.003;
551555
testClassificationNet(net, blob, ref, l1, lInf);
552556
}
553557

@@ -578,7 +582,7 @@ TEST_P(Test_Int8_nets, ResNet50)
578582
Mat blob =blobFromImage(inp,1.0,Size(224,224),Scalar(),false);
579583
Mat ref =blobFromNPY(_tf("resnet50_prob.npy"));
580584

581-
float l1 =2e-4, lInf =0.035;
585+
float l1 =3e-4, lInf =0.035;
582586
testClassificationNet(net, blob, ref, l1, lInf);
583587
}
584588

@@ -626,7 +630,7 @@ TEST_P(Test_Int8_nets, CaffeNet)
626630
&&getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
627631
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
628632
#endif
629-
float l1 =1e-5, lInf =0.0022;
633+
float l1 =4e-5, lInf =0.0025;
630634
testONNXNet("caffenet", l1, lInf);
631635
}
632636

@@ -751,7 +755,7 @@ TEST_P(Test_Int8_nets, opencv_face_detector)
751755
0,1,0.97203469,0.67965847,0.06876482,0.73999709,0.1513494,
752756
0,1,0.95097077,0.51901293,0.45863652,0.5777427,0.5347801);
753757

754-
float confThreshold =0.5, scoreDiff =0.002, iouDiff =0.2;
758+
float confThreshold =0.5, scoreDiff =0.002, iouDiff =0.21;
755759
testDetectionNet(net, blob, ref, confThreshold, scoreDiff, iouDiff);
756760
}
757761

@@ -1122,7 +1126,7 @@ TEST_P(Test_Int8_nets, YOLOv4)
11221126

11231127
std::string config_file ="yolov4.cfg";
11241128
std::string weights_file ="yolov4.weights";
1125-
double scoreDiff =0.1, iouDiff =0.15;
1129+
double scoreDiff =0.1, iouDiff =0.17;
11261130
{
11271131
SCOPED_TRACE("batch size 1");
11281132
testDarknetModel(config_file, weights_file, ref.rowRange(0, N0), scoreDiff, iouDiff);
@@ -1175,7 +1179,8 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
11751179

11761180
std::string config_file ="yolov4-tiny.cfg";
11771181
std::string weights_file ="yolov4-tiny.weights";
1178-
double scoreDiff =0.11, iouDiff =0.082;
1182+
double scoreDiff =0.12;
1183+
double iouDiff = target == DNN_TARGET_OPENCL_FP16 ?0.2 :0.082;
11791184

11801185
#if defined(INF_ENGINE_RELEASE)
11811186
if (target == DNN_TARGET_MYRIAD)// bad accuracy
@@ -1210,7 +1215,6 @@ TEST_P(Test_Int8_nets, YOLOv4_tiny)
12101215
#endif
12111216
}
12121217

1213-
// TODO : Only CPU backend has int8 implementation, so testing is done on OCV/CPU and OCV/OCL backend.
1214-
// Need to test the behaviour of int8 layers on other backends.
1215-
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_nets, Combine(Values(DNN_BACKEND_OPENCV), Values(DNN_TARGET_CPU, DNN_TARGET_OPENCL)));
1218+
// TODO : Only CPU backend has int8 implementation. Need to test the behaviour of int8 layers on other backends.
1219+
INSTANTIATE_TEST_CASE_P(/**/, Test_Int8_nets, Combine(Values(DNN_BACKEND_OPENCV), ValuesIn(getAvailableTargets(DNN_BACKEND_OPENCV))));
12161220
}}// namespace

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