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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
# include "test_precomp.hpp"
# include "npy_blob.hpp"
# include <opencv2/dnn/shape_utils.hpp>
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# include <numeric>
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namespace opencv_test { namespace {
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void yoloPostProcessing (
std : : vector < Mat > & outs ,
std : : vector < int > & keep_classIds ,
std : : vector < float > & keep_confidences ,
std : : vector < Rect2d > & keep_boxes ,
float conf_threshold ,
float iou_threshold ,
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const std : : string & model_name ,
const int nc = 80 ) ;
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template < typename TString >
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static std : : string _tf ( TString filename , bool required = true )
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{
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return findDataFile ( std : : string ( " dnn/onnx/ " ) + filename , required ) ;
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}
class Test_ONNX_layers : public DNNTestLayer
{
public :
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bool required ;
Test_ONNX_layers ( ) : required ( true ) { }
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enum Extension
{
npy ,
pb
} ;
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void testInputShapes ( const Net & net , const std : : vector < Mat > & inps )
{
std : : vector < MatShape > inLayerShapes ;
std : : vector < MatShape > outLayerShapes ;
Merge pull request #24411 from alexlyulkov:al/dnn-type-inference
Added int32, int64 support and type inference to dnn #24411
**Added a type inference to dnn similar to the shape inference, added int32 and int64 support.**
- Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type
- Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types
- All layers output blobs are now allocated using the calculated types from the type inference.
- Inputs and constants with int32 and int64 types are not automatically converted into float32 now.
- Added int32 and int64 support for all the layers with indexing and for all the layers required in tests.
Added int32 and int64 support for CUDA:
- Added host<->device data moving for int32 and int64
- Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates)
Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model)
**CURRENT PROBLEMS**:
- ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102)
- I didn't add type inference and int support to VULCAN, so it doesn't work at all now.
- Some layers don't support int yet, so some unknown models may not work.
**CURRENT WORKAROUNDS**:
- CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion)
- CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion
- CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion)
**DISABLED TESTS**:
- RAFT model
**REMOVED TESTS**:
- Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant)
**TODO IN NEXT PULL REQUESTS**:
- Add int64 support for ONNX parser
- Add int support for more layers
- Add int support for OCL (currently int layers just run on CPU)
- Add int tests
- Add int support for other backends
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net . getLayerShapes ( MatShape ( ) , CV_32F , 0 , inLayerShapes , outLayerShapes ) ;
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ASSERT_EQ ( inLayerShapes . size ( ) , inps . size ( ) ) ;
for ( int i = 0 ; i < inps . size ( ) ; + + i ) {
bool hasDynamicShapes = inLayerShapes [ i ] . empty ( ) ;
if ( hasDynamicShapes )
continue ;
if ( inLayerShapes [ i ] . size ( ) = = 1 ) { // 1D input
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ASSERT_EQ ( shape ( inLayerShapes [ i ] [ 0 ] ) , shape ( inps [ i ] ) ) ;
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} else {
// Compare all axes except batch dimension which is variable.
inLayerShapes [ i ] [ 0 ] = inps [ i ] . size [ 0 ] ;
ASSERT_EQ ( inLayerShapes [ i ] , shape ( inps [ i ] ) ) ;
}
}
}
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void testONNXModels ( const String & basename , const Extension ext = npy ,
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double l1 = 0 , double lInf = 0 , const bool useSoftmax = false ,
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bool checkNoFallbacks = true , int numInps = 1 ,
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bool testShapes = true , bool useWinograd = true )
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{
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String onnxmodel = _tf ( " models/ " + basename + " .onnx " , required ) ;
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std : : vector < Mat > inps ( numInps ) ;
Mat ref ;
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if ( ext = = npy ) {
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for ( int i = 0 ; i < numInps ; + + i )
inps [ i ] = blobFromNPY ( _tf ( " data/input_ " + basename + ( numInps > 1 ? format ( " _%d " , i ) : " " ) + " .npy " ) ) ;
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ref = blobFromNPY ( _tf ( " data/output_ " + basename + " .npy " ) ) ;
}
else if ( ext = = pb ) {
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for ( int i = 0 ; i < numInps ; + + i )
inps [ i ] = readTensorFromONNX ( _tf ( " data/input_ " + basename + ( numInps > 1 ? format ( " _%d " , i ) : " " ) + " .pb " ) ) ;
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ref = readTensorFromONNX ( _tf ( " data/output_ " + basename + " .pb " ) ) ;
}
else
CV_Error ( Error : : StsUnsupportedFormat , " Unsupported extension " ) ;
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checkBackend ( & inps [ 0 ] , & ref ) ;
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Net net = readNetFromONNX ( onnxmodel ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
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if ( testShapes )
testInputShapes ( net , inps ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . enableWinograd ( useWinograd ) ;
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std : : vector < String > inputNames ;
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for ( int i = 0 ; i < numInps ; + + i )
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inputNames . push_back ( format ( " %d " , i ) ) ;
net . setInputsNames ( inputNames ) ;
for ( int i = 0 ; i < numInps ; + + i )
net . setInput ( inps [ i ] , inputNames [ i ] ) ;
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Mat out = net . forward ( " " ) ;
if ( useSoftmax )
{
LayerParams lp ;
Net netSoftmax ;
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netSoftmax . addLayerToPrev ( " softmaxLayer " , " Softmax " , lp ) ;
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netSoftmax . setPreferableBackend ( DNN_BACKEND_OPENCV ) ;
netSoftmax . setInput ( out ) ;
out = netSoftmax . forward ( ) ;
netSoftmax . setInput ( ref ) ;
ref = netSoftmax . forward ( ) ;
}
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if ( ref . dims ! = out . dims ) {
if ( ref . dims < = 1 )
ref = ref . reshape ( 1 , out . rows ) ;
if ( out . dims < = 1 )
out = out . reshape ( 1 , ref . rows ) ;
}
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL )
{
l1 = std : : max ( l1 , 1.4e-3 ) ;
lInf = std : : max ( lInf , 8e-3 ) ;
}
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normAssert ( ref , out , basename . c_str ( ) , l1 ? l1 : default_l1 , lInf ? lInf : default_lInf ) ;
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if ( checkNoFallbacks )
expectNoFallbacksFromIE ( net ) ;
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}
} ;
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TEST_P ( Test_ONNX_layers , InstanceNorm )
{
if ( target = = DNN_TARGET_MYRIAD )
testONNXModels ( " instancenorm " , npy , 0 , 0 , false , false ) ;
else
testONNXModels ( " instancenorm " , npy ) ;
}
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TEST_P ( Test_ONNX_layers , MaxPooling )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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testONNXModels ( " maxpooling " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , MaxPooling_2 )
{
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testONNXModels ( " two_maxpooling " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , Convolution )
{
testONNXModels ( " convolution " ) ;
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testONNXModels ( " conv_asymmetric_pads " ) ;
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}
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TEST_P ( Test_ONNX_layers , Convolution_variable_weight )
{
if ( ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH | |
backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ) & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // not supported
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ; // not supported
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String basename = " conv_variable_w " ;
Net net = readNetFromONNX ( _tf ( " models/ " + basename + " .onnx " ) ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
for ( int i = 0 ; i < 2 ; i + + )
{
Mat input = blobFromNPY ( _tf ( " data/input_ " + basename + format ( " _%d " , i ) + " _0.npy " ) ) ;
Mat weights = blobFromNPY ( _tf ( " data/input_ " + basename + format ( " _%d " , i ) + " _1.npy " ) ) ;
Mat ref = blobFromNPY ( _tf ( " data/output_ " + basename + format ( " _%d " , i ) + " .npy " ) ) ;
net . setInput ( input , " 0 " ) ;
net . setInput ( weights , " 1 " ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
}
}
TEST_P ( Test_ONNX_layers , Convolution_variable_weight_bias )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// openvino/src/plugins/intel_myriad/common/src/ngraph/transformations/extract_dynamic_batch/slice_convolution.cpp:14 Expecting operation v1::GroupConvolution GroupConvolution_6904725 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904719[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904719[0]:f32{4,1,1,2,2}
// openvino\src\plugins\intel_myriad\common\src\ngraph\transformations\extract_dynamic_batch\slice_convolution.cpp:15 Expecting operation v1::GroupConvolution GroupConvolution_6904692 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904686[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904686[0]:f32{4,1,1,2,2}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
// accuracy (depends on OpenCL version / HW)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# elif defined(INF_ENGINE_RELEASE)
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if ( ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH | |
backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ) & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU & &
getInferenceEngineCPUType ( ) = = CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // supports only <= 2 inputs
if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ; // not supported
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String basename = " conv_variable_wb " ;
Net net = readNetFromONNX ( _tf ( " models/ " + basename + " .onnx " ) ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
for ( int i = 0 ; i < 2 ; i + + )
{
Mat input = blobFromNPY ( _tf ( " data/input_ " + basename + format ( " _%d " , i ) + " _0.npy " ) ) ;
Mat weights = blobFromNPY ( _tf ( " data/input_ " + basename + format ( " _%d " , i ) + " _1.npy " ) ) ;
Mat bias = blobFromNPY ( _tf ( " data/input_ " + basename + format ( " _%d " , i ) + " _2.npy " ) ) ;
Mat ref = blobFromNPY ( _tf ( " data/output_ " + basename + format ( " _%d " , i ) + " .npy " ) ) ;
net . setInput ( input , " 0 " ) ;
net . setInput ( weights , " 1 " ) ;
net . setInput ( bias , " bias " ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
}
}
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TEST_P ( Test_ONNX_layers , Gather )
{
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testONNXModels ( " gather " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , Gather_Scalar )
{
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testONNXModels ( " gather_scalar " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , GatherMulti )
{
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// GPU plugin unsupported slice for constant
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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testONNXModels ( " gather_multi " , npy , 0 , 0 , false , false ) ;
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}
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TEST_P ( Test_ONNX_layers , Gather_shared_indices ) {
testONNXModels ( " gather_shared_indices " , npy , 0 , 0 , false , false , 1 ) ;
}
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TEST_P ( Test_ONNX_layers , Two_resizes_with_shared_subgraphs ) {
testONNXModels ( " two_resizes_with_shared_subgraphs " , npy , 0 , 0 , false , false , 3 , /*testShapes*/ false ) ;
}
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TEST_P ( Test_ONNX_layers , Convolution3D )
{
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if ( backend = = DNN_BACKEND_CUDA & & target = = DNN_TARGET_CUDA_FP16 )
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA_FP16 ) ;
}
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testONNXModels ( " conv3d " ) ;
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}
TEST_P ( Test_ONNX_layers , Convolution3D_bias )
{
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if ( backend = = DNN_BACKEND_CUDA & & target = = DNN_TARGET_CUDA_FP16 )
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA_FP16 ) ;
}
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testONNXModels ( " conv3d_bias " ) ;
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testONNXModels ( " conv3d_depthwise_bias " ) ; // kernel 1x1
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}
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TEST_P ( Test_ONNX_layers , Two_convolution )
{
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# if defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD
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& & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
// Reference output values are in range [-0.855, 0.611]
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testONNXModels ( " two_convolution " ) ;
}
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TEST_P ( Test_ONNX_layers , Deconvolution )
{
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testONNXModels ( " deconvolution " , npy , 0 , 0 , false , false ) ;
testONNXModels ( " two_deconvolution " , npy , 0 , 0 , false , false ) ;
testONNXModels ( " deconvolution_group " , npy , 0 , 0 , false , false ) ;
testONNXModels ( " deconvolution_output_shape " , npy , 0 , 0 , false , false ) ;
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if ( target ! = DNN_TARGET_CUDA_FP16 ) // bug
testONNXModels ( " deconv_adjpad_2d " , npy , 0 , 0 , false , false ) ;
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}
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TEST_P ( Test_ONNX_layers , Deconvolution3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "2":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
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{
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// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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}
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# endif
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if ( backend = = DNN_BACKEND_OPENCV )
throw SkipTestException ( " OpenCV backend is not supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " deconv3d " ) ;
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}
TEST_P ( Test_ONNX_layers , Deconvolution3D_bias )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (270 and 810 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# endif
if ( backend = = DNN_BACKEND_OPENCV )
throw SkipTestException ( " OpenCV backend is not supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " deconv3d_bias " ) ;
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}
TEST_P ( Test_ONNX_layers , Deconvolution3D_pad )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (108 and 432 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# endif
if ( backend = = DNN_BACKEND_OPENCV )
throw SkipTestException ( " OpenCV backend is not supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " deconv3d_pad " ) ;
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}
TEST_P ( Test_ONNX_layers , Deconvolution3D_adjpad )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (90 and 180 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# endif
if ( backend = = DNN_BACKEND_OPENCV )
throw SkipTestException ( " OpenCV backend is not supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " deconv3d_adjpad " ) ;
}
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TEST_P ( Test_ONNX_layers , Dropout )
{
testONNXModels ( " dropout " ) ;
}
TEST_P ( Test_ONNX_layers , Linear )
{
if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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testONNXModels ( " linear " ) ;
}
TEST_P ( Test_ONNX_layers , ReLU )
{
testONNXModels ( " ReLU " ) ;
}
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TEST_P ( Test_ONNX_layers , PReLU )
{
testONNXModels ( " PReLU_slope " ) ;
}
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TEST_P ( Test_ONNX_layers , Clip )
{
testONNXModels ( " clip " , npy ) ;
}
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TEST_P ( Test_ONNX_layers , Clip_init )
{
testONNXModels ( " clip_init_min_max " ) ;
testONNXModels ( " clip_init_min " ) ;
testONNXModels ( " clip_init_max " ) ;
}
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TEST_P ( Test_ONNX_layers , Shape )
{
testONNXModels ( " shape_of_constant " ) ;
}
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TEST_P ( Test_ONNX_layers , ReduceMean )
{
testONNXModels ( " reduce_mean " ) ;
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testONNXModels ( " reduce_mean_axis1 " ) ;
testONNXModels ( " reduce_mean_axis2 " ) ;
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}
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TEST_P ( Test_ONNX_layers , ReduceSum )
{
testONNXModels ( " reduce_sum " ) ;
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testONNXModels ( " reduce_sum_axis_dynamic_batch " ) ;
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}
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TEST_P ( Test_ONNX_layers , ReduceMax )
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{
testONNXModels ( " reduce_max " ) ;
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}
TEST_P ( Test_ONNX_layers , ReduceMax_axis_0 )
{
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testONNXModels ( " reduce_max_axis_0 " ) ;
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}
TEST_P ( Test_ONNX_layers , ReduceMax_axis_1 )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty()
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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testONNXModels ( " reduce_max_axis_1 " ) ;
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}
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TEST_P ( Test_ONNX_layers , Min )
{
testONNXModels ( " min " , npy , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , ArgLayer )
{
if ( backend ! = DNN_BACKEND_OPENCV | | target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
testONNXModels ( " argmax " ) ;
testONNXModels ( " argmin " ) ;
}
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TEST_P ( Test_ONNX_layers , Scale )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy (inf/nan)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
// IE exception: mkldnn_node.cpp:238 Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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testONNXModels ( " scale " ) ;
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}
TEST_P ( Test_ONNX_layers , Scale_broadcast )
{
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // doesn't support broadcasting
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testONNXModels ( " scale_broadcast " , npy , 0 , 0 , false , true , 3 ) ;
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}
TEST_P ( Test_ONNX_layers , Scale_broadcast_mid )
{
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // doesn't support broadcasting
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testONNXModels ( " scale_broadcast_mid " , npy , 0 , 0 , false , true , 2 ) ;
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}
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TEST_P ( Test_ONNX_layers , ReduceMean3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " reduce_mean3d " ) ;
}
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TEST_P ( Test_ONNX_layers , MaxPooling_Sigmoid )
{
testONNXModels ( " maxpooling_sigmoid " ) ;
}
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TEST_P ( Test_ONNX_layers , Cast )
{
testONNXModels ( " cast " ) ;
}
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TEST_P ( Test_ONNX_layers , Power )
{
testONNXModels ( " pow2 " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , Exp )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
testONNXModels ( " exp " ) ;
}
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TEST_P ( Test_ONNX_layers , Elementwise_Ceil )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " ceil " ) ;
}
TEST_P ( Test_ONNX_layers , Elementwise_Floor )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " floor " ) ;
}
TEST_P ( Test_ONNX_layers , Elementwise_Log )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " log " ) ;
}
TEST_P ( Test_ONNX_layers , Elementwise_Round )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " round " ) ;
}
TEST_P ( Test_ONNX_layers , Elementwise_Sqrt )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
testONNXModels ( " sqrt " ) ;
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# endif
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}
TEST_P ( Test_ONNX_layers , Elementwise_not )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " not " ) ;
}
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TEST_P ( Test_ONNX_layers , Compare_EQ )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " equal " ) ;
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}
TEST_P ( Test_ONNX_layers , Compare_GT )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
# endif
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testONNXModels ( " greater " ) ;
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}
TEST_P ( Test_ONNX_layers , Compare_LT )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
# endif
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testONNXModels ( " less " ) ;
}
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TEST_P ( Test_ONNX_layers , Compare_GTorEQ )
{
testONNXModels ( " greater_or_equal " ) ;
}
TEST_P ( Test_ONNX_layers , Compare_LEorEQ )
{
testONNXModels ( " less_or_equal " ) ;
}
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TEST_P ( Test_ONNX_layers , CompareSameDims_EQ )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " equal_same_dims " , npy , 0 , 0 , false , true , 2 ) ;
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}
TEST_P ( Test_ONNX_layers , CompareSameDims_GT )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
# endif
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testONNXModels ( " greater_same_dims " , npy , 0 , 0 , false , true , 2 ) ;
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}
TEST_P ( Test_ONNX_layers , CompareSameDims_LT )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
# endif
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testONNXModels ( " less_same_dims " , npy , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Concatenation )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
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if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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}
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testONNXModels ( " concatenation " ) ;
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testONNXModels ( " concat_const_blobs " ) ;
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}
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TEST_P ( Test_ONNX_layers , CumSumExclusiveInplace )
{
testONNXModels ( " cumsum_exclusive_inplace " ) ;
}
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TEST_P ( Test_ONNX_layers , RangeFloat )
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{
testONNXModels ( " range_float " ) ;
testONNXModels ( " range_float_negative " ) ;
}
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TEST_P ( Test_ONNX_layers , RangeInt32 )
{
testONNXModels ( " range_int32 " ) ;
testONNXModels ( " range_int32_negative " ) ;
}
TEST_P ( Test_ONNX_layers , RangeInt64 )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // OpenVINO uses int32 precision for int64 operations
testONNXModels ( " range_int64 " ) ;
testONNXModels ( " range_int64_negative " ) ;
}
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TEST_P ( Test_ONNX_layers , Eltwise3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
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testONNXModels ( " eltwise3d " ) ;
}
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TEST_P ( Test_ONNX_layers , AveragePooling )
{
testONNXModels ( " average_pooling " ) ;
}
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TEST_P ( Test_ONNX_layers , MaxPooling3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// accuracy
if ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
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{
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// accuracy
if ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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}
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# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " max_pool3d " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , AvePooling3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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testONNXModels ( " ave_pool3d " ) ;
}
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TEST_P ( Test_ONNX_layers , PoolConv3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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if ( backend = = DNN_BACKEND_CUDA & & target = = DNN_TARGET_CUDA_FP16 )
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA_FP16 ) ;
}
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testONNXModels ( " pool_conv_3d " ) ;
}
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TEST_P ( Test_ONNX_layers , BatchNormalization )
{
testONNXModels ( " batch_norm " ) ;
}
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TEST_P ( Test_ONNX_layers , BatchNormalization3D )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
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if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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}
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testONNXModels ( " batch_norm_3d " ) ;
}
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TEST_P ( Test_ONNX_layers , BatchNormalizationUnfused )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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testONNXModels ( " frozenBatchNorm2d " ) ;
}
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TEST_P ( Test_ONNX_layers , BatchNormalizationSubgraph )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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testONNXModels ( " batch_norm_subgraph " ) ;
}
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TEST_P ( Test_ONNX_layers , NormalizeFusionSubgraph )
{
testONNXModels ( " normalize_fusion " ) ;
}
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TEST_P ( Test_ONNX_layers , Transpose )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
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if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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}
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testONNXModels ( " transpose " ) ;
}
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TEST_P ( Test_ONNX_layers , Multiplication )
{
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if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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testONNXModels ( " mul " ) ;
}
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TEST_P ( Test_ONNX_layers , MatMul_2d )
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{
testONNXModels ( " matmul_2d " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_3d )
{
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testONNXModels ( " matmul_3d " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_4d )
{
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testONNXModels ( " matmul_4d " ) ;
}
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TEST_P ( Test_ONNX_layers , MatMul_2d_init )
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{
testONNXModels ( " matmul_2d_init " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_3d_init )
{
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testONNXModels ( " matmul_3d_init " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_4d_init )
{
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testONNXModels ( " matmul_4d_init " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_init_2 )
{
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testONNXModels ( " matmul_init_2 " ) ;
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}
TEST_P ( Test_ONNX_layers , MatMul_init_bcast )
{
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testONNXModels ( " matmul_init_bcast " ) ;
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}
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TEST_P ( Test_ONNX_layers , MatMulAdd )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021010000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
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if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
testONNXModels ( " matmul_add " ) ;
}
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TEST_P ( Test_ONNX_layers , Expand )
{
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testONNXModels ( " expand " ) ;
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}
TEST_P ( Test_ONNX_layers , ExpandIdentity ) {
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testONNXModels ( " expand_identity " ) ;
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}
TEST_P ( Test_ONNX_layers , ExpandBatch ) {
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testONNXModels ( " expand_batch " ) ;
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}
TEST_P ( Test_ONNX_layers , ExpandChannels ) {
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testONNXModels ( " expand_channels " ) ;
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}
TEST_P ( Test_ONNX_layers , ExpandNegBatch ) {
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testONNXModels ( " expand_neg_batch " ) ;
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}
TEST_P ( Test_ONNX_layers , ExpandHW )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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testONNXModels ( " expand_hw " ) ;
}
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TEST_P ( Test_ONNX_layers , Constant )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD
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& & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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testONNXModels ( " constant " ) ;
}
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TEST_P ( Test_ONNX_layers , Padding )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels ( " padding " , npy , 0 , 0 , false , false ) ;
# else
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testONNXModels ( " padding " ) ;
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# endif
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}
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TEST_P ( Test_ONNX_layers , Resize )
{
testONNXModels ( " resize_nearest " ) ;
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testONNXModels ( " tf_half_pixel_for_nn " ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
testONNXModels ( " resize_bilinear " ) ;
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}
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TEST_P ( Test_ONNX_layers , ResizeUnfused )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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testONNXModels ( " upsample_unfused_torch1.2 " ) ;
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testONNXModels ( " upsample_unfused_opset9_torch1.4 " ) ;
testONNXModels ( " resize_nearest_unfused_opset11_torch1.4 " ) ;
testONNXModels ( " resize_nearest_unfused_opset11_torch1.3 " ) ;
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testONNXModels ( " resize_bilinear_unfused_opset11_torch1.4 " ) ;
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}
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TEST_P ( Test_ONNX_layers , ResizeUnfusedTwoInputs )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " upsample_unfused_two_inputs_opset9_torch1.4 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " upsample_unfused_two_inputs_opset11_torch1.4 " , npy , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , MultyInputs )
{
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testONNXModels ( " multy_inputs " , npy , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Broadcast )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
testONNXModels ( " channel_broadcast " , npy , 0 , 0 , false , true , 2 ) ;
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}
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TEST_P ( Test_ONNX_layers , DynamicResize )
{
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testONNXModels ( " dynamic_resize_9 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " dynamic_resize_10 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " dynamic_resize_11 " , npy , 0 , 0 , false , true , 2 ) ;
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testONNXModels ( " dynamic_resize_13 " , npy , 0 , 0 , false , true , 2 ) ;
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testONNXModels ( " dynamic_resize_scale_9 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " dynamic_resize_scale_10 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " dynamic_resize_scale_11 " , npy , 0 , 0 , false , true , 2 ) ;
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testONNXModels ( " dynamic_resize_scale_13 " , npy , 0 , 0 , false , true , 2 ) ;
testONNXModels ( " resize_size_opset11 " ) ;
testONNXModels ( " resize_size_opset13 " ) ;
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}
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TEST_P ( Test_ONNX_layers , Resize_HumanSeg )
{
testONNXModels ( " resize_humanseg " ) ;
}
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TEST_P ( Test_ONNX_layers , Div )
{
const String model = _tf ( " models/div.onnx " ) ;
Net net = readNetFromONNX ( model ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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// Reference output values range is -68.80928, 2.991873. So to avoid computational
// difference for FP16 we'll perform reversed division (just swap inputs).
Mat inp1 = blobFromNPY ( _tf ( " data/input_div_1.npy " ) ) ;
Mat inp2 = blobFromNPY ( _tf ( " data/input_div_0.npy " ) ) ;
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Mat ref = blobFromNPY ( _tf ( " data/output_div.npy " ) ) ;
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cv : : divide ( 1.0 , ref , ref ) ;
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checkBackend ( & inp1 , & ref ) ;
net . setInput ( inp1 , " 0 " ) ;
net . setInput ( inp2 , " 1 " ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
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// NaryEltwise layer suuports only CPU for now
testONNXModels ( " div_test_1x1 " , npy , 0 , 0 , false , false , 2 ) ;
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}
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TEST_P ( Test_ONNX_layers , DynamicReshape )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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testONNXModels ( " dynamic_reshape " ) ;
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testONNXModels ( " dynamic_reshape_opset_11 " ) ;
testONNXModels ( " flatten_by_prod " ) ;
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testONNXModels ( " flatten_const " ) ;
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}
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TEST_P ( Test_ONNX_layers , Reshape )
{
testONNXModels ( " unsqueeze " ) ;
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testONNXModels ( " unsqueeze_opset_13 " ) ;
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}
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TEST_P ( Test_ONNX_layers , Unsqueeze_Neg_Axes )
{
testONNXModels ( " unsqueeze_neg_axes " ) ;
}
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TEST_P ( Test_ONNX_layers , Squeeze )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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testONNXModels ( " squeeze " ) ;
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testONNXModels ( " squeeze_axes_op13 " ) ;
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}
TEST_P ( Test_ONNX_layers , ReduceL2 )
{
testONNXModels ( " reduceL2 " ) ;
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testONNXModels ( " reduceL2_subgraph " ) ;
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testONNXModels ( " reduceL2_subgraph_2 " ) ;
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testONNXModels ( " reduceL2_subgraph2_2 " ) ;
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}
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TEST_P ( Test_ONNX_layers , Split )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " split_0 " ) ;
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testONNXModels ( " split_1 " ) ;
testONNXModels ( " split_2 " ) ;
testONNXModels ( " split_3 " ) ;
testONNXModels ( " split_4 " ) ;
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testONNXModels ( " split_5 " ) ;
testONNXModels ( " split_6 " ) ;
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testONNXModels ( " split_neg_axis " ) ;
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}
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// Mul inside with 0-d tensor, output should be A x 1, but is 1 x A. PR #22652
TEST_P ( Test_ONNX_layers , DISABLED_Split_sizes_0d )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
testONNXModels ( " split_sizes " ) ;
}
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TEST_P ( Test_ONNX_layers , Slice )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels ( " slice " , npy , 0 , 0 , false , false ) ;
# else
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testONNXModels ( " slice " ) ;
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testONNXModels ( " slice_neg_starts " ) ;
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testONNXModels ( " slice_opset_11 " ) ;
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testONNXModels ( " slice_neg_steps " , pb ) ;
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# endif
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}
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TEST_P ( Test_ONNX_layers , Slice_Steps_2DInput )
{
testONNXModels ( " slice_opset_11_steps_2d " ) ;
}
TEST_P ( Test_ONNX_layers , Slice_Steps_3DInput )
{
testONNXModels ( " slice_opset_11_steps_3d " ) ;
}
TEST_P ( Test_ONNX_layers , Slice_Steps_4DInput )
{
testONNXModels ( " slice_opset_11_steps_4d " ) ;
}
TEST_P ( Test_ONNX_layers , Slice_Steps_5DInput )
{
testONNXModels ( " slice_opset_11_steps_5d " ) ;
}
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TEST_P ( Test_ONNX_layers , Slice_Nonseq_Axes )
{
testONNXModels ( " slice_nonseq_axes " ) ;
testONNXModels ( " slice_nonseq_axes_steps " ) ;
testONNXModels ( " slice_nonseq_miss_axes_steps " ) ;
}
TEST_P ( Test_ONNX_layers , Slice_Neg_Axes )
{
testONNXModels ( " slice_neg_axes " ) ;
testONNXModels ( " slice_neg_axes_steps " ) ;
testONNXModels ( " slice_neg_miss_axes_steps " ) ;
}
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TEST_P ( Test_ONNX_layers , Softmax )
{
testONNXModels ( " softmax " ) ;
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testONNXModels ( " log_softmax " , npy , 0 , 0 , false , false ) ;
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testONNXModels ( " softmax_unfused " ) ;
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}
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TEST_P ( Test_ONNX_layers , Split_EltwiseMax )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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testONNXModels ( " split_max " ) ;
}
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TEST_P ( Test_ONNX_layers , LSTM_Activations )
{
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // TODO: fix this test for OpenVINO
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node Block1326/lstm/reshape_0/permute was not assigned on any pointed device
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// IE Exception: Ngraph operation Reshape with name Block1237_Output_0_before_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
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testONNXModels ( " lstm_cntk_tanh " , pb , 0 , 0 , false , false ) ;
}
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// disabled due to poor handling of 1-d mats
TEST_P ( Test_ONNX_layers , DISABLED_LSTM )
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{
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testONNXModels ( " lstm " , npy , 0 , 0 , false , false ) ;
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}
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// disabled due to poor handling of 1-d mats
TEST_P ( Test_ONNX_layers , DISABLED_LSTM_bidirectional )
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{
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testONNXModels ( " lstm_bidirectional " , npy , 0 , 0 , false , false ) ;
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}
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TEST_P ( Test_ONNX_layers , LSTM_hidden )
{
testONNXModels ( " hidden_lstm " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , LSTM_hidden_bidirectional )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node Transpose_45 was not assigned on any pointed device.
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
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testONNXModels ( " hidden_lstm_bi " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , GRU )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
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testONNXModels ( " gru " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , gru_cell_batchsize_50_seqlen_1 )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " gru_cell_batchsize_50_seqlen_1 " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , gru_cell_batchsize_5_seqlen_5 )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " gru_cell_batchsize_5_seqlen_5 " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , gru_cell_batchsize_1_seqlen_50 )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " gru_cell_batchsize_1_seqlen_50 " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , GRU_bidirectional )
{
testONNXModels ( " gru_bi " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , LSTM_cell_forward )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy!
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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testONNXModels ( " lstm_cell_forward " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , LSTM_cell_bidirectional )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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testONNXModels ( " lstm_cell_bidirectional " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , LSTM_cell_with_peepholes )
{
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testONNXModels ( " lstm_cell_with_peepholes " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , LSTM_cell_batchsize_50_seqlen_1 )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_cell_batchsize_50_seqlen_1 " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , LSTM_cell_batchsize_1_seqlen_50 )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_cell_batchsize_1_seqlen_50 " , npy , 0 , 0 , false , false ) ;
}
TEST_P ( Test_ONNX_layers , LSTM_cell_batchsize_5_seqlen_5 )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_cell_batchsize_5_seqlen_5 " , npy , 0 , 0 , false , false ) ;
}
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TEST_P ( Test_ONNX_layers , LSTM_init_h0_c0 )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_init_h0_c0 " , npy , 0 , 0 , false , false , 3 ) ;
}
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// epsilon is larger because onnx does not match with torch/opencv exactly
TEST_P ( Test_ONNX_layers , LSTM_layout_seq )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_layout_0 " , npy , 0.005 , 0.005 , false , false , 3 ) ;
}
// epsilon is larger because onnx does not match with torch/opencv exactly
TEST_P ( Test_ONNX_layers , LSTM_layout_batch )
{
if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
testONNXModels ( " lstm_layout_1 " , npy , 0.005 , 0.005 , false , false , 3 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_1D )
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{
testONNXModels ( " einsum_1d " , npy , 0 , 0 , false , false , 2 ) ;
}
TEST_P ( Test_ONNX_layers , Einsum_2D )
{
testONNXModels ( " einsum_2d " , npy , 0 , 0 , false , false , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_2D_Ellipses )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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testONNXModels ( " einsum_2d_ellipses " , npy , 0 , 0 , false , false , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_3D )
{
testONNXModels ( " einsum_3d " , npy , 0 , 0 , false , false , 2 ) ;
}
TEST_P ( Test_ONNX_layers , Einsum_4D )
{
testONNXModels ( " einsum_4d " , npy , 0 , 0 , false , false , 2 ) ;
}
TEST_P ( Test_ONNX_layers , Einsum_5D )
{
testONNXModels ( " einsum_5d " , npy , 0 , 0 , false , false , 2 ) ;
}
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// https://github.com/opencv/opencv/issues/24883
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TEST_P ( Test_ONNX_layers , Einsum_InnerProduct )
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{
testONNXModels ( " einsum_inner " , npy , 0 , 0 , false , false , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_HadamardProduct )
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{
testONNXModels ( " einsum_hadamard " , npy , 0 , 0 , false , false , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_Batch_Diagonal )
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{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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testONNXModels ( " einsum_batch_diagonal " , npy , 0 , 0 , false , false , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Einsum_Sum )
{
testONNXModels ( " einsum_sum " , npy , 0 , 0 , false , false , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Einsum_transpose )
{
testONNXModels ( " einsum_transpose " , npy , 0 , 0 , false , false , 1 ) ;
}
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TEST_P ( Test_ONNX_layers , Einsum_const_inputs ) {
testONNXModels ( " einsum_const_inputs " , npy , 0 , 0 , false , false , 1 ) ;
}
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TEST_P ( Test_ONNX_layers , ReduceSum_Consts ) {
testONNXModels ( " reducesum_consts " ) ;
}
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TEST_P ( Test_ONNX_layers , Pad2d_Unfused )
{
testONNXModels ( " ReflectionPad2d " ) ;
testONNXModels ( " ZeroPad2d " ) ;
}
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TEST_P ( Test_ONNX_layers , LinearWithConstant )
{
if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE ) ;
# endif
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ;
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testONNXModels ( " lin_with_constant " ) ;
}
TEST_P ( Test_ONNX_layers , MatmulWithTwoInputs )
{
if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE ) ;
# endif
testONNXModels ( " matmul_with_two_inputs " ) ;
}
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TEST_P ( Test_ONNX_layers , ResizeOpset11_Torch1_6 )
{
testONNXModels ( " resize_opset11_torch1.6 " ) ;
}
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TEST_P ( Test_ONNX_layers , Mish )
{
testONNXModels ( " mish " ) ;
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testONNXModels ( " mish_no_softplus " ) ;
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}
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TEST_P ( Test_ONNX_layers , CalculatePads )
{
testONNXModels ( " calc_pads " ) ;
}
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TEST_P ( Test_ONNX_layers , Conv1d )
{
testONNXModels ( " conv1d " ) ;
}
TEST_P ( Test_ONNX_layers , Conv1d_bias )
{
testONNXModels ( " conv1d_bias " ) ;
}
TEST_P ( Test_ONNX_layers , Conv1d_variable_weight )
{
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // not supported
if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ; // not supported
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String basename = " conv1d_variable_w " ;
Net net = readNetFromONNX ( _tf ( " models/ " + basename + " .onnx " ) ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
Mat input = blobFromNPY ( _tf ( " data/input_ " + basename + " _0.npy " ) ) ;
Mat weights = blobFromNPY ( _tf ( " data/input_ " + basename + " _1.npy " ) ) ;
Mat ref = blobFromNPY ( _tf ( " data/output_ " + basename + " .npy " ) ) ;
net . setInput ( input , " 0 " ) ;
net . setInput ( weights , " 1 " ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
}
TEST_P ( Test_ONNX_layers , Conv1d_variable_weight_bias )
{
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if ( backend = = DNN_BACKEND_CUDA )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_CUDA ) ; // not supported
if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ; // not supported
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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if ( target = = DNN_TARGET_CPU & & getInferenceEngineCPUType ( ) = = CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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}
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String basename = " conv1d_variable_wb " ;
Net net = readNetFromONNX ( _tf ( " models/ " + basename + " .onnx " ) ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
Mat input = blobFromNPY ( _tf ( " data/input_ " + basename + " _0.npy " ) ) ;
Mat weights = blobFromNPY ( _tf ( " data/input_ " + basename + " _1.npy " ) ) ;
Mat bias = blobFromNPY ( _tf ( " data/input_ " + basename + " _2.npy " ) ) ;
Mat ref = blobFromNPY ( _tf ( " data/output_ " + basename + " .npy " ) ) ;
net . setInput ( input , " 0 " ) ;
net . setInput ( weights , " 1 " ) ;
net . setInput ( bias , " bias " ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
}
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TEST_P ( Test_ONNX_layers , GatherMultiOutput )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Reshape with name 6 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2021030000)
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if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE ) ;
# endif
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testONNXModels ( " gather_multi_output " , npy , 0 , 0 , false , false ) ;
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}
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TEST_P ( Test_ONNX_layers , DynamicAxes_squeeze_and_conv )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
testONNXModels ( " squeeze_and_conv_dynamic_axes " ) ;
}
TEST_P ( Test_ONNX_layers , DynamicAxes_unsqueeze_and_conv )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
testONNXModels ( " unsqueeze_and_conv_dynamic_axes " ) ;
}
TEST_P ( Test_ONNX_layers , DynamicAxes_gather )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " gather_dynamic_axes " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_gather_scalar )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
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// accuracy
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
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# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# elif defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
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# if INF_ENGINE_VER_MAJOR_LT(2021000000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
# endif
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testONNXModels ( " gather_scalar_dynamic_axes " , npy , 0 , 0 , false , false ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_slice )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " slice_dynamic_axes " ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_slice_opset_11 )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " slice_opset_11_dynamic_axes " ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_resize_opset11_torch16 )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " resize_opset11_torch1.6_dynamic_axes " ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_average_pooling )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " average_pooling_dynamic_axes " ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_maxpooling_sigmoid )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " maxpooling_sigmoid_dynamic_axes " ) ;
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}
TEST_P ( Test_ONNX_layers , DynamicAxes_dynamic_batch )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
# if INF_ENGINE_VER_MAJOR_LT(2021000000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
# endif
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testONNXModels ( " dynamic_batch " ) ;
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}
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TEST_P ( Test_ONNX_layers , MaxPool1d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
{
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !expired()
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
# endif
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testONNXModels ( " maxpooling_1d " ) ;
}
TEST_P ( Test_ONNX_layers , MaxPoolSigmoid1d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
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testONNXModels ( " maxpooling_sigmoid_1d " ) ;
}
TEST_P ( Test_ONNX_layers , MaxPool1d_Twise )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
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testONNXModels ( " two_maxpooling_1d " ) ;
}
TEST_P ( Test_ONNX_layers , AvePool1d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
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testONNXModels ( " average_pooling_1d " ) ;
}
TEST_P ( Test_ONNX_layers , PoolConv1d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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# endif
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testONNXModels ( " pool_conv_1d " ) ;
}
TEST_P ( Test_ONNX_layers , ConvResizePool1d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Reshape with name 15 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# endif
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# if defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# if INF_ENGINE_VER_MAJOR_EQ(2021030000)
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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}
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# endif
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const double lInf = ( target = = DNN_TARGET_CPU_FP16 ) ? 0.024 : default_lInf ;
testONNXModels ( " conv_resize_pool_1d " , npy , default_l1 , lInf ) ;
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}
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TEST_P ( Test_ONNX_layers , DepthWiseAdd )
{
testONNXModels ( " depthwiseconv_add " ) ;
}
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TEST_P ( Test_ONNX_layers , DepthStride2 )
{
testONNXModels ( " depthwise_stride2 " ) ;
}
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TEST_P ( Test_ONNX_layers , SubFromConst )
{
testONNXModels ( " sub_from_const1 " ) ;
testONNXModels ( " sub_from_const_eltwise " ) ;
testONNXModels ( " sub_from_const_broadcast " ) ;
}
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TEST_P ( Test_ONNX_layers , DivConst )
{
testONNXModels ( " div_const " ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm )
{
testONNXModels ( " gemm_no_transB " ) ;
testONNXModels ( " gemm_transB_0 " ) ;
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testONNXModels ( " gemm_first_const " ) ;
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}
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TEST_P ( Test_ONNX_layers , Gemm_bias )
{
testONNXModels ( " gemm_vector_bias " ) ;
}
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TEST_P ( Test_ONNX_layers , Quantized_Convolution )
{
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // TODO: fix this test for OpenVINO
2022-11-01 00:06:31 +08:00
// The difference of QOperator and QDQ format:
// https://onnxruntime.ai/docs/performance/quantization.html#onnx-quantization-representation-format.
{
SCOPED_TRACE ( " QOperator quantized model. " ) ;
testONNXModels ( " quantized_conv_uint8_weights " , npy , 0.004 , 0.02 ) ;
testONNXModels ( " quantized_conv_int8_weights " , npy , 0.03 , 0.5 ) ;
testONNXModels ( " quantized_conv_per_channel_weights " , npy , 0.06 , 0.4 ) ;
testONNXModels ( " quantized_conv_asymmetric_pads_int8_weights " ) ;
}
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2022-11-01 00:06:31 +08:00
{
SCOPED_TRACE ( " QDQ quantized model. " ) ;
testONNXModels ( " quantized_conv_uint8_weights_qdq " , npy , 0.004 , 0.02 ) ;
testONNXModels ( " quantized_conv_int8_weights_qdq " , npy , 0.03 , 0.5 ) ;
testONNXModels ( " quantized_conv_per_channel_weights_qdq " , npy , 0.06 , 0.4 ) ;
}
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}
TEST_P ( Test_ONNX_layers , Quantized_MatMul )
{
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testONNXModels ( " quantized_matmul_uint8_weights " , npy , 0.008 , 0.015 ) ;
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testONNXModels ( " quantized_matmul_int8_weights " , npy , 0.06 , 0.2 ) ;
testONNXModels ( " quantized_matmul_per_channel_weights " , npy , 0.06 , 0.22 ) ;
}
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TEST_P ( Test_ONNX_layers , Quantized_Gemm )
{
testONNXModels ( " quantized_gemm " , npy ) ;
}
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TEST_P ( Test_ONNX_layers , Quantized_MatMul_Variable_Weights )
{
// Unsupported
EXPECT_THROW (
{
testONNXModels ( " quantized_matmul_variable_inputs " ) ;
} , cv : : Exception ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Eltwise )
{
testONNXModels ( " quantized_eltwise " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Eltwise_Scalar )
{
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // TODO: fix this test for OpenVINO
2021-10-05 02:07:38 +08:00
testONNXModels ( " quantized_eltwise_scalar " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Eltwise_Broadcast )
{
testONNXModels ( " quantized_eltwise_broadcast " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_LeakyReLU )
{
testONNXModels ( " quantized_leaky_relu " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Sigmoid )
{
testONNXModels ( " quantized_sigmoid " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_MaxPool )
{
testONNXModels ( " quantized_maxpool " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_AvgPool )
{
testONNXModels ( " quantized_avgpool " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Split )
{
testONNXModels ( " quantized_split " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Pad )
{
testONNXModels ( " quantized_padding " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Reshape )
{
testONNXModels ( " quantized_reshape " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Transpose )
{
testONNXModels ( " quantized_transpose " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Squeeze )
{
testONNXModels ( " quantized_squeeze " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Unsqueeze )
{
testONNXModels ( " quantized_unsqueeze " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Resize )
{
testONNXModels ( " quantized_resize_nearest " ) ;
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double l1 = backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 2e-4 ;
testONNXModels ( " quantized_resize_bilinear " , npy , l1 , 0.003 ) ;
l1 = backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 3e-4 ;
testONNXModels ( " quantized_resize_bilinear_align " , npy , l1 , 0.003 ) ;
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}
TEST_P ( Test_ONNX_layers , Quantized_Concat )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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testONNXModels ( " quantized_concat " ) ;
testONNXModels ( " quantized_concat_const_blob " ) ;
}
TEST_P ( Test_ONNX_layers , Quantized_Constant )
{
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testONNXModels ( " quantized_constant " , npy , 0.008 , 0.02 ) ;
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}
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TEST_P ( Test_ONNX_layers , OutputRegistration )
{
testONNXModels ( " output_registration " , npy , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , QLinearSoftmax )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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testONNXModels ( " qlinearsoftmax_v11 " , npy , 0.002 , 0.002 ) ; // 2D coerced
testONNXModels ( " qlinearsoftmax_v13 " , npy , 0.002 , 0.002 ) ;
}
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INSTANTIATE_TEST_CASE_P ( /*nothing*/ , Test_ONNX_layers , dnnBackendsAndTargets ( ) ) ;
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class Test_ONNX_nets : public Test_ONNX_layers
{
public :
Test_ONNX_nets ( ) { required = false ; }
} ;
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TEST_P ( Test_ONNX_nets , Alexnet )
{
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# if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
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applyTestTag ( CV_TEST_TAG_MEMORY_2GB ) ;
# else
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applyTestTag ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ;
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# endif
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const String model = _tf ( " models/alexnet.onnx " , false ) ;
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Net net = readNetFromONNX ( model ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . enableWinograd ( false ) ;
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Mat inp = imread ( _tf ( " ../grace_hopper_227.png " ) ) ;
Mat ref = blobFromNPY ( _tf ( " ../caffe_alexnet_prob.npy " ) ) ;
checkBackend ( & inp , & ref ) ;
net . setInput ( blobFromImage ( inp , 1.0f , Size ( 227 , 227 ) , Scalar ( ) , false ) ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
Mat out = net . forward ( ) ;
normAssert ( out , ref , " " , default_l1 , default_lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( Test_ONNX_nets , RAFT )
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{
applyTestTag ( CV_TEST_TAG_LONG , CV_TEST_TAG_DEBUG_VERYLONG , CV_TEST_TAG_MEMORY_2GB ) ;
std : : string weight_path = _tf ( " models/optical_flow_estimation_raft_2023aug.onnx " , false ) ;
std : : string img0_path = findDataFile ( std : : string ( " gpu/opticalflow/frame0.png " ) ) ;
std : : string img1_path = findDataFile ( std : : string ( " gpu/opticalflow/frame1.png " ) ) ;
Size target_size { 480 , 360 } ;
auto img0 = imread ( img0_path ) ;
auto img1 = imread ( img1_path ) ;
auto blob0 = blobFromImage ( img0 , 1.0 , target_size , 0 , true ) ;
auto blob1 = blobFromImage ( img1 , 1.0 , target_size , 0 , true ) ;
auto net = readNet ( weight_path ) ;
net . setInput ( blob0 , " 0 " ) ;
net . setInput ( blob1 , " 1 " ) ;
std : : vector < std : : string > outnames { " 12007 " , " 12006 " } ;
std : : vector < Mat > outs ;
net . forward ( outs , outnames ) ;
// output 12006 is not checked to save space in opencv_extra since its ref is > 1MB,
// and output 12006 is calculated from 12007 so checking 12007 is sufficient.
std : : string ref_12700_path = _tf ( " data/output_optical_flow_estimation_raft_2023aug.npy " ) ;
auto ref0 = blobFromNPY ( ref_12700_path ) ;
normAssert ( ref0 , outs [ 0 ] , " " , 1e-5 , 1.8e-4 ) ;
}
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TEST_P ( Test_ONNX_nets , Squeezenet )
{
testONNXModels ( " squeezenet " , pb ) ;
}
TEST_P ( Test_ONNX_nets , Googlenet )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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const String model = _tf ( " models/googlenet.onnx " , false ) ;
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Net net = readNetFromONNX ( model ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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if ( target = = DNN_TARGET_CPU_FP16 )
net . enableWinograd ( false ) ;
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std : : vector < Mat > images ;
images . push_back ( imread ( _tf ( " ../googlenet_0.png " ) ) ) ;
images . push_back ( imread ( _tf ( " ../googlenet_1.png " ) ) ) ;
Mat inp = blobFromImages ( images , 1.0f , Size ( ) , Scalar ( ) , false ) ;
Mat ref = blobFromNPY ( _tf ( " ../googlenet_prob.npy " ) ) ;
checkBackend ( & inp , & ref ) ;
net . setInput ( inp ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
Mat out = net . forward ( ) ;
normAssert ( ref , out , " " , default_l1 , default_lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( Test_ONNX_nets , CaffeNet )
{
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# if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
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applyTestTag ( CV_TEST_TAG_MEMORY_2GB ) ;
# else
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applyTestTag ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ;
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# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD
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& & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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testONNXModels ( " caffenet " , pb ) ;
}
TEST_P ( Test_ONNX_nets , RCNN_ILSVRC13 )
{
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# if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
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applyTestTag ( CV_TEST_TAG_MEMORY_2GB ) ;
# else
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applyTestTag ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ;
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# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD
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& & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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// Reference output values are in range [-4.992, -1.161]
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testONNXModels ( " rcnn_ilsvrc13 " , pb , 0.0046 ) ;
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}
TEST_P ( Test_ONNX_nets , VGG16_bn )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_6GB ) ; // > 2.3Gb
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// output range: [-16; 27], after Softmax [0; 0.67]
const double lInf = ( target = = DNN_TARGET_MYRIAD ) ? 0.038 : default_lInf ;
testONNXModels ( " vgg16-bn " , pb , default_l1 , lInf , true ) ;
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}
TEST_P ( Test_ONNX_nets , ZFNet )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_2GB ) ;
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testONNXModels ( " zfnet512 " , pb ) ;
}
TEST_P ( Test_ONNX_nets , ResNet18v1 )
{
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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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testONNXModels ( " resnet18v1 " , pb , default_l1 , default_lInf , true , target ! = DNN_TARGET_MYRIAD ) ;
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}
TEST_P ( Test_ONNX_nets , ResNet50v1 )
{
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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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size_t hwm0 = getTopMemoryUsageMB ( ) ;
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testONNXModels ( " resnet50v1 " , pb , default_l1 , default_lInf , true , target ! = DNN_TARGET_MYRIAD ) ;
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size_t hwm1 = getTopMemoryUsageMB ( ) ;
if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_CPU )
{
EXPECT_LE ( hwm1 - hwm0 , 350 ) < < " Top allocated memory " ;
}
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}
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TEST_P ( Test_ONNX_nets , ResNet50_Int8 )
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{
testONNXModels ( " resnet50_int8 " , pb , default_l1 , default_lInf , true ) ;
}
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TEST_P ( Test_ONNX_nets , ResNet101_DUC_HDC )
{
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applyTestTag ( CV_TEST_TAG_VERYLONG ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
# if defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
if ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_OPENCL )
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{
if ( backend = = DNN_BACKEND_OPENCV )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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throw SkipTestException ( " Test is disabled for OpenCL targets " ) ;
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}
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testONNXModels ( " resnet101_duc_hdc " , pb ) ;
}
TEST_P ( Test_ONNX_nets , TinyYolov2 )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_512MB ) ;
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if ( cvtest : : skipUnstableTests )
throw SkipTestException ( " Skip unstable test " ) ;
# if defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
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& & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 )
)
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applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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if ( target = = DNN_TARGET_MYRIAD & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X ,
backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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// output range: [-11; 8]
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double l1 = default_l1 , lInf = default_lInf ;
Merge pull request #22275 from zihaomu:fp16_support_conv
DNN: FP16 support on Convolution 2D #22275
## FP16 support on ARM platform
This PR proposes to support FP16 backend in Convolution.
For now, we only support FP16 at ARM aarch64.
In addition to adding fp16, I also added `seperateIm2col` optimization in this patch.
## How to use FP16 to speed up convolution?
```
Net net = readNet(modelPath);
net.setPreferableTarget(DNN_TARGET_CPU_FP16);
net.setInput(blob);
Mat output = net.forward();
```
### TODO List
| Task | Status | Remarks |
|:-------:|:--------:|:------------:|
| Convolution 2D FP16 | :heavy_check_mark: | Done |
| Winograd FP16 | Because the current modification has reached 2k lines, winograd fp16 will be completed in the next PR. | |
| Accuracy Test | :heavy_check_mark: | Done |
| Performance Test | :heavy_check_mark: | Done |
| Compiler bug | :heavy_check_mark: | Done |
### Speed Test for FP 16.
**Test on M1 chip, 4 threads.**
| Model Name | FP32 (Conv+Wino) | Conv(FP16) + Wino(FP 32) |
|:-------:|:--------:|:------------:|
| ReseNet 50 | 26.0 ms | **18.05 ms** (25% speed up)|
| MobileNet V2 | 4.17 ms | **3.09 ms (29% speed up)** |
### Speed Test for `seperateIm2col` trick on X86.
**Test on AMD 5600x, 12 threads.**
| Model Name | 4.x | Patch |
|:-------:|:--------:|:------------:|
| MobileNet V2 | 5.6 ms | **3.0 ms (46% speed up)** |
### Performance Test
#### Performance Test of X86 platform: AMD 5600X, with `-perf_threas=1`
|Name of Test|4.x|patch|patch vs 4.x (x-factor)|
|---|:-:|:-:|:-:|
|Name of Test|4.x 0|fp16pr final|fp16pr final vs 4.x 0 (x-factor)|
|---|:-:|:-:|:-:|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)|0.001|0.001|1.00|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)|0.001|0.001|1.03|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.001|0.001|0.92|
|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.002|0.003|0.95|
|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.006|0.006|1.00|
|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.045|0.033|1.39|
|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)|0.011|0.009|1.17|
|conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)|0.109|0.078|1.39|
|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)|0.040|0.042|0.94|
|conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.326|0.342|0.95|
|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.580|0.589|0.99|
|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)|1.293|1.382|0.94|
|conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)|3.590|3.710|0.97|
|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)|1.120|1.191|0.94|
|conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)|2.576|2.872|0.90|
|conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)|4.599|4.670|0.98|
|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)|9.230|9.582|0.96|
|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)|65.946|69.381|0.95|
|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)|18.915|19.289|0.98|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)|1.404|1.457|0.96|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)|2.060|1.501|1.37|
|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)|1.409|1.464|0.96|
|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)|1.793|1.838|0.98|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)|1.207|1.199|1.01|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)|1.277|1.275|1.00|
|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|2.319|2.370|0.98|
|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)|1.351|1.346|1.00|
|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)|3.520|3.612|0.97|
|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.876|1.880|1.00|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)|1.981|1.995|0.99|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)|2.620|2.627|1.00|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)|4.202|4.123|1.02|
|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|2.429|2.445|0.99|
|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|2.591|2.576|1.01|
|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)|3.005|2.998|1.00|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)|3.515|3.532|1.00|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)|3.115|3.134|0.99|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|3.937|3.899|1.01|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)|5.533|5.471|1.01|
|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|3.472|3.464|1.00|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)|4.302|4.322|1.00|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)|6.100|6.035|1.01|
|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)|6.580|6.484|1.01|
|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|9.741|9.634|1.01|
|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|10.131|10.156|1.00|
|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|12.391|12.350|1.00|
|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)|91.074|87.893|1.04|
|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|5.903|5.903|1.00|
|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)|6.890|6.794|1.01|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)|5.160|5.131|1.01|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|4.970|5.036|0.99|
|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|5.045|5.015|1.01|
|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)|11.583|11.343|1.02|
|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)|5.348|5.320|1.01|
|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)|5.357|5.396|0.99|
|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)|6.050|6.006|1.01|
|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)|5.952|5.953|1.00|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)|8.014|8.014|1.00|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)|12.472|12.577|0.99|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)|10.803|10.655|1.01|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)|18.429|13.405|1.37|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)|6.659|6.647|1.00|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)|14.192|13.819|1.03|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)|6.045|6.068|1.00|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)|12.742|12.828|0.99|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)|8.046|7.773|1.04|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)|17.440|17.192|1.01|
|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)|15.418|14.972|1.03|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)|0.430|0.430|1.00|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)|6.692|6.663|1.00|
|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)|6.350|6.347|1.00|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)|0.267|0.265|1.01|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)|7.755|7.558|1.03|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)|0.203|0.202|1.00|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|10.663|10.576|1.01|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)|10.827|10.614|1.02|
|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)|7.049|6.947|1.01|
|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)|6.900|6.901|1.00|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)|0.165|0.165|1.00|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)|17.953|17.251|1.04|
|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)|7.430|7.320|1.01|
|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)|22.187|21.705|1.02|
|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)|8.349|8.126|1.03|
|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)|8.273|8.297|1.00|
|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)|8.169|8.094|1.01|
|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)|13.602|13.359|1.02|
|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)|8.633|8.584|1.01|
|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)|29.339|28.897|1.02|
|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|13.000|12.920|1.01|
|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|14.262|13.319|1.07|
|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|27.453|27.253|1.01|
|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)|32.052|27.269|1.18|
|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|15.363|15.208|1.01|
|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|18.543|18.434|1.01|
|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)|39.114|37.954|1.03|
|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|36.271|36.972|0.98|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|19.262|19.427|0.99|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|19.298|19.349|1.00|
|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|20.261|19.847|1.02|
|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|21.867|21.525|1.02|
|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)|51.756|49.979|1.04|
|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|28.133|27.060|1.04|
|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)|25.035|24.980|1.00|
|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|25.858|25.821|1.00|
|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|27.313|27.149|1.01|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)|28.219|28.111|1.00|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|46.025|46.674|0.99|
|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|30.220|29.446|1.03|
|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|49.410|48.708|1.01|
|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|38.203|38.001|1.01|
|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|39.961|39.021|1.02|
|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|48.685|47.075|1.03|
|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|75.114|72.586|1.03|
|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)|41.222|41.144|1.00|
|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)|46.220|46.353|1.00|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)|98.201|98.771|0.99|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|100.106|96.971|1.03|
|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|146.977|140.445|1.05|
|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)|198.618|194.665|1.02|
#### Performance Test of ARM platform: apple M1, with `-perf_threas=1`
Min (ms)
|Name of Test|4.x|patch|4.x vs patch (x-factor)|
|---|:-:|:-:|:-:|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 19}, OCN=2, G=2, S=2, P=(1, 1), BIAS, OCV/CPU)|0.001|0.001|1.07|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 2, 25}, OCN=2, G=2, P=(2, 2), PM=SAME, OCV/CPU)|0.001|0.001|1.10|
|conv1d::Conv1D::(GFLOPS=0.000, K=[3], IN={1, 6, 10}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.002|0.002|0.97|
|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 4, 9, 10, 10}, OCN=4, S=[1 x 1 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.003|0.003|0.84|
|conv3d::Conv3D::(GFLOPS=0.000, K=[1 x 1 x 1], IN={1, 8, 1, 10, 10}, OCN=8, G=8, P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.009|0.009|1.00|
|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 3 x 3], IN={1, 2, 19, 19, 19}, OCN=2, G=2, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), BIAS, OCV/CPU)|0.027|0.030|0.90|
|conv3d::Conv3D::(GFLOPS=0.000, K=[3 x 4 x 2], IN={1, 4, 8, 10, 10}, OCN=4, G=4, S=[1 x 2 x 1], BIAS, OCV/CPU)|0.008|0.007|1.07|
|conv3d::Conv3D::(GFLOPS=0.001, K=[3 x 3 x 3], IN={1, 2, 25, 19, 19}, OCN=2, G=2, S=[1 x 2 x 2], P=(2, 2) x (2, 2) x (2, 2), PM=SAME, OCV/CPU)|0.066|0.072|0.91|
|conv3d::Conv3D::(GFLOPS=0.002, K=[3 x 1 x 4], IN={1, 14, 5, 10, 10}, OCN=14, PM=SAME, OCV/CPU)|0.090|0.054|1.68|
|conv3d::Conv3D::(GFLOPS=0.006, K=[5 x 5 x 5], IN={1, 4, 50, 19, 19}, OCN=4, S=[2 x 2 x 2], P=(1, 1) x (1, 1) x (1, 1), PM=VALID, OCV/CPU)|0.328|0.409|0.80|
|conv3d::Conv3D::(GFLOPS=0.027, K=[3 x 3 x 3], IN={1, 6, 10, 38, 50}, OCN=6, PM=VALID, BIAS, OCV/CPU)|0.659|0.697|0.95|
|conv3d::Conv3D::(GFLOPS=0.030, K=[5 x 5 x 5], IN={1, 6, 19, 19, 19}, OCN=6, G=2, OCV/CPU)|1.266|1.403|0.90|
|conv3d::Conv3D::(GFLOPS=0.045, K=[7 x 7 x 7], IN={1, 2, 38, 38, 38}, OCN=2, S=[1 x 2 x 1], OCV/CPU)|3.550|4.145|0.86|
|conv3d::Conv3D::(GFLOPS=0.053, K=[3 x 3 x 3], IN={1, 10, 98, 10, 10}, OCN=10, PM=SAME, OCV/CPU)|1.188|1.375|0.86|
|conv3d::Conv3D::(GFLOPS=0.071, K=[7 x 7 x 7], IN={1, 6, 15, 19, 19}, OCN=6, S=[2 x 1 x 1], P=(3, 3) x (3, 3) x (3, 3), PM=SAME, BIAS, OCV/CPU)|2.683|3.236|0.83|
|conv3d::Conv3D::(GFLOPS=0.093, K=[5 x 5 x 5], IN={1, 4, 40, 75, 75}, OCN=4, S=[2 x 2 x 2], OCV/CPU)|4.491|5.501|0.82|
|conv3d::Conv3D::(GFLOPS=0.116, K=[5 x 5 x 5], IN={1, 2, 21, 75, 100}, OCN=2, BIAS, OCV/CPU)|8.916|10.181|0.88|
|conv3d::Conv3D::(GFLOPS=1.267, K=[5 x 5 x 5], IN={1, 3, 75, 75, 100}, OCN=3, PM=SAME, BIAS, OCV/CPU)|69.995|72.296|0.97|
|conv3d::Conv3D::(GFLOPS=1.343, K=[3 x 3 x 3], IN={1, 11, 9, 150, 200}, OCN=11, PM=VALID, BIAS, OCV/CPU)|22.531|23.139|0.97|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU)|2.239|1.933|1.16|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 512, 26, 26}, OCN=256, OCV/CPU_FP16)|-|1.010|-|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU)|3.134|2.068|1.52|
|conv::Conv::(GFLOPS=0.177, K=[1 x 1], IN={1, 1024, 13, 13}, OCN=512, OCV/CPU_FP16)|-|1.062|-|
|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU)|1.918|1.920|1.00|
|conv::Conv::(GFLOPS=0.178, K=[1 x 1], IN={1, 256, 52, 52}, OCN=128, OCV/CPU_FP16)|-|1.014|-|
|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU)|2.340|2.352|0.99|
|conv::Conv::(GFLOPS=0.210, K=[1 x 1], IN={1, 576, 38, 50}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)|-|1.247|-|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU)|1.116|1.111|1.00|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 128, 56, 56}, OCN=32, P=[1 x 1], OCV/CPU_FP16)|-|1.114|-|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU)|1.116|1.112|1.00|
|conv::Conv::(GFLOPS=0.231, K=[3 x 3], IN={1, 256, 14, 14}, OCN=256, P=[1 x 1], OCV/CPU_FP16)|-|1.113|-|
|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|3.067|3.085|0.99|
|conv::Conv::(GFLOPS=0.280, K=[1 x 1], IN={1, 576, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)|-|1.622|-|
|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU)|1.153|1.187|0.97|
|conv::Conv::(GFLOPS=0.302, K=[3 x 3], IN={1, 64, 64, 64}, OCN=64, PM=SAME, OCV/CPU_FP16)|-|1.150|-|
|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU)|4.804|4.849|0.99|
|conv::Conv::(GFLOPS=0.357, K=[1 x 1], IN={1, 64, 208, 208}, OCN=64, OCV/CPU_FP16)|-|2.922|-|
|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.463|1.469|1.00|
|conv::Conv::(GFLOPS=0.420, K=[3 x 3], IN={1, 96, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)|-|1.459|-|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU)|1.577|1.580|1.00|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 128, 40, 40}, OCN=128, PM=SAME, OCV/CPU_FP16)|-|1.580|-|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU)|1.826|1.818|1.00|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 256, 20, 20}, OCN=256, PM=SAME, OCV/CPU_FP16)|-|1.817|-|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU)|6.541|5.081|1.29|
|conv::Conv::(GFLOPS=0.472, K=[3 x 3], IN={1, 512, 10, 10}, OCN=512, PM=SAME, OCV/CPU_FP16)|-|2.809|-|
|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU)|1.912|1.919|1.00|
|conv::Conv::(GFLOPS=0.561, K=[3 x 3], IN={1, 128, 38, 50}, OCN=128, PM=SAME, BIAS, OCV/CPU_FP16)|-|1.919|-|
|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|1.961|1.971|0.99|
|conv::Conv::(GFLOPS=0.624, K=[3 x 3], IN={1, 128, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|1.961|-|
|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU)|2.317|2.329|0.99|
|conv::Conv::(GFLOPS=0.701, K=[3 x 3], IN={1, 128, 38, 50}, OCN=160, PM=SAME, BIAS, OCV/CPU_FP16)|-|2.322|-|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU)|2.920|2.947|0.99|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 64, 104, 104}, OCN=64, P=[1 x 1], OCV/CPU_FP16)|-|2.924|-|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU)|2.467|2.466|1.00|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 128, 52, 52}, OCN=128, P=[1 x 1], OCV/CPU_FP16)|-|2.496|-|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|3.028|2.997|1.01|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 256, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU_FP16)|-|2.986|-|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU)|4.353|4.355|1.00|
|conv::Conv::(GFLOPS=0.798, K=[3 x 3], IN={1, 512, 13, 13}, OCN=512, P=[1 x 1], OCV/CPU_FP16)|-|4.355|-|
|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|2.762|2.793|0.99|
|conv::Conv::(GFLOPS=0.830, K=[3 x 3], IN={1, 64, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)|-|2.797|-|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU)|3.428|3.226|1.06|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 192, 38, 38}, OCN=192, PM=SAME, OCV/CPU_FP16)|-|3.223|-|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU)|3.967|3.957|1.00|
|conv::Conv::(GFLOPS=0.958, K=[3 x 3], IN={1, 384, 19, 19}, OCN=384, PM=SAME, OCV/CPU_FP16)|-|3.960|-|
|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU)|4.806|4.387|1.10|
|conv::Conv::(GFLOPS=1.022, K=[3 x 3], IN={1, 576, 19, 19}, OCN=273, PM=SAME, BIAS, OCV/CPU_FP16)|-|4.366|-|
|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|14.509|11.756|1.23|
|conv::Conv::(GFLOPS=1.112, K=[3 x 3], IN={1, 512, 10, 10}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|6.510|-|
|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|13.718|13.287|1.03|
|conv::Conv::(GFLOPS=1.181, K=[3 x 3], IN={1, 64, 160, 200}, OCN=128, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU_FP16)|-|7.190|-|
|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU)|15.133|14.853|1.02|
|conv::Conv::(GFLOPS=1.182, K=[3 x 3], IN={1, 32, 320, 400}, OCN=64, S=[2 x 2], P=[1 x 1], BIAS, OCV/CPU_FP16)|-|8.671|-|
|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU)|41.928|43.328|0.97|
|conv::Conv::(GFLOPS=1.195, K=[9 x 9], IN={1, 32, 240, 320}, OCN=3, P=[4 x 4], BIAS, OCV/CPU_FP16)|-|38.072|-|
|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU)|4.409|4.428|1.00|
|conv::Conv::(GFLOPS=1.196, K=[3 x 3], IN={1, 384, 26, 26}, OCN=256, P=[1 x 1], OCV/CPU_FP16)|-|4.427|-|
|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU)|6.144|5.363|1.15|
|conv::Conv::(GFLOPS=1.210, K=[3 x 3], IN={1, 32, 256, 256}, OCN=32, PM=SAME, OCV/CPU_FP16)|-|5.368|-|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU)|3.926|3.932|1.00|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 64, 75, 75}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)|-|3.938|-|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU)|3.920|3.915|1.00|
|conv::Conv::(GFLOPS=1.245, K=[3 x 3], IN={1, 96, 75, 100}, OCN=96, PM=SAME, BIAS, OCV/CPU_FP16)|-|3.950|-|
|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|3.767|3.764|1.00|
|conv::Conv::(GFLOPS=1.248, K=[3 x 3], IN={1, 256, 46, 46}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|3.762|-|
|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU)|19.959|13.875|1.44|
|conv::Conv::(GFLOPS=1.258, K=[3 x 3], IN={1, 1280, 10, 10}, OCN=546, PM=SAME, BIAS, OCV/CPU_FP16)|-|7.781|-|
|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU)|3.951|3.955|1.00|
|conv::Conv::(GFLOPS=1.261, K=[3 x 3], IN={1, 192, 38, 50}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)|-|3.969|-|
|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU)|4.050|4.034|1.00|
|conv::Conv::(GFLOPS=1.416, K=[3 x 3], IN={1, 128, 62, 82}, OCN=128, BIAS, OCV/CPU_FP16)|-|4.093|-|
|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU)|4.923|4.506|1.09|
|conv::Conv::(GFLOPS=1.500, K=[3 x 3], IN={1, 128, 64, 84}, OCN=128, BIAS, OCV/CPU_FP16)|-|4.509|-|
|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU)|4.759|4.476|1.06|
|conv::Conv::(GFLOPS=1.586, K=[3 x 3], IN={1, 128, 66, 86}, OCN=128, BIAS, OCV/CPU_FP16)|-|4.447|-|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU)|6.079|5.628|1.08|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 26, 26}, OCN=512, P=[1 x 1], OCV/CPU_FP16)|-|5.625|-|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU)|19.843|17.523|1.13|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 256, 52, 52}, OCN=512, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)|-|8.917|-|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU)|8.334|8.247|1.01|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 13, 13}, OCN=1024, P=[1 x 1], OCV/CPU_FP16)|-|8.246|-|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU)|23.164|18.199|1.27|
|conv::Conv::(GFLOPS=1.595, K=[3 x 3], IN={1, 512, 26, 26}, OCN=1024, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)|-|9.305|-|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU)|5.184|5.178|1.00|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 104, 104}, OCN=128, P=[1 x 1], OCV/CPU_FP16)|-|5.149|-|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU)|17.990|18.103|0.99|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 64, 208, 208}, OCN=128, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)|-|9.777|-|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU)|4.831|4.522|1.07|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 52, 52}, OCN=256, P=[1 x 1], OCV/CPU_FP16)|-|4.523|-|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU)|17.328|17.319|1.00|
|conv::Conv::(GFLOPS=1.596, K=[3 x 3], IN={1, 128, 104, 104}, OCN=256, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)|-|8.948|-|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU)|5.944|5.961|1.00|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 208, 208}, OCN=64, P=[1 x 1], OCV/CPU_FP16)|-|5.936|-|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU)|19.811|20.064|0.99|
|conv::Conv::(GFLOPS=1.598, K=[3 x 3], IN={1, 32, 416, 416}, OCN=64, S=[2 x 2], P=[1 x 1], OCV/CPU_FP16)|-|11.705|-|
|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU)|22.398|17.686|1.27|
|conv::Conv::(GFLOPS=1.659, K=[3 x 3], IN={1, 960, 10, 10}, OCN=960, PM=SAME, OCV/CPU_FP16)|-|9.859|-|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU)|0.416|0.416|1.00|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, G=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|0.417|-|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU)|5.356|5.110|1.05|
|conv::Conv::(GFLOPS=1.660, K=[3 x 3], IN={1, 128, 75, 75}, OCN=128, PM=SAME, OCV/CPU_FP16)|-|5.114|-|
|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU)|5.092|4.748|1.07|
|conv::Conv::(GFLOPS=1.675, K=[3 x 3], IN={1, 128, 68, 88}, OCN=128, BIAS, OCV/CPU_FP16)|-|4.754|-|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU)|0.260|0.229|1.13|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, G=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|0.229|-|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU)|5.872|5.460|1.08|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 256, 38, 38}, OCN=256, PM=SAME, OCV/CPU_FP16)|-|5.460|-|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU)|0.161|0.161|1.00|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, G=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|0.161|-|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|7.176|7.175|1.00|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|7.162|-|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU)|7.174|7.185|1.00|
|conv::Conv::(GFLOPS=1.704, K=[3 x 3], IN={1, 512, 19, 19}, OCN=512, PM=SAME, OCV/CPU_FP16)|-|7.157|-|
|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU)|5.400|5.180|1.04|
|conv::Conv::(GFLOPS=1.766, K=[3 x 3], IN={1, 128, 70, 90}, OCN=128, BIAS, OCV/CPU_FP16)|-|5.201|-|
|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU)|5.330|5.188|1.03|
|conv::Conv::(GFLOPS=1.859, K=[3 x 3], IN={1, 128, 72, 92}, OCN=128, BIAS, OCV/CPU_FP16)|-|5.177|-|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU)|0.115|0.115|1.00|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, G=1024, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|0.115|-|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU)|26.156|20.222|1.29|
|conv::Conv::(GFLOPS=1.888, K=[3 x 3], IN={1, 1024, 10, 10}, OCN=1024, PM=SAME, OCV/CPU_FP16)|-|11.203|-|
|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU)|5.627|5.543|1.02|
|conv::Conv::(GFLOPS=1.954, K=[3 x 3], IN={1, 128, 74, 94}, OCN=128, BIAS, OCV/CPU_FP16)|-|5.506|-|
|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU)|27.925|27.741|1.01|
|conv::Conv::(GFLOPS=1.995, K=[9 x 9], IN={1, 3, 320, 400}, OCN=32, P=[4 x 4], BIAS, OCV/CPU_FP16)|-|17.217|-|
|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU)|6.359|6.062|1.05|
|conv::Conv::(GFLOPS=2.052, K=[3 x 3], IN={1, 128, 76, 96}, OCN=128, BIAS, OCV/CPU_FP16)|-|6.048|-|
|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU)|6.559|6.322|1.04|
|conv::Conv::(GFLOPS=2.100, K=[3 x 3], IN={1, 144, 75, 75}, OCN=144, PM=SAME, OCV/CPU_FP16)|-|6.280|-|
|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU)|6.412|6.200|1.03|
|conv::Conv::(GFLOPS=2.153, K=[3 x 3], IN={1, 128, 78, 98}, OCN=128, BIAS, OCV/CPU_FP16)|-|6.197|-|
|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU)|9.167|8.624|1.06|
|conv::Conv::(GFLOPS=2.156, K=[3 x 3], IN={1, 576, 19, 19}, OCN=576, PM=SAME, OCV/CPU_FP16)|-|8.626|-|
|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU)|6.755|6.491|1.04|
|conv::Conv::(GFLOPS=2.255, K=[3 x 3], IN={1, 128, 80, 100}, OCN=128, BIAS, OCV/CPU_FP16)|-|6.520|-|
|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU)|35.664|34.752|1.03|
|conv::Conv::(GFLOPS=2.719, K=[3 x 3], IN={1, 96, 256, 256}, OCN=96, S=[2 x 2], PM=SAME, OCV/CPU_FP16)|-|20.260|-|
|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|9.514|9.414|1.01|
|conv::Conv::(GFLOPS=3.319, K=[3 x 3], IN={1, 128, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|9.462|-|
|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|10.631|9.963|1.07|
|conv::Conv::(GFLOPS=3.321, K=[3 x 3], IN={1, 64, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|9.935|-|
|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|37.465|36.798|1.02|
|conv::Conv::(GFLOPS=3.398, K=[7 x 7], IN={1, 128, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU_FP16)|-|19.569|-|
|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU)|38.157|36.157|1.06|
|conv::Conv::(GFLOPS=3.407, K=[3 x 3], IN={1, 512, 19, 19}, OCN=1024, D=[6 x 6], P=[6 x 6], BIAS, OCV/CPU_FP16)|-|18.902|-|
|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|10.356|10.401|1.00|
|conv::Conv::(GFLOPS=3.408, K=[3 x 3], IN={1, 256, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|10.360|-|
|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|12.641|12.150|1.04|
|conv::Conv::(GFLOPS=4.247, K=[3 x 3], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU_FP16)|-|12.162|-|
|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU)|50.545|50.505|1.00|
|conv::Conv::(GFLOPS=4.247, K=[5 x 5], IN={1, 144, 128, 128}, OCN=144, S=[2 x 2], PM=SAME, OCV/CPU_FP16)|-|27.950|-|
|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU)|54.233|49.603|1.09|
|conv::Conv::(GFLOPS=4.566, K=[7 x 7], IN={1, 172, 46, 46}, OCN=128, P=[3 x 3], BIAS, OCV/CPU_FP16)|-|26.515|-|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|13.779|12.968|1.06|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 256, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|12.984|-|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|15.809|15.329|1.03|
|conv::Conv::(GFLOPS=4.993, K=[3 x 3], IN={1, 512, 46, 46}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|15.433|-|
|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|14.563|14.527|1.00|
|conv::Conv::(GFLOPS=4.994, K=[3 x 3], IN={1, 128, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|14.480|-|
|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|16.714|16.484|1.01|
|conv::Conv::(GFLOPS=4.997, K=[3 x 3], IN={1, 64, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|16.362|-|
|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU)|77.832|65.729|1.18|
|conv::Conv::(GFLOPS=5.780, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, S=[2 x 2], PM=SAME, OCV/CPU_FP16)|-|32.065|-|
|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|21.903|20.386|1.07|
|conv::Conv::(GFLOPS=6.116, K=[3 x 3], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU_FP16)|-|20.416|-|
|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU)|20.405|18.148|1.12|
|conv::Conv::(GFLOPS=6.118, K=[3 x 3], IN={1, 144, 128, 128}, OCN=144, PM=SAME, OCV/CPU_FP16)|-|18.128|-|
|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|20.334|18.521|1.10|
|conv::Conv::(GFLOPS=6.637, K=[3 x 3], IN={1, 256, 75, 75}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|18.495|-|
|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|21.527|19.584|1.10|
|conv::Conv::(GFLOPS=6.638, K=[3 x 3], IN={1, 128, 150, 150}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|19.630|-|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU)|22.715|20.057|1.13|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 150, 200}, OCN=192, PM=SAME, BIAS, OCV/CPU_FP16)|-|20.068|-|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|26.228|24.992|1.05|
|conv::Conv::(GFLOPS=6.641, K=[3 x 3], IN={1, 64, 300, 300}, OCN=64, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|24.957|-|
|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|21.524|21.581|1.00|
|conv::Conv::(GFLOPS=6.814, K=[3 x 3], IN={1, 512, 38, 38}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|21.782|-|
|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU)|34.094|31.964|1.07|
|conv::Conv::(GFLOPS=8.025, K=[3 x 3], IN={1, 1024, 19, 19}, OCN=1206, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|31.925|-|
|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU)|28.677|27.813|1.03|
|conv::Conv::(GFLOPS=9.986, K=[3 x 3], IN={1, 512, 46, 46}, OCN=512, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|27.808|-|
|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU)|31.274|27.892|1.12|
|conv::Conv::(GFLOPS=9.987, K=[3 x 3], IN={1, 256, 92, 92}, OCN=256, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|27.910|-|
|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU)|30.533|30.007|1.02|
|conv::Conv::(GFLOPS=9.989, K=[3 x 3], IN={1, 128, 184, 184}, OCN=128, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|30.089|-|
|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU)|39.837|38.312|1.04|
|conv::Conv::(GFLOPS=9.993, K=[3 x 3], IN={1, 64, 368, 368}, OCN=64, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|38.477|-|
|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU)|32.480|29.237|1.11|
|conv::Conv::(GFLOPS=10.087, K=[3 x 3], IN={1, 576, 38, 50}, OCN=512, PM=SAME, BIAS, OCV/CPU_FP16)|-|29.452|-|
|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU)|33.544|32.832|1.02|
|conv::Conv::(GFLOPS=10.701, K=[3 x 3], IN={1, 512, 38, 38}, OCN=804, P=[1 x 1], BIAS, OCV/CPU_FP16)|-|32.784|-|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU)|134.481|130.678|1.03|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 240, 64, 64}, OCN=240, PM=SAME, OCV/CPU_FP16)|-|70.134|-|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU)|127.930|126.530|1.01|
|conv::Conv::(GFLOPS=11.797, K=[5 x 5], IN={1, 480, 32, 32}, OCN=480, PM=SAME, OCV/CPU_FP16)|-|65.261|-|
|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU)|201.346|187.007|1.08|
|conv::Conv::(GFLOPS=16.987, K=[5 x 5], IN={1, 1152, 16, 16}, OCN=1152, PM=SAME, OCV/CPU_FP16)|-|91.525|-|
|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU)|252.038|245.587|1.03|
|conv::Conv::(GFLOPS=23.122, K=[5 x 5], IN={1, 672, 32, 32}, OCN=672, PM=SAME, OCV/CPU_FP16)|-|125.477|-|
### 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
- [ ] There is a reference to the original bug report and related work
- [ ] 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
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if ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD | | target = = DNN_TARGET_CPU_FP16 )
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{
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l1 = 0.02 ;
lInf = 0.2 ;
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}
else if ( target = = DNN_TARGET_CUDA_FP16 )
{
l1 = 0.018 ;
lInf = 0.16 ;
}
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
l1 = 0.018f ; lInf = 0.16f ;
}
# endif
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testONNXModels ( " tiny_yolo2 " , pb , l1 , lInf , false , true , 1 , true , false ) ;
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}
TEST_P ( Test_ONNX_nets , CNN_MNIST )
{
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// output range: [-1952; 6574], after Softmax [0; 1]
testONNXModels ( " cnn_mnist " , pb , default_l1 , default_lInf , true ) ;
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}
TEST_P ( Test_ONNX_nets , MobileNet_v2 )
{
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// output range: [-166; 317], after Softmax [0; 1]
testONNXModels ( " mobilenetv2 " , pb , default_l1 , default_lInf , true ) ;
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}
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TEST_P ( Test_ONNX_nets , MobileNet_v2_FP16 )
{
testONNXModels ( " mobilenetv2_fp16 " , npy , default_l1 , default_lInf , true ) ;
}
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TEST_P ( Test_ONNX_nets , LResNet100E_IR )
{
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applyTestTag (
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# if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB ,
# else
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( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ,
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# endif
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CV_TEST_TAG_DEBUG_VERYLONG
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) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
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if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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}
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
}
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double l1 = default_l1 , lInf = default_lInf ;
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// output range: [-3; 3]
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bool useWinograd = true ;
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if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
{
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l1 = 0.009 ;
lInf = 0.035 ;
}
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_CPU )
{
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l1 = 4.6e-5 ;
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lInf = 1.9e-4 ;
}
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else if ( target = = DNN_TARGET_CUDA_FP16 )
{
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l1 = 0.009 ;
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lInf = 0.04 ;
}
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else if ( target = = DNN_TARGET_CPU_FP16 )
{
useWinograd = false ;
l1 = 0.009 ;
lInf = 0.035 ;
}
testONNXModels ( " LResNet100E_IR " , pb , l1 , lInf , false , true , 1 , true , useWinograd ) ;
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}
TEST_P ( Test_ONNX_nets , Emotion_ferplus )
{
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# if defined(INF_ENGINE_RELEASE)
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if ( target = = DNN_TARGET_MYRIAD & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X ,
backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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double l1 = default_l1 ;
double lInf = default_lInf ;
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bool useWinograd = true ;
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// Output values are in range [-2.011, 2.111]
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if ( ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 ) | | ( target = = DNN_TARGET_CUDA_FP16 ) )
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l1 = 0.007 ;
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_OPENCL_FP16 )
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{
l1 = 0.021 ;
lInf = 0.034 ;
}
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else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & ( target = = DNN_TARGET_CPU | | target = = DNN_TARGET_OPENCL ) ) {
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l1 = 2.4e-4 ;
lInf = 6e-4 ;
}
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else if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_CPU_FP16 )
{
useWinograd = false ;
l1 = 0.007 ;
}
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
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l1 = 0.013f ; lInf = 0.035f ;
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}
# endif
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testONNXModels ( " emotion_ferplus " , pb , l1 , lInf , false , true , 1 , true , useWinograd ) ;
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}
TEST_P ( Test_ONNX_nets , Inception_v2 )
{
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testONNXModels ( " inception_v2 " , pb , default_l1 , default_lInf , true ) ;
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}
TEST_P ( Test_ONNX_nets , DenseNet121 )
{
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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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testONNXModels ( " densenet121 " , pb , default_l1 , default_lInf , true , target ! = DNN_TARGET_MYRIAD ) ;
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}
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TEST_P ( Test_ONNX_nets , Inception_v1 )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | |
backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) & & target = = DNN_TARGET_MYRIAD )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD ) ;
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# endif
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testONNXModels ( " inception_v1 " , pb ) ;
}
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TEST_P ( Test_ONNX_nets , Shufflenet )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
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if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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}
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# endif
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testONNXModels ( " shufflenet " , pb ) ;
}
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TEST_P ( Test_ONNX_nets , Resnet34_kinetics )
{
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applyTestTag ( CV_TEST_TAG_DEBUG_VERYLONG ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Failed to allocate graph: MYRIAD device is not opened
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
// accuracy
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) )
applyTestTag ( target = = DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION
) ;
# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// IE exception: Function contains several inputs and outputs with one friendly name!
if ( target = = DNN_TARGET_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
}
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# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ; // Only CPU on DLIE backend is supported
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // Only CPU on DLIE backend is supported
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# endif
if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
throw SkipTestException ( " Only CPU is supported " ) ; // FIXIT use tags
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if ( backend = = DNN_BACKEND_VKCOM )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_VULKAN ) ;
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String onnxmodel = findDataFile ( " dnn/resnet-34_kinetics.onnx " , false ) ;
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Mat image0 = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
Mat image1 = imread ( findDataFile ( " dnn/street.png " ) ) ;
Mat ref0 = blobFromNPY ( _tf ( " data/output_kinetics0.npy " ) ) ;
Mat ref1 = blobFromNPY ( _tf ( " data/output_kinetics1.npy " ) ) ;
std : : vector < Mat > images_0 ( 16 , image0 ) ;
std : : vector < Mat > images_1 ( 16 , image1 ) ;
Mat blob0 = blobFromImages ( images_0 , 1.0 , Size ( 112 , 112 ) , Scalar ( 114.7748 , 107.7354 , 99.4750 ) , true , true ) ;
Mat blob1 = blobFromImages ( images_1 , 1.0 , Size ( 112 , 112 ) , Scalar ( 114.7748 , 107.7354 , 99.4750 ) , true , true ) ;
Net permute ;
LayerParams lp ;
int order [ ] = { 1 , 0 , 2 , 3 } ;
lp . set ( " order " , DictValue : : arrayInt < int * > ( & order [ 0 ] , 4 ) ) ;
permute . addLayerToPrev ( " perm " , " Permute " , lp ) ;
2019-12-02 21:16:06 +08:00
permute . setPreferableBackend ( backend ) ;
permute . setPreferableTarget ( target ) ;
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permute . setInput ( blob0 ) ;
Mat input0 = permute . forward ( ) . clone ( ) ;
permute . setInput ( blob1 ) ;
Mat input1 = permute . forward ( ) . clone ( ) ;
int dims [ ] = { 1 , 3 , 16 , 112 , 112 } ;
input0 = input0 . reshape ( 0 , 5 , & dims [ 0 ] ) ;
input1 = input1 . reshape ( 0 , 5 , & dims [ 0 ] ) ;
Net net = readNetFromONNX ( onnxmodel ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
// output range [-5, 11]
float l1 = 0.0013 ;
float lInf = 0.009 ;
2021-11-30 20:08:35 +08:00
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
l1 = 0.02 ;
lInf = 0.07 ;
}
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if ( target = = DNN_TARGET_CUDA_FP16 )
{
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l1 = 0.01 ;
lInf = 0.06 ;
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}
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testInputShapes ( net , { input0 } ) ;
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checkBackend ( & input0 , & ref0 ) ;
net . setInput ( input0 ) ;
Mat out = net . forward ( ) . clone ( ) ;
normAssert ( ref0 , out , " " , l1 , lInf ) ;
checkBackend ( & input1 , & ref1 ) ;
net . setInput ( input1 ) ;
out = net . forward ( ) . clone ( ) ;
normAssert ( ref1 , out , " " , l1 , lInf ) ;
expectNoFallbacksFromIE ( net ) ;
}
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TEST_P ( Test_ONNX_layers , CumSum )
{
testONNXModels ( " cumsum_1d_exclusive_1 " ) ;
testONNXModels ( " cumsum_1d_reverse " ) ;
testONNXModels ( " cumsum_1d_exclusive_1_reverse " ) ;
testONNXModels ( " cumsum_2d_dim_1 " ) ;
testONNXModels ( " cumsum_3d_dim_2 " ) ;
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testONNXModels ( " cumsum_3d_dim_2_int32 " ) ;
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
}
TEST_P ( Test_ONNX_layers , CumSum_int64 )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // OpenVINO uses int32 precision for int64 operations
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testONNXModels ( " cumsum_3d_dim_2_int64 " ) ;
}
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
TEST_P ( Test_ONNX_layers , ReduceSumInt64 )
2024-04-04 19:23:48 +08:00
{
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // OpenVINO uses int32 precision for int64 operations
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testONNXModels ( " reduce_sum_int64 " ) ;
}
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
TEST_P ( Test_ONNX_layers , ScatterInt32 )
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{
testONNXModels ( " scatter_int32 " , npy , 0 , 0 , false , true , 3 ) ;
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
}
TEST_P ( Test_ONNX_layers , ScatterInt64 )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // OpenVINO uses int32 precision for int64 operations
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testONNXModels ( " scatter_int64 " , npy , 0 , 0 , false , true , 3 ) ;
}
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
TEST_P ( Test_ONNX_layers , TileInt32 )
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{
testONNXModels ( " tile_int32 " ) ;
Merge pull request #25458 from alexlyulkov:al/dnn-openvino-int-support
Added int support for OpenVINO dnn backend #25458
Modified dnn OpenVINO integration to support type inference and int operations.
Added OpenVINO support to Cast, CumSum, Expand, Gather, GatherElements, Scatter, ScatterND, Tile layers.
I tried to add Reduce layer, but looks like OpenVINO uses float values inside Reduce operation so it can't pass our int tests.
OpenVINO uses int32 precision for int64 operations, so I've modified input values for int64 tests when backend is OpenVINO.
OpenVINO has a strange behavior with custom layers and int64 values. After model compilation OpenVINO may change types, so the model can have different output type. That's why these tests were disabled:
- Test_ArgMax_Int.random/0, where GetParam() = (4, NGRAPH/CPU)
- Test_ArgMax_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.random/6, where GetParam() = (11, NGRAPH/CPU)
- Test_Reduce_Int.two_axes/6, where GetParam() = (11, NGRAPH/CPU)
Also these tests were temporary disabled, they didn't work on both 4.x and 5.x branches:
- Test_Caffe_layers.layer_prelu_fc/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.LSTM_Activations/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Convolution/0, where GetParam() = NGRAPH/CPU
- Test_ONNX_layers.Quantized_Eltwise_Scalar/0, where GetParam() = NGRAPH/CPU
- Test_TFLite.EfficientDet_int8/0, where GetParam() = NGRAPH/CPU
### 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
- [ ] 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.
- [x] The feature is well documented and sample code can be built with the project CMake
2024-05-15 16:51:59 +08:00
}
TEST_P ( Test_ONNX_layers , TileInt64 )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // OpenVINO uses int32 precision for int64 operations
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testONNXModels ( " tile_int64 " ) ;
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}
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static void testYOLO ( const std : : string & weightPath , const std : : vector < int > & refClassIds ,
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const std : : vector < float > & refScores , const std : : vector < Rect2d > & refBoxes ,
Image2BlobParams imgParams , float conf_threshold = 0.3 , float iou_threshold = 0.5 ,
double scores_diff = 1e-5 , double boxes_iou_diff = 1e-4 , const std : : string test_name = " " )
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{
std : : string imgPath = _tf ( " ../dog_orig_size.png " ) ;
Mat img = imread ( imgPath ) ;
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Mat inp = blobFromImageWithParams ( img , imgParams ) ;
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Net net = readNet ( weightPath ) ;
net . setInput ( inp ) ;
std : : vector < Mat > outs ;
net . forward ( outs , net . getUnconnectedOutLayersNames ( ) ) ;
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// Retrieve
std : : vector < int > keep_classIds ;
std : : vector < float > keep_confidences ;
std : : vector < Rect2d > keep_boxes ;
yoloPostProcessing ( outs , keep_classIds , keep_confidences , keep_boxes , conf_threshold , iou_threshold , test_name ) ;
normAssertDetections (
refClassIds , refScores , refBoxes ,
keep_classIds , keep_confidences , keep_boxes ,
" " , 0.0 , scores_diff , boxes_iou_diff ) ;
}
void yoloPostProcessing (
std : : vector < Mat > & outs ,
std : : vector < int > & keep_classIds ,
std : : vector < float > & keep_confidences ,
std : : vector < Rect2d > & keep_boxes ,
float conf_threshold ,
float iou_threshold ,
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const std : : string & model_name ,
const int nc
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) {
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// Retrieve
std : : vector < int > classIds ;
std : : vector < float > confidences ;
std : : vector < Rect2d > boxes ;
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if ( model_name = = " yolov8 " | | model_name = = " yolov10 " | |
model_name = = " yolov9 " )
{
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cv : : transposeND ( outs [ 0 ] , { 0 , 2 , 1 } , outs [ 0 ] ) ;
}
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if ( model_name = = " yolonas " ) {
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// outs contains 2 elemets of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
Mat concat_out ;
// squeeze the first dimension
outs [ 0 ] = outs [ 0 ] . reshape ( 1 , outs [ 0 ] . size [ 1 ] ) ;
outs [ 1 ] = outs [ 1 ] . reshape ( 1 , outs [ 1 ] . size [ 1 ] ) ;
cv : : hconcat ( outs [ 1 ] , outs [ 0 ] , concat_out ) ;
outs [ 0 ] = concat_out ;
// remove the second element
outs . pop_back ( ) ;
// unsqueeze the first dimension
outs [ 0 ] = outs [ 0 ] . reshape ( 0 , std : : vector < int > { 1 , 8400 , 84 } ) ;
}
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// assert if last dim is 85 or 84
CV_CheckEQ ( outs [ 0 ] . dims , 3 , " Invalid output shape. The shape should be [1, #anchors, 85 or 84] " ) ;
CV_CheckEQ ( ( outs [ 0 ] . size [ 2 ] = = nc + 5 | | outs [ 0 ] . size [ 2 ] = = 80 + 4 ) , true , " Invalid output shape: " ) ;
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for ( auto preds : outs ) {
preds = preds . reshape ( 1 , preds . size [ 1 ] ) ; // [1, 8400, 85] -> [8400, 85]
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for ( int i = 0 ; i < preds . rows ; + + i )
{
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// filter out non object
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float obj_conf = ( model_name = = " yolov8 " | | model_name = = " yolonas " | |
model_name = = " yolov9 " | | model_name = = " yolov10 " ) ? 1.0f : preds . at < float > ( i , 4 ) ;
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if ( obj_conf < conf_threshold )
continue ;
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Mat scores = preds . row ( i ) . colRange ( ( model_name = = " yolov8 " | | model_name = = " yolonas " | | model_name = = " yolov9 " | | model_name = = " yolov10 " ) ? 4 : 5 , preds . cols ) ;
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double conf ;
Point maxLoc ;
minMaxLoc ( scores , 0 , & conf , 0 , & maxLoc ) ;
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conf = ( model_name = = " yolov8 " | | model_name = = " yolonas " | | model_name = = " yolov9 " | | model_name = = " yolov10 " ) ? conf : conf * obj_conf ;
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if ( conf < conf_threshold )
continue ;
// get bbox coords
float * det = preds . ptr < float > ( i ) ;
double cx = det [ 0 ] ;
double cy = det [ 1 ] ;
double w = det [ 2 ] ;
double h = det [ 3 ] ;
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// [x1, y1, x2, y2]
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if ( model_name = = " yolonas " | | model_name = = " yolov10 " ) {
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boxes . push_back ( Rect2d ( cx , cy , w , h ) ) ;
} else {
boxes . push_back ( Rect2d ( cx - 0.5 * w , cy - 0.5 * h ,
cx + 0.5 * w , cy + 0.5 * h ) ) ;
}
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classIds . push_back ( maxLoc . x ) ;
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confidences . push_back ( conf ) ;
}
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}
// NMS
std : : vector < int > keep_idx ;
NMSBoxes ( boxes , confidences , conf_threshold , iou_threshold , keep_idx ) ;
for ( auto i : keep_idx )
{
keep_classIds . push_back ( classIds [ i ] ) ;
keep_confidences . push_back ( confidences [ i ] ) ;
keep_boxes . push_back ( boxes [ i ] ) ;
}
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}
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TEST_P ( Test_ONNX_nets , YOLOv10 )
{
std : : string weightPath = _tf ( " models/yolov10s.onnx " , false ) ;
Size targetSize { 640 , 480 } ;
float conf_threshold = 0.50 ;
float iou_threshold = 0.50 ;
std : : vector < int > refClassIds { 1 , 16 , 7 } ;
std : : vector < float > refScores { 0.9510f , 0.9454f , 0.8404f } ;
std : : vector < Rect2d > refBoxes {
Rect2d ( 105.5014 , 112.8838 , 472.9274 , 350.0603 ) ,
Rect2d ( 109.8231 , 185.7994 , 258.5916 , 452.9302 ) ,
Rect2d ( 388.5018 , 62.1034 , 576.6399 , 143.3986 )
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-4 , " yolov10 " ) ;
}
TEST_P ( Test_ONNX_nets , YOLOv9 )
{
std : : string weightPath = _tf ( " models/yolov9t.onnx " , false ) ;
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Size targetSize { 640 , 480 } ;
float conf_threshold = 0.50 ;
float iou_threshold = 0.50 ;
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std : : vector < int > refClassIds { 1 , 16 , 2 } ; // wrong class mapping for yolov9
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std : : vector < float > refScores { 0.959274f , 0.901125f , 0.559396f } ;
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std : : vector < Rect2d > refBoxes {
Rect2d ( 106.255 , 107.927 , 472.497 , 350.309 ) ,
Rect2d ( 108.633 , 185.256 , 259.287 , 450.672 ) ,
Rect2d ( 390.701 , 62.1454 , 576.928 , 141.795 )
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-4 , " yolov9 " ) ;
}
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TEST_P ( Test_ONNX_nets , YOLOX )
{
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applyTestTag ( CV_TEST_TAG_DEBUG_VERYLONG ) ;
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std : : string weightPath = _tf ( " models/yolox_s_inf_decoder.onnx " , false ) ;
Size targetSize { 640 , 640 } ;
float conf_threshold = 0.50 ;
float iou_threshold = 0.50 ;
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std : : vector < int > refClassIds { 1 , 16 , 7 } ;
std : : vector < float > refScores { 0.9649f , 0.9163f , 0.6879f } ;
std : : vector < Rect2d > refBoxes {
Rect2d ( 105.5384 , 179.4100 , 470.6339 , 428.5553 ) ,
Rect2d ( 111.4482 , 263.4098 , 258.7438 , 526.1140 ) ,
Rect2d ( 389.1421 , 143.9286 , 577.9495 , 222.0294 )
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-4 ) ;
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}
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TEST_P ( Test_ONNX_nets , YOLONas )
{
// model information: https://dl.opencv.org/models/yolo-nas/Readme.md
std : : string weightPath = _tf ( " models/yolo_nas_s.onnx " , false ) ;
Size targetSize { 640 , 640 } ;
float conf_threshold = 0.50 ;
float iou_threshold = 0.50 ;
std : : vector < int > refClassIds { 1 , 16 , 7 } ;
std : : vector < float > refScores { 0.9720f , 0.9283f , 0.8990f } ;
// [x1, y1, x2, y2]
std : : vector < Rect2d > refBoxes {
Rect2d ( 105.516 , 173.696 , 471.323 , 430.433 ) ,
Rect2d ( 109.241 , 263.406 , 259.872 , 531.858 ) ,
Rect2d ( 390.153 , 142.492 , 574.932 , 222.709 )
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
false ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-4 , " yolonas " ) ;
}
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TEST_P ( Test_ONNX_nets , YOLOv8 )
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{
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std : : string weightPath = _tf ( " models/yolov8n.onnx " , false ) ;
Size targetSize { 640 , 640 } ;
float conf_threshold = 0.25 ;
float iou_threshold = 0.50 ;
std : : vector < int > refClassIds { 16 , 1 , 2 } ;
std : : vector < float > refScores { 0.9332f , 0.8959f , 0.6157f } ;
// [x1, y1, x2, y2]
std : : vector < Rect2d > refBoxes {
Rect2d ( 108.8965 , 261.9094 , 257.1633 , 530.3049 ) ,
Rect2d ( 110.4020 , 192.9843 , 473.4418 , 429.5965 ) ,
Rect2d ( 389.1603 , 143.2506 , 577.3542 , 223.0615 ) ,
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-4 , " yolov8 " ) ;
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}
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// This test is mainly to test:
// 1. identity node with constant input
// 2. limited support to range operator (all inputs are constant)
// 3. parseExpand with multiple broadcast axes
// 4. 1D mat dimension issue with the output of range operator
TEST_P ( Test_ONNX_nets , YOLOv7 )
{
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applyTestTag (
CV_TEST_TAG_MEMORY_2GB ,
CV_TEST_TAG_DEBUG_VERYLONG
) ;
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std : : string weightPath = _tf ( " models/yolov7.onnx " , false ) ;
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// Reference, which is collected with input size of 640x640
std : : vector < int > refClassIds { 1 , 16 , 7 } ;
std : : vector < float > refScores { 0.9614331f , 0.9589417f , 0.8679074f } ;
// [x1, y1, x2, y2] x 3
std : : vector < Rect2d > refBoxes { Rect2d ( 105.973236f , 150.16716f , 472.59012f , 466.48834f ) ,
Rect2d ( 109.97953f , 246.17862f , 259.83676f , 600.76624f ) ,
Rect2d ( 385.96185f , 83.02809f , 576.07355f , 189.82793f ) } ;
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Size targetSize { 640 , 640 } ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_NULL ,
Scalar : : all ( 0 )
) ;
testYOLO ( weightPath , refClassIds , refScores , refBoxes , imgParams ) ;
}
TEST_P ( Test_ONNX_nets , YOLOv6 )
{
std : : string weightPath = _tf ( " models/yolov6n.onnx " , false ) ;
Size targetSize { 640 , 640 } ;
float conf_threshold = 0.30 ;
float iou_threshold = 0.50 ;
std : : vector < int > refClassIds { 1 , 16 , 7 , 1 } ;
std : : vector < float > refScores { 0.95031f , 0.87123f , 0.65453f , 0.34142f } ;
// [x1, y1, x2, y2] x 3
std : : vector < Rect2d > refBoxes { Rect2d ( 98.84 , 177.91 , 473.29 , 431.19 ) ,
Rect2d ( 109.80 , 265.50 , 258.86 , 531.97 ) ,
Rect2d ( 387.79 , 141.61 , 576.98 , 223.52 ) ,
Rect2d ( 105.62 , 199.24 , 218.37 , 389.84 ) ,
} ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_LETTERBOX ,
Scalar : : all ( 114 )
) ;
testYOLO (
weightPath , refClassIds , refScores , refBoxes ,
imgParams , conf_threshold , iou_threshold ,
1.0e-4 , 1.0e-3 ) ;
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}
TEST_P ( Test_ONNX_nets , YOLOv5n )
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{
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std : : string weightPath = findDataFile ( " dnn/yolov5n.onnx " , false ) ;
// Reference, which is collected with input size of 640x640
std : : vector < int > refClassIds { 16 , 2 , 1 } ;
std : : vector < float > refScores { 0.749053f , 0.616853f , 0.32506f } ;
// [x1, y1, x2, y2] x 4
std : : vector < Rect2d > refBoxes { Rect2d ( 108.088f , 239.293f , 266.196f , 607.658f ) ,
Rect2d ( 392.028f , 89.9233f , 579.152f , 190.447f ) ,
Rect2d ( 120.278f , 159.76 , 214.481f , 241.473f ) } ;
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Size targetSize { 640 , 640 } ;
Image2BlobParams imgParams (
Scalar : : all ( 1 / 255.0 ) ,
targetSize ,
Scalar : : all ( 0 ) ,
true ,
CV_32F ,
DNN_LAYOUT_NCHW ,
DNN_PMODE_NULL ,
Scalar : : all ( 0 )
) ;
testYOLO ( weightPath , refClassIds , refScores , refBoxes , imgParams ) ;
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}
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TEST_P ( Test_ONNX_layers , Tile )
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{
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testONNXModels ( " tile " , pb ) ;
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}
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TEST_P ( Test_ONNX_layers , Gelu )
{
testONNXModels ( " gelu " ) ;
testONNXModels ( " gelu_approximation " ) ;
}
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TEST_P ( Test_ONNX_layers , OpenAI_CLIP_head )
{
testONNXModels ( " clip-vit-base-head " ) ;
}
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TEST_P ( Test_ONNX_layers , where_node )
{
testONNXModels ( " where_layer " ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_all_attributes ) {
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testONNXModels ( " test_gemm_all_attributes " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_alpha ) {
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testONNXModels ( " test_gemm_alpha " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_beta ) {
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testONNXModels ( " test_gemm_beta " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_matrix_bias ) {
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testONNXModels ( " test_gemm_default_matrix_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_no_bias ) {
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testONNXModels ( " test_gemm_default_no_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_scalar_bias ) {
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testONNXModels ( " test_gemm_default_scalar_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_single_elem_vector_bias ) {
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testONNXModels ( " test_gemm_default_single_elem_vector_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_vector_bias ) {
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testONNXModels ( " test_gemm_default_vector_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_default_zero_bias ) {
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testONNXModels ( " test_gemm_default_zero_bias " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_transposeA ) {
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testONNXModels ( " test_gemm_transposeA " , pb , 0 , 0 , false , true , 2 ) ;
}
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TEST_P ( Test_ONNX_layers , Gemm_transposeB ) {
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testONNXModels ( " test_gemm_transposeB " , pb , 0 , 0 , false , true , 2 ) ;
}
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// Note: These tests are converted from onnx/onnx so that they have constant shape as input.
// TODO: They can be moved into conformance tests once dynamic input is properly supported.
TEST_P ( Test_ONNX_layers , Expand_dim_changed ) {
testONNXModels ( " test_expand_dim_changed " , pb , 0 , 0 , false , true , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Expand_dim_unchanged ) {
testONNXModels ( " test_expand_dim_unchanged " , pb , 0 , 0 , false , true , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Expand_shape_model1 ) {
testONNXModels ( " test_expand_shape_model1 " , pb , 0 , 0 , false , true , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Expand_shape_model2 ) {
testONNXModels ( " test_expand_shape_model2 " , pb , 0 , 0 , false , true , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Expand_shape_model3 ) {
testONNXModels ( " test_expand_shape_model3 " , pb , 0 , 0 , false , true , 1 ) ;
}
TEST_P ( Test_ONNX_layers , Expand_shape_model4 ) {
testONNXModels ( " test_expand_shape_model4 " , pb , 0 , 0 , false , true , 1 ) ;
}
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TEST_P ( Test_ONNX_layers , Attention ) {
testONNXModels ( " attention " ) ;
}
TEST_P ( Test_ONNX_layers , AttentionSingleHead ) {
testONNXModels ( " attention_single_head " ) ;
}
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TEST_P ( Test_ONNX_layers , PyTorchAttentionSingleHead ) {
// 5.x specific bug: https://github.com/opencv/opencv/issues/25921
if ( target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL ) ;
if ( target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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testONNXModels ( " pytorch_attention_single_head " ) ;
}
TEST_P ( Test_ONNX_layers , PyTorchUnflatten ) {
testONNXModels ( " unflatten " ) ;
}
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TEST_P ( Test_ONNX_nets , ViT_B_32 ) {
applyTestTag ( CV_TEST_TAG_LONG , CV_TEST_TAG_DEBUG_LONG ) ;
const std : : string model_path = _tf ( " models/vit_b_32.onnx " , false ) ;
auto net = readNet ( model_path ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
auto image = imread ( _tf ( " ../googlenet_0.png " ) ) ;
auto blob = blobFromImage ( image , 1.f , Size ( 224 , 224 ) ) ;
auto ref = blobFromNPY ( _tf ( " data/output_vit_b_32.npy " ) ) ;
checkBackend ( & blob , & ref ) ;
net . setInput ( blob ) ;
auto out = net . forward ( ) ;
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double l1 = default_l1 ;
double lInf = default_lInf ;
if ( target = = DNN_TARGET_CUDA_FP16 )
{
l1 = 0.01 ;
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lInf = 0.06 ;
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}
if ( target = = DNN_TARGET_OPENCL_FP16 )
{
l1 = 0.008 ;
lInf = 0.04 ;
}
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) {
if ( target = = DNN_TARGET_CPU ) {
l1 = 4.4e-5 ; // Expected: (normL1) <= (l1), actual: 4.31208e-05 vs 1e-05
lInf = 0.0002 ; // Expected: (normInf) <= (lInf), actual: 0.000194907 vs 0.0001
} else if ( target = = DNN_TARGET_OPENCL | | target = = DNN_TARGET_OPENCL_FP16 ) {
l1 = 0.0092 ; // Expected: (normL1) <= (l1), actual: 0.00918349 vs 4.4e-05
lInf = 0.056 ; // Expected: (normInf) <= (lInf), actual: 0.0556431 vs 0.0002
}
}
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normAssert ( ref , out , " ViTB_32 " , l1 , lInf ) ;
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}
TEST_P ( Test_ONNX_nets , VitTrack ) {
auto image = imread ( _tf ( " ../dog_orig_size.png " ) ) ;
auto input0 = blobFromImage ( image , 1.f , Size ( 128 , 128 ) ) ;
auto input1 = blobFromImage ( image , 1.f , Size ( 256 , 256 ) ) ;
auto net = readNet ( _tf ( " models/object_tracking_vittrack_2023sep.onnx " , false ) ) ;
net . setInput ( input0 , " template " ) ;
net . setInput ( input1 , " search " ) ;
std : : vector < std : : string > output_names { " output1 " , " output2 " , " output3 " } ;
std : : vector < Mat > outputs ;
net . forward ( outputs , output_names ) ;
auto ref_output1 = blobFromNPY ( _tf ( " data/output_object_tracking_vittrack_2023sep_0.npy " ) ) ;
auto ref_output2 = blobFromNPY ( _tf ( " data/output_object_tracking_vittrack_2023sep_1.npy " ) ) ;
auto ref_output3 = blobFromNPY ( _tf ( " data/output_object_tracking_vittrack_2023sep_2.npy " ) ) ;
normAssert ( ref_output1 , outputs [ 0 ] , " VitTrack output1 " ) ;
normAssert ( ref_output2 , outputs [ 1 ] , " VitTrack output2 " ) ;
normAssert ( ref_output3 , outputs [ 2 ] , " VitTrack output3 " ) ;
}
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TEST_P ( Test_ONNX_layers , LayerNormNoFusion ) {
testONNXModels ( " layer_norm_no_fusion " ) ;
}
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TEST_P ( Test_ONNX_layers , MatMulAddFusion ) {
double l1 = ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL ) ? 0.0018 : default_l1 ;
double lInf = ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL ) ? 0.011 : default_lInf ;
testONNXModels ( " biased_matmul " , npy , l1 , lInf ) ;
}
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TEST_P ( Test_ONNX_layers , ClipDivSharedConstant ) {
testONNXModels ( " clip_div_shared_constant " ) ;
}
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INSTANTIATE_TEST_CASE_P ( /**/ , Test_ONNX_nets , dnnBackendsAndTargets ( ) ) ;
} } // namespace
