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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 "opencv2/core/ocl.hpp"
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namespace opencv_test { namespace {
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class DNNTestNetwork : public DNNTestLayer
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{
public :
void processNet ( const std : : string & weights , const std : : string & proto ,
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Size inpSize , const std : : string & outputLayer = " " ,
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const std : : string & halideScheduler = " " ,
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double l1 = 0.0 , double lInf = 0.0 )
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{
// Create a common input blob.
int blobSize [ ] = { 1 , 3 , inpSize . height , inpSize . width } ;
Mat inp ( 4 , blobSize , CV_32FC1 ) ;
randu ( inp , 0.0f , 1.0f ) ;
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processNet ( weights , proto , inp , outputLayer , halideScheduler , l1 , lInf ) ;
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}
void processNet ( std : : string weights , std : : string proto ,
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Mat inp , const std : : string & outputLayer = " " ,
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std : : string halideScheduler = " " ,
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double l1 = 0.0 , double lInf = 0.0 , double detectionConfThresh = 0.2 )
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{
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checkBackend ( ) ;
l1 = l1 ? l1 : default_l1 ;
lInf = lInf ? lInf : default_lInf ;
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weights = findDataFile ( weights , false ) ;
if ( ! proto . empty ( ) )
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proto = findDataFile ( proto ) ;
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// Create two networks - with default backend and target and a tested one.
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Net netDefault = readNet ( weights , proto ) ;
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netDefault . setPreferableBackend ( DNN_BACKEND_OPENCV ) ;
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netDefault . setInput ( inp ) ;
Mat outDefault = netDefault . forward ( outputLayer ) . clone ( ) ;
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net = readNet ( weights , proto ) ;
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net . setInput ( inp ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
if ( backend = = DNN_BACKEND_HALIDE & & ! halideScheduler . empty ( ) )
{
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halideScheduler = findDataFile ( halideScheduler ) ;
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net . setHalideScheduler ( halideScheduler ) ;
}
Mat out = net . forward ( outputLayer ) . clone ( ) ;
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check ( outDefault , out , outputLayer , l1 , lInf , detectionConfThresh , " First run " ) ;
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// Test 2: change input.
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float * inpData = ( float * ) inp . data ;
for ( int i = 0 ; i < inp . size [ 0 ] * inp . size [ 1 ] ; + + i )
{
Mat slice ( inp . size [ 2 ] , inp . size [ 3 ] , CV_32F , inpData ) ;
cv : : flip ( slice , slice , 1 ) ;
inpData + = slice . total ( ) ;
}
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netDefault . setInput ( inp ) ;
net . setInput ( inp ) ;
outDefault = netDefault . forward ( outputLayer ) . clone ( ) ;
out = net . forward ( outputLayer ) . clone ( ) ;
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check ( outDefault , out , outputLayer , l1 , lInf , detectionConfThresh , " Second run " ) ;
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}
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void check ( Mat & ref , Mat & out , const std : : string & outputLayer , double l1 , double lInf ,
double detectionConfThresh , const char * msg )
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{
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if ( outputLayer = = " detection_out " )
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{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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{
// Inference Engine produces detections terminated by a row which starts from -1.
out = out . reshape ( 1 , out . total ( ) / 7 ) ;
int numDetections = 0 ;
while ( numDetections < out . rows & & out . at < float > ( numDetections , 0 ) ! = - 1 )
{
numDetections + = 1 ;
}
out = out . rowRange ( 0 , numDetections ) ;
}
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normAssertDetections ( ref , out , msg , detectionConfThresh , l1 , lInf ) ;
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}
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else
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normAssert ( ref , out , msg , l1 , lInf ) ;
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}
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Net net ;
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} ;
TEST_P ( DNNTestNetwork , AlexNet )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_1GB ) ;
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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processNet ( " dnn/bvlc_alexnet.caffemodel " , " dnn/bvlc_alexnet.prototxt " ,
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Size ( 227 , 227 ) , " prob " ,
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target = = DNN_TARGET_OPENCL ? " dnn/halide_scheduler_opencl_alexnet.yml " :
" dnn/halide_scheduler_alexnet.yml " ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , ResNet_50 )
{
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applyTestTag (
( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ,
CV_TEST_TAG_DEBUG_LONG
) ;
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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processNet ( " dnn/ResNet-50-model.caffemodel " , " dnn/ResNet-50-deploy.prototxt " ,
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Size ( 224 , 224 ) , " prob " ,
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target = = DNN_TARGET_OPENCL ? " dnn/halide_scheduler_opencl_resnet_50.yml " :
" dnn/halide_scheduler_resnet_50.yml " ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , SqueezeNet_v1_1 )
{
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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processNet ( " dnn/squeezenet_v1.1.caffemodel " , " dnn/squeezenet_v1.1.prototxt " ,
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Size ( 227 , 227 ) , " prob " ,
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target = = DNN_TARGET_OPENCL ? " dnn/halide_scheduler_opencl_squeezenet_v1_1.yml " :
" dnn/halide_scheduler_squeezenet_v1_1.yml " ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , GoogLeNet )
{
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applyTestTag ( target = = DNN_TARGET_CPU ? " " : CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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processNet ( " dnn/bvlc_googlenet.caffemodel " , " dnn/bvlc_googlenet.prototxt " ,
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Size ( 224 , 224 ) , " prob " ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , Inception_5h )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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double l1 = default_l1 , lInf = default_lInf ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & ( target = = DNN_TARGET_CPU | | target = = DNN_TARGET_OPENCL ) )
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{
l1 = 1.72e-5 ;
lInf = 8e-4 ;
}
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processNet ( " dnn/tensorflow_inception_graph.pb " , " " , Size ( 224 , 224 ) , " softmax2 " ,
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target = = DNN_TARGET_OPENCL ? " dnn/halide_scheduler_opencl_inception_5h.yml " :
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" dnn/halide_scheduler_inception_5h.yml " ,
l1 , lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , ENet )
{
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applyTestTag ( target = = DNN_TARGET_CPU ? " " : CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE ) // Realization contains wrong number of Images (1) for realizing pipeline with 2 outputs
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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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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if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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processNet ( " dnn/Enet-model-best.net " , " " , Size ( 512 , 512 ) , " l367_Deconvolution " ,
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target = = DNN_TARGET_OPENCL ? " dnn/halide_scheduler_opencl_enet.yml " :
" dnn/halide_scheduler_enet.yml " ,
2e-5 , 0.15 ) ;
}
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TEST_P ( DNNTestNetwork , MobileNet_SSD_Caffe )
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{
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applyTestTag ( CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f / 127.5 , Size ( 300 , 300 ) , Scalar ( 127.5 , 127.5 , 127.5 ) , false ) ;
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float diffScores = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 1.5e-2 : 0.0 ;
float diffSquares = ( target = = DNN_TARGET_MYRIAD ) ? 0.063 : 0.0 ;
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float detectionConfThresh = ( target = = DNN_TARGET_MYRIAD ) ? 0.262 : FLT_MIN ;
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processNet ( " dnn/MobileNetSSD_deploy.caffemodel " , " dnn/MobileNetSSD_deploy.prototxt " ,
inp , " detection_out " , " " , diffScores , diffSquares , detectionConfThresh ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , MobileNet_SSD_Caffe_Different_Width_Height )
{
if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// May hang on some configurations
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 Transpose with name conv15_2_mbox_conf_perm 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
) ;
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if ( ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | | backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) & &
target = = DNN_TARGET_MYRIAD & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# elif defined(INF_ENGINE_RELEASE)
if ( ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | | backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ) & &
target = = DNN_TARGET_MYRIAD & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f / 127.5 , Size ( 300 , 560 ) , Scalar ( 127.5 , 127.5 , 127.5 ) , false ) ;
float diffScores = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.029 : 0.0 ;
float diffSquares = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.09 : 0.0 ;
processNet ( " dnn/MobileNetSSD_deploy.caffemodel " , " dnn/MobileNetSSD_deploy.prototxt " ,
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inp , " detection_out " , " " , diffScores , diffSquares ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , MobileNet_SSD_v1_TensorFlow )
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{
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applyTestTag ( target = = DNN_TARGET_CPU ? " " : CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , false ) ;
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float l1 = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.095 : 0.0 ;
float lInf = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.09 : 0.0 ;
float detectionConfThresh = ( target = = DNN_TARGET_MYRIAD ) ? 0.216 : 0.2 ;
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processNet ( " dnn/ssd_mobilenet_v1_coco_2017_11_17.pb " , " dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt " ,
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inp , " detection_out " , " " , l1 , lInf , detectionConfThresh ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , MobileNet_SSD_v1_TensorFlow_Different_Width_Height )
{
if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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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 & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X ) ;
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# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
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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 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f , Size ( 300 , 560 ) , Scalar ( ) , false ) ;
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float l1 = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.013 : 0.0 ;
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float lInf = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.06 : 0.0 ;
processNet ( " dnn/ssd_mobilenet_v1_coco_2017_11_17.pb " , " dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt " ,
inp , " detection_out " , " " , l1 , lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , MobileNet_SSD_v2_TensorFlow )
{
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applyTestTag ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , false ) ;
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float l1 = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.013 : 2e-5 ;
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float lInf = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.062 : 0.0 ;
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processNet ( " dnn/ssd_mobilenet_v2_coco_2018_03_29.pb " , " dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt " ,
inp , " detection_out " , " " , l1 , lInf , 0.25 ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , SSD_VGG16 )
{
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applyTestTag ( CV_TEST_TAG_LONG , ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB ) ,
CV_TEST_TAG_DEBUG_VERYLONG ) ;
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if ( backend = = DNN_BACKEND_HALIDE & & target = = DNN_TARGET_CPU )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ; // TODO HALIDE_CPU
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , false ) ;
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float scoreDiff = 0.0 , iouDiff = 0.0 ;
if ( target = = DNN_TARGET_OPENCL_FP16 )
{
scoreDiff = 0.04 ;
}
else if ( target = = DNN_TARGET_MYRIAD )
{
scoreDiff = 0.0325 ;
iouDiff = 0.032 ;
}
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processNet ( " dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel " ,
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" dnn/ssd_vgg16.prototxt " , inp , " detection_out " , " " , scoreDiff , iouDiff ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , OpenPose_pose_coco )
{
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applyTestTag ( CV_TEST_TAG_LONG , ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB ) ,
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CV_TEST_TAG_DEBUG_LONG ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION ) ;
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# endif
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const float l1 = ( target = = DNN_TARGET_MYRIAD ) ? 0.009 : 0.0 ;
const float lInf = ( target = = DNN_TARGET_MYRIAD ) ? 0.09 : 0.0 ;
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processNet ( " dnn/openpose_pose_coco.caffemodel " , " dnn/openpose_pose_coco.prototxt " ,
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Size ( 46 , 46 ) , " " , " " , l1 , lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , OpenPose_pose_mpi )
{
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applyTestTag ( CV_TEST_TAG_LONG , ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB ) ,
CV_TEST_TAG_DEBUG_VERYLONG ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION ) ;
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# endif
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// output range: [-0.001, 0.97]
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const float l1 = ( target = = DNN_TARGET_MYRIAD ) ? 0.02 : 0.0 ;
const float lInf = ( target = = DNN_TARGET_MYRIAD | | target = = DNN_TARGET_OPENCL_FP16 ) ? 0.2 : 0.0 ;
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processNet ( " dnn/openpose_pose_mpi.caffemodel " , " dnn/openpose_pose_mpi.prototxt " ,
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Size ( 46 , 46 ) , " " , " " , l1 , lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , OpenPose_pose_mpi_faster_4_stages )
{
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applyTestTag ( CV_TEST_TAG_LONG , CV_TEST_TAG_MEMORY_1GB ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION ) ;
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# endif
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// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet ( " dnn/openpose_pose_mpi.caffemodel " , " dnn/openpose_pose_mpi_faster_4_stages.prototxt " ,
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Size ( 46 , 46 ) ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
TEST_P ( DNNTestNetwork , OpenFace )
{
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# if defined(INF_ENGINE_RELEASE)
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# if INF_ENGINE_VER_MAJOR_EQ(2018050000)
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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_VERSION ) ;
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# endif
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# endif
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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const float l1 = ( target = = DNN_TARGET_MYRIAD ) ? 0.0024 : 0.0 ;
const float lInf = ( target = = DNN_TARGET_MYRIAD ) ? 0.0071 : 0.0 ;
processNet ( " dnn/openface_nn4.small2.v1.t7 " , " " , Size ( 96 , 96 ) , " " , " " , l1 , lInf ) ;
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}
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TEST_P ( DNNTestNetwork , opencv_face_detector )
{
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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Mat img = imread ( findDataFile ( " gpu/lbpcascade/er.png " ) ) ;
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Mat inp = blobFromImage ( img , 1.0 , Size ( ) , Scalar ( 104.0 , 177.0 , 123.0 ) , false , false ) ;
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processNet ( " dnn/opencv_face_detector.caffemodel " , " dnn/opencv_face_detector.prototxt " ,
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inp , " detection_out " ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , Inception_v2_SSD_TensorFlow )
{
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applyTestTag (
( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ,
CV_TEST_TAG_DEBUG_LONG
) ;
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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 ) ;
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# endif
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
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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 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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Mat sample = imread ( findDataFile ( " dnn/street.png " ) ) ;
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Mat inp = blobFromImage ( sample , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , false ) ;
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float l1 = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.02 : 0.0 ;
float lInf = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.1 : 0.0 ;
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processNet ( " dnn/ssd_inception_v2_coco_2017_11_17.pb " , " dnn/ssd_inception_v2_coco_2017_11_17.pbtxt " ,
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inp , " detection_out " , " " , l1 , lInf ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , DenseNet_121 )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_512MB ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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// Reference output values are in range [-3.807, 4.605]
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float l1 = 0.0 , lInf = 0.0 ;
if ( target = = DNN_TARGET_OPENCL_FP16 )
{
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l1 = 2e-2 ; lInf = 9e-2 ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
lInf = 0.1f ;
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}
else if ( target = = DNN_TARGET_MYRIAD )
{
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l1 = 0.1 ; lInf = 0.6 ;
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}
processNet ( " dnn/DenseNet_121.caffemodel " , " dnn/DenseNet_121.prototxt " , Size ( 224 , 224 ) , " " , " " , l1 , lInf ) ;
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if ( target ! = DNN_TARGET_MYRIAD | | getInferenceEngineVPUType ( ) ! = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( DNNTestNetwork , FastNeuralStyle_eccv16 )
{
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applyTestTag ( CV_TEST_TAG_MEMORY_512MB , CV_TEST_TAG_DEBUG_VERYLONG ) ;
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if ( backend = = DNN_BACKEND_HALIDE )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_HALIDE ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_MYRIAD )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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 ) ;
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# if defined(INF_ENGINE_RELEASE)
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# if INF_ENGINE_VER_MAJOR_LE(2018050000)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
# endif
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Mat img = imread ( findDataFile ( " dnn/googlenet_1.png " ) ) ;
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Mat inp = blobFromImage ( img , 1.0 , Size ( 320 , 240 ) , Scalar ( 103.939 , 116.779 , 123.68 ) , false , false ) ;
// Output image has values in range [-143.526, 148.539].
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float l1 = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.4 : 4e-5 ;
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float lInf = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 7.45 : 2e-3 ;
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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_OPENCL )
{
l1 = 5e-3 ;
lInf = 5e-3 ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
lInf = 25 ;
}
# endif
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processNet ( " dnn/fast_neural_style_eccv16_starry_night.t7 " , " " , inp , " " , " " , l1 , lInf ) ;
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# if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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expectNoFallbacksFromIE ( net ) ;
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# endif
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}
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INSTANTIATE_TEST_CASE_P ( /*nothing*/ , DNNTestNetwork , dnnBackendsAndTargets ( true , true , false ) ) ;
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} } // namespace