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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) 2017-2019, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
/*
Test for Tensorflow models loading
*/
# include "test_precomp.hpp"
# include "npy_blob.hpp"
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# include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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namespace opencv_test
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{
using namespace cv ;
using namespace cv : : dnn ;
template < typename TString >
static std : : string _tf ( TString filename )
{
return ( getOpenCVExtraDir ( ) + " /dnn/ " ) + filename ;
}
TEST ( Test_TensorFlow , read_inception )
{
Net net ;
{
const string model = findDataFile ( " dnn/tensorflow_inception_graph.pb " , false ) ;
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net = readNetFromTensorflow ( model ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
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}
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net . setPreferableBackend ( DNN_BACKEND_OPENCV ) ;
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Mat sample = imread ( _tf ( " grace_hopper_227.png " ) ) ;
ASSERT_TRUE ( ! sample . empty ( ) ) ;
Mat input ;
resize ( sample , input , Size ( 224 , 224 ) ) ;
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input - = Scalar : : all ( 117 ) ; // mean sub
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Mat inputBlob = blobFromImage ( input ) ;
net . setInput ( inputBlob , " input " ) ;
Mat out = net . forward ( " softmax2 " ) ;
std : : cout < < out . dims < < std : : endl ;
}
TEST ( Test_TensorFlow , inception_accuracy )
{
Net net ;
{
const string model = findDataFile ( " dnn/tensorflow_inception_graph.pb " , false ) ;
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net = readNetFromTensorflow ( model ) ;
ASSERT_FALSE ( net . empty ( ) ) ;
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}
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net . setPreferableBackend ( DNN_BACKEND_OPENCV ) ;
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Mat sample = imread ( _tf ( " grace_hopper_227.png " ) ) ;
ASSERT_TRUE ( ! sample . empty ( ) ) ;
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Mat inputBlob = blobFromImage ( sample , 1.0 , Size ( 224 , 224 ) , Scalar ( ) , /*swapRB*/ true ) ;
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net . setInput ( inputBlob , " input " ) ;
Mat out = net . forward ( " softmax2 " ) ;
Mat ref = blobFromNPY ( _tf ( " tf_inception_prob.npy " ) ) ;
normAssert ( ref , out ) ;
}
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static std : : string path ( const std : : string & file )
{
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return findDataFile ( " dnn/tensorflow/ " + file ) ;
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}
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class Test_TensorFlow_layers : public DNNTestLayer
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{
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public :
void runTensorFlowNet ( const std : : string & prefix , bool hasText = false ,
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double l1 = 0.0 , double lInf = 0.0 , bool memoryLoad = false , const std : : string & groupPrefix = " " )
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{
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if ( cvtest : : debugLevel > 0 )
{
std : : cout < < prefix < < groupPrefix < < std : : endl ;
}
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std : : string netPath = path ( prefix + groupPrefix + " _net.pb " ) ;
std : : string netConfig = ( hasText ? path ( prefix + groupPrefix + " _net.pbtxt " ) : " " ) ;
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std : : string inpPath = path ( prefix + " _in.npy " ) ;
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std : : string outPath = path ( prefix + groupPrefix + " _out.npy " ) ;
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cv : : Mat input = blobFromNPY ( inpPath ) ;
cv : : Mat ref = blobFromNPY ( outPath ) ;
checkBackend ( & input , & ref ) ;
Net net ;
if ( memoryLoad )
{
// Load files into a memory buffers
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std : : vector < char > dataModel ;
readFileContent ( netPath , dataModel ) ;
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std : : vector < char > dataConfig ;
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if ( hasText )
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{
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readFileContent ( netConfig , dataConfig ) ;
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}
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net = readNetFromTensorflow ( dataModel . data ( ) , dataModel . size ( ) ,
dataConfig . data ( ) , dataConfig . size ( ) ) ;
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}
else
net = readNetFromTensorflow ( netPath , netConfig ) ;
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ASSERT_FALSE ( net . empty ( ) ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
net . setInput ( input ) ;
cv : : Mat output = net . forward ( ) ;
normAssert ( ref , output , " " , l1 ? l1 : default_l1 , lInf ? lInf : default_lInf ) ;
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if ( cvtest : : debugLevel > 0 | | HasFailure ( ) )
{
std : : cout < < " input: " < < input . size < < std : : endl ;
std : : cout < < input . reshape ( 1 , 1 ) < < std : : endl ;
std : : cout < < " ref " < < ref . size < < std : : endl ;
std : : cout < < ref . reshape ( 1 , 1 ) < < std : : endl ;
std : : cout < < " output: " < < output . size < < std : : endl ;
std : : cout < < output . reshape ( 1 , 1 ) < < std : : endl ;
}
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}
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} ;
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TEST_P ( Test_TensorFlow_layers , reduce_mean )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
runTensorFlowNet ( " global_pool_by_axis " ) ;
}
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TEST_P ( Test_TensorFlow_layers , reduce_max )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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runTensorFlowNet ( " max_pool_by_axis " , false , 0.0f , 0.0f ) ;
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}
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TEST_P ( Test_TensorFlow_layers , reduce_sum )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
runTensorFlowNet ( " sum_pool_by_axis " ) ;
}
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TEST_P ( Test_TensorFlow_layers , reduce_max_channel )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD ) // incorrect result
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
runTensorFlowNet ( " reduce_max_channel " , false , 0.0f , 0.0f ) ;
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}
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TEST_P ( Test_TensorFlow_layers , reduce_sum_channel )
{
runTensorFlowNet ( " reduce_sum_channel " ) ;
}
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TEST_P ( Test_TensorFlow_layers , reduce_max_channel_keep_dims )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD ) // incorrect result
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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runTensorFlowNet ( " reduce_max_channel " , false , 0.0 , 0.0 , false , " _keep_dims " ) ;
}
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TEST_P ( Test_TensorFlow_layers , reduce_sum_channel_keep_dims )
{
runTensorFlowNet ( " reduce_sum_channel " , false , 0.0 , 0.0 , false , " _keep_dims " ) ;
}
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TEST_P ( Test_TensorFlow_layers , conv_single_conv )
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{
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runTensorFlowNet ( " single_conv " ) ;
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}
TEST_P ( Test_TensorFlow_layers , conv_atrous_conv2d_valid )
{
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runTensorFlowNet ( " atrous_conv2d_valid " ) ;
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}
TEST_P ( Test_TensorFlow_layers , conv_atrous_conv2d_same )
{
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runTensorFlowNet ( " atrous_conv2d_same " ) ;
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}
TEST_P ( Test_TensorFlow_layers , conv_depthwise_conv2d )
{
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runTensorFlowNet ( " depthwise_conv2d " ) ;
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}
TEST_P ( Test_TensorFlow_layers , conv_keras_atrous_conv2d_same )
{
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runTensorFlowNet ( " keras_atrous_conv2d_same " ) ;
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}
TEST_P ( Test_TensorFlow_layers , conv_pool_nchw )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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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runTensorFlowNet ( " conv_pool_nchw " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , Convolution3D )
{
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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
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
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runTensorFlowNet ( " conv3d " ) ;
}
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TEST_P ( Test_TensorFlow_layers , padding )
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{
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runTensorFlowNet ( " padding_valid " ) ;
runTensorFlowNet ( " spatial_padding " ) ;
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runTensorFlowNet ( " mirror_pad " ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( target = = DNN_TARGET_MYRIAD )
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{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
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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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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
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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}
# endif
runTensorFlowNet ( " keras_pad_concat " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , padding_asymmetric_1 )
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{
runTensorFlowNet ( " conv2d_asymmetric_pads_nchw " ) ;
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}
TEST_P ( Test_TensorFlow_layers , padding_asymmetric_2 )
{
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runTensorFlowNet ( " conv2d_asymmetric_pads_nhwc " ) ;
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}
TEST_P ( Test_TensorFlow_layers , padding_asymmetric_3 )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU ) // Exception: Unsupported pad value
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD ) // Exception: Unsupported pad value
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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runTensorFlowNet ( " max_pool2d_asymmetric_pads_nchw " ) ;
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}
TEST_P ( Test_TensorFlow_layers , padding_asymmetric_4 )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// Unsupported pad value
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)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD ) // Exception: Unsupported pad value
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_NGRAPH & & target = = DNN_TARGET_CPU ) // Exception: Unsupported pad value
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_CPU , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD ) // Exception: Unsupported pad value
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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# endif
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# endif
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runTensorFlowNet ( " max_pool2d_asymmetric_pads_nhwc " ) ;
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}
TEST_P ( Test_TensorFlow_layers , padding_asymmetric_5 )
{
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runTensorFlowNet ( " conv2d_backprop_input_asymmetric_pads_nchw " ) ;
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}
TEST_P ( Test_TensorFlow_layers , padding_asymmetric_6 )
{
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runTensorFlowNet ( " conv2d_backprop_input_asymmetric_pads_nhwc " ) ;
}
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TEST_P ( Test_TensorFlow_layers , padding_same )
{
// Reference output values are in range [0.0006, 2.798]
runTensorFlowNet ( " padding_same " ) ;
}
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TEST_P ( Test_TensorFlow_layers , eltwise )
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{
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runTensorFlowNet ( " eltwise_add_mul " ) ;
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runTensorFlowNet ( " eltwise_sub " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , eltwise_add_vec )
{
runTensorFlowNet ( " eltwise_add_vec " ) ;
}
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TEST_P ( Test_TensorFlow_layers , eltwise_mul_vec )
{
runTensorFlowNet ( " eltwise_mul_vec " ) ;
}
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TEST_P ( Test_TensorFlow_layers , channel_broadcast )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
runTensorFlowNet ( " channel_broadcast " ) ;
}
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TEST_P ( Test_TensorFlow_layers , pad_and_concat )
{
runTensorFlowNet ( " pad_and_concat " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , concat_axis_1 )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Transpose with name Flatten_1/flatten/Reshape/nhwc 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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runTensorFlowNet ( " concat_axis_1 " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , concat_3d )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( backend = = DNN_BACKEND_OPENCV & & target ! = DNN_TARGET_CPU )
{
if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL ) ;
}
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_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
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runTensorFlowNet ( " concat_3d " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , batch_norm_1 )
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{
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runTensorFlowNet ( " batch_norm " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_2 )
{
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runTensorFlowNet ( " batch_norm " , false , 0.0 , 0.0 , true ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_3 )
{
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runTensorFlowNet ( " fused_batch_norm " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_4 )
{
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runTensorFlowNet ( " fused_batch_norm " , false , 0.0 , 0.0 , true ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_5 )
{
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runTensorFlowNet ( " batch_norm_text " , true ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_6 )
{
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runTensorFlowNet ( " batch_norm_text " , true , 0.0 , 0.0 , true ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_7 )
{
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runTensorFlowNet ( " unfused_batch_norm " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_8 )
{
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runTensorFlowNet ( " fused_batch_norm_no_gamma " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_9 )
{
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runTensorFlowNet ( " unfused_batch_norm_no_gamma " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_10 )
{
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runTensorFlowNet ( " mvn_batch_norm " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_11 )
{
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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 ) ; // nan
# endif
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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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runTensorFlowNet ( " mvn_batch_norm_1x1 " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_12 )
{
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runTensorFlowNet ( " switch_identity " ) ;
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}
TEST_P ( Test_TensorFlow_layers , batch_norm_13 )
{
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runTensorFlowNet ( " keras_batch_norm_training " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , batch_norm3D )
{
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target ! = DNN_TARGET_CPU )
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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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throw SkipTestException ( " " ) ;
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}
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runTensorFlowNet ( " batch_norm3d " ) ;
}
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TEST_P ( Test_TensorFlow_layers , slim_batch_norm )
{
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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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// Output values range: [-40.0597, 207.827]
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double l1 = default_l1 ;
double lInf = default_lInf ;
if ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD )
{
l1 = 0.041 ;
lInf = 0.33 ;
}
# if defined(INF_ENGINE_RELEASE)
else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_CPU )
{
lInf = 0.0002 ;
}
# endif
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runTensorFlowNet ( " slim_batch_norm " , false , l1 , lInf ) ;
}
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TEST_P ( Test_TensorFlow_layers , pooling_max_pool_even )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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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runTensorFlowNet ( " max_pool_even " ) ;
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}
TEST_P ( Test_TensorFlow_layers , pooling_max_pool_odd_valid )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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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runTensorFlowNet ( " max_pool_odd_valid " ) ;
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}
TEST_P ( Test_TensorFlow_layers , pooling_max_pool_odd_same )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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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runTensorFlowNet ( " max_pool_odd_same " ) ;
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}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_mean )
{
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runTensorFlowNet ( " reduce_mean " ) ; // an average pooling over all spatial dimensions.
}
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TEST_P ( Test_TensorFlow_layers , pooling_reduce_max )
{
runTensorFlowNet ( " reduce_max " ) ; // a MAX pooling over all spatial dimensions.
}
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TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum )
{
runTensorFlowNet ( " reduce_sum " ) ; // a SUM pooling over all spatial dimensions.
}
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TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_0_false )
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{
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runTensorFlowNet ( " reduce_sum_0_False " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_1_false )
{
runTensorFlowNet ( " reduce_sum_1_False " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_2_false )
{
runTensorFlowNet ( " reduce_sum_2_False " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_3_false )
{
runTensorFlowNet ( " reduce_sum_3_False " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_1_2_false )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
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{
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default_l1 = 0.01f ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
default_l1 = 0.01f ;
}
# endif
runTensorFlowNet ( " reduce_sum_1_2_False " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_0_true )
{
runTensorFlowNet ( " reduce_sum_0_True " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_1_true )
{
runTensorFlowNet ( " reduce_sum_1_True " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_2_true )
{
runTensorFlowNet ( " reduce_sum_2_True " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_3_true )
{
runTensorFlowNet ( " reduce_sum_3_True " ) ;
}
TEST_P ( Test_TensorFlow_layers , pooling_reduce_sum_1_2_true )
{
# if defined(INF_ENGINE_RELEASE)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_MYRIAD )
{
default_l1 = 0.01f ;
}
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target = = DNN_TARGET_OPENCL_FP16 )
{
default_l1 = 0.01f ;
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}
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# endif
runTensorFlowNet ( " reduce_sum_1_2_True " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , max_pool_grad )
{
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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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runTensorFlowNet ( " max_pool_grad " ) ;
}
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// TODO: fix tests and replace to pooling
TEST_P ( Test_TensorFlow_layers , ave_pool_same )
{
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// Reference output values are in range [-0.519531, 0.112976]
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if ( target = = DNN_TARGET_MYRIAD & & getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
else if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
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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runTensorFlowNet ( " ave_pool_same " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , MaxPooling3D )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
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 )
{
// 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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# elif 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
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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runTensorFlowNet ( " max_pool3d " ) ;
}
TEST_P ( Test_TensorFlow_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
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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runTensorFlowNet ( " ave_pool3d " ) ;
}
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TEST_P ( Test_TensorFlow_layers , deconvolution )
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{
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runTensorFlowNet ( " deconvolution " ) ;
runTensorFlowNet ( " deconvolution_same " ) ;
runTensorFlowNet ( " deconvolution_stride_2_same " ) ;
runTensorFlowNet ( " deconvolution_adj_pad_valid " ) ;
runTensorFlowNet ( " deconvolution_adj_pad_same " ) ;
runTensorFlowNet ( " keras_deconv_valid " ) ;
runTensorFlowNet ( " keras_deconv_same " ) ;
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runTensorFlowNet ( " keras_deconv_same_v2 " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , matmul )
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{
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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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runTensorFlowNet ( " matmul " ) ;
runTensorFlowNet ( " nhwc_transpose_reshape_matmul " ) ;
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// Reference output values are in range [-5.688, 4.484]
double l1 = target = = DNN_TARGET_MYRIAD ? 6.1e-3 : default_l1 ;
runTensorFlowNet ( " nhwc_reshape_matmul " , false , l1 ) ;
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runTensorFlowNet ( " matmul_layout " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , reshape )
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{
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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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runTensorFlowNet ( " shift_reshape_no_reorder " ) ;
runTensorFlowNet ( " reshape_no_reorder " ) ;
runTensorFlowNet ( " reshape_reduce " ) ;
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runTensorFlowNet ( " reshape_as_shape " ) ;
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}
TEST_P ( Test_TensorFlow_layers , flatten )
{
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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_2
)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
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runTensorFlowNet ( " flatten " , true ) ;
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}
TEST_P ( Test_TensorFlow_layers , unfused_flatten )
{
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runTensorFlowNet ( " unfused_flatten " ) ;
runTensorFlowNet ( " unfused_flatten_unknown_batch " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , reshape_layer )
{
runTensorFlowNet ( " reshape_layer " ) ;
}
TEST_P ( Test_TensorFlow_layers , reshape_nchw )
{
runTensorFlowNet ( " reshape_nchw " ) ;
}
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TEST_P ( Test_TensorFlow_layers , reshape_conv )
{
runTensorFlowNet ( " reshape_conv " ) ;
}
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TEST_P ( Test_TensorFlow_layers , leaky_relu )
{
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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_OPENCL )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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runTensorFlowNet ( " leaky_relu " ) ;
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runTensorFlowNet ( " leaky_relu_order1 " ) ;
runTensorFlowNet ( " leaky_relu_order2 " ) ;
runTensorFlowNet ( " leaky_relu_order3 " ) ;
}
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TEST_P ( Test_TensorFlow_layers , l2_normalize )
{
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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 & & 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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runTensorFlowNet ( " l2_normalize " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , BiasAdd )
{
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & 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_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
runTensorFlowNet ( " bias_add_1 " ) ;
}
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// TODO: fix it and add to l2_normalize
TEST_P ( Test_TensorFlow_layers , l2_normalize_3d )
{
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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_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)
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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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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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# endif
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runTensorFlowNet ( " l2_normalize_3d " ) ;
}
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class Test_TensorFlow_nets : public DNNTestLayer { } ;
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TEST_P ( Test_TensorFlow_nets , MobileNet_SSD )
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{
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# if defined(INF_ENGINE_RELEASE)
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if ( target = = DNN_TARGET_MYRIAD )
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{
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# if INF_ENGINE_VER_MAJOR_GE(2019020000)
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if ( getInferenceEngineVPUType ( ) = = CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X )
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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 ,
CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
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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checkBackend ( ) ;
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std : : string imgPath = findDataFile ( " dnn/street.png " ) ;
std : : string netConfig = findDataFile ( " dnn/ssd_mobilenet_v1_coco.pbtxt " ) ;
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std : : string netPath = findDataFile ( " dnn/ssd_mobilenet_v1_coco.pb " , false ) ;
Mat inp ;
resize ( imread ( imgPath ) , inp , Size ( 300 , 300 ) ) ;
inp = blobFromImage ( inp , 1.0f / 127.5 , Size ( ) , Scalar ( 127.5 , 127.5 , 127.5 ) , true ) ;
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Mat ref = blobFromNPY ( findDataFile ( " dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy " ) ) ;
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Net net = readNetFromTensorflow ( netPath , netConfig ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . setInput ( inp ) ;
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Mat out = net . forward ( ) ;
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double scoreDiff = default_l1 , iouDiff = default_lInf ;
if ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD )
{
scoreDiff = 0.01 ;
iouDiff = 0.1 ;
}
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normAssertDetections ( ref , out , " " , 0.2 , scoreDiff , iouDiff ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2019010000
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expectNoFallbacksFromIE ( net ) ;
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# endif
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}
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TEST_P ( Test_TensorFlow_nets , Inception_v2_SSD )
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{
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applyTestTag ( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB ) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
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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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checkBackend ( ) ;
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Mat img = imread ( findDataFile ( " dnn/street.png " ) ) ;
std : : string proto = findDataFile ( " dnn/ssd_inception_v2_coco_2017_11_17.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/ssd_inception_v2_coco_2017_11_17.pb " , false ) ;
Net net = readNetFromTensorflow ( model , proto ) ;
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Mat blob = blobFromImage ( img , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , true , false ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . setInput ( blob ) ;
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net . forward ( ) ;
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Mat ref = ( Mat_ < float > ( 5 , 7 ) < < 0 , 1 , 0.90176028 , 0.19872092 , 0.36311883 , 0.26461923 , 0.63498729 ,
0 , 3 , 0.93569964 , 0.64865261 , 0.45906419 , 0.80675775 , 0.65708131 ,
0 , 3 , 0.75838411 , 0.44668293 , 0.45907149 , 0.49459291 , 0.52197015 ,
0 , 10 , 0.95932811 , 0.38349164 , 0.32528657 , 0.40387636 , 0.39165527 ,
0 , 10 , 0.93973452 , 0.66561931 , 0.37841269 , 0.68074018 , 0.42907384 ) ;
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double scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.0097 : default_l1 ;
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double iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.09 : default_lInf ;
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normAssertDetections ( ref , out , " " , 0.5 , scoreDiff , iouDiff ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( Test_TensorFlow_nets , MobileNet_v1_SSD )
{
checkBackend ( ) ;
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std : : string proto = findDataFile ( " dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/ssd_mobilenet_v1_coco_2017_11_17.pb " , false ) ;
Net net = readNetFromTensorflow ( model , proto ) ;
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Mat img = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
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Mat blob = blobFromImage ( img , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , true , false ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
net . setInput ( blob ) ;
Mat out = net . forward ( ) ;
Mat ref = blobFromNPY ( findDataFile ( " dnn/tensorflow/ssd_mobilenet_v1_coco_2017_11_17.detection_out.npy " ) ) ;
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float scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.011 : 1.5e-5 ;
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float iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.012 : 1e-3 ;
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float detectionConfThresh = ( target = = DNN_TARGET_MYRIAD ) ? 0.35 : 0.3 ;
# 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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scoreDiff = 0.061 ;
iouDiff = 0.12 ;
detectionConfThresh = 0.36 ;
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}
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# endif
normAssertDetections ( ref , out , " " , detectionConfThresh , scoreDiff , iouDiff ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( Test_TensorFlow_nets , Faster_RCNN_inception_v2_coco_2018_01_28 )
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{
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applyTestTag (
( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB ) ,
CV_TEST_TAG_LONG ,
CV_TEST_TAG_DEBUG_VERYLONG
) ;
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# ifdef INF_ENGINE_RELEASE
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & &
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( INF_ENGINE_VER_MAJOR_LT ( 2019020000 ) | | target ! = DNN_TARGET_CPU ) )
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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if ( INF_ENGINE_VER_MAJOR_GT ( 2019030000 ) & &
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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# endif
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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// segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
// Assertion `prior_height > 0' failed.
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 ) ;
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# endif
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checkBackend ( ) ;
double scoresDiff = 1e-5 ;
double iouDiff = 1e-4 ;
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 | | backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
scoresDiff = 0.02 ;
iouDiff = 0.1 ;
}
std : : string name = " faster_rcnn_inception_v2_coco_2018_01_28 " ;
{
std : : string proto = findDataFile ( " dnn/ " + name + " .pbtxt " ) ;
std : : string model = findDataFile ( " dnn/ " + name + " .pb " , false ) ;
Net net = readNetFromTensorflow ( model , proto ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
Mat img = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
Mat blob = blobFromImage ( img , 1.0f , Size ( 800 , 600 ) , Scalar ( ) , true , false ) ;
net . setInput ( blob ) ;
Mat out = net . forward ( ) ;
Mat ref = blobFromNPY ( findDataFile ( " dnn/tensorflow/ " + name + " .detection_out.npy " ) ) ;
// accuracy (both OpenCV & IE)
if ( target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
normAssertDetections ( ref , out , name . c_str ( ) , 0.3 , scoresDiff , iouDiff ) ;
}
}
TEST_P ( Test_TensorFlow_nets , Faster_RCNN_resnet50_coco_2018_01_28 )
{
applyTestTag (
( target = = DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB ) ,
CV_TEST_TAG_LONG ,
CV_TEST_TAG_DEBUG_VERYLONG
) ;
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size()
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 ) ;
// [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep < subgraphs.size()
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)
// [ GENERAL_ERROR ] AssertionFailed: subgraphTopoSortsStep++ < subgraphs.size()
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 ) ;
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// IE exception: Ngraph operation Transpose with name FirstStageBoxPredictor/ClassPredictor/reshape_1/nhwc 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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# elif defined(INF_ENGINE_RELEASE)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & &
( INF_ENGINE_VER_MAJOR_LT ( 2019020000 ) | | target ! = DNN_TARGET_CPU ) )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
if ( INF_ENGINE_VER_MAJOR_GT ( 2019030000 ) & &
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 ) ;
# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
// segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
// Assertion `prior_height > 0' failed.
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 ) ;
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# endif
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# endif
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checkBackend ( ) ;
double scoresDiff = backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 2.9e-5 : 1e-5 ;
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double iouDiff = 1e-4 ;
std : : string name = " faster_rcnn_resnet50_coco_2018_01_28 " ;
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{
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std : : string proto = findDataFile ( " dnn/ " + name + " .pbtxt " ) ;
std : : string model = findDataFile ( " dnn/ " + name + " .pb " , false ) ;
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Net net = readNetFromTensorflow ( model , proto ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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Mat img = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
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Mat blob = blobFromImage ( img , 1.0f , Size ( 800 , 600 ) , Scalar ( ) , true , false ) ;
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net . setInput ( blob ) ;
Mat out = net . forward ( ) ;
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Mat ref = blobFromNPY ( findDataFile ( " dnn/tensorflow/ " + name + " .detection_out.npy " ) ) ;
// accuracy
if ( target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
normAssertDetections ( ref , out , name . c_str ( ) , 0.3 , scoresDiff , iouDiff ) ;
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}
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}
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TEST_P ( Test_TensorFlow_nets , MobileNet_v1_SSD_PPN )
{
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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_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_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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checkBackend ( ) ;
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std : : string proto = findDataFile ( " dnn/ssd_mobilenet_v1_ppn_coco.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/ssd_mobilenet_v1_ppn_coco.pb " , false ) ;
Net net = readNetFromTensorflow ( model , proto ) ;
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Mat img = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
Mat ref = blobFromNPY ( findDataFile ( " dnn/tensorflow/ssd_mobilenet_v1_ppn_coco.detection_out.npy " ) ) ;
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Mat blob = blobFromImage ( img , 1.0f , Size ( 300 , 300 ) , Scalar ( ) , true , false ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
net . setInput ( blob ) ;
Mat out = net . forward ( ) ;
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double scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.048 : 1.1e-5 ;
double iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.058 : default_lInf ;
normAssertDetections ( ref , out , " " , 0.45 , scoreDiff , iouDiff ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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TEST_P ( Test_TensorFlow_nets , opencv_face_detector_uint8 )
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{
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checkBackend ( ) ;
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std : : string proto = findDataFile ( " dnn/opencv_face_detector.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/opencv_face_detector_uint8.pb " , false ) ;
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Net net = readNetFromTensorflow ( model , proto ) ;
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Mat img = imread ( findDataFile ( " gpu/lbpcascade/er.png " ) ) ;
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Mat blob = blobFromImage ( img , 1.0 , Size ( ) , Scalar ( 104.0 , 177.0 , 123.0 ) , false , false ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . setInput ( blob ) ;
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net . forward ( ) ;
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// References are from test for Caffe model.
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Mat ref = ( Mat_ < float > ( 6 , 7 ) < < 0 , 1 , 0.99520785 , 0.80997437 , 0.16379407 , 0.87996572 , 0.26685631 ,
0 , 1 , 0.9934696 , 0.2831718 , 0.50738752 , 0.345781 , 0.5985168 ,
0 , 1 , 0.99096733 , 0.13629119 , 0.24892329 , 0.19756334 , 0.3310290 ,
0 , 1 , 0.98977017 , 0.23901358 , 0.09084064 , 0.29902688 , 0.1769477 ,
0 , 1 , 0.97203469 , 0.67965847 , 0.06876482 , 0.73999709 , 0.1513494 ,
0 , 1 , 0.95097077 , 0.51901293 , 0.45863652 , 0.5777427 , 0.5347801 ) ;
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double scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 4e-3 : 3.4e-3 ;
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double iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.024 : 1e-2 ;
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normAssertDetections ( ref , out , " " , 0.9 , scoreDiff , iouDiff ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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// inp = cv.imread('opencv_extra/testdata/cv/ximgproc/sources/08.png')
// inp = inp[:,:,[2, 1, 0]].astype(np.float32).reshape(1, 512, 512, 3)
// outs = sess.run([sess.graph.get_tensor_by_name('feature_fusion/Conv_7/Sigmoid:0'),
// sess.graph.get_tensor_by_name('feature_fusion/concat_3:0')],
// feed_dict={'input_images:0': inp})
// scores = np.ascontiguousarray(outs[0].transpose(0, 3, 1, 2))
// geometry = np.ascontiguousarray(outs[1].transpose(0, 3, 1, 2))
// np.save('east_text_detection.scores.npy', scores)
// np.save('east_text_detection.geometry.npy', geometry)
TEST_P ( Test_TensorFlow_nets , EAST_text_detection )
{
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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 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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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 ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 & & target = = DNN_TARGET_OPENCL_FP16 & &
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( INF_ENGINE_VER_MAJOR_EQ ( 2019020000 ) | | INF_ENGINE_VER_MAJOR_GE ( 2020010000 ) )
)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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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 , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
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# endif
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checkBackend ( ) ;
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std : : string netPath = findDataFile ( " dnn/frozen_east_text_detection.pb " , false ) ;
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std : : string imgPath = findDataFile ( " cv/ximgproc/sources/08.png " ) ;
std : : string refScoresPath = findDataFile ( " dnn/east_text_detection.scores.npy " ) ;
std : : string refGeometryPath = findDataFile ( " dnn/east_text_detection.geometry.npy " ) ;
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Net net = readNet ( netPath ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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Mat img = imread ( imgPath ) ;
Mat inp = blobFromImage ( img , 1.0 , Size ( ) , Scalar ( 123.68 , 116.78 , 103.94 ) , true , false ) ;
net . setInput ( inp ) ;
std : : vector < Mat > outs ;
std : : vector < String > outNames ( 2 ) ;
outNames [ 0 ] = " feature_fusion/Conv_7/Sigmoid " ;
outNames [ 1 ] = " feature_fusion/concat_3 " ;
net . forward ( outs , outNames ) ;
Mat scores = outs [ 0 ] ;
Mat geometry = outs [ 1 ] ;
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// Scores are in range [0, 1]. Geometry values are in range [-0.23, 290]
double l1_scores = default_l1 , lInf_scores = default_lInf ;
double l1_geometry = default_l1 , lInf_geometry = default_lInf ;
if ( target = = DNN_TARGET_OPENCL_FP16 )
{
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lInf_scores = backend = = DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 0.16 : 0.11 ;
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l1_geometry = 0.28 ; lInf_geometry = 5.94 ;
}
else if ( target = = DNN_TARGET_MYRIAD )
{
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lInf_scores = 0.41 ;
l1_geometry = 0.28 ; lInf_geometry = 5.94 ;
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}
else
{
l1_geometry = 1e-4 , lInf_geometry = 3e-3 ;
}
normAssert ( scores , blobFromNPY ( refScoresPath ) , " scores " , l1_scores , lInf_scores ) ;
normAssert ( geometry , blobFromNPY ( refGeometryPath ) , " geometry " , l1_geometry , lInf_geometry ) ;
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expectNoFallbacksFromIE ( net ) ;
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}
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INSTANTIATE_TEST_CASE_P ( /**/ , Test_TensorFlow_nets , dnnBackendsAndTargets ( ) ) ;
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TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_single_conv )
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{
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float l1 = 0.00078 , lInf = 0.012 ;
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runTensorFlowNet ( " fp16_single_conv " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_max_pool_odd_same )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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
float l1 = 0.00078 , lInf = 0.012 ;
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runTensorFlowNet ( " fp16_max_pool_odd_same " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_eltwise_add_mul )
{
float l1 = 0.00078 , lInf = 0.012 ;
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runTensorFlowNet ( " fp16_eltwise_add_mul " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_pad_and_concat )
{
float l1 = 0.00078 , lInf = 0.012 ;
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runTensorFlowNet ( " fp16_pad_and_concat " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_padding_valid )
{
float l1 = 0.00078 , lInf = 0.012 ;
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runTensorFlowNet ( " fp16_padding_valid " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_max_pool_even )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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
float l1 = 0.00078 , lInf = 0.012 ;
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// Reference output values are in range [0.0889, 1.651]
runTensorFlowNet ( " fp16_max_pool_even " , false , ( target = = DNN_TARGET_MYRIAD ) ? 0.003 : l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_deconvolution )
{
float l1 = 0.00078 , lInf = 0.012 ;
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if ( target = = DNN_TARGET_MYRIAD ) {
l1 = 0.0041 ;
lInf = 0.024 ;
}
// Reference output values are in range [0, 10.75]
runTensorFlowNet ( " fp16_deconvolution " , false , l1 , lInf ) ;
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}
TEST_P ( Test_TensorFlow_layers , fp16_weights_fp16_max_pool_odd_valid )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// [ GENERAL_ERROR ] AssertionFailed: !expired()
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_GE(2020020000)
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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
float l1 = 0.00078 , lInf = 0.012 ;
if ( target = = DNN_TARGET_MYRIAD ) {
l1 = 0.0041 ;
lInf = 0.024 ;
}
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// Reference output values are in range [0.418, 2.297]
runTensorFlowNet ( " fp16_max_pool_odd_valid " , false , l1 , lInf ) ;
}
TEST_P ( Test_TensorFlow_layers , fp16_padding_same )
{
// Reference output values are in range [-3.504, -0.002]
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runTensorFlowNet ( " fp16_padding_same " , false , 7e-4 , 4e-3 ) ;
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}
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TEST_P ( Test_TensorFlow_layers , defun )
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{
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runTensorFlowNet ( " defun_dropout " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , quantized )
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{
runTensorFlowNet ( " uint8_single_conv " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , lstm )
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{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// Xlink, Failed to allocate graph: NC_ERROR
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)
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
{
// Exception: Ngraph operation Reshape with name Reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
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
) ;
// Xlink
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 )
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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if ( backend = = DNN_BACKEND_OPENCV & & target = = DNN_TARGET_OPENCL_FP16 )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
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runTensorFlowNet ( " lstm " , true ) ;
runTensorFlowNet ( " lstm " , true , 0.0 , 0.0 , true ) ;
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}
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TEST_P ( Test_TensorFlow_layers , split )
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{
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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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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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runTensorFlowNet ( " split " ) ;
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}
TEST_P ( Test_TensorFlow_layers , split_equals )
{
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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runTensorFlowNet ( " split_equals " ) ;
}
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TEST_P ( Test_TensorFlow_layers , resize_nearest_neighbor )
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{
runTensorFlowNet ( " resize_nearest_neighbor " ) ;
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}
TEST_P ( Test_TensorFlow_layers , resize_nearest_neighbor_keras_upsampling2d )
{
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runTensorFlowNet ( " keras_upsampling2d " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , resize_nearest_neighbor_align_corners )
{
runTensorFlowNet ( " resize_nearest_neighbor " , false , 0.0 , 0.0 , false , " _align_corners " ) ;
}
TEST_P ( Test_TensorFlow_layers , resize_nearest_neighbor_half_pixel )
{
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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_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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# endif
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runTensorFlowNet ( " resize_nearest_neighbor " , false , 0.0 , 0.0 , false , " _half_pixel " ) ;
}
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TEST_P ( Test_TensorFlow_layers , fused_resize_conv )
{
runTensorFlowNet ( " fused_resize_conv " ) ;
}
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TEST_P ( Test_TensorFlow_layers , slice_crop2d )
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{
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double l1 = target = = DNN_TARGET_MYRIAD ? 4.9e-3 : default_l1 ;
runTensorFlowNet ( " crop2d " , false , l1 ) ;
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}
TEST_P ( Test_TensorFlow_layers , slice_4d )
{
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runTensorFlowNet ( " slice_4d " ) ;
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}
TEST_P ( Test_TensorFlow_layers , slice_strided )
{
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runTensorFlowNet ( " strided_slice " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , softmax_keras )
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{
runTensorFlowNet ( " keras_softmax " ) ;
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}
TEST_P ( Test_TensorFlow_layers , softmax_slim )
{
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runTensorFlowNet ( " slim_softmax " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , softmax_slim_v2 )
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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_2
)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
runTensorFlowNet ( " slim_softmax_v2 " ) ;
}
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TEST_P ( Test_TensorFlow_layers , relu6 )
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{
runTensorFlowNet ( " keras_relu6 " ) ;
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runTensorFlowNet ( " keras_relu6 " , /*hasText*/ true ) ;
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}
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TEST_P ( Test_TensorFlow_layers , subpixel )
{
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# if defined(INF_ENGINE_RELEASE)
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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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runTensorFlowNet ( " subpixel " ) ;
}
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TEST_P ( Test_TensorFlow_layers , keras_mobilenet_head )
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{
runTensorFlowNet ( " keras_mobilenet_head " ) ;
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runTensorFlowNet ( " keras_learning_phase " ) ;
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}
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// TF case: align_corners=False, half_pixel_centers=False
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TEST_P ( Test_TensorFlow_layers , resize_bilinear )
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{
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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_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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runTensorFlowNet ( " resize_bilinear " ) ;
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}
// TF case: align_corners=True, half_pixel_centers=False
TEST_P ( Test_TensorFlow_layers , resize_bilinear_align_corners )
{
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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_MYRIAD )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ; // exception
# endif
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runTensorFlowNet ( " resize_bilinear " ,
false , 0.0 , 0.0 , false , // default parameters
" _align_corners " ) ;
}
// TF case: align_corners=False, half_pixel_centers=True
TEST_P ( Test_TensorFlow_layers , resize_bilinear_half_pixel )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
runTensorFlowNet ( " resize_bilinear " , false , 0.0 , 0.0 , false , " _half_pixel " ) ;
}
// TF case: align_corners=False, half_pixel_centers=False
TEST_P ( Test_TensorFlow_layers , resize_bilinear_factor )
{
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runTensorFlowNet ( " resize_bilinear_factor " ) ;
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}
// TF case: align_corners=False, half_pixel_centers=True
TEST_P ( Test_TensorFlow_layers , resize_bilinear_factor_half_pixel )
{
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
runTensorFlowNet ( " resize_bilinear_factor " , false , 0.0 , 0.0 , false , " _half_pixel " ) ;
}
// TF case: align_corners=True, half_pixel_centers=False
TEST_P ( Test_TensorFlow_layers , resize_bilinear_factor_align_corners )
{
runTensorFlowNet ( " resize_bilinear_factor " , false , 0.0 , 0.0 , false , " _align_corners " ) ;
}
// TF case: align_corners=False, half_pixel_centers=False
TEST_P ( Test_TensorFlow_layers , resize_bilinear_down )
{
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runTensorFlowNet ( " resize_bilinear_down " ) ;
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}
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TEST_P ( Test_TensorFlow_layers , resize_concat_optimization )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH & & target ! = DNN_TARGET_CPU ) // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?)
applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_NGRAPH , CV_TEST_TAG_DNN_SKIP_IE_VERSION ) ;
# endif
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runTensorFlowNet ( " resize_concat_optimization " ) ;
}
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TEST_P ( Test_TensorFlow_layers , tf2_dense )
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{
runTensorFlowNet ( " tf2_dense " ) ;
}
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TEST_P ( Test_TensorFlow_layers , clip_by_value )
{
runTensorFlowNet ( " clip_by_value " ) ;
}
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TEST_P ( Test_TensorFlow_layers , tf2_prelu )
{
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# if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// Eltwise executor got invalid input/output dims configuration
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 ) ;
// Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map
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: Input prelu:StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add hasn't been found in primitiveIDs map
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: Eltwise node with name `StatefulPartitionedCall/StatefulPartitionedCall/sequential/p_re_lu/add` has invalid input/output dims configuration
if ( 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 ) ;
}
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# elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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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runTensorFlowNet ( " tf2_prelu " ) ;
}
TEST_P ( Test_TensorFlow_layers , tf2_permute_nhwc_ncwh )
{
runTensorFlowNet ( " tf2_permute_nhwc_ncwh " ) ;
}
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TEST_P ( Test_TensorFlow_layers , squeeze )
{
# 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_2
)
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applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2 , CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER ) ;
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# endif
int inpShapes [ ] [ 4 ] = { { 1 , 3 , 4 , 2 } , { 1 , 3 , 1 , 2 } , { 1 , 3 , 4 , 1 } , { 1 , 3 , 4 , 1 } } ; // TensorFlow's shape (NHWC)
int outShapes [ ] [ 3 ] = { { 3 , 4 , 2 } , { 1 , 3 , 2 } , { 1 , 3 , 4 } , { 1 , 3 , 4 } } ;
int squeeze_dims [ ] = { 0 , 2 , 3 , - 1 } ;
for ( int i = 0 ; i < 4 ; + + i )
{
SCOPED_TRACE ( format ( " i=%d " , i ) ) ;
std : : string pbtxt =
" node { name: \" input \" op: \" Placeholder \" "
" attr { key: \" data_format \" value { s: \" NHWC \" } } } "
" node { name: \" squeeze \" op: \" Squeeze \" input: \" input \" "
" attr { key: \" squeeze_dims \" value { list { i: " + format ( " %d " , squeeze_dims [ i ] ) + " }}}} " ;
Net net = readNetFromTensorflow ( 0 , 0 , pbtxt . c_str ( ) , pbtxt . size ( ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
Mat tfInp ( 4 , & inpShapes [ i ] [ 0 ] , CV_32F ) ;
randu ( tfInp , - 1 , 1 ) ;
// NHWC to NCHW
CV_Assert ( inpShapes [ i ] [ 0 ] = = 1 ) ;
std : : swap ( inpShapes [ i ] [ 2 ] , inpShapes [ i ] [ 3 ] ) ;
std : : swap ( inpShapes [ i ] [ 1 ] , inpShapes [ i ] [ 2 ] ) ;
Mat cvInp = tfInp . reshape ( 1 , tfInp . total ( ) / inpShapes [ i ] [ 1 ] ) . t ( ) ;
cvInp = cvInp . reshape ( 1 , 4 , & inpShapes [ i ] [ 0 ] ) ;
net . setInput ( cvInp ) ;
Mat out = net . forward ( ) ;
normAssert ( tfInp . reshape ( 1 , 3 , & outShapes [ i ] [ 0 ] ) , out , " " , default_l1 , default_lInf ) ;
}
}
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INSTANTIATE_TEST_CASE_P ( /**/ , Test_TensorFlow_layers , dnnBackendsAndTargets ( ) ) ;
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TEST ( Test_TensorFlow , two_inputs )
{
Net net = readNet ( path ( " two_inputs_net.pbtxt " ) ) ;
net . setPreferableBackend ( DNN_BACKEND_OPENCV ) ;
Mat firstInput ( 2 , 3 , CV_32FC1 ) , secondInput ( 2 , 3 , CV_32FC1 ) ;
randu ( firstInput , - 1 , 1 ) ;
randu ( secondInput , - 1 , 1 ) ;
net . setInput ( firstInput , " first_input " ) ;
net . setInput ( secondInput , " second_input " ) ;
Mat out = net . forward ( ) ;
normAssert ( out , firstInput + secondInput ) ;
}
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TEST_P ( Test_TensorFlow_nets , Mask_RCNN )
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{
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static const double kMaskThreshold = 0.5 ;
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 ) ;
if ( 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 , CV_TEST_TAG_DNN_SKIP_IE_NGRAPH ) ;
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applyTestTag ( CV_TEST_TAG_MEMORY_1GB , CV_TEST_TAG_DEBUG_VERYLONG ) ;
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Mat img = imread ( findDataFile ( " dnn/street.png " ) ) ;
std : : string proto = findDataFile ( " dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/mask_rcnn_inception_v2_coco_2018_01_28.pb " , false ) ;
Net net = readNetFromTensorflow ( model , proto ) ;
Mat refDetections = blobFromNPY ( path ( " mask_rcnn_inception_v2_coco_2018_01_28.detection_out.npy " ) ) ;
Mat refMasks = blobFromNPY ( path ( " mask_rcnn_inception_v2_coco_2018_01_28.detection_masks.npy " ) ) ;
Mat blob = blobFromImage ( img , 1.0f , Size ( 800 , 800 ) , Scalar ( ) , true , false ) ;
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net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
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net . setInput ( blob ) ;
// Mask-RCNN predicts bounding boxes and segmentation masks.
std : : vector < String > outNames ( 2 ) ;
outNames [ 0 ] = " detection_out_final " ;
outNames [ 1 ] = " detection_masks " ;
std : : vector < Mat > outs ;
net . forward ( outs , outNames ) ;
Mat outDetections = outs [ 0 ] ;
Mat outMasks = outs [ 1 ] ;
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double scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.2 : 2e-5 ;
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double iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.018 : default_lInf ;
normAssertDetections ( refDetections , outDetections , " " , /*threshold for zero confidence*/ 1e-5 , scoreDiff , iouDiff ) ;
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// Output size of masks is NxCxHxW where
// N - number of detected boxes
// C - number of classes (excluding background)
// HxW - segmentation shape
const int numDetections = outDetections . size [ 2 ] ;
int masksSize [ ] = { 1 , numDetections , outMasks . size [ 2 ] , outMasks . size [ 3 ] } ;
Mat masks ( 4 , & masksSize [ 0 ] , CV_32F ) ;
std : : vector < cv : : Range > srcRanges ( 4 , cv : : Range : : all ( ) ) ;
std : : vector < cv : : Range > dstRanges ( 4 , cv : : Range : : all ( ) ) ;
outDetections = outDetections . reshape ( 1 , outDetections . total ( ) / 7 ) ;
for ( int i = 0 ; i < numDetections ; + + i )
{
// Get a class id for this bounding box and copy mask only for that class.
int classId = static_cast < int > ( outDetections . at < float > ( i , 1 ) ) ;
srcRanges [ 0 ] = dstRanges [ 1 ] = cv : : Range ( i , i + 1 ) ;
srcRanges [ 1 ] = cv : : Range ( classId , classId + 1 ) ;
outMasks ( srcRanges ) . copyTo ( masks ( dstRanges ) ) ;
}
cv : : Range topRefMasks [ ] = { Range : : all ( ) , Range ( 0 , numDetections ) , Range : : all ( ) , Range : : all ( ) } ;
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refMasks = refMasks ( & topRefMasks [ 0 ] ) ;
// make binary masks
cv : : threshold ( masks . reshape ( 1 , 1 ) , masks , kMaskThreshold , 1 , THRESH_BINARY ) ;
cv : : threshold ( refMasks . reshape ( 1 , 1 ) , refMasks , kMaskThreshold , 1 , THRESH_BINARY ) ;
double inter = cv : : countNonZero ( masks & refMasks ) ;
double area = cv : : countNonZero ( masks | refMasks ) ;
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EXPECT_GE ( inter / area , ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 0.98 : 0.99 ) ;
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if ( backend = = DNN_BACKEND_INFERENCE_ENGINE_NGRAPH )
expectNoFallbacks ( net ) ;
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}
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TEST_P ( Test_TensorFlow_nets , EfficientDet )
{
if ( target ! = DNN_TARGET_CPU )
{
if ( target = = DNN_TARGET_OPENCL_FP16 ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL_FP16 ) ;
if ( target = = DNN_TARGET_OPENCL ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_OPENCL ) ;
if ( target = = DNN_TARGET_MYRIAD ) applyTestTag ( CV_TEST_TAG_DNN_SKIP_IE_MYRIAD ) ;
}
checkBackend ( ) ;
std : : string proto = findDataFile ( " dnn/efficientdet-d0.pbtxt " ) ;
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std : : string model = findDataFile ( " dnn/efficientdet-d0.pb " , false ) ;
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Net net = readNetFromTensorflow ( model , proto ) ;
Mat img = imread ( findDataFile ( " dnn/dog416.png " ) ) ;
Mat blob = blobFromImage ( img , 1.0 / 255 , Size ( 512 , 512 ) , Scalar ( 123.675 , 116.28 , 103.53 ) ) ;
net . setPreferableBackend ( backend ) ;
net . setPreferableTarget ( target ) ;
net . setInput ( blob ) ;
// Output has shape 1x1xNx7 where N - number of detections.
// An every detection is a vector of values [id, classId, confidence, left, top, right, bottom]
Mat out = net . forward ( ) ;
// References are from test for TensorFlow model.
Mat ref = ( Mat_ < float > ( 3 , 7 ) < < 0 , 1 , 0.8437444 , 0.153996080160141 , 0.20534580945968628 , 0.7463544607162476 , 0.7414066195487976 ,
0 , 17 , 0.8245924 , 0.16657517850399017 , 0.3996818959712982 , 0.4111558794975281 , 0.9306337833404541 ,
0 , 7 , 0.8039304 , 0.6118435263633728 , 0.13175517320632935 , 0.9065558314323425 , 0.2943994700908661 ) ;
double scoreDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 4e-3 : 1e-5 ;
double iouDiff = ( target = = DNN_TARGET_OPENCL_FP16 | | target = = DNN_TARGET_MYRIAD ) ? 2e-3 : 1e-4 ;
normAssertDetections ( ref , out , " " , 0.5 , scoreDiff , iouDiff ) ;
expectNoFallbacksFromIE ( net ) ;
}
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TEST ( Test_TensorFlow_Importer , tf_graph_simplifier_buffer_overflow_21852 )
{
uint8_t payload [ ] = { 0x08 , 0x08 , 0x0a , 0x00 , 0x0a , 0x00 } ;
EXPECT_ANY_THROW ( readNetFromTensorflow ( reinterpret_cast < const char * > ( payload ) , sizeof ( payload ) / sizeof ( payload [ 0 ] ) ) ) ;
}
// can be triggered with -fsanitize=address
TEST ( Test_TensorFlow_Importer , tf_graph_simplifier_buffer_overflow_21947 )
{
uint8_t payload [ ] = { 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 ,
0xba , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 ,
0x0a , 0xbd , 0x00 , 0x1a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0xba ,
0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 ,
0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0xba , 0x0a , 0x00 ,
0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0xba ,
0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 ,
0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x2a , 0x00 , 0xba , 0x0a , 0x00 ,
0x0a , 0x00 , 0x5d , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x00 , 0x0a , 0x40 } ;
EXPECT_ANY_THROW ( readNetFromTensorflow ( reinterpret_cast < const char * > ( payload ) , sizeof ( payload ) / sizeof ( payload [ 0 ] ) ) ) ;
}
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}