opencv/modules/dnn/test/test_onnx_importer.cpp

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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.
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
namespace opencv_test { namespace {
template<typename TString>
static std::string _tf(TString filename, bool required = true)
{
return findDataFile(std::string("dnn/onnx/") + filename, required);
}
class Test_ONNX_layers : public DNNTestLayer
{
public:
bool required;
Test_ONNX_layers() : required(true) { }
enum Extension
{
npy,
pb
};
void testONNXModels(const String& basename, const Extension ext = npy,
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
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bool checkNoFallbacks = true, int numInps = 1)
{
String onnxmodel = _tf("models/" + basename + ".onnx", required);
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std::vector<Mat> inps(numInps);
Mat ref;
if (ext == npy) {
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for (int i = 0; i < numInps; ++i)
inps[i] = blobFromNPY(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".npy"));
ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
}
else if (ext == pb) {
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for (int i = 0; i < numInps; ++i)
inps[i] = readTensorFromONNX(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".pb"));
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
}
else
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
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checkBackend(&inps[0], &ref);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
std::vector<String> inputNames;
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for (int i = 0; i < numInps; ++i)
inputNames.push_back(format("%d", i));
net.setInputsNames(inputNames);
for (int i = 0; i < numInps; ++i)
net.setInput(inps[i], inputNames[i]);
Mat out = net.forward("");
if (useSoftmax)
{
LayerParams lp;
Net netSoftmax;
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netSoftmax.addLayerToPrev("softmaxLayer", "Softmax", lp);
netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);
netSoftmax.setInput(out);
out = netSoftmax.forward();
netSoftmax.setInput(ref);
ref = netSoftmax.forward();
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
if (checkNoFallbacks)
expectNoFallbacksFromIE(net);
}
};
TEST_P(Test_ONNX_layers, InstanceNorm)
{
Merge pull request #14827 from YashasSamaga:cuda4dnn-csl-low CUDA backend for the DNN module * stub cuda4dnn design * minor fixes for tests and doxygen * add csl public api directory to module headers * add low-level CSL components * add high-level CSL components * integrate csl::Tensor into backbone code * switch to CPU iff unsupported; otherwise, fail on error * add fully connected layer * add softmax layer * add activation layers * support arbitary rank TensorDescriptor * pass input wrappers to `initCUDA()` * add 1d/2d/3d-convolution * add pooling layer * reorganize and refactor code * fixes for gcc, clang and doxygen; remove cxx14/17 code * add blank_layer * add LRN layer * add rounding modes for pooling layer * split tensor.hpp into tensor.hpp and tensor_ops.hpp * add concat layer * add scale layer * add batch normalization layer * split math.cu into activations.cu and math.hpp * add eltwise layer * add flatten layer * add tensor transform api * add asymmetric padding support for convolution layer * add reshape layer * fix rebase issues * add permute layer * add padding support for concat layer * refactor and reorganize code * add normalize layer * optimize bias addition in scale layer * add prior box layer * fix and optimize normalize layer * add asymmetric padding support for pooling layer * add event API * improve pooling performance for some padding scenarios * avoid over-allocation of compute resources to kernels * improve prior box performance * enable layer fusion * add const layer * add resize layer * add slice layer * add padding layer * add deconvolution layer * fix channelwise ReLU initialization * add vector traits * add vectorized versions of relu, clipped_relu, power * add vectorized concat kernels * improve concat_with_offsets performance * vectorize scale and bias kernels * add support for multi-billion element tensors * vectorize prior box kernels * fix address alignment check * improve bias addition performance of conv/deconv/fc layers * restructure code for supporting multiple targets * add DNN_TARGET_CUDA_FP64 * add DNN_TARGET_FP16 * improve vectorization * add region layer * improve tensor API, add dynamic ranks 1. use ManagedPtr instead of a Tensor in backend wrapper 2. add new methods to tensor classes - size_range: computes the combined size of for a given axis range - tensor span/view can be constructed from a raw pointer and shape 3. the tensor classes can change their rank at runtime (previously rank was fixed at compile-time) 4. remove device code from tensor classes (as they are unused) 5. enforce strict conditions on tensor class APIs to improve debugging ability * fix parametric relu activation * add squeeze/unsqueeze tensor API * add reorg layer * optimize permute and enable 2d permute * enable 1d and 2d slice * add split layer * add shuffle channel layer * allow tensors of different ranks in reshape primitive * patch SliceOp to allow Crop Layer * allow extra shape inputs in reshape layer * use `std::move_backward` instead of `std::move` for insert in resizable_static_array * improve workspace management * add spatial LRN * add nms (cpu) to region layer * add max pooling with argmax ( and a fix to limits.hpp) * add max unpooling layer * rename DNN_TARGET_CUDA_FP32 to DNN_TARGET_CUDA * update supportBackend to be more rigorous * remove stray include from preventing non-cuda build * include op_cuda.hpp outside condition #if * refactoring, fixes and many optimizations * drop DNN_TARGET_CUDA_FP64 * fix gcc errors * increase max. tensor rank limit to six * add Interp layer * drop custom layers; use BackendNode * vectorize activation kernels * fixes for gcc * remove wrong assertion * fix broken assertion in unpooling primitive * fix build errors in non-CUDA build * completely remove workspace from public API * fix permute layer * enable accuracy and perf. tests for DNN_TARGET_CUDA * add asynchronous forward * vectorize eltwise ops * vectorize fill kernel * fixes for gcc * remove CSL headers from public API * remove csl header source group from cmake * update min. cudnn version in cmake * add numerically stable FP32 log1pexp * refactor code * add FP16 specialization to cudnn based tensor addition * vectorize scale1 and bias1 + minor refactoring * fix doxygen build * fix invalid alignment assertion * clear backend wrappers before allocateLayers * ignore memory lock failures * do not allocate internal blobs * integrate NVTX * add numerically stable half precision log1pexp * fix indentation, following coding style, improve docs * remove accidental modification of IE code * Revert "add asynchronous forward" This reverts commit 1154b9da9da07e9b52f8a81bdcea48cf31c56f70. * [cmake] throw error for unsupported CC versions * fix rebase issues * add more docs, refactor code, fix bugs * minor refactoring and fixes * resolve warnings/errors from clang * remove haveCUDA() checks from supportBackend() * remove NVTX integration * changes based on review comments * avoid exception when no CUDA device is present * add color code for CUDA in Net::dump
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if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); /* MVN is not supported */
if (target == DNN_TARGET_MYRIAD)
testONNXModels("instancenorm", npy, 0, 0, false, false);
else
testONNXModels("instancenorm", npy);
}
TEST_P(Test_ONNX_layers, MaxPooling)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, MaxPooling_2)
{
testONNXModels("two_maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Convolution)
{
testONNXModels("convolution");
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
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TEST_P(Test_ONNX_layers, Gather)
{
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);
testONNXModels("gather");
// GPU plugin unsupported slice for constant
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("gather_scalar", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Convolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("conv3d");
}
TEST_P(Test_ONNX_layers, Convolution3D_bias)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("conv3d_bias");
}
TEST_P(Test_ONNX_layers, Two_convolution)
{
#if defined(INF_ENGINE_RELEASE)
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);
#endif
// Reference output values are in range [-0.855, 0.611]
testONNXModels("two_convolution");
}
TEST_P(Test_ONNX_layers, Deconvolution)
{
testONNXModels("deconvolution", npy, 0, 0, false, false);
testONNXModels("two_deconvolution", npy, 0, 0, false, false);
testONNXModels("deconvolution_group", npy, 0, 0, false, false);
testONNXModels("deconvolution_output_shape", npy, 0, 0, false, false);
if (target != DNN_TARGET_CUDA_FP16) // bug
testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Deconvolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else if (backend == DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("deconv3d");
testONNXModels("deconv3d_bias");
testONNXModels("deconv3d_pad");
testONNXModels("deconv3d_adjpad");
}
TEST_P(Test_ONNX_layers, Dropout)
{
testONNXModels("dropout");
}
TEST_P(Test_ONNX_layers, Linear)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testONNXModels("linear");
}
TEST_P(Test_ONNX_layers, ReLU)
{
testONNXModels("ReLU");
}
TEST_P(Test_ONNX_layers, Clip)
{
testONNXModels("clip", npy);
}
TEST_P(Test_ONNX_layers, Shape)
{
testONNXModels("shape_of_constant");
}
TEST_P(Test_ONNX_layers, ReduceMean)
{
testONNXModels("reduce_mean");
testONNXModels("reduce_mean_axis1");
testONNXModels("reduce_mean_axis2");
}
TEST_P(Test_ONNX_layers, ReduceSum)
{
testONNXModels("reduce_sum");
}
TEST_P(Test_ONNX_layers, ReduceMax)
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{
testONNXModels("reduce_max");
testONNXModels("reduce_max_axis_0");
testONNXModels("reduce_max_axis_1");
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}
TEST_P(Test_ONNX_layers, Scale)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("scale");
}
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else 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
else 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
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("reduce_mean3d");
}
TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid)
{
testONNXModels("maxpooling_sigmoid");
}
TEST_P(Test_ONNX_layers, Cast)
{
testONNXModels("cast");
}
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TEST_P(Test_ONNX_layers, Power)
{
testONNXModels("pow2", npy, 0, 0, false, false);
}
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TEST_P(Test_ONNX_layers, Exp)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("exp");
}
TEST_P(Test_ONNX_layers, Concatenation)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
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);
}
testONNXModels("concatenation");
}
TEST_P(Test_ONNX_layers, Eltwise3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
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
testONNXModels("eltwise3d");
}
TEST_P(Test_ONNX_layers, AveragePooling)
{
testONNXModels("average_pooling");
}
TEST_P(Test_ONNX_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else 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
else 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
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("max_pool3d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else 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
else 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
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("ave_pool3d");
}
TEST_P(Test_ONNX_layers, PoolConv3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else 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
else 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
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("pool_conv_3d");
}
TEST_P(Test_ONNX_layers, BatchNormalization)
{
testONNXModels("batch_norm");
}
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TEST_P(Test_ONNX_layers, BatchNormalization3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
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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testONNXModels("batch_norm_3d");
}
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TEST_P(Test_ONNX_layers, BatchNormalizationUnfused)
{
testONNXModels("frozenBatchNorm2d");
}
TEST_P(Test_ONNX_layers, BatchNormalizationSubgraph)
{
testONNXModels("batch_norm_subgraph");
}
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TEST_P(Test_ONNX_layers, NormalizeFusionSubgraph)
{
testONNXModels("normalize_fusion");
}
TEST_P(Test_ONNX_layers, Transpose)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
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);
}
testONNXModels("transpose");
}
TEST_P(Test_ONNX_layers, Multiplication)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
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);
testONNXModels("mul");
}
TEST_P(Test_ONNX_layers, MatMul)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
testONNXModels("matmul_2d");
testONNXModels("matmul_3d");
testONNXModels("matmul_4d");
}
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TEST_P(Test_ONNX_layers, MatMulAdd)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testONNXModels("matmul_add");
}
TEST_P(Test_ONNX_layers, Expand)
{
testONNXModels("expand_batch");
testONNXModels("expand_channels");
testONNXModels("expand_neg_batch");
}
TEST_P(Test_ONNX_layers, ExpandHW)
{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("expand_hw");
}
TEST_P(Test_ONNX_layers, Constant)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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
testONNXModels("constant");
}
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TEST_P(Test_ONNX_layers, Padding)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels("padding", npy, 0, 0, false, false);
#else
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testONNXModels("padding");
#endif
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}
TEST_P(Test_ONNX_layers, Resize)
{
testONNXModels("resize_nearest");
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("resize_bilinear");
}
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TEST_P(Test_ONNX_layers, ResizeUnfused)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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testONNXModels("upsample_unfused_torch1.2");
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testONNXModels("upsample_unfused_opset9_torch1.4");
testONNXModels("resize_nearest_unfused_opset11_torch1.4");
testONNXModels("resize_nearest_unfused_opset11_torch1.3");
testONNXModels("resize_bilinear_unfused_opset11_torch1.4");
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}
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TEST_P(Test_ONNX_layers, ResizeUnfusedTwoInputs)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("upsample_unfused_two_inputs_opset9_torch1.4", npy, 0, 0, false, true, 2);
testONNXModels("upsample_unfused_two_inputs_opset11_torch1.4", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, MultyInputs)
{
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testONNXModels("multy_inputs", npy, 0, 0, false, true, 2);
}
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TEST_P(Test_ONNX_layers, Broadcast)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("channel_broadcast", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, DynamicResize)
{
testONNXModels("dynamic_resize_9", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_10", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_11", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_9", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_10", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_11", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Div)
{
const String model = _tf("models/div.onnx");
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
// Reference output values range is -68.80928, 2.991873. So to avoid computational
// difference for FP16 we'll perform reversed division (just swap inputs).
Mat inp1 = blobFromNPY(_tf("data/input_div_1.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_div_0.npy"));
Mat ref = blobFromNPY(_tf("data/output_div.npy"));
cv::divide(1.0, ref, ref);
checkBackend(&inp1, &ref);
net.setInput(inp1, "0");
net.setInput(inp2, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
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expectNoFallbacksFromCUDA(net);
}
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TEST_P(Test_ONNX_layers, DynamicReshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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testONNXModels("dynamic_reshape");
testONNXModels("dynamic_reshape_opset_11");
testONNXModels("flatten_by_prod");
testONNXModels("flatten_const");
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}
TEST_P(Test_ONNX_layers, Reshape)
{
testONNXModels("unsqueeze");
}
TEST_P(Test_ONNX_layers, Squeeze)
{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("squeeze");
}
TEST_P(Test_ONNX_layers, ReduceL2)
{
testONNXModels("reduceL2");
testONNXModels("reduceL2_subgraph");
testONNXModels("reduceL2_subgraph_2");
}
TEST_P(Test_ONNX_layers, Split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("split_1");
testONNXModels("split_2");
testONNXModels("split_3");
testONNXModels("split_4");
}
TEST_P(Test_ONNX_layers, Slice)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels("slice", npy, 0, 0, false, false);
#else
testONNXModels("slice");
testONNXModels("slice_opset_11");
#endif
}
TEST_P(Test_ONNX_layers, Softmax)
{
testONNXModels("softmax");
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testONNXModels("log_softmax", npy, 0, 0, false, false);
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testONNXModels("softmax_unfused");
}
TEST_P(Test_ONNX_layers, Split_EltwiseMax)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("split_max");
}
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TEST_P(Test_ONNX_layers, LSTM)
{
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testONNXModels("lstm", npy, 0, 0, false, false);
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}
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TEST_P(Test_ONNX_layers, LSTM_bidirectional)
{
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testONNXModels("lstm_bidirectional", npy, 0, 0, false, false);
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}
TEST_P(Test_ONNX_layers, Pad2d_Unfused)
{
testONNXModels("ReflectionPad2d");
testONNXModels("ZeroPad2d");
}
TEST_P(Test_ONNX_layers, LinearWithConstant)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
#endif
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lin_with_constant");
}
TEST_P(Test_ONNX_layers, MatmulWithTwoInputs)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
#endif
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("matmul_with_two_inputs");
}
TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6)
{
testONNXModels("resize_opset11_torch1.6");
}
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TEST_P(Test_ONNX_layers, Mish)
{
testONNXModels("mish");
}
TEST_P(Test_ONNX_layers, CalculatePads)
{
testONNXModels("calc_pads");
}
TEST_P(Test_ONNX_layers, Conv1d)
{
testONNXModels("conv1d");
}
TEST_P(Test_ONNX_layers, Conv1d_bias)
{
testONNXModels("conv1d_bias");
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv1d_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
String basename = "conv1d_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
TEST_P(Test_ONNX_layers, GatherMultiOutput)
{
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE);
#endif
testONNXModels("gather_multi_output");
}
TEST_P(Test_ONNX_layers, DynamicAxes)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("squeeze_and_conv_dynamic_axes");
testONNXModels("unsqueeze_and_conv_dynamic_axes");
testONNXModels("gather_dynamic_axes");
testONNXModels("gather_scalar_dynamic_axes");
testONNXModels("slice_dynamic_axes");
testONNXModels("slice_opset_11_dynamic_axes");
testONNXModels("resize_opset11_torch1.6_dynamic_axes");
testONNXModels("average_pooling_dynamic_axes");
testONNXModels("maxpooling_sigmoid_dynamic_axes");
}
TEST_P(Test_ONNX_layers, MaxPool1d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("maxpooling_1d");
}
TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("maxpooling_sigmoid_1d");
}
TEST_P(Test_ONNX_layers, MaxPool1d_Twise)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("two_maxpooling_1d");
}
TEST_P(Test_ONNX_layers, AvePool1d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("average_pooling_1d");
}
TEST_P(Test_ONNX_layers, PoolConv1d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("pool_conv_1d");
}
TEST_P(Test_ONNX_layers, ConvResizePool1d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
testONNXModels("conv_resize_pool_1d");
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers
{
public:
Test_ONNX_nets() { required = false; }
};
TEST_P(Test_ONNX_nets, Alexnet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
const String model = _tf("models/alexnet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat inp = imread(_tf("../grace_hopper_227.png"));
Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false));
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, Squeezenet)
{
testONNXModels("squeezenet", pb);
}
TEST_P(Test_ONNX_nets, Googlenet)
{
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);
const String model = _tf("models/googlenet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
std::vector<Mat> images;
images.push_back( imread(_tf("../googlenet_0.png")) );
images.push_back( imread(_tf("../googlenet_1.png")) );
Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false);
Mat ref = blobFromNPY(_tf("../googlenet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(inp);
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, CaffeNet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
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)
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
testONNXModels("caffenet", pb);
}
TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
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applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
2019-10-04 15:29:27 +08:00
&& 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);
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#endif
// Reference output values are in range [-4.992, -1.161]
testONNXModels("rcnn_ilsvrc13", pb, 0.0046);
}
TEST_P(Test_ONNX_nets, VGG16_bn)
{
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applyTestTag(CV_TEST_TAG_MEMORY_6GB); // > 2.3Gb
// output range: [-16; 27], after Softmax [0; 0.67]
const double lInf = (target == DNN_TARGET_MYRIAD) ? 0.038 : default_lInf;
testONNXModels("vgg16-bn", pb, default_l1, lInf, true);
}
TEST_P(Test_ONNX_nets, ZFNet)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
testONNXModels("zfnet512", pb);
}
TEST_P(Test_ONNX_nets, ResNet18v1)
{
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-16; 22], after Softmax [0, 0.51]
testONNXModels("resnet18v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
}
TEST_P(Test_ONNX_nets, ResNet50v1)
{
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-67; 75], after Softmax [0, 0.98]
testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
}
TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
{
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applyTestTag(CV_TEST_TAG_VERYLONG);
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
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);
#endif
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
{
if (backend == DNN_BACKEND_OPENCV)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
throw SkipTestException("Test is disabled for OpenCL targets");
}
testONNXModels("resnet101_duc_hdc", pb);
}
TEST_P(Test_ONNX_nets, TinyYolov2)
{
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (cvtest::skipUnstableTests)
throw SkipTestException("Skip unstable test");
#if defined(INF_ENGINE_RELEASE)
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);
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
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// output range: [-11; 8]
double l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
l1 = 0.017;
lInf = 0.14;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.018;
lInf = 0.16;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.018f; lInf = 0.16f;
}
#endif
testONNXModels("tiny_yolo2", pb, l1, lInf);
}
TEST_P(Test_ONNX_nets, CNN_MNIST)
{
// output range: [-1952; 6574], after Softmax [0; 1]
testONNXModels("cnn_mnist", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, MobileNet_v2)
{
// output range: [-166; 317], after Softmax [0; 1]
testONNXModels("mobilenetv2", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, LResNet100E_IR)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
#endif
CV_TEST_TAG_DEBUG_LONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
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);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
double l1 = default_l1, lInf = default_lInf;
// output range: [-3; 3]
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.009;
lInf = 0.035;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_CPU)
{
l1 = 4.6e-5;
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lInf = 1.9e-4;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.008;
lInf = 0.04;
}
testONNXModels("LResNet100E_IR", pb, l1, lInf);
}
TEST_P(Test_ONNX_nets, Emotion_ferplus)
{
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
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double l1 = default_l1;
double lInf = default_lInf;
// Output values are in range [-2.011, 2.111]
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
l1 = 0.007;
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
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{
l1 = 0.021;
lInf = 0.034;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) {
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l1 = 2.4e-4;
lInf = 6e-4;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.012f; lInf = 0.035f;
}
#endif
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testONNXModels("emotion_ferplus", pb, l1, lInf);
}
TEST_P(Test_ONNX_nets, Inception_v2)
{
testONNXModels("inception_v2", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, DenseNet121)
{
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applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-87; 138], after Softmax [0; 1]
testONNXModels("densenet121", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
}
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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#endif
testONNXModels("inception_v1", pb);
}
TEST_P(Test_ONNX_nets, Shufflenet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
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);
}
testONNXModels("shufflenet", pb);
}
TEST_P(Test_ONNX_nets, Resnet34_kinetics)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
if (backend == DNN_BACKEND_CUDA)
{
// ok
}
else 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
else 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
else if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
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String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
Mat image0 = imread(findDataFile("dnn/dog416.png"));
Mat image1 = imread(findDataFile("dnn/street.png"));
Mat ref0 = blobFromNPY(_tf("data/output_kinetics0.npy"));
Mat ref1 = blobFromNPY(_tf("data/output_kinetics1.npy"));
std::vector<Mat> images_0(16, image0);
std::vector<Mat> images_1(16, image1);
Mat blob0 = blobFromImages(images_0, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
Mat blob1 = blobFromImages(images_1, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
Net permute;
LayerParams lp;
int order[] = {1, 0, 2, 3};
lp.set("order", DictValue::arrayInt<int*>(&order[0], 4));
permute.addLayerToPrev("perm", "Permute", lp);
permute.setPreferableBackend(backend);
permute.setPreferableTarget(target);
permute.setInput(blob0);
Mat input0 = permute.forward().clone();
permute.setInput(blob1);
Mat input1 = permute.forward().clone();
int dims[] = {1, 3, 16, 112, 112};
input0 = input0.reshape(0, 5, &dims[0]);
input1 = input1.reshape(0, 5, &dims[0]);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
// output range [-5, 11]
float l1 = 0.0013, lInf = 0.009;
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.01;
lInf = 0.06;
}
checkBackend(&input0, &ref0);
net.setInput(input0);
Mat out = net.forward().clone();
normAssert(ref0, out, "", l1, lInf);
checkBackend(&input1, &ref1);
net.setInput(input1);
out = net.forward().clone();
normAssert(ref1, out, "", l1, lInf);
expectNoFallbacksFromIE(net);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
}} // namespace