Administrator/opencv-MIRROR
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-09-15 07:29:07 -05:00
Code Issues Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'upstream/3.4' into merge-3.4

Browse Source
This commit is contained in:
Alexander Alekhin
2021-11-27 13:17:44 +00:00
parent d470cfe86e b55d8f46f4
commit 57ee14d62d
37 changed files with 527 additions and 111 deletions
Download Patch File Download Diff File Expand all files Collapse all files

256
modules/dnn/test/test_tf_importer.cpp
View File

@@ -84,6 +84,10 @@ public:
void runTensorFlowNet(const std::string& prefix, bool hasText = false,
double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false, const std::string& groupPrefix = "")
{
if (cvtest::debugLevel > 0)
{
std::cout << prefix << groupPrefix << std::endl;
}
std::string netPath = path(prefix + groupPrefix + "_net.pb");
std::string netConfig = (hasText ? path(prefix + groupPrefix + "_net.pbtxt") : "");
std::string inpPath = path(prefix + "_in.npy");
@@ -119,6 +123,16 @@ public:
net.setInput(input);
cv::Mat output = net.forward();
normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
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;
}
}
};
@@ -133,7 +147,7 @@ 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);
runTensorFlowNet("max_pool_by_axis");
runTensorFlowNet("max_pool_by_axis", false, 0.0f, 0.0f);
}
TEST_P(Test_TensorFlow_layers, reduce_sum)
@@ -145,7 +159,11 @@ TEST_P(Test_TensorFlow_layers, reduce_sum)
TEST_P(Test_TensorFlow_layers, reduce_max_channel)
{
runTensorFlowNet("reduce_max_channel");
#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);
}
TEST_P(Test_TensorFlow_layers, reduce_sum_channel)
@@ -155,6 +173,10 @@ TEST_P(Test_TensorFlow_layers, reduce_sum_channel)
TEST_P(Test_TensorFlow_layers, reduce_max_channel_keep_dims)
{
#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, 0.0, 0.0, false, "_keep_dims");
}
@@ -221,13 +243,49 @@ TEST_P(Test_TensorFlow_layers, padding)
runTensorFlowNet("keras_pad_concat");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric)
TEST_P(Test_TensorFlow_layers, padding_asymmetric_1)
{
runTensorFlowNet("conv2d_asymmetric_pads_nchw");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric_2)
{
runTensorFlowNet("conv2d_asymmetric_pads_nhwc");
}
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
runTensorFlowNet("max_pool2d_asymmetric_pads_nchw");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric_4)
{
#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
runTensorFlowNet("max_pool2d_asymmetric_pads_nhwc");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric_5)
{
runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nchw");
}
TEST_P(Test_TensorFlow_layers, padding_asymmetric_6)
{
runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nhwc");
}
@@ -268,6 +326,13 @@ TEST_P(Test_TensorFlow_layers, pad_and_concat)
TEST_P(Test_TensorFlow_layers, concat_axis_1)
{
#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
#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
@@ -423,19 +488,77 @@ TEST_P(Test_TensorFlow_layers, pooling_reduce_sum)
runTensorFlowNet("reduce_sum"); // a SUM pooling over all spatial dimensions.
}
TEST_P(Test_TensorFlow_layers, pooling_reduce_sum2)
TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_0_false)
{
int axises[] = {0, 1, 2, 3};
for (int keepdims = 0; keepdims <= 1; ++keepdims)
{
for (int i = 0; i < sizeof(axises)/sizeof(axises[0]); ++i)
{
runTensorFlowNet(cv::format("reduce_sum_%d_%s", axises[i], (keepdims ? "True" : "False")));
}
runTensorFlowNet(cv::format("reduce_sum_1_2_%s", keepdims ? "True" : "False"));
}
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)
{
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;
}
#endif
runTensorFlowNet("reduce_sum_1_2_True");
}
TEST_P(Test_TensorFlow_layers, max_pool_grad)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -715,13 +838,14 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
double scoreDiff = default_l1, iouDiff = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
{
scoreDiff = 0.0043;
iouDiff = 0.037;
scoreDiff = 0.01;
iouDiff = 0.1;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
iouDiff = 0.04;
}
normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2019010000
expectNoFallbacksFromIE(net);
@@ -815,16 +939,13 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
expectNoFallbacksFromIE(net);
}
TEST_P(Test_TensorFlow_nets, Faster_RCNN)
TEST_P(Test_TensorFlow_nets, Faster_RCNN_inception_v2_coco_2018_01_28)
{
// FIXIT split test
applyTestTag(
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
"faster_rcnn_resnet50_coco_2018_01_28"};
#ifdef INF_ENGINE_RELEASE
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
@@ -835,13 +956,82 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
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);
#endif
#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);
#endif
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
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
);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// 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
);
#endif
#ifdef INF_ENGINE_RELEASE
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);
#endif
#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);
#endif
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
@@ -856,10 +1046,11 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
scoresDiff = 0.06;
iouDiff = 0.08;
}
for (int i = 0; i < 2; ++i)
std::string name = "faster_rcnn_resnet50_coco_2018_01_28";
{
std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
std::string proto = findDataFile("dnn/" + name + ".pbtxt");
std::string model = findDataFile("dnn/" + name + ".pb", false);
Net net = readNetFromTensorflow(model, proto);
net.setPreferableBackend(backend);
@@ -870,8 +1061,13 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
net.setInput(blob);
Mat out = net.forward();
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
normAssertDetections(ref, out, names[i].c_str(), 0.3, scoresDiff, iouDiff);
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);
}
}
@@ -1282,6 +1478,10 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear_down)
TEST_P(Test_TensorFlow_layers, resize_concat_optimization)
{
#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
runTensorFlowNet("resize_concat_optimization");
}
@@ -1406,7 +1606,7 @@ TEST_P(Test_TensorFlow_nets, Mask_RCNN)
Mat outDetections = outs[0];
Mat outMasks = outs[1];
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.019 : 2e-5;
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.2 : 2e-5;
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);
@@ -1440,7 +1640,7 @@ TEST_P(Test_TensorFlow_nets, Mask_RCNN)
double inter = cv::countNonZero(masks & refMasks);
double area = cv::countNonZero(masks | refMasks);
EXPECT_GE(inter / area, 0.99);
EXPECT_GE(inter / area, (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.98 : 0.99);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
expectNoFallbacks(net);