Merge pull request #13694 from dkurt:dnn_ie_async
Asynchronous API from Intel's Inference Engine (#13694) * Add forwardAsync for asynchronous mode from Intel's Inference Engine * Python test for forwardAsync * Replace Future_Mat to AsyncMat * Shadow AsyncMat * Isolate InferRequest callback * Manage exceptions in Async API of IE
This commit is contained in:
Dmitry Kurtaev
committed by
Alexander Alekhin
Alexander Alekhin
parent
3abae3c511
commit
a5c92c2029
@@ -2,7 +2,13 @@
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typedef dnn::DictValue LayerId;
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typedef std::vector<dnn::MatShape> vector_MatShape;
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typedef std::vector<std::vector<dnn::MatShape> > vector_vector_MatShape;
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#ifdef CV_CXX11
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typedef std::chrono::milliseconds chrono_milliseconds;
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typedef std::future_status AsyncMatStatus;
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#else
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typedef size_t chrono_milliseconds;
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typedef size_t AsyncMatStatus;
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#endif
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template<>
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bool pyopencv_to(PyObject *o, dnn::DictValue &dv, const char *name)
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@@ -40,6 +46,46 @@ bool pyopencv_to(PyObject *o, std::vector<Mat> &blobs, const char *name) //requi
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return pyopencvVecConverter<Mat>::to(o, blobs, ArgInfo(name, false));
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}
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#ifdef CV_CXX11
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template<>
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PyObject* pyopencv_from(const std::future<Mat>& f_)
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{
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std::future<Mat>& f = const_cast<std::future<Mat>&>(f_);
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Ptr<cv::dnn::AsyncMat> p(new std::future<Mat>(std::move(f)));
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return pyopencv_from(p);
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}
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template<>
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PyObject* pyopencv_from(const std::future_status& status)
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{
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return pyopencv_from((int)status);
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}
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template<>
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bool pyopencv_to(PyObject* src, std::chrono::milliseconds& dst, const char* name)
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{
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size_t millis = 0;
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if (pyopencv_to(src, millis, name))
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{
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dst = std::chrono::milliseconds(millis);
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return true;
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}
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else
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return false;
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}
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#else
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template<>
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PyObject* pyopencv_from(const cv::dnn::AsyncMat&)
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{
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CV_Error(Error::StsNotImplemented, "C++11 is required.");
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return 0;
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}
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#endif // CV_CXX11
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template<typename T>
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PyObject* pyopencv_from(const dnn::DictValue &dv)
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{
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@@ -0,0 +1,22 @@
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#error This is a shadow header file, which is not intended for processing by any compiler. \
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Only bindings parser should handle this file.
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namespace cv { namespace dnn {
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class CV_EXPORTS_W AsyncMat
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{
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public:
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//! Wait for Mat object readiness and return it.
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CV_WRAP Mat get();
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//! Wait for Mat object readiness.
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CV_WRAP void wait() const;
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/** @brief Wait for Mat object readiness specific amount of time.
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* @param timeout Timeout in milliseconds
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* @returns [std::future_status](https://en.cppreference.com/w/cpp/thread/future_status)
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*/
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CV_WRAP AsyncMatStatus wait_for(std::chrono::milliseconds timeout) const;
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};
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}}
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@@ -5,8 +5,8 @@ import numpy as np
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from tests_common import NewOpenCVTests, unittest
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def normAssert(test, a, b, lInf=1e-5):
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test.assertLess(np.max(np.abs(a - b)), lInf)
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def normAssert(test, a, b, msg=None, lInf=1e-5):
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test.assertLess(np.max(np.abs(a - b)), lInf, msg)
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def inter_area(box1, box2):
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x_min, x_max = max(box1[0], box2[0]), min(box1[2], box2[2])
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@@ -53,53 +53,6 @@ def normAssertDetections(test, ref, out, confThreshold=0.0, scores_diff=1e-5, bo
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if errMsg:
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test.fail(errMsg)
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# Returns a simple one-layer network created from Caffe's format
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def getSimpleNet():
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prototxt = """
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name: "simpleNet"
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input: "data"
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layer {
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type: "Identity"
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name: "testLayer"
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top: "testLayer"
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bottom: "data"
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}
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"""
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return cv.dnn.readNetFromCaffe(bytearray(prototxt, 'utf8'))
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def testBackendAndTarget(backend, target):
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net = getSimpleNet()
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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inp = np.random.standard_normal([1, 2, 3, 4]).astype(np.float32)
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try:
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net.setInput(inp)
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net.forward()
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except BaseException as e:
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return False
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return True
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haveInfEngine = testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU)
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dnnBackendsAndTargets = [
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[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
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]
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if haveInfEngine:
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
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if testBackendAndTarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
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if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
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if haveInfEngine and cv.ocl_Device.getDefault().isIntel():
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
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dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
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def printParams(backend, target):
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backendNames = {
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cv.dnn.DNN_BACKEND_OPENCV: 'OCV',
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@@ -116,8 +69,44 @@ def printParams(backend, target):
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class dnn_test(NewOpenCVTests):
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def __init__(self, *args, **kwargs):
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super(dnn_test, self).__init__(*args, **kwargs)
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self.dnnBackendsAndTargets = [
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[cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_CPU],
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]
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_CPU])
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_MYRIAD])
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if cv.ocl.haveOpenCL() and cv.ocl.useOpenCL():
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL])
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_TARGET_OPENCL_FP16])
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if cv.ocl_Device.getDefault().isIntel():
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL])
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if self.checkIETarget(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16):
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self.dnnBackendsAndTargets.append([cv.dnn.DNN_BACKEND_INFERENCE_ENGINE, cv.dnn.DNN_TARGET_OPENCL_FP16])
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def find_dnn_file(self, filename, required=True):
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return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd())], required=required)
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return self.find_file(filename, [os.environ.get('OPENCV_DNN_TEST_DATA_PATH', os.getcwd()),
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os.environ['OPENCV_TEST_DATA_PATH']],
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required=required)
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def checkIETarget(self, backend, target):
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proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=True)
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model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=True)
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net = cv.dnn.readNet(proto, model)
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net.setPreferableBackend(backend)
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net.setPreferableTarget(target)
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inp = np.random.standard_normal([1, 2, 10, 11]).astype(np.float32)
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try:
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net.setInput(inp)
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net.forward()
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except BaseException as e:
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return False
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return True
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def test_blobFromImage(self):
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np.random.seed(324)
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@@ -148,7 +137,7 @@ class dnn_test(NewOpenCVTests):
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def test_face_detection(self):
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testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
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proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt2', required=testdata_required)
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proto = self.find_dnn_file('dnn/opencv_face_detector.prototxt', required=testdata_required)
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model = self.find_dnn_file('dnn/opencv_face_detector.caffemodel', required=testdata_required)
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if proto is None or model is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/opencv_face_detector.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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@@ -164,7 +153,7 @@ class dnn_test(NewOpenCVTests):
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[0, 1, 0.95097077, 0.51901293, 0.45863652, 0.5777427, 0.5347801]]
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print('\n')
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for backend, target in dnnBackendsAndTargets:
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for backend, target in self.dnnBackendsAndTargets:
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printParams(backend, target)
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net = cv.dnn.readNet(proto, model)
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@@ -178,5 +167,52 @@ class dnn_test(NewOpenCVTests):
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normAssertDetections(self, ref, out, 0.5, scoresDiff, iouDiff)
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def test_async(self):
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timeout = 5000 # in milliseconds
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testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
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proto = self.find_dnn_file('dnn/layers/layer_convolution.prototxt', required=testdata_required)
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model = self.find_dnn_file('dnn/layers/layer_convolution.caffemodel', required=testdata_required)
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if proto is None or model is None:
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raise unittest.SkipTest("Missing DNN test files (dnn/layers/layer_convolution.{prototxt/caffemodel}). Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
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print('\n')
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for backend, target in self.dnnBackendsAndTargets:
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if backend != cv.dnn.DNN_BACKEND_INFERENCE_ENGINE:
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continue
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printParams(backend, target)
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netSync = cv.dnn.readNet(proto, model)
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netSync.setPreferableBackend(backend)
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netSync.setPreferableTarget(target)
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netAsync = cv.dnn.readNet(proto, model)
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netAsync.setPreferableBackend(backend)
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netAsync.setPreferableTarget(target)
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# Generate inputs
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numInputs = 10
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inputs = []
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for _ in range(numInputs):
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inputs.append(np.random.standard_normal([2, 6, 75, 113]).astype(np.float32))
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# Run synchronously
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refs = []
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for i in range(numInputs):
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netSync.setInput(inputs[i])
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refs.append(netSync.forward())
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# Run asynchronously. To make test more robust, process inputs in the reversed order.
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outs = []
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for i in reversed(range(numInputs)):
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netAsync.setInput(inputs[i])
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outs.insert(0, netAsync.forwardAsync())
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for i in reversed(range(numInputs)):
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if outs[i].wait_for(timeout) == 1:
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self.fail("Timeout")
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normAssert(self, refs[i], outs[i].get(), 'Index: %d' % i, 1e-10)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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