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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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Alexander Alekhin
6 years ago
committed by
Alexander Alekhin