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Merge pull request #20480 from JulieBar:lstm_pytest

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Add Python's test for LSTM layer

* Add Python's test for LSTM layer

* Set different test threshold for FP16 target

* rename test to test_input_3d

Co-authored-by: Julie Bareeva <julia.bareeva@xperience.ai>
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Julia Bareeva 2021-08-05 18:13:17 +03:00 committed by GitHub
parent d5f34cf34c
commit 633fedaa96
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1 changed files with 36 additions and 0 deletions
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36
modules/dnn/misc/python/test/test_dnn.py
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@ -62,6 +62,12 @@ def printParams(backend, target):
}
print('%s/%s' % (backendNames[backend], targetNames[target]))
def getDefaultThreshold(target):
if target == cv.dnn.DNN_TARGET_OPENCL_FP16 or target == cv.dnn.DNN_TARGET_MYRIAD:
return 4e-3
else:
return 1e-5
testdata_required = bool(os.environ.get('OPENCV_DNN_TEST_REQUIRE_TESTDATA', False))
g_dnnBackendsAndTargets = None
@ -305,5 +311,35 @@ class dnn_test(NewOpenCVTests):
cv.dnn_unregisterLayer('CropCaffe')
# check that dnn module can work with 3D tensor as input for network
def test_input_3d(self):
model = self.find_dnn_file('dnn/onnx/models/hidden_lstm.onnx')
input_file = self.find_dnn_file('dnn/onnx/data/input_hidden_lstm.npy')
output_file = self.find_dnn_file('dnn/onnx/data/output_hidden_lstm.npy')
if model is None:
raise unittest.SkipTest("Missing DNN test files (dnn/onnx/models/hidden_lstm.onnx). "
"Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
if input_file is None or output_file is None:
raise unittest.SkipTest("Missing DNN test files (dnn/onnx/data/{input/output}_hidden_lstm.npy). "
"Verify OPENCV_DNN_TEST_DATA_PATH configuration parameter.")
net = cv.dnn.readNet(model)
input = np.load(input_file)
# we have to expand the shape of input tensor because Python bindings cut 3D tensors to 2D
# it should be fixed in future. see : https://github.com/opencv/opencv/issues/19091
# please remove `expand_dims` after that
input = np.expand_dims(input, axis=3)
gold_output = np.load(output_file)
net.setInput(input)
for backend, target in self.dnnBackendsAndTargets:
printParams(backend, target)
net.setPreferableBackend(backend)
net.setPreferableTarget(target)
real_output = net.forward()
normAssert(self, real_output, gold_output, "", getDefaultThreshold(target))
if __name__ == '__main__':
NewOpenCVTests.bootstrap()