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Fix TensorFlow's Squeeze and a new fusion for SoftMax from slim backend

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This commit is contained in:
Dmitry Kurtaev
2019-04-12 18:40:27 +03:00
parent 39630e076a
commit 0cfd95c097
4 changed files with 98 additions and 7 deletions
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modules/dnn/test/test_tf_importer.cpp
+49
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@@ -658,6 +658,17 @@ TEST_P(Test_TensorFlow_layers, softmax)
runTensorFlowNet("slim_softmax");
}
TEST_P(Test_TensorFlow_layers, slim_softmax_v2)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD &&
getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
throw SkipTestException("Test is disabled for Myriad2");
#endif
runTensorFlowNet("slim_softmax_v2");
}
TEST_P(Test_TensorFlow_layers, relu6)
{
runTensorFlowNet("keras_relu6");
@@ -675,6 +686,44 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear)
runTensorFlowNet("resize_bilinear_factor");
}
TEST_P(Test_TensorFlow_layers, squeeze)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
)
throw SkipTestException("Test is disabled for Myriad2");
#endif
int inpShapes[][4] = {{1, 3, 4, 2}, {1, 3, 1, 2}, {1, 3, 4, 1}, {1, 3, 4, 1}}; // TensorFlow's shape (NHWC)
int outShapes[][3] = {{3, 4, 2}, {1, 3, 2}, {1, 3, 4}, {1, 3, 4}};
int squeeze_dims[] = {0, 2, 3, -1};
for (int i = 0; i < 4; ++i)
{
SCOPED_TRACE(format("i=%d", i));
std::string pbtxt =
"node { name: \"input\" op: \"Placeholder\""
"attr { key: \"data_format\" value { s: \"NHWC\" } } }"
"node { name: \"squeeze\" op: \"Squeeze\" input: \"input\""
"attr { key: \"squeeze_dims\" value { list { i:" + format("%d", squeeze_dims[i]) + "}}}}";
Net net = readNetFromTensorflow(0, 0, pbtxt.c_str(), pbtxt.size());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat tfInp(4, &inpShapes[i][0], CV_32F);
randu(tfInp, -1, 1);
// NHWC to NCHW
CV_Assert(inpShapes[i][0] == 1);
std::swap(inpShapes[i][2], inpShapes[i][3]);
std::swap(inpShapes[i][1], inpShapes[i][2]);
Mat cvInp = tfInp.reshape(1, tfInp.total() / inpShapes[i][1]).t();
cvInp = cvInp.reshape(1, 4, &inpShapes[i][0]);
net.setInput(cvInp);
Mat out = net.forward();
normAssert(tfInp.reshape(1, 3, &outShapes[i][0]), out, "", default_l1, default_lInf);
}
}
INSTANTIATE_TEST_CASE_P(/**/, Test_TensorFlow_layers, dnnBackendsAndTargets());
TEST(Test_TensorFlow, two_inputs)