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Broadcasting from ONNX

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This commit is contained in:
Dmitry Kurtaev
2020-03-05 23:53:50 +03:00
parent a694e5074f
commit 9e332dc5fb
3 changed files with 111 additions and 62 deletions
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modules/dnn/test/test_onnx_importer.cpp
+17 -24
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@@ -32,29 +32,33 @@ public:
void testONNXModels(const String& basename, const Extension ext = npy,
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true)
bool checkNoFallbacks = true, int numInps = 1)
{
String onnxmodel = _tf("models/" + basename + ".onnx", required);
Mat inp, ref;
std::vector<Mat> inps(numInps);
Mat ref;
if (ext == npy) {
inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
for (int i = 0; i < numInps; ++i)
inps[i] = blobFromNPY(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".npy"));
ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
}
else if (ext == pb) {
inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
for (int i = 0; i < numInps; ++i)
inps[i] = readTensorFromONNX(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".pb"));
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
}
else
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
checkBackend(&inp, &ref);
checkBackend(&inps[0], &ref);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
for (int i = 0; i < numInps; ++i)
net.setInput(inps[i], numInps > 1 ? format("%d", i) : "");
Mat out = net.forward("");
if (useSoftmax)
@@ -328,25 +332,14 @@ TEST_P(Test_ONNX_layers, ResizeUnfused)
TEST_P(Test_ONNX_layers, MultyInputs)
{
const String model = _tf("models/multy_inputs.onnx");
testONNXModels("multy_inputs", npy, 0, 0, false, true, 2);
}
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy"));
checkBackend(&inp1, &ref);
net.setInput(inp1, "0");
net.setInput(inp2, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
TEST_P(Test_ONNX_layers, Broadcast)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("channel_broadcast", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Div)