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Support for Unflatten operation requred by Attention layer #25851

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Support for Unflatten operation requred by Attention layer #25851

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2 changes: 2 additions & 0 deletions modules/dnn/src/layers/concat_layer.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -316,6 +316,8 @@ class ConcatLayerImpl CV_FINAL : public ConcatLayer
ranges[cAxis].start = 0;
for (size_t i = 0; i < inputs.size(); i++)
{
if (inputs[i].empty())
continue;
ranges[cAxis].end = ranges[cAxis].start + inputs[i].size[cAxis];
for (int j = 0; j < outMat.dims; ++j)
{
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14 changes: 9 additions & 5 deletions modules/dnn/src/layers/slice_layer.cpp
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Expand Up @@ -69,10 +69,12 @@ Range normalizeRange(const Range& input_range, int n)
{
Range range = input_range;

range.start = std::min(std::max(range.start, -n), n - 1);
if (range.start < 0)
{
range.start += n;
if (!(range.start == n)){
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@dkurt dkurt Jul 2, 2024

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if (range.start != n)

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@dkurt dkurt Jul 2, 2024

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If range.start == n doe it mean uninitialized value?

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@Abdurrahheem Abdurrahheem Jul 3, 2024 •
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no, it means that start index is equal to the last dimention of the input, in which case we do not want it to be (n-1)

range.start = std::min(std::max(range.start, -n), n - 1);
if (range.start < 0)
{
range.start += n;
}
}

range.end = std::min(std::max(range.end, -n), n);
Expand Down Expand Up @@ -632,7 +634,9 @@ class SliceLayerImpl : public SliceLayer
{
for (size_t i = 0; i < outputs.size(); i++)
{
inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
if (finalSliceRanges[i][0].start != finalSliceRanges[i][0].end){
inpMat(finalSliceRanges[i]).copyTo(outputs[i]);
}
}
}
else
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7 changes: 7 additions & 0 deletions modules/dnn/test/test_onnx_importer.cpp
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Expand Up @@ -3099,6 +3099,13 @@ TEST_P(Test_ONNX_layers, Attention) {
TEST_P(Test_ONNX_layers, AttentionSingleHead) {
testONNXModels("attention_single_head");
}
TEST_P(Test_ONNX_layers, TorchAttentionSingleHead){
testONNXModels("torch_attention_single_head");
}

TEST_P(Test_ONNX_layers, TorchUnflatten){
testONNXModels("unflatten");
}

TEST_P(Test_ONNX_nets, ViT_B_32) {
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_LONG);
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