COMPMID-1451: (3RDPARTY_UPDATE) Fixes for GenerateProposals graph node and BoxWithNMSLimitKernel
COMPMID-1792: Accuracy issue in CLGenerateProposals
This patch does the following:
- Some fixes for GenerateProposals function and tests
- Adapting BoxWithNMSLimitKernel to only accept U32 tensors as keeps_size
- Update 3rdparty
- Adds a small tolerance for a GenerateProposals test
Change-Id: Ia8ec1cdfe941fe05003645e86deb9ea6a6044d74
diff --git a/tests/validation/CL/GenerateProposalsLayer.cpp b/tests/validation/CL/GenerateProposalsLayer.cpp
index 28cdc71..b4772fc 100644
--- a/tests/validation/CL/GenerateProposalsLayer.cpp
+++ b/tests/validation/CL/GenerateProposalsLayer.cpp
@@ -68,38 +68,45 @@
TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Mismatching types
TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong deltas (number of transformation non multiple of 4)
TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong anchors (number of values per roi != 5)
- TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16)}), // Output tensor num_valid_proposals not scalar
+ TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Output tensor num_valid_proposals not scalar
+ TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16)}), // num_valid_proposals not U32
framework::dataset::make("deltas",{ TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32)})),
framework::dataset::make("anchors", { TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 9U), 1, DataType::F32),
+ TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 9U), 1, DataType::F32)})),
framework::dataset::make("proposals", { TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
+ TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32)})),
framework::dataset::make("scores_out", { TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
+ TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32)})),
framework::dataset::make("num_valid_proposals", { TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
- TensorInfo(TensorShape(1U, 10U), 1, DataType::U32)})),
+ TensorInfo(TensorShape(1U, 10U), 1, DataType::U32),
+ TensorInfo(TensorShape(1U, 1U), 1, DataType::F16)})),
framework::dataset::make("generate_proposals_info", { GenerateProposalsInfo(10.f, 10.f, 1.f),
GenerateProposalsInfo(10.f, 10.f, 1.f),
GenerateProposalsInfo(10.f, 10.f, 1.f),
GenerateProposalsInfo(10.f, 10.f, 1.f),
+ GenerateProposalsInfo(10.f, 10.f, 1.f),
GenerateProposalsInfo(10.f, 10.f, 1.f)})),
- framework::dataset::make("Expected", { true, false, false, false, false })),
+ framework::dataset::make("Expected", { true, false, false, false, false, false })),
scores, deltas, anchors, proposals, scores_out, num_valid_proposals, generate_proposals_info, expected)
{
ARM_COMPUTE_EXPECT(bool(CLGenerateProposalsLayer::validate(&scores.clone()->set_is_resizable(true),
@@ -262,7 +269,7 @@
CLTensor proposals;
CLTensor num_valid_proposals;
CLTensor scores_out;
- num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::F32));
+ num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::U32));
CLGenerateProposalsLayer generate_proposals;
generate_proposals.configure(&scores, &bbox_deltas, &anchors, &proposals, &scores_out, &num_valid_proposals,
@@ -286,26 +293,27 @@
// Gather num_valid_proposals
num_valid_proposals.map();
- const float N = *reinterpret_cast<float *>(num_valid_proposals.ptr_to_element(Coordinates(0, 0)));
+ const uint32_t N = *reinterpret_cast<uint32_t *>(num_valid_proposals.ptr_to_element(Coordinates(0, 0)));
num_valid_proposals.unmap();
// Select the first N entries of the proposals
CLTensor proposals_final;
CLSlice select_proposals;
- select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, size_t(N)));
+ select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, N));
proposals_final.allocator()->allocate();
select_proposals.run();
// Select the first N entries of the proposals
CLTensor scores_final;
CLSlice select_scores;
- select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(size_t(N)));
+ select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(N));
scores_final.allocator()->allocate();
select_scores.run();
+ const RelativeTolerance<float> tolerance_f32(1e-6f);
// Validate the output
- validate(CLAccessor(proposals_final), proposals_expected);
- validate(CLAccessor(scores_final), scores_expected);
+ validate(CLAccessor(proposals_final), proposals_expected, tolerance_f32);
+ validate(CLAccessor(scores_final), scores_expected, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsFixture<float>, framework::DatasetMode::ALL,