Proposal reference implementation

docs/ops/detection/Proposal_1.md

@@ -8,7 +8,7 @@
 
 **Detailed description**
 
-*Proposal* has three inputs: a tensor with probabilities whether particular bounding box corresponds to background and foreground, a tensor with bbox_deltas for each of the bounding boxes, a tensor with input image size in the [`image_height`, `image_width`, `scale_height_and_width`] or [`image_height`, `image_width`, `scale_height`, `scale_width`] format. The produced tensor has two dimensions `[batch_size * post_nms_topn, 5]`.
+*Proposal* has three inputs: a tensor with probabilities whether particular bounding box corresponds to background and foreground, a tensor with bbox_deltas for each of the bounding boxes, a tensor with input image size in the [`image_height`, `image_width`, `scale_height_and_width`] or [`image_height`, `image_width`, `scale_height`, `scale_width`] format. The produced tensor has two dimensions `[batch_size * post_nms_topn, 5]`, and for each output box contains batch index and box coordinates.
 *Proposal* layer does the following with the input tensor:
 1.  Generates initial anchor boxes. Left top corner of all boxes is at (0, 0). Width and height of boxes are calculated from *base_size* with *scale* and *ratio* attributes.
 2.  For each point in the first input tensor:
@@ -136,9 +136,9 @@
 
 **Inputs**:
 
-*   **1**: 4D input floating point tensor with class prediction scores. Required.
+*   **1**: 4D input floating point tensor with class prediction scores of shape `[batch_size, 2*K, H, W]`. Required.
 
-*   **2**: 4D input floating point tensor with box bbox_deltas. Required.
+*   **2**: 4D input floating point tensor with box bbox_deltas of shape `[batch_size, 4*K, H, W]`. Required.
 
 *   **3**: 1D input floating tensor 3 or 4 elements:  [`image_height`, `image_width`, `scale_height_and_width`] or [`image_height`, `image_width`, `scale_height`, `scale_width`]. Required.
 

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/proposal.cpp

@@ -45,5 +45,4 @@ INSTANTIATE_TEST_CASE_P(smoke_Proposal_tests, ProposalLayerTest,
                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                         ProposalLayerTest::getTestCaseName
 );
-
 }  // namespace

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp

@@ -59,6 +59,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*decomposition1_batch=5_hidden_size=10_input_size=30_.*tanh.relu.*_clip=0_linear_before_reset=1.*_targetDevice=CPU_.*)",
         // TODO: Sporadic Issue: 45163
         R"(.*Behavior.*CancellationTests.*canResetAfterCancelAsyncRequest.*)",
+        // TODO: Issue: 47271
+        R"(.*ProposalLayerTest.*)",
     };
 
     if (!InferenceEngine::with_cpu_x86_avx512_core()) {

inference-engine/tests/functional/plugin/myriad/shared_tests_instances/skip_tests_config.cpp

@@ -34,6 +34,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*(DSR_GatherND).*)",
         // TODO: Issue 26090
         ".*DSR_GatherStaticDataDynamicIdx.*f32.*1.3.200.304.*",
+        // TODO: Issue 47315
+        ".*ProposalLayerTest.*",
         // TODO: Issue 46755
         ".*DSR_GatherElements.*"
     };

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/proposal.hpp

@@ -61,7 +61,6 @@ class ProposalLayerTest
 
 protected:
     void SetUp() override;
-    void Validate() override;
 };
 
 }  // namespace LayerTestsDefinitions

inference-engine/tests/functional/shared_test_classes/src/single_layer/proposal.cpp

@@ -98,7 +98,8 @@ void ProposalLayerTest::SetUp() {
     std::vector<size_t> imageInfoShape = {3};
 
     auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(InferenceEngine::Precision::FP16);
-    auto params = ngraph::builder::makeParams(ngPrc, {{"scores", scoresShape}, {"boxes", boxesShape}});
+        // a_ and b_ are a workaround to solve alphabetic param sorting that destroys ordering
+    auto params = ngraph::builder::makeParams(ngPrc, {{"a_scores", scoresShape}, {"b_boxes", boxesShape}});
     auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
 
     auto proposal = std::dynamic_pointer_cast<ngraph::opset1::Proposal>(
@@ -126,15 +127,12 @@ InferenceEngine::Blob::Ptr ProposalLayerTest::GenerateInput(const InferenceEngin
     InferenceEngine::Blob::Ptr blobPtr;
 
     const std::string name = info.name();
-    if (name == "scores") {
+    if (name == "a_scores") {
         blobPtr = FuncTestUtils::createAndFillBlobFloat(info.getTensorDesc(), 1, 0, 1000, 8234231);
-    } else if (name == "boxes") {
+    } else if (name == "b_boxes") {
         blobPtr = FuncTestUtils::createAndFillBlobFloatNormalDistribution(info.getTensorDesc(), 0.0f, 0.2f, 7235346);
     }
 
     return blobPtr;
 }
-
-// TODO: for validation, reference version is required (#28373)
-void ProposalLayerTest::Validate() {}
 }  // namespace LayerTestsDefinitions