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[CPU] proposal_onnx tensor check was added and test was disabled (#4603)

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Alexey Varyzgin 2021-03-16 11:21:57 +03:00 committed by GitHub
parent 7a533f7abf
commit 8cfb63cb56
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 120 additions and 108 deletions
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177
inference-engine/src/mkldnn_plugin/nodes/proposal_onnx.cpp
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@ -313,91 +313,108 @@ public:
StatusCode execute(std::vector<Blob::Ptr> &inputs, std::vector<Blob::Ptr> &outputs, StatusCode execute(std::vector<Blob::Ptr> &inputs, std::vector<Blob::Ptr> &outputs,
ResponseDesc *resp) noexcept override { ResponseDesc *resp) noexcept override {
if (inputs.size() != 4 || outputs.size() != 2) { try {
if (inputs.size() != 4 || outputs.size() != 2) {
THROW_IE_EXCEPTION << "Incorrect number of input or output edges!";
}
size_t anchor_dims_size = 1;
for (size_t i = 0; i < inputs[INPUT_ANCHORS]->getTensorDesc().getDims().size(); i++) {
anchor_dims_size *= inputs[INPUT_ANCHORS]->getTensorDesc().getDims()[i];
}
size_t deltas_dims_size = 1;
for (size_t i = 0; i < inputs[INPUT_DELTAS]->getTensorDesc().getDims().size(); i++) {
deltas_dims_size *= inputs[INPUT_DELTAS]->getTensorDesc().getDims()[i];
}
if (anchor_dims_size != deltas_dims_size)
THROW_IE_EXCEPTION << "'Anchors' blob size for ONNXProposal is incompatible with 'deltas' blob size!";
size_t score_dims_size = 1;
for (size_t i = 0; i < inputs[INPUT_SCORES]->getTensorDesc().getDims().size(); i++) {
score_dims_size *= inputs[INPUT_SCORES]->getTensorDesc().getDims()[i];
}
if (deltas_dims_size != (4 * score_dims_size))
THROW_IE_EXCEPTION << "'Deltas' blob size for ONNXProposal is incompatible with 'scores' blob size!";
// Prepare memory
const float* p_deltas_item = inputs[INPUT_DELTAS]->buffer();
const float* p_scores_item = inputs[INPUT_SCORES]->buffer();
const float* p_anchors_item = inputs[INPUT_ANCHORS]->buffer();
const float* p_img_info_cpu = inputs[INPUT_IM_INFO]->buffer();
float* p_roi_item = outputs[OUTPUT_ROIS]->buffer();
float* p_roi_score_item = outputs[OUTPUT_SCORES]->buffer();
const int anchors_num = inputs[INPUT_SCORES]->getTensorDesc().getDims()[0];
// bottom shape: (num_anchors) x H x W
const int bottom_H = inputs[INPUT_DELTAS]->getTensorDesc().getDims()[1];
const int bottom_W = inputs[INPUT_DELTAS]->getTensorDesc().getDims()[2];
// input image height & width
const float img_H = p_img_info_cpu[0];
const float img_W = p_img_info_cpu[1];
// scale factor for height & width
// minimum box width & height
const float min_box_H = min_size_;
const float min_box_W = min_size_;
// number of all proposals = num_anchors * H * W
const int num_proposals = anchors_num * bottom_H * bottom_W;
// number of top-n proposals before NMS
const int pre_nms_topn = std::min<int>(num_proposals, pre_nms_topn_);
// number of final RoIs
int num_rois = 0;
// enumerate all proposals
// num_proposals = num_anchors * H * W
// (x1, y1, x2, y2, score) for each proposal
// NOTE: for bottom, only foreground scores are passed
struct ProposalBox {
float x0;
float y0;
float x1;
float y1;
float score;
};
std::vector<ProposalBox> proposals_(num_proposals);
std::vector<float> unpacked_boxes(5 * pre_nms_topn);
std::vector<int> is_dead(pre_nms_topn);
// Execute
int batch_size = 1; // inputs[INPUT_DELTAS]->getTensorDesc().getDims()[0];
for (int n = 0; n < batch_size; ++n) {
refine_anchors(p_deltas_item, p_scores_item, p_anchors_item,
reinterpret_cast<float *>(&proposals_[0]), anchors_num, bottom_H,
bottom_W, img_H, img_W,
min_box_H, min_box_W,
static_cast<const float>(log(1000. / 16.)),
1.0f);
std::partial_sort(proposals_.begin(), proposals_.begin() + pre_nms_topn, proposals_.end(),
[](const ProposalBox& struct1, const ProposalBox& struct2) {
return (struct1.score > struct2.score);
});
unpack_boxes(reinterpret_cast<float *>(&proposals_[0]), &unpacked_boxes[0], pre_nms_topn);
nms_cpu(pre_nms_topn, &is_dead[0], &unpacked_boxes[0], &roi_indices_[0], &num_rois, 0,
nms_thresh_, post_nms_topn_, coordinates_offset);
fill_output_blobs(&unpacked_boxes[0], &roi_indices_[0], p_roi_item, p_roi_score_item,
pre_nms_topn, num_rois, post_nms_topn_);
}
return OK;
} catch (const InferenceEngine::details::InferenceEngineException& e) {
if (resp) { if (resp) {
std::string errorMsg = "Incorrect number of input or output edges!"; std::string errorMsg = e.what();
errorMsg.copy(resp->msg, sizeof(resp->msg) - 1); errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
} }
return GENERAL_ERROR; return GENERAL_ERROR;
} }
// Prepare memory
const float* p_deltas_item = inputs[INPUT_DELTAS]->buffer();
const float* p_scores_item = inputs[INPUT_SCORES]->buffer();
const float* p_anchors_item = inputs[INPUT_ANCHORS]->buffer();
const float* p_img_info_cpu = inputs[INPUT_IM_INFO]->buffer();
float* p_roi_item = outputs[OUTPUT_ROIS]->buffer();
float* p_roi_score_item = outputs[OUTPUT_SCORES]->buffer();
size_t img_info_size = 1;
for (size_t i = 0; i < inputs[INPUT_IM_INFO]->getTensorDesc().getDims().size(); i++) {
img_info_size *= inputs[INPUT_IM_INFO]->getTensorDesc().getDims()[i];
}
const int anchors_num = inputs[INPUT_SCORES]->getTensorDesc().getDims()[0];
// bottom shape: (num_anchors) x H x W
const int bottom_H = inputs[INPUT_DELTAS]->getTensorDesc().getDims()[1];
const int bottom_W = inputs[INPUT_DELTAS]->getTensorDesc().getDims()[2];
// input image height & width
const float img_H = p_img_info_cpu[0];
const float img_W = p_img_info_cpu[1];
// scale factor for height & width
// minimum box width & height
const float min_box_H = min_size_;
const float min_box_W = min_size_;
// number of all proposals = num_anchors * H * W
const int num_proposals = anchors_num * bottom_H * bottom_W;
// number of top-n proposals before NMS
const int pre_nms_topn = std::min<int>(num_proposals, pre_nms_topn_);
// number of final RoIs
int num_rois = 0;
// enumerate all proposals
// num_proposals = num_anchors * H * W
// (x1, y1, x2, y2, score) for each proposal
// NOTE: for bottom, only foreground scores are passed
struct ProposalBox {
float x0;
float y0;
float x1;
float y1;
float score;
};
std::vector<ProposalBox> proposals_(num_proposals);
std::vector<float> unpacked_boxes(5 * pre_nms_topn);
std::vector<int> is_dead(pre_nms_topn);
// Execute
int batch_size = 1; // inputs[INPUT_DELTAS]->getTensorDesc().getDims()[0];
for (int n = 0; n < batch_size; ++n) {
refine_anchors(p_deltas_item, p_scores_item, p_anchors_item,
reinterpret_cast<float *>(&proposals_[0]), anchors_num, bottom_H,
bottom_W, img_H, img_W,
min_box_H, min_box_W,
static_cast<const float>(log(1000. / 16.)),
1.0f);
std::partial_sort(proposals_.begin(), proposals_.begin() + pre_nms_topn, proposals_.end(),
[](const ProposalBox& struct1, const ProposalBox& struct2) {
return (struct1.score > struct2.score);
});
unpack_boxes(reinterpret_cast<float *>(&proposals_[0]), &unpacked_boxes[0], pre_nms_topn);
nms_cpu(pre_nms_topn, &is_dead[0], &unpacked_boxes[0], &roi_indices_[0], &num_rois, 0,
nms_thresh_, post_nms_topn_, coordinates_offset);
fill_output_blobs(&unpacked_boxes[0], &roi_indices_[0], p_roi_item, p_roi_score_item,
pre_nms_topn, num_rois, post_nms_topn_);
}
return OK;
} }
private: private:

4
ngraph/test/models/onnx/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.prototxt
View File

@ -52,7 +52,7 @@ graph {
elem_type: 1 elem_type: 1
shape { shape {
dim { dim {
dim_value: 16 dim_value: 36
} }
dim { dim {
dim_value: 4 dim_value: 4
@ -87,7 +87,7 @@ graph {
elem_type: 1 elem_type: 1
shape { shape {
dim { dim {
dim_value: 13 dim_value: 3
} }
dim { dim {
dim_value: 2 dim_value: 2

44
ngraph/test/onnx/onnx_import_org_openvino.in.cpp
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@ -431,13 +431,21 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_experimental_detectron_generate_proposal
// im_info // im_info
test_case.add_input<float>({1.0f, 1.0f, 1.0f}); test_case.add_input<float>({1.0f, 1.0f, 1.0f});
// anchors // anchors
test_case.add_input<float>({ test_case.add_input<float>(
5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
});
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f});
// deltas // deltas
test_case.add_input<float>( test_case.add_input<float>(
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
@ -455,26 +463,16 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_experimental_detectron_generate_proposal
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f}); 1.0f, 1.0f, 1.0f});
// scores // scores
test_case.add_input<float>( test_case.add_input<float>({
{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, });
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
test_case.add_expected_output<float>( test_case.add_expected_output<float>(
Shape{6, 4}, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, Shape{6, 4}, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}); 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f});
test_case.add_expected_output<float>(Shape{6}, {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}); test_case.add_expected_output<float>(Shape{6}, {8.0f, 5.0f, 4.0f, 1.0f, 1.0f, 1.0f});
test_case.run(); test_case.run();
} }

3
ngraph/test/runtime/ie/unit_test.manifest
View File

@ -1590,9 +1590,6 @@ evaluate_mvn_6_across_chanells
evaluate_mvn_6_across_batch evaluate_mvn_6_across_batch
IE_CPU.onnx_mvn_v6 IE_CPU.onnx_mvn_v6
# The test randomly fails in CI (MSVC2019 Debug)
onnx_model_experimental_detectron_generate_proposals_single_image
# Issue 49621: Incorrect blob sizes for node BinaryConvolution_X # Issue 49621: Incorrect blob sizes for node BinaryConvolution_X
bin_convolution_2D_1batch_1channel bin_convolution_2D_1batch_1channel
bin_convolution_2D_1batch_1channel_padding_pad_val_0 bin_convolution_2D_1batch_1channel_padding_pad_val_0