roi_indices_.resize(post_nms_topn_);
addConfig(layer, {DataConfigurator(ConfLayout::PLN), DataConfigurator(ConfLayout::PLN), DataConfigurator(ConfLayout::PLN)},
{DataConfigurator(ConfLayout::PLN)});
- } catch (InferenceEngine::details::InferenceEngineException &ex) {
+ } catch (const InferenceEngine::details::InferenceEngineException &ex) {
errorMsg = ex.what();
}
}
StatusCode execute(std::vector<Blob::Ptr> &inputs, std::vector<Blob::Ptr> &outputs,
ResponseDesc *resp) noexcept override {
- if (inputs.size() != 3 || outputs.empty()) {
+ try {
+ if (inputs.size() != 3 || outputs.empty()) {
+ THROW_IE_EXCEPTION << "Incorrect number of input or output edges!";
+ }
+
+ // Prepare memory
+ const float *p_bottom_item = inputs[0]->buffer();
+ const float *p_d_anchor_item = inputs[1]->buffer();
+ const float *p_img_info_cpu = inputs[2]->buffer();
+ float *p_roi_item = outputs[0]->buffer();
+
+ size_t img_info_size = inputs[2]->getTensorDesc().getDims()[1];
+
+ // No second output so ignoring this
+ // Dtype* p_score_item = (top.size() > 1) ? top[1]->mutable_cpu_data() : NULL;
+
+ // bottom shape: (2 x num_anchors) x H x W
+ const int bottom_H = inputs[0]->getTensorDesc().getDims()[2];
+ const int bottom_W = inputs[0]->getTensorDesc().getDims()[3];
+
+ // input image height & width
+ const float img_H = p_img_info_cpu[swap_xy ? 1 : 0];
+ const float img_W = p_img_info_cpu[swap_xy ? 0 : 1];
+
+ // scale factor for height & width
+ const float scale_H = p_img_info_cpu[2];
+ const float scale_W = img_info_size > 3 ? p_img_info_cpu[3] : scale_H;
+
+ // minimum box width & height
+ const float min_box_H = min_size_ * scale_H;
+ const float min_box_W = min_size_ * scale_W;
+
+ // number of all proposals = num_anchors * H * W
+ const int num_proposals = anchors_shape_0 * 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(4 * pre_nms_topn);
+ std::vector<int> is_dead(pre_nms_topn);
+
+ // Execute
+ int nn = inputs[0]->getTensorDesc().getDims()[0];
+ for (int n = 0; n < nn; ++n) {
+ enumerate_proposals_cpu(p_bottom_item + num_proposals + n * num_proposals * 2,
+ p_d_anchor_item + n * num_proposals * 4,
+ &anchors_[0], reinterpret_cast<float *>(&proposals_[0]),
+ anchors_shape_0, bottom_H, bottom_W, img_H, img_W,
+ min_box_H, min_box_W, feat_stride_,
+ box_coordinate_scale_, box_size_scale_,
+ coordinates_offset, initial_clip, swap_xy, clip_before_nms);
+ 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);
+ retrieve_rois_cpu(num_rois, n, pre_nms_topn, &unpacked_boxes[0], &roi_indices_[0],
+ p_roi_item + n * post_nms_topn_ * 5,
+ post_nms_topn_, normalize_, img_H, img_W, clip_after_nms);
+ }
+
+ return OK;
+ } catch (const InferenceEngine::details::InferenceEngineException& e) {
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);
}
return GENERAL_ERROR;
}
-
- // Prepare memory
- const float* p_bottom_item = inputs[0]->buffer();
- const float* p_d_anchor_item = inputs[1]->buffer();
- const float* p_img_info_cpu = inputs[2]->buffer();
- float* p_roi_item = outputs[0]->buffer();
-
- size_t img_info_size = inputs[2]->getTensorDesc().getDims()[1];
-
- // No second output so ignoring this
- // Dtype* p_score_item = (top.size() > 1) ? top[1]->mutable_cpu_data() : NULL;
-
- // bottom shape: (2 x num_anchors) x H x W
- const int bottom_H = inputs[0]->getTensorDesc().getDims()[2];
- const int bottom_W = inputs[0]->getTensorDesc().getDims()[3];
-
- // input image height & width
- const float img_H = p_img_info_cpu[swap_xy ? 1 : 0];
- const float img_W = p_img_info_cpu[swap_xy ? 0 : 1];
-
- // scale factor for height & width
- const float scale_H = p_img_info_cpu[2];
- const float scale_W = img_info_size > 3 ? p_img_info_cpu[3] : scale_H;
-
- // minimum box width & height
- const float min_box_H = min_size_ * scale_H;
- const float min_box_W = min_size_ * scale_W;
-
- // number of all proposals = num_anchors * H * W
- const int num_proposals = anchors_shape_0 * 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(4 * pre_nms_topn);
- std::vector<int> is_dead(pre_nms_topn);
-
- // Execute
- int nn = inputs[0]->getTensorDesc().getDims()[0];
- for (int n = 0; n < nn; ++n) {
- enumerate_proposals_cpu(p_bottom_item + num_proposals + n*num_proposals*2, p_d_anchor_item + n*num_proposals*4,
- &anchors_[0], reinterpret_cast<float *>(&proposals_[0]),
- anchors_shape_0, bottom_H, bottom_W, img_H, img_W,
- min_box_H, min_box_W, feat_stride_,
- box_coordinate_scale_, box_size_scale_,
- coordinates_offset, initial_clip, swap_xy, clip_before_nms);
- 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);
- retrieve_rois_cpu(num_rois, n, pre_nms_topn, &unpacked_boxes[0], &roi_indices_[0], p_roi_item + n*post_nms_topn_*5,
- post_nms_topn_, normalize_, img_H, img_W, clip_after_nms);
- }
-
- return OK;
}
private:
// set output shapes by input shapes.
StatusCode getShapes(const std::vector<TensorDesc>& inShapes, std::vector<TensorDesc>& outShapes,
ResponseDesc *resp) noexcept override {
- if (inShapes.size() != 1) {
+ try {
+ if (inShapes.size() != 1) {
+ THROW_IE_EXCEPTION << "Incorrect input shapes!";
+ }
+ outShapes.clear();
+ outShapes.emplace_back(cnnLayer.precision, inShapes[0].getDims(), inShapes[0].getLayout());
+ return OK;
+ } catch (const InferenceEngine::details::InferenceEngineException& e) {
if (resp) {
- std::string errorMsg = "Incorrect input shapes!";
+ std::string errorMsg = e.what();
errorMsg.copy(resp->msg, sizeof(resp->msg) - 1);
}
return GENERAL_ERROR;
}
- outShapes.clear();
- outShapes.emplace_back(cnnLayer.precision, inShapes[0].getDims(), inShapes[0].getLayout());
- return OK;
}
};
projects / platform / upstream / dldt.git / blobdiff