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Posted to commits@mxnet.apache.org by zh...@apache.org on 2021/04/20 18:12:45 UTC

[incubator-mxnet] branch v1.x updated: add faster_rcnn_fpn models (#20190)

This is an automated email from the ASF dual-hosted git repository.

zha0q1 pushed a commit to branch v1.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/v1.x by this push:
     new b0807c3  add faster_rcnn_fpn models (#20190)
b0807c3 is described below

commit b0807c39c74c99c90917128c7b43a5caa5e913d9
Author: Zhaoqi Zhu <zh...@gmail.com>
AuthorDate: Tue Apr 20 11:10:20 2021 -0700

    add faster_rcnn_fpn models (#20190)
---
 tests/python-pytest/onnx/test_onnxruntime_cv.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tests/python-pytest/onnx/test_onnxruntime_cv.py b/tests/python-pytest/onnx/test_onnxruntime_cv.py
index e03923b..e0e24cf 100644
--- a/tests/python-pytest/onnx/test_onnxruntime_cv.py
+++ b/tests/python-pytest/onnx/test_onnxruntime_cv.py
@@ -238,6 +238,8 @@ def obj_detection_test_images(tmpdir_factory):
     'faster_rcnn_resnet101_v1d_coco',
     'yolo3_darknet53_coco',
     'yolo3_mobilenet1.0_coco',
+    'faster_rcnn_fpn_resnet50_v1b_coco',
+    'faster_rcnn_fpn_resnet101_v1d_coco',
     'mask_rcnn_resnet18_v1b_coco',
     'mask_rcnn_fpn_resnet18_v1b_coco',
     'mask_rcnn_resnet50_v1b_coco',
@@ -315,6 +317,11 @@ def test_obj_detection_model_inference_onnxruntime(tmp_path, model, obj_detectio
                     assert_obj_detetion_result(mx_class_ids[0], mx_scores[0], mx_boxes[0],
                                                onnx_class_ids[0], onnx_scores[0], onnx_boxes[0],
                                                score_thresh=0.8, score_tol=0.05, box_tol=15)
+                elif model.startswith('faster_rcnn_fpn'):
+                    onnx_class_ids, onnx_scores, onnx_boxes = session.run([], {input_name: img_data.asnumpy()})
+                    assert_obj_detetion_result(mx_class_ids[0], mx_scores[0], mx_boxes[0],
+                                               onnx_class_ids[0], onnx_scores[0], onnx_boxes[0],
+                                               score_thresh=0.8, score_tol=0.05, box_tol=30)
                 else:
                     onnx_class_ids, onnx_scores, onnx_boxes = session.run([], {input_name: img_data.asnumpy()})
                     assert_obj_detetion_result(mx_class_ids[0], mx_scores[0], mx_boxes[0],