waytrue17 commented on a change in pull request #20190:
URL: https://github.com/apache/incubator-mxnet/pull/20190#discussion_r616164777
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File path: tests/python-pytest/onnx/test_onnxruntime_cv.py
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@@ -315,6 +317,11 @@ def normalize_image(imgfile):
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)
Review comment:
Other object detection models are able to pass the test with
`box_tol=0.01`, but here we loose it to `box_tol=30` for faster_rcnn models. Do
we know what causes the accuracy regression here?
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