trevor-m edited a comment on pull request #7123: URL: https://github.com/apache/tvm/pull/7123#issuecomment-756277499
Thanks for looking into it and finding that info @mbrookhart ! Here is the relevant relay graph: ``` boxes = relay.var("boxes", shape=(1, relay.Any(), 5), dtype="float32") max_output_size = relay.shape_of(boxes) max_output_size = relay.strided_slice(max_output_size, begin=[1], end=[2], strides=[1]) max_output_size = relay.squeeze(max_output_size) max_output_size = relay.minimum(relay.const(100, dtype="int32"), max_output_size) ct, data, indices = relay.vision.get_valid_counts( boxes, score_threshold=0.0, id_index=-1, score_index=0 ) nms_ret = relay.vision.non_max_suppression( data=boxes, valid_count=ct, indices=indices, max_output_size=max_output_size, iou_threshold=0.6, force_suppress=True, top_k=-1, coord_start=1, score_index=0, id_index=-1, return_indices=True, invalid_to_bottom=False, ) ``` The input shape is `[1, 0, 5]` during the model execution when the crash occurs. I haven't been able to reproduce with this standalone test yet. Maybe there is an edge case for size 0 max_output_size or num_anchors? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org