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?
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