csullivan commented on a change in pull request #6085:
URL: https://github.com/apache/incubator-tvm/pull/6085#discussion_r459608948
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File path: python/tvm/relay/frontend/tensorflow.py
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@@ -637,10 +637,11 @@ def _impl(inputs, attr, params, mod):
iou_threshold = np.atleast_1d(inputs[3].data.asnumpy())[0]
# score_threshold was introduced from V3
score_threshold = np.atleast_1d(inputs[4].data.asnumpy())[0] if
len(inputs) > 4 else 0.0
+ pad_output = 'pad_to_max_output_size'
# Generate data with shape (1, num_anchors, 5)
scores = AttrCvt(op_name="expand_dims",
- ignores=['T_threshold'],
+ ignores=['T_threshold', pad_output],
Review comment:
NMSv4 semantics allow for the user to define whether or not to pad the
output using `pad_to_max_output_size=True/False`, see the example I provided in
the test. Some pre-trained TF models use NMSv4 with
pad_to_max_output_size=True, in which case, yes, they expect padding of the
indices if the number of boxes is less than `max_output_size`, as well as an
additional scalar output specifying the number of valid boxes.
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