kevinthesun commented on a change in pull request #6443:
URL: https://github.com/apache/incubator-tvm/pull/6443#discussion_r486803979
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File path: python/tvm/relay/op/vision/rcnn.py
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@@ -24,7 +24,7 @@ def roi_align(data, rois, pooled_size, spatial_scale,
sample_ratio=-1, layout='N
Parameters
----------
data : relay.Expr
- 4-D tensor with shape [batch, channel, height, width]
+ 4-D tensor with shape [batch, channel, height, width] or [batch,
height, width, channel]
Review comment:
OK. One thing to note is that on relay level NHWC roi_align will fail
during type inference since we do check NCHW layout:
https://github.com/apache/incubator-tvm/blob/master/src/relay/op/vision/rcnn_op.cc#L46
If we would like to allow NHWC roi_align even on relay level, we might want to
remove this check and move it to op strategy for corresponding targets, since
we don't have implementation for NHWC and usually layout for roi_align is not
an issue in end to end inference for these general purpose targets.
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