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 we do check NCHW layout for
roi_align:
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, we might want to remove this
constraint for relay type inference 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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