kevinthesun commented on a change in pull request #6443:
URL: https://github.com/apache/incubator-tvm/pull/6443#discussion_r486803979
##########
File path: python/tvm/relay/op/vision/rcnn.py
##########
@@ -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.
##########
File path: python/tvm/relay/op/vision/rcnn.py
##########
@@ -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
contraint 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.
##########
File path: python/tvm/relay/op/vision/rcnn.py
##########
@@ -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.
##########
File path: python/tvm/relay/op/vision/rcnn.py
##########
@@ -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:
Today we don't check roi_align layout in op strategy since we do the
check in relay infer type level. If we want to allow NHWC in relay level, we
need to remove that constraint in ROIAlignRel and move it here. Silently
selecting nchw implementation and fail in later stage(even runtime) is not
great.
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