Beya2019 commented on a change in pull request #6443:
URL: https://github.com/apache/incubator-tvm/pull/6443#discussion_r486813081
##########
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:
"we need remove this constraint for relay type inference and move it to
op strategy for corresponding targets"
For the issue, there already exists the constraint as:
`python/tvm/relay/op/strategy/cuda.py`
```
@roi_align_strategy.register(["cuda", "gpu"])
def roi_align_strategy_cuda(attrs, inputs, out_type, target):
"""roi_align cuda strategy"""
strategy = _op.OpStrategy()
strategy.add_implementation(wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
wrap_topi_schedule(topi.cuda.schedule_roi_align),
name="roi_align_nchw.cuda")
return strategy
```
and `python/tvm/relay/op/strategy/x86.py`
```
@roi_align_strategy.register("cpu")
def roi_align_strategy_cpu(attrs, inputs, out_type, target):
"""roi_align x86 strategy"""
strategy = _op.OpStrategy()
strategy.add_implementation(wrap_compute_roi_align(topi.x86.roi_align_nchw),
wrap_topi_schedule(topi.generic.schedule_roi_align),
name="roi_align.x86")
return strategy
```
In future, we just need to realize the topi.vision.rcnn.roi_align_nhwc and
add the constraint(call topi.vision.rcnn.roi_align_nhwc) to the special target.
And I will realize this job as soon as possible.
##########
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:
@kevinthesun
"we need remove this constraint for relay type inference and move it to op
strategy for corresponding targets"
For the issue, there already exists the constraint as:
`python/tvm/relay/op/strategy/cuda.py`
```
@roi_align_strategy.register(["cuda", "gpu"])
def roi_align_strategy_cuda(attrs, inputs, out_type, target):
"""roi_align cuda strategy"""
strategy = _op.OpStrategy()
strategy.add_implementation(wrap_compute_roi_align(topi.vision.rcnn.roi_align_nchw),
wrap_topi_schedule(topi.cuda.schedule_roi_align),
name="roi_align_nchw.cuda")
return strategy
```
and `python/tvm/relay/op/strategy/x86.py`
```
@roi_align_strategy.register("cpu")
def roi_align_strategy_cpu(attrs, inputs, out_type, target):
"""roi_align x86 strategy"""
strategy = _op.OpStrategy()
strategy.add_implementation(wrap_compute_roi_align(topi.x86.roi_align_nchw),
wrap_topi_schedule(topi.generic.schedule_roi_align),
name="roi_align.x86")
return strategy
```
In future, we just need to realize the topi.vision.rcnn.roi_align_nhwc and
add the constraint(call topi.vision.rcnn.roi_align_nhwc) to the special target.
And I will realize this job as soon as possible.
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