jwfromm commented on a change in pull request #5135: [ONNX]Pool3d & upsample3d
op support
URL: https://github.com/apache/incubator-tvm/pull/5135#discussion_r396796719
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File path: python/tvm/relay/frontend/onnx.py
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@@ -780,19 +784,36 @@ def _impl_v9(cls, inputs, attr, params):
assert len(inputs) == 2, "Upsample op take 2 inputs, {}
given".format(len(inputs))
scales = params[inputs[1].name_hint].asnumpy()
inputs = inputs[:1]
- assert len(scales) == 4 and scales[0] == 1.0 and scales[1] == 1.0
+ assert scales[0] == 1.0 and scales[1] == 1.0
+ input_shape = infer_shape(inputs[0])
+ dims = len(input_shape)
+ isUpSample3D = bool(dims == 5)
mode = attr.get('mode')
if mode == b'nearest':
method = "nearest_neighbor"
elif mode == b'linear':
- method = "bilinear"
+ method = "trilinear" if isUpSample3D else "bilinear"
else:
raise tvm.error.OpAttributeInvalid(
'Value {} in attribute "mode" of operator Upsample is not
valid.'.format(mode))
- attr = {'scale_h': scales[-2], 'scale_w': scales[-1], 'method': method,
- 'layout': 'NCHW', 'align_corners': True}
- return AttrCvt('upsampling')(inputs, attr)
-
+ if isUpSample3D:
+ assert len(scales) == 5
+ attr = {'scale_d': scales[-3],
+ 'scale_h': scales[-2],
+ 'scale_w': scales[-1],
+ 'method': method,
+ 'layout': 'NCDHW',
+ 'coordinate_transformation_mode':'half_pixel'}
+ op_name = 'upsampling3d'
+ else:
+ assert len(scales) == 4
+ attr = {'scale_h': scales[-2],
+ 'scale_w': scales[-1],
+ 'method': method,
+ 'layout': 'NCHW',
+ 'align_corners': True}
Review comment:
Since many of these attributes are shared between 2d and 3d, it'd be more
space efficient to define the shared attributes then update the missing /
different bits inside the if/else.
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