name change

Author: BowenBao

test/test_jit.py

@@ -17108,34 +17108,34 @@ class TestJitGeneratedFunctional(JitTestCase):
  ('interpolate', torch.zeros(3, 3, 3).view(1, 1, 3, 3, 3), (2,), 'trilinear_5d', (True, 'aten::__interpolate')),
  ('interpolate', torch.randn(S, M, M, M, M), (None, 2.), 'trilinear_5d_with_scale', (True, 'aten::__interpolate')),
  ('interpolate', torch.randn(S, M, M, M, M), (4,), 'trilinear_5d_with_size', (True, 'aten::__interpolate')),
- ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2, None, 'nearest', None, True),
- 'nearest_4d_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, True),
- 'nearest_4d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, True),
- 'bilinear_4d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, True),
- 'bilinear_4d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, True),
- 'bicubic_4d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, True),
- 'bicubic_4d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, True),
- 'nearest_3d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, True),
- 'nearest_3d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, True),
- 'linear_3d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, True),
- 'linear_3d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, True),
- 'nearest_5d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, True),
- 'nearest_5d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, True),
- 'trilinear_5d_with_scale_use_scale_factor', (True, 'aten::__interpolate')),
- ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, True),
- 'trilinear_5d_with_size_use_scale_factor', (True, 'aten::__interpolate')),
+ ('interpolate', torch.zeros(3, 3).view(1, 1, 3, 3), (2, None, 'nearest', None, False),
+ 'nearest_4d_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, S, M, M), (4, None, 'nearest', None, False),
+ 'nearest_4d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bilinear', None, False),
+ 'bilinear_4d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, S, M, M), (4, None, 'bilinear', None, False),
+ 'bilinear_4d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, S, M, M), (None, 2., 'bicubic', None, False),
+ 'bicubic_4d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, S, M, M), (4, None, 'bicubic', None, False),
+ 'bicubic_4d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M), (None, 2., 'nearest', None, False),
+ 'nearest_3d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M), (4, None, 'nearest', None, False),
+ 'nearest_3d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M), (None, 2., 'linear', None, False),
+ 'linear_3d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M), (4, None, 'linear', None, False),
+ 'linear_3d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'nearest', None, False),
+ 'nearest_5d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'nearest', None, False),
+ 'nearest_5d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M, M, M), (None, 2., 'trilinear', None, False),
+ 'trilinear_5d_with_scale_not_recompute_scales', (True, 'aten::__interpolate')),
+ ('interpolate', torch.randn(S, M, M, M, M), (4, None, 'trilinear', None, False),
+ 'trilinear_5d_with_size_not_recompute_scales', (True, 'aten::__interpolate')),
 ]
 
 

test/test_nn.py

@@ -7283,7 +7283,7 @@ def test_upsamplingBicubic2d(self):
  input = torch.randn(2, 2, 2, 2, requires_grad=True)
  gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
 
- def test_upsampling_use_scale_factor(self):
+ def test_upsampling_not_recompute_scales(self):
  # test output against known input: result must match opencv
  in_t = torch.arange(8).view(1, 2, 2, 2).type(torch.FloatTensor)
  expected_out_t = torch.Tensor(