From 6254ebef8d1d106e9cca8d4097786c6172cf70ce Mon Sep 17 00:00:00 2001
From: "q.yao" <yaoqian@sensetime.com>
Date: Tue, 8 Nov 2022 15:54:34 +0800
Subject: [PATCH] [Fix] Create Tensor with new_* method to support amp (#2389)
---
mmcv/ops/diff_iou_rotated.py | 4 +-
mmcv/ops/group_points.py | 6 +-
mmcv/ops/three_interpolate.py | 4 +-
tests/test_ops/test_group_points.py | 251 ++++++++---------------
tests/test_ops/test_three_interpolate.py | 35 ++--
5 files changed, 116 insertions(+), 184 deletions(-)
diff --git a/mmcv/ops/diff_iou_rotated.py b/mmcv/ops/diff_iou_rotated.py
index cdc6c72f8..ddcf4b4fc 100644
--- a/mmcv/ops/diff_iou_rotated.py
+++ b/mmcv/ops/diff_iou_rotated.py
@@ -235,9 +235,9 @@ def box2corners(box: Tensor) -> Tensor:
"""
B = box.size()[0]
x, y, w, h, alpha = box.split([1, 1, 1, 1, 1], dim=-1)
- x4 = torch.FloatTensor([0.5, -0.5, -0.5, 0.5]).to(box.device)
+ x4 = box.new_tensor([0.5, -0.5, -0.5, 0.5]).to(box.device)
x4 = x4 * w # (B, N, 4)
- y4 = torch.FloatTensor([0.5, 0.5, -0.5, -0.5]).to(box.device)
+ y4 = box.new_tensor([0.5, 0.5, -0.5, -0.5]).to(box.device)
y4 = y4 * h # (B, N, 4)
corners = torch.stack([x4, y4], dim=-1) # (B, N, 4, 2)
sin = torch.sin(alpha)
diff --git a/mmcv/ops/group_points.py b/mmcv/ops/group_points.py
index 95f359e86..999728c22 100644
--- a/mmcv/ops/group_points.py
+++ b/mmcv/ops/group_points.py
@@ -233,7 +233,7 @@ class GroupingOperation(Function):
else:
B, nfeatures, nsample = indices.size()
_, C, N = features.size()
- output = torch.cuda.FloatTensor(B, C, nfeatures, nsample)
+ output = features.new_zeros(B, C, nfeatures, nsample)
ext_module.group_points_forward(
features,
@@ -262,7 +262,7 @@ class GroupingOperation(Function):
idx, N = ctx.for_backwards
B, C, npoint, nsample = grad_out.size()
- grad_features = torch.cuda.FloatTensor(B, C, N).zero_()
+ grad_features = grad_out.new_zeros(B, C, N)
grad_out_data = grad_out.data.contiguous()
ext_module.group_points_backward(
@@ -279,7 +279,7 @@ class GroupingOperation(Function):
B, N, idx, features_batch_cnt, idx_batch_cnt = ctx.for_backwards
M, C, nsample = grad_out.size()
- grad_features = torch.cuda.FloatTensor(N, C).zero_()
+ grad_features = grad_out.new_zeros(N, C)
grad_out_data = grad_out.data.contiguous()
ext_module.stack_group_points_backward(
diff --git a/mmcv/ops/three_interpolate.py b/mmcv/ops/three_interpolate.py
index 12b2f7611..286bd0472 100644
--- a/mmcv/ops/three_interpolate.py
+++ b/mmcv/ops/three_interpolate.py
@@ -38,7 +38,7 @@ class ThreeInterpolate(Function):
B, c, m = features.size()
n = indices.size(1)
ctx.three_interpolate_for_backward = (indices, weight, m)
- output = torch.cuda.FloatTensor(B, c, n)
+ output = features.new_empty(B, c, n)
ext_module.three_interpolate_forward(
features, indices, weight, output, b=B, c=c, m=m, n=n)
@@ -58,7 +58,7 @@ class ThreeInterpolate(Function):
idx, weight, m = ctx.three_interpolate_for_backward
B, c, n = grad_out.size()
- grad_features = torch.cuda.FloatTensor(B, c, m).zero_()
+ grad_features = grad_out.new_zeros(B, c, m)
grad_out_data = grad_out.data.contiguous()
ext_module.three_interpolate_backward(
diff --git a/tests/test_ops/test_group_points.py b/tests/test_ops/test_group_points.py
index 48c0161ba..8109540ce 100644
--- a/tests/test_ops/test_group_points.py
+++ b/tests/test_ops/test_group_points.py
@@ -7,7 +7,8 @@ from mmcv.ops import grouping_operation
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
-def test_grouping_points():
+@pytest.mark.parametrize('dtype', [torch.half, torch.float, torch.double])
+def test_grouping_points(dtype):
idx = torch.tensor([[[0, 0, 0], [3, 3, 3], [8, 8, 8], [0, 0, 0], [0, 0, 0],
[0, 0, 0]],
[[0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0], [0, 0, 0],
@@ -35,51 +36,37 @@ def test_grouping_points():
[
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
- ]]]).cuda()
+ ]]],
+ dtype=dtype).cuda()
output = grouping_operation(features, idx)
- expected_output = torch.tensor([[[[0.5798, 0.5798, 0.5798],
- [-1.3311, -1.3311, -1.3311],
- [0.9268, 0.9268, 0.9268],
- [0.5798, 0.5798, 0.5798],
- [0.5798, 0.5798, 0.5798],
- [0.5798, 0.5798, 0.5798]],
- [[5.4247, 5.4247, 5.4247],
- [1.4740, 1.4740, 1.4740],
- [2.1581, 2.1581, 2.1581],
- [5.4247, 5.4247, 5.4247],
- [5.4247, 5.4247, 5.4247],
- [5.4247, 5.4247, 5.4247]],
- [[-1.6266, -1.6266, -1.6266],
- [-1.6931, -1.6931, -1.6931],
- [-1.6786, -1.6786, -1.6786],
- [-1.6266, -1.6266, -1.6266],
- [-1.6266, -1.6266, -1.6266],
- [-1.6266, -1.6266, -1.6266]]],
- [[[-0.0380, -0.0380, -0.0380],
- [-0.3693, -0.3693, -0.3693],
- [-1.8527, -1.8527, -1.8527],
- [-0.0380, -0.0380, -0.0380],
- [-0.0380, -0.0380, -0.0380],
- [-0.0380, -0.0380, -0.0380]],
- [[1.1773, 1.1773, 1.1773],
- [6.0865, 6.0865, 6.0865],
- [2.8229, 2.8229, 2.8229],
- [1.1773, 1.1773, 1.1773],
- [1.1773, 1.1773, 1.1773],
- [1.1773, 1.1773, 1.1773]],
- [[-0.6646, -0.6646, -0.6646],
- [0.4990, 0.4990, 0.4990],
- [0.0386, 0.0386, 0.0386],
- [-0.6646, -0.6646, -0.6646],
- [-0.6646, -0.6646, -0.6646],
- [-0.6646, -0.6646, -0.6646]]]]).cuda()
+ expected_output = torch.tensor(
+ [[[[0.5798, 0.5798, 0.5798], [-1.3311, -1.3311, -1.3311],
+ [0.9268, 0.9268, 0.9268], [0.5798, 0.5798, 0.5798],
+ [0.5798, 0.5798, 0.5798], [0.5798, 0.5798, 0.5798]],
+ [[5.4247, 5.4247, 5.4247], [1.4740, 1.4740, 1.4740],
+ [2.1581, 2.1581, 2.1581], [5.4247, 5.4247, 5.4247],
+ [5.4247, 5.4247, 5.4247], [5.4247, 5.4247, 5.4247]],
+ [[-1.6266, -1.6266, -1.6266], [-1.6931, -1.6931, -1.6931],
+ [-1.6786, -1.6786, -1.6786], [-1.6266, -1.6266, -1.6266],
+ [-1.6266, -1.6266, -1.6266], [-1.6266, -1.6266, -1.6266]]],
+ [[[-0.0380, -0.0380, -0.0380], [-0.3693, -0.3693, -0.3693],
+ [-1.8527, -1.8527, -1.8527], [-0.0380, -0.0380, -0.0380],
+ [-0.0380, -0.0380, -0.0380], [-0.0380, -0.0380, -0.0380]],
+ [[1.1773, 1.1773, 1.1773], [6.0865, 6.0865, 6.0865],
+ [2.8229, 2.8229, 2.8229], [1.1773, 1.1773, 1.1773],
+ [1.1773, 1.1773, 1.1773], [1.1773, 1.1773, 1.1773]],
+ [[-0.6646, -0.6646, -0.6646], [0.4990, 0.4990, 0.4990],
+ [0.0386, 0.0386, 0.0386], [-0.6646, -0.6646, -0.6646],
+ [-0.6646, -0.6646, -0.6646], [-0.6646, -0.6646, -0.6646]]]],
+ dtype=dtype).cuda()
assert torch.allclose(output, expected_output)
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
-def test_stack_grouping_points():
+@pytest.mark.parametrize('dtype', [torch.half, torch.float, torch.double])
+def test_stack_grouping_points(dtype):
idx = torch.tensor([[0, 0, 0], [3, 3, 3], [8, 8, 8], [1, 1, 1], [0, 0, 0],
[2, 2, 2], [0, 0, 0], [6, 6, 6], [9, 9, 9], [0, 0, 0],
[1, 1, 1], [0, 0, 0]]).int().cuda()
@@ -106,130 +93,72 @@ def test_stack_grouping_points():
[
-0.6646, -0.6870, -0.1125, -0.2224, -0.3445,
-1.4049, 0.4990, -0.7037, -0.9924, 0.0386
- ]]).float().cuda()
+ ]],
+ dtype=dtype).cuda()
features_batch_cnt = torch.tensor([3, 3]).int().cuda()
indices_batch_cnt = torch.tensor([6, 6]).int().cuda()
output = grouping_operation(features, idx, features_batch_cnt,
indices_batch_cnt)
- expected_output = torch.Tensor([[[0.5798, 0.5798, 0.5798],
- [-0.7981, -0.7981, -0.7981],
- [-0.9280, -0.9280, -0.9280],
- [-1.3311, -1.3311, -1.3311],
- [1.3687, 1.3687, 1.3687],
- [0.9277, 0.9277, 0.9277],
- [-0.4164, -0.4164, -0.4164],
- [-1.8274, -1.8274, -1.8274],
- [0.9268, 0.9268, 0.9268],
- [0.8414, 0.8414, 0.8414]],
- [[0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000]],
- [[0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000]],
- [[5.4247, 5.4247, 5.4247],
- [1.5113, 1.5113, 1.5113],
- [2.3944, 2.3944, 2.3944],
- [1.4740, 1.4740, 1.4740],
- [5.0300, 5.0300, 5.0300],
- [5.1030, 5.1030, 5.1030],
- [1.9360, 1.9360, 1.9360],
- [2.1939, 2.1939, 2.1939],
- [2.1581, 2.1581, 2.1581],
- [3.4666, 3.4666, 3.4666]],
- [[0.5798, 0.5798, 0.5798],
- [-0.7981, -0.7981, -0.7981],
- [-0.9280, -0.9280, -0.9280],
- [-1.3311, -1.3311, -1.3311],
- [1.3687, 1.3687, 1.3687],
- [0.9277, 0.9277, 0.9277],
- [-0.4164, -0.4164, -0.4164],
- [-1.8274, -1.8274, -1.8274],
- [0.9268, 0.9268, 0.9268],
- [0.8414, 0.8414, 0.8414]],
- [[-1.6266, -1.6266, -1.6266],
- [-1.0281, -1.0281, -1.0281],
- [-1.0393, -1.0393, -1.0393],
- [-1.6931, -1.6931, -1.6931],
- [-1.3982, -1.3982, -1.3982],
- [-0.5732, -0.5732, -0.5732],
- [-1.0830, -1.0830, -1.0830],
- [-1.7561, -1.7561, -1.7561],
- [-1.6786, -1.6786, -1.6786],
- [-1.6967, -1.6967, -1.6967]],
- [[-0.0380, -0.0380, -0.0380],
- [-0.1880, -0.1880, -0.1880],
- [-1.5724, -1.5724, -1.5724],
- [0.6905, 0.6905, 0.6905],
- [-0.3190, -0.3190, -0.3190],
- [0.7798, 0.7798, 0.7798],
- [-0.3693, -0.3693, -0.3693],
- [-0.9457, -0.9457, -0.9457],
- [-0.2942, -0.2942, -0.2942],
- [-1.8527, -1.8527, -1.8527]],
- [[0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000]],
- [[0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000],
- [0.0000, 0.0000, 0.0000]],
- [[-0.0380, -0.0380, -0.0380],
- [-0.1880, -0.1880, -0.1880],
- [-1.5724, -1.5724, -1.5724],
- [0.6905, 0.6905, 0.6905],
- [-0.3190, -0.3190, -0.3190],
- [0.7798, 0.7798, 0.7798],
- [-0.3693, -0.3693, -0.3693],
- [-0.9457, -0.9457, -0.9457],
- [-0.2942, -0.2942, -0.2942],
- [-1.8527, -1.8527, -1.8527]],
- [[1.1773, 1.1773, 1.1773],
- [1.5009, 1.5009, 1.5009],
- [2.6399, 2.6399, 2.6399],
- [5.9242, 5.9242, 5.9242],
- [1.0962, 1.0962, 1.0962],
- [2.7346, 2.7346, 2.7346],
- [6.0865, 6.0865, 6.0865],
- [1.5555, 1.5555, 1.5555],
- [4.3303, 4.3303, 4.3303],
- [2.8229, 2.8229, 2.8229]],
- [[-0.0380, -0.0380, -0.0380],
- [-0.1880, -0.1880, -0.1880],
- [-1.5724, -1.5724, -1.5724],
- [0.6905, 0.6905, 0.6905],
- [-0.3190, -0.3190, -0.3190],
- [0.7798, 0.7798, 0.7798],
- [-0.3693, -0.3693, -0.3693],
- [-0.9457, -0.9457, -0.9457],
- [-0.2942, -0.2942, -0.2942],
- [-1.8527, -1.8527,
- -1.8527]]]).cuda().float()
+ expected_output = torch.tensor(
+ [[[0.5798, 0.5798, 0.5798], [-0.7981, -0.7981, -0.7981],
+ [-0.9280, -0.9280, -0.9280], [-1.3311, -1.3311, -1.3311],
+ [1.3687, 1.3687, 1.3687], [0.9277, 0.9277, 0.9277],
+ [-0.4164, -0.4164, -0.4164], [-1.8274, -1.8274, -1.8274],
+ [0.9268, 0.9268, 0.9268], [0.8414, 0.8414, 0.8414]],
+ [[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]],
+ [[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]],
+ [[5.4247, 5.4247, 5.4247], [1.5113, 1.5113, 1.5113],
+ [2.3944, 2.3944, 2.3944], [1.4740, 1.4740, 1.4740],
+ [5.0300, 5.0300, 5.0300], [5.1030, 5.1030, 5.1030],
+ [1.9360, 1.9360, 1.9360], [2.1939, 2.1939, 2.1939],
+ [2.1581, 2.1581, 2.1581], [3.4666, 3.4666, 3.4666]],
+ [[0.5798, 0.5798, 0.5798], [-0.7981, -0.7981, -0.7981],
+ [-0.9280, -0.9280, -0.9280], [-1.3311, -1.3311, -1.3311],
+ [1.3687, 1.3687, 1.3687], [0.9277, 0.9277, 0.9277],
+ [-0.4164, -0.4164, -0.4164], [-1.8274, -1.8274, -1.8274],
+ [0.9268, 0.9268, 0.9268], [0.8414, 0.8414, 0.8414]],
+ [[-1.6266, -1.6266, -1.6266], [-1.0281, -1.0281, -1.0281],
+ [-1.0393, -1.0393, -1.0393], [-1.6931, -1.6931, -1.6931],
+ [-1.3982, -1.3982, -1.3982], [-0.5732, -0.5732, -0.5732],
+ [-1.0830, -1.0830, -1.0830], [-1.7561, -1.7561, -1.7561],
+ [-1.6786, -1.6786, -1.6786], [-1.6967, -1.6967, -1.6967]],
+ [[-0.0380, -0.0380, -0.0380], [-0.1880, -0.1880, -0.1880],
+ [-1.5724, -1.5724, -1.5724], [0.6905, 0.6905, 0.6905],
+ [-0.3190, -0.3190, -0.3190], [0.7798, 0.7798, 0.7798],
+ [-0.3693, -0.3693, -0.3693], [-0.9457, -0.9457, -0.9457],
+ [-0.2942, -0.2942, -0.2942], [-1.8527, -1.8527, -1.8527]],
+ [[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]],
+ [[0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000],
+ [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]],
+ [[-0.0380, -0.0380, -0.0380], [-0.1880, -0.1880, -0.1880],
+ [-1.5724, -1.5724, -1.5724], [0.6905, 0.6905, 0.6905],
+ [-0.3190, -0.3190, -0.3190], [0.7798, 0.7798, 0.7798],
+ [-0.3693, -0.3693, -0.3693], [-0.9457, -0.9457, -0.9457],
+ [-0.2942, -0.2942, -0.2942], [-1.8527, -1.8527, -1.8527]],
+ [[1.1773, 1.1773, 1.1773], [1.5009, 1.5009, 1.5009],
+ [2.6399, 2.6399, 2.6399], [5.9242, 5.9242, 5.9242],
+ [1.0962, 1.0962, 1.0962], [2.7346, 2.7346, 2.7346],
+ [6.0865, 6.0865, 6.0865], [1.5555, 1.5555, 1.5555],
+ [4.3303, 4.3303, 4.3303], [2.8229, 2.8229, 2.8229]],
+ [[-0.0380, -0.0380, -0.0380], [-0.1880, -0.1880, -0.1880],
+ [-1.5724, -1.5724, -1.5724], [0.6905, 0.6905, 0.6905],
+ [-0.3190, -0.3190, -0.3190], [0.7798, 0.7798, 0.7798],
+ [-0.3693, -0.3693, -0.3693], [-0.9457, -0.9457, -0.9457],
+ [-0.2942, -0.2942, -0.2942], [-1.8527, -1.8527, -1.8527]]],
+ dtype=dtype).cuda()
assert torch.allclose(output, expected_output)
diff --git a/tests/test_ops/test_three_interpolate.py b/tests/test_ops/test_three_interpolate.py
index 900f451ff..51a6b8732 100644
--- a/tests/test_ops/test_three_interpolate.py
+++ b/tests/test_ops/test_three_interpolate.py
@@ -7,19 +7,20 @@ from mmcv.ops import three_interpolate
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
-def test_three_interpolate():
- features = torch.tensor([[[2.4350, 4.7516, 4.4995, 2.4350, 2.4350, 2.4350],
- [3.1236, 2.6278, 3.0447, 3.1236, 3.1236, 3.1236],
- [2.6732, 2.8677, 2.6436, 2.6732, 2.6732, 2.6732],
- [0.0124, 7.0150, 7.0199, 0.0124, 0.0124, 0.0124],
- [0.3207, 0.0000, 0.3411, 0.3207, 0.3207,
- 0.3207]],
- [[0.0000, 0.9544, 2.4532, 0.0000, 0.0000, 0.0000],
- [0.5346, 1.9176, 1.4715, 0.5346, 0.5346, 0.5346],
- [0.0000, 0.2744, 2.0842, 0.0000, 0.0000, 0.0000],
- [0.3414, 1.5063, 1.6209, 0.3414, 0.3414, 0.3414],
- [0.5814, 0.0103, 0.0000, 0.5814, 0.5814,
- 0.5814]]]).cuda()
+@pytest.mark.parametrize('dtype', [torch.half, torch.float, torch.double])
+def test_three_interpolate(dtype):
+ features = torch.tensor(
+ [[[2.4350, 4.7516, 4.4995, 2.4350, 2.4350, 2.4350],
+ [3.1236, 2.6278, 3.0447, 3.1236, 3.1236, 3.1236],
+ [2.6732, 2.8677, 2.6436, 2.6732, 2.6732, 2.6732],
+ [0.0124, 7.0150, 7.0199, 0.0124, 0.0124, 0.0124],
+ [0.3207, 0.0000, 0.3411, 0.3207, 0.3207, 0.3207]],
+ [[0.0000, 0.9544, 2.4532, 0.0000, 0.0000, 0.0000],
+ [0.5346, 1.9176, 1.4715, 0.5346, 0.5346, 0.5346],
+ [0.0000, 0.2744, 2.0842, 0.0000, 0.0000, 0.0000],
+ [0.3414, 1.5063, 1.6209, 0.3414, 0.3414, 0.3414],
+ [0.5814, 0.0103, 0.0000, 0.5814, 0.5814, 0.5814]]],
+ dtype=dtype).cuda()
idx = torch.tensor([[[0, 1, 2], [2, 3, 4], [2, 3, 4], [0, 1, 2], [0, 1, 2],
[0, 1, 3]],
@@ -37,7 +38,8 @@ def test_three_interpolate():
[1.0000e+00, 1.7148e-08, 1.4070e-08],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
[3.3333e-01, 3.3333e-01, 3.3333e-01],
- [3.3333e-01, 3.3333e-01, 3.3333e-01]]]).cuda()
+ [3.3333e-01, 3.3333e-01, 3.3333e-01]]],
+ dtype=dtype).cuda()
output = three_interpolate(features, idx, weight)
expected_output = torch.tensor([[[
@@ -70,6 +72,7 @@ def test_three_interpolate():
[
3.8760e-01, 1.0300e-02, 8.3569e-09,
3.8760e-01, 3.8760e-01, 1.9723e-01
- ]]]).cuda()
+ ]]],
+ dtype=dtype).cuda()
- assert torch.allclose(output, expected_output, 1e-4)
+ assert torch.allclose(output, expected_output, 1e-3, 1e-4)