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Commit 3bb0611a authored by q.yao's avatar q.yao Committed by Zaida Zhou
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[Fix] Create Tensor with new_* method to support amp (#2389)

parent ac470881
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Showing with 116 additions and 184 deletions
mmcv/ops/diff_iou_rotated.py
View file @ 3bb0611a
......@@ -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)
......
mmcv/ops/group_points.py
View file @ 3bb0611a
......@@ -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(
......
mmcv/ops/three_interpolate.py
View file @ 3bb0611a
......@@ -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(
......
tests/test_ops/test_group_points.py
View file @ 3bb0611a
......@@ -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)
tests/test_ops/test_three_interpolate.py
View file @ 3bb0611a
......@@ -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],
@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]],
[0.3207, 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()
[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)
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