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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.ops.multi_scale_deform_attn import (
MultiScaleDeformableAttention, MultiScaleDeformableAttnFunction,
multi_scale_deformable_attn_pytorch)
_USING_PARROTS = True
try:
from parrots.autograd import gradcheck
except ImportError:
from torch.autograd import gradcheck
_USING_PARROTS = False
@pytest.mark.parametrize('device_type', [
'cpu',
pytest.param(
'cuda:0',
marks=pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support'))
])
def test_multiscale_deformable_attention(device_type):
with pytest.raises(ValueError):
# embed_dims must be divisible by num_heads,
MultiScaleDeformableAttention(
embed_dims=256,
num_heads=7,
)
device = torch.device(device_type)
msda = MultiScaleDeformableAttention(
embed_dims=3, num_levels=2, num_heads=3)
msda.init_weights()
num_query = 5
bs = 1
embed_dims = 3
query = torch.rand(num_query, bs, embed_dims).to(device)
key = torch.rand(num_query, bs, embed_dims).to(device)
spatial_shapes = torch.Tensor([[2, 2], [1, 1]]).long().to(device)
level_start_index = torch.Tensor([0, 4]).long().to(device)
reference_points = torch.rand(bs, num_query, 2, 2).to(device)
msda.to(device)
msda(
query,
key,
key,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index)
def test_forward_multi_scale_deformable_attn_pytorch():
N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long)
S = sum((H * W).item() for H, W in shapes)
torch.manual_seed(3)
value = torch.rand(N, S, M, D) * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2)
attention_weights = torch.rand(N, Lq, M, L, P) + 1e-5
attention_weights /= attention_weights.sum(
-1, keepdim=True).sum(
-2, keepdim=True)
multi_scale_deformable_attn_pytorch(value.double(), shapes,
sampling_locations.double(),
attention_weights.double()).detach()
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_forward_equal_with_pytorch_double():
N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros(
(1, )), shapes.prod(1).cumsum(0)[:-1]))
S = sum((H * W).item() for H, W in shapes)
torch.manual_seed(3)
value = torch.rand(N, S, M, D).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(
-1, keepdim=True).sum(
-2, keepdim=True)
im2col_step = 2
output_pytorch = multi_scale_deformable_attn_pytorch(
value.double(), shapes, sampling_locations.double(),
attention_weights.double()).detach().cpu()
output_cuda = MultiScaleDeformableAttnFunction.apply(
value.double(), shapes, level_start_index, sampling_locations.double(),
attention_weights.double(), im2col_step).detach().cpu()
assert torch.allclose(output_cuda, output_pytorch)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() /
output_pytorch.abs()).max()
assert max_abs_err < 1e-18
assert max_rel_err < 1e-15
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_forward_equal_with_pytorch_float():
N, M, D = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros(
(1, )), shapes.prod(1).cumsum(0)[:-1]))
S = sum((H * W).item() for H, W in shapes)
torch.manual_seed(3)
value = torch.rand(N, S, M, D).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(
-1, keepdim=True).sum(
-2, keepdim=True)
im2col_step = 2
output_pytorch = multi_scale_deformable_attn_pytorch(
value, shapes, sampling_locations, attention_weights).detach().cpu()
output_cuda = MultiScaleDeformableAttnFunction.apply(
value, shapes, level_start_index, sampling_locations,
attention_weights, im2col_step).detach().cpu()
assert torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
max_abs_err = (output_cuda - output_pytorch).abs().max()
max_rel_err = ((output_cuda - output_pytorch).abs() /
output_pytorch.abs()).max()
assert max_abs_err < 1e-9
assert max_rel_err < 1e-6
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
@pytest.mark.parametrize('channels', [
4,
30,
32,
64,
71,
1025,
])
def test_gradient_numerical(channels,
grad_value=True,
grad_sampling_loc=True,
grad_attn_weight=True):
N, M, _ = 1, 2, 2
Lq, L, P = 2, 2, 2
shapes = torch.as_tensor([(3, 2), (2, 1)], dtype=torch.long).cuda()
level_start_index = torch.cat((shapes.new_zeros(
(1, )), shapes.prod(1).cumsum(0)[:-1]))
S = sum((H * W).item() for H, W in shapes)
value = torch.rand(N, S, M, channels).cuda() * 0.01
sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
attention_weights /= attention_weights.sum(
-1, keepdim=True).sum(
-2, keepdim=True)
im2col_step = 2
func = MultiScaleDeformableAttnFunction.apply
value.requires_grad = grad_value
sampling_locations.requires_grad = grad_sampling_loc
attention_weights.requires_grad = grad_attn_weight
if _USING_PARROTS:
assert gradcheck(
func, (value.double(), shapes, level_start_index,
sampling_locations.double(), attention_weights.double(),
im2col_step),
no_grads=[shapes, level_start_index])
else:
assert gradcheck(func, (value.double(), shapes, level_start_index,
sampling_locations.double(),
attention_weights.double(), im2col_step))
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