Spaces:
Running
on
L40S
Running
on
L40S
File size: 1,383 Bytes
d7e58f0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
_USING_PARROTS = True
try:
from parrots.autograd import gradcheck
except ImportError:
from torch.autograd import gradcheck, gradgradcheck
_USING_PARROTS = False
class TestFusedBiasLeakyReLU:
@classmethod
def setup_class(cls):
if not torch.cuda.is_available():
return
cls.input_tensor = torch.randn((2, 2, 2, 2), requires_grad=True).cuda()
cls.bias = torch.zeros(2, requires_grad=True).cuda()
@pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
def test_gradient(self):
from mmcv.ops import FusedBiasLeakyReLU
if _USING_PARROTS:
gradcheck(
FusedBiasLeakyReLU(2).cuda(),
self.input_tensor,
delta=1e-4,
pt_atol=1e-3)
else:
gradcheck(
FusedBiasLeakyReLU(2).cuda(),
self.input_tensor,
eps=1e-4,
atol=1e-3)
@pytest.mark.skipif(
not torch.cuda.is_available() or _USING_PARROTS,
reason='requires cuda')
def test_gradgradient(self):
from mmcv.ops import FusedBiasLeakyReLU
gradgradcheck(
FusedBiasLeakyReLU(2).cuda(),
self.input_tensor,
eps=1e-4,
atol=1e-3)
|