mirror of https://github.com/open-mmlab/mmcv.git
125 lines
4.0 KiB
Python
125 lines
4.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE, IS_NPU_AVAILABLE
|
|
|
|
_USING_PARROTS = True
|
|
try:
|
|
from parrots.autograd import gradcheck
|
|
except ImportError:
|
|
from torch.autograd import gradcheck
|
|
_USING_PARROTS = False
|
|
|
|
# yapf:disable
|
|
|
|
inputs = [([[[[1., 2.], [3., 4.]]]],
|
|
[[0., 0., 0., 1., 1.]]),
|
|
([[[[1., 2.], [3., 4.]],
|
|
[[4., 3.], [2., 1.]]]],
|
|
[[0., 0., 0., 1., 1.]]),
|
|
([[[[1., 2., 5., 6.], [3., 4., 7., 8.],
|
|
[9., 10., 13., 14.], [11., 12., 15., 16.]]]],
|
|
[[0., 0., 0., 3., 3.]])]
|
|
outputs = [([[[[1.0, 1.25], [1.5, 1.75]]]],
|
|
[[[[3.0625, 0.4375], [0.4375, 0.0625]]]]),
|
|
([[[[1.0, 1.25], [1.5, 1.75]],
|
|
[[4.0, 3.75], [3.5, 3.25]]]],
|
|
[[[[3.0625, 0.4375], [0.4375, 0.0625]],
|
|
[[3.0625, 0.4375], [0.4375, 0.0625]]]]),
|
|
([[[[1.9375, 4.75], [7.5625, 10.375]]]],
|
|
[[[[0.47265625, 0.42968750, 0.42968750, 0.04296875],
|
|
[0.42968750, 0.39062500, 0.39062500, 0.03906250],
|
|
[0.42968750, 0.39062500, 0.39062500, 0.03906250],
|
|
[0.04296875, 0.03906250, 0.03906250, 0.00390625]]]])]
|
|
# yapf:enable
|
|
|
|
pool_h = 2
|
|
pool_w = 2
|
|
spatial_scale = 1.0
|
|
sampling_ratio = 2
|
|
|
|
|
|
def _test_roialign_gradcheck(device, dtype):
|
|
try:
|
|
from mmcv.ops import RoIAlign
|
|
except ModuleNotFoundError:
|
|
pytest.skip('RoIAlign op is not successfully compiled')
|
|
if dtype is torch.half:
|
|
pytest.skip('grad check does not support fp16')
|
|
for case in inputs:
|
|
np_input = np.array(case[0])
|
|
np_rois = np.array(case[1])
|
|
|
|
x = torch.tensor(
|
|
np_input, dtype=dtype, device=device, requires_grad=True)
|
|
rois = torch.tensor(np_rois, dtype=dtype, device=device)
|
|
|
|
froipool = RoIAlign((pool_h, pool_w), spatial_scale, sampling_ratio)
|
|
|
|
if torch.__version__ == 'parrots':
|
|
gradcheck(
|
|
froipool, (x, rois), no_grads=[rois], delta=1e-5, pt_atol=1e-5)
|
|
else:
|
|
gradcheck(froipool, (x, rois), eps=1e-5, atol=1e-5)
|
|
|
|
|
|
def _test_roialign_allclose(device, dtype):
|
|
try:
|
|
from mmcv.ops import roi_align
|
|
except ModuleNotFoundError:
|
|
pytest.skip('test requires compilation')
|
|
pool_h = 2
|
|
pool_w = 2
|
|
spatial_scale = 1.0
|
|
sampling_ratio = 2
|
|
for case, output in zip(inputs, outputs):
|
|
np_input = np.array(case[0])
|
|
np_rois = np.array(case[1])
|
|
np_output = np.array(output[0])
|
|
np_grad = np.array(output[1])
|
|
|
|
x = torch.tensor(
|
|
np_input, dtype=dtype, device=device, requires_grad=True)
|
|
rois = torch.tensor(np_rois, dtype=dtype, device=device)
|
|
|
|
output = roi_align(x, rois, (pool_h, pool_w), spatial_scale,
|
|
sampling_ratio, 'avg', True)
|
|
output.backward(torch.ones_like(output))
|
|
assert np.allclose(
|
|
output.data.type(torch.float).cpu().numpy(), np_output, atol=1e-3)
|
|
assert np.allclose(
|
|
x.grad.data.type(torch.float).cpu().numpy(), np_grad, atol=1e-3)
|
|
|
|
|
|
@pytest.mark.parametrize('dtype', [
|
|
torch.float,
|
|
pytest.param(
|
|
torch.double,
|
|
marks=pytest.mark.skipif(
|
|
IS_MLU_AVAILABLE or IS_NPU_AVAILABLE,
|
|
reason='MLU and NPU do not support for 64-bit floating point')),
|
|
torch.half
|
|
])
|
|
@pytest.mark.parametrize('device', [
|
|
'cpu',
|
|
pytest.param(
|
|
'cuda',
|
|
marks=pytest.mark.skipif(
|
|
not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
|
|
pytest.param(
|
|
'mlu',
|
|
marks=pytest.mark.skipif(
|
|
not IS_MLU_AVAILABLE, reason='requires MLU support')),
|
|
pytest.param(
|
|
'npu',
|
|
marks=pytest.mark.skipif(
|
|
not IS_NPU_AVAILABLE, reason='requires NPU support'))
|
|
])
|
|
def test_roialign(device, dtype):
|
|
# check double only
|
|
if dtype is torch.double:
|
|
_test_roialign_gradcheck(device=device, dtype=dtype)
|
|
_test_roialign_allclose(device=device, dtype=dtype)
|