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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmcv.utils import IS_CUDA_AVAILABLE
_USING_PARROTS = True
try:
from parrots.autograd import gradcheck
except ImportError:
from torch.autograd import gradcheck
_USING_PARROTS = False
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 = [
([[[[1.75, 2.25], [2.75, 3.25]]]], [[[[1., 1.],
[1., 1.]]]], [[0., 2., 4., 2., 4.]]),
([[[[1.75, 2.25], [2.75, 3.25]],
[[3.25, 2.75], [2.25, 1.75]]]], [[[[1., 1.], [1., 1.]],
[[1., 1.],
[1., 1.]]]], [[0., 0., 0., 0., 0.]]),
([[[[3.75, 6.91666651],
[10.08333302,
13.25]]]], [[[[0.11111111, 0.22222224, 0.22222222, 0.11111111],
[0.22222224, 0.444444448, 0.44444448, 0.22222224],
[0.22222224, 0.44444448, 0.44444448, 0.22222224],
[0.11111111, 0.22222224, 0.22222224, 0.11111111]]]],
[[0.0, 3.33333302, 6.66666603, 3.33333349, 6.66666698]])
]
class TestPrRoiPool:
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
])
def test_roipool_gradcheck(self, device):
from mmcv.ops import PrRoIPool
pool_h = 2
pool_w = 2
spatial_scale = 1.0
for case in inputs:
np_input = np.array(case[0], dtype=np.float32)
np_rois = np.array(case[1], dtype=np.float32)
x = torch.tensor(np_input, device=device, requires_grad=True)
rois = torch.tensor(np_rois, device=device)
froipool = PrRoIPool((pool_h, pool_w), spatial_scale)
if _USING_PARROTS:
pass
# gradcheck(froipool, (x, rois), no_grads=[rois])
else:
gradcheck(froipool, (x, rois), eps=1e-2, atol=1e-2)
def _test_roipool_allclose(self, device, dtype=torch.float):
from mmcv.ops import prroi_pool
pool_h = 2
pool_w = 2
spatial_scale = 1.0
for case, output in zip(inputs, outputs):
np_input = np.array(case[0], dtype=np.float32)
np_rois = np.array(case[1], dtype=np.float32)
np_output = np.array(output[0], dtype=np.float32)
np_input_grad = np.array(output[1], dtype=np.float32)
np_rois_grad = np.array(output[2], dtype=np.float32)
x = torch.tensor(
np_input, dtype=dtype, device=device, requires_grad=True)
rois = torch.tensor(
np_rois, dtype=dtype, device=device, requires_grad=True)
output = prroi_pool(x, rois, (pool_h, pool_w), spatial_scale)
output.backward(torch.ones_like(output))
assert np.allclose(output.data.cpu().numpy(), np_output, 1e-3)
assert np.allclose(x.grad.data.cpu().numpy(), np_input_grad, 1e-3)
assert np.allclose(rois.grad.data.cpu().numpy(), np_rois_grad,
1e-3)
@pytest.mark.parametrize('device', [
pytest.param(
'cuda',
marks=pytest.mark.skipif(
not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
])
def test_roipool_allclose_float(self, device):
self._test_roipool_allclose(device, dtype=torch.float)
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