def __init__(self,
roi_layer_type='RoIAlign',
featmap_stride=16,
output_size=16,
sampling_ratio=0,
pool_mode='avg',
aligned=True,
with_temporal_pool=True):
super().__init__()
self.roi_layer_type = roi_layer_type
assert self.roi_layer_type in ['RoIPool', 'RoIAlign']
self.featmap_stride = featmap_stride
self.spatial_scale = 1. / self.featmap_stride
self.output_size = output_size
self.sampling_ratio = sampling_ratio
self.pool_mode = pool_mode
self.aligned = aligned
self.with_temporal_pool = with_temporal_pool
if self.roi_layer_type == 'RoIPool':
self.roi_layer = RoIPool(self.output_size, self.spatial_scale)
else:
self.roi_layer = RoIAlign(
self.output_size,
self.spatial_scale,
sampling_ratio=self.sampling_ratio,
pool_mode=self.pool_mode,
aligned=self.aligned)
def __init__(self,
roi_layer_type='RoIAlign',
featmap_stride=16,
output_size=16,
sampling_ratio=0,
pool_mode='avg',
aligned=True,
with_temporal_pool=True,
temporal_pool_mode='avg',
with_global=False):
super().__init__()
self.roi_layer_type = roi_layer_type
assert self.roi_layer_type in ['RoIPool', 'RoIAlign']
self.featmap_stride = featmap_stride
self.spatial_scale = 1. / self.featmap_stride
self.output_size = output_size
self.sampling_ratio = sampling_ratio
self.pool_mode = pool_mode
self.aligned = aligned
self.with_temporal_pool = with_temporal_pool
self.temporal_pool_mode = temporal_pool_mode
self.with_global = with_global
try:
from mmcv.ops import RoIAlign, RoIPool
except (ImportError, ModuleNotFoundError):
raise ImportError('Failed to import `RoIAlign` and `RoIPool` from '
'`mmcv.ops`. The two modules will be used in '
'`SingleRoIExtractor3D`! ')
if self.roi_layer_type == 'RoIPool':
self.roi_layer = RoIPool(self.output_size, self.spatial_scale)
else:
self.roi_layer = RoIAlign(
self.output_size,
self.spatial_scale,
sampling_ratio=self.sampling_ratio,
pool_mode=self.pool_mode,
aligned=self.aligned)
self.global_pool = nn.AdaptiveAvgPool2d(self.output_size)
def test_roipool_gradcheck(self):
if not torch.cuda.is_available():
return
from mmcv.ops import RoIPool
pool_h = 2
pool_w = 2
spatial_scale = 1.0
for case in inputs:
np_input = np.array(case[0])
np_rois = np.array(case[1])
x = torch.tensor(np_input, device='cuda', requires_grad=True)
rois = torch.tensor(np_rois, device='cuda')
froipool = RoIPool((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)