本文整理匯總了Python中maskrcnn_benchmark._C.deform_psroi_pooling_forward方法的典型用法代碼示例。如果您正苦於以下問題：Python _C.deform_psroi_pooling_forward方法的具體用法？Python _C.deform_psroi_pooling_forward怎麽用？Python _C.deform_psroi_pooling_forward使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類maskrcnn_benchmark._C的用法示例。
在下文中一共展示了_C.deform_psroi_pooling_forward方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: forward
# 需要導入模塊: from maskrcnn_benchmark import _C [as 別名]
# 或者: from maskrcnn_benchmark._C import deform_psroi_pooling_forward [as 別名]
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
sample_per_part=4,
trans_std=.0
):
ctx.spatial_scale = spatial_scale
ctx.out_size = out_size
ctx.out_channels = out_channels
ctx.no_trans = no_trans
ctx.group_size = group_size
ctx.part_size = out_size if part_size is None else part_size
ctx.sample_per_part = sample_per_part
ctx.trans_std = trans_std
assert 0.0 <= ctx.trans_std <= 1.0
if not data.is_cuda:
raise NotImplementedError
n = rois.shape[0]
output = data.new_empty(n, out_channels, out_size, out_size)
output_count = data.new_empty(n, out_channels, out_size, out_size)
_C.deform_psroi_pooling_forward(
data,
rois,
offset,
output,
output_count,
ctx.no_trans,
ctx.spatial_scale,
ctx.out_channels,
ctx.group_size,
ctx.out_size,
ctx.part_size,
ctx.sample_per_part,
ctx.trans_std
)
if data.requires_grad or rois.requires_grad or offset.requires_grad:
ctx.save_for_backward(data, rois, offset)
ctx.output_count = output_count
return output 示例2: forward
# 需要導入模塊: from maskrcnn_benchmark import _C [as 別名]
# 或者: from maskrcnn_benchmark._C import deform_psroi_pooling_forward [as 別名]
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
sample_per_part=4,
trans_std=.0
):
ctx.spatial_scale = spatial_scale
ctx.out_size = out_size
ctx.out_channels = out_channels
ctx.no_trans = no_trans
ctx.group_size = group_size
ctx.part_size = out_size if part_size is None else part_size
ctx.sample_per_part = sample_per_part
ctx.trans_std = trans_std
assert 0.0 <= ctx.trans_std <= 1.0
if not data.is_cuda:
raise NotImplementedError
n = rois.shape[0]
output = data.new_empty(n, out_channels, out_size, out_size)
output_count = data.new_empty(n, out_channels, out_size, out_size)
_C.deform_psroi_pooling_forward(
data,
rois,
offset,
output,
output_count,
ctx.no_trans,
ctx.spatial_scale,
ctx.out_channels,
ctx.group_size,
ctx.out_size,
ctx.part_size,
ctx.sample_per_part,
ctx.trans_std
)
if data.requires_grad or rois.requires_grad or offset.requires_grad:
ctx.save_for_backward(data, rois, offset)
ctx.output_count = output_count
return output