本文整理汇总了Python中maskrcnn_benchmark._C.deform_conv_backward_parameters方法的典型用法代码示例。如果您正苦于以下问题：Python _C.deform_conv_backward_parameters方法的具体用法？Python _C.deform_conv_backward_parameters怎么用？Python _C.deform_conv_backward_parameters使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类maskrcnn_benchmark._C的用法示例。
在下文中一共展示了_C.deform_conv_backward_parameters方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: backward
# 需要导入模块: from maskrcnn_benchmark import _C [as 别名]
# 或者: from maskrcnn_benchmark._C import deform_conv_backward_parameters [as 别名]
def backward(ctx, grad_output):
input, offset, weight = ctx.saved_tensors
grad_input = grad_offset = grad_weight = None
if not grad_output.is_cuda:
raise NotImplementedError
else:
cur_im2col_step = min(ctx.im2col_step, input.shape[0])
assert (input.shape[0] %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
_C.deform_conv_backward_input(
input,
offset,
grad_output,
grad_input,
grad_offset,
weight,
ctx.bufs_[0],
weight.size(3),
weight.size(2),
ctx.stride[1],
ctx.stride[0],
ctx.padding[1],
ctx.padding[0],
ctx.dilation[1],
ctx.dilation[0],
ctx.groups,
ctx.deformable_groups,
cur_im2col_step
)
if ctx.needs_input_grad[2]:
grad_weight = torch.zeros_like(weight)
_C.deform_conv_backward_parameters(
input,
offset,
grad_output,
grad_weight,
ctx.bufs_[0],
ctx.bufs_[1],
weight.size(3),
weight.size(2),
ctx.stride[1],
ctx.stride[0],
ctx.padding[1],
ctx.padding[0],
ctx.dilation[1],
ctx.dilation[0],
ctx.groups,
ctx.deformable_groups,
1,
cur_im2col_step
)
return (grad_input, grad_offset, grad_weight, None, None, None, None, None) 示例2: backward
# 需要导入模块: from maskrcnn_benchmark import _C [as 别名]
# 或者: from maskrcnn_benchmark._C import deform_conv_backward_parameters [as 别名]
def backward(ctx, grad_output):
input, offset, weight = ctx.saved_tensors
grad_input = grad_offset = grad_weight = None
if not grad_output.is_cuda:
raise NotImplementedError
else:
cur_im2col_step = min(ctx.im2col_step, input.shape[0])
assert (input.shape[0] %
cur_im2col_step) == 0, 'im2col step must divide batchsize'
if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
grad_input = torch.zeros_like(input)
grad_offset = torch.zeros_like(offset)
_C.deform_conv_backward_input(
input,
offset,
grad_output,
grad_input,
grad_offset,
weight,
ctx.bufs_[0],
weight.size(3),
weight.size(2),
ctx.stride[1],
ctx.stride[0],
ctx.padding[1],
ctx.padding[0],
ctx.dilation[1],
ctx.dilation[0],
ctx.groups,
ctx.deformable_groups,
cur_im2col_step
)
if ctx.needs_input_grad[2]:
grad_weight = torch.zeros_like(weight)
_C.deform_conv_backward_parameters(
input,
offset,
grad_output,
grad_weight,
ctx.bufs_[0],
ctx.bufs_[1],
weight.size(3),
weight.size(2),
ctx.stride[1],
ctx.stride[0],
ctx.padding[1],
ctx.padding[0],
ctx.dilation[1],
ctx.dilation[0],
ctx.groups,
ctx.deformable_groups,
1,
cur_im2col_step
)
return (grad_input, grad_offset, grad_weight, None, None, None, None, None)