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- import math
-
- import torch
- import torch.nn as nn
- from torch.autograd import Function
- from torch.autograd.function import once_differentiable
- from torch.nn.modules.utils import _pair, _single
-
- from mmcv.utils import deprecated_api_warning
- from ..cnn import CONV_LAYERS
- from ..utils import ext_loader, print_log
-
- ext_module = ext_loader.load_ext(
- '_ext',
- ['modulated_deform_conv_forward', 'modulated_deform_conv_backward'])
-
-
- class ModulatedDeformConv2dFunction(Function):
-
- @staticmethod
- def symbolic(g, input, offset, mask, weight, bias, stride, padding,
- dilation, groups, deform_groups):
- return g.op(
- 'MMCVModulatedDeformConv2d',
- input,
- offset,
- mask,
- weight,
- bias,
- stride_i=stride,
- padding_i=padding,
- dilation_i=dilation,
- groups_i=groups,
- deform_groups_i=deform_groups)
-
- @staticmethod
- def forward(ctx,
- input,
- offset,
- mask,
- weight,
- bias=None,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1):
- if input is not None and input.dim() != 4:
- raise ValueError(
- f'Expected 4D tensor as input, got {input.dim()}D tensor \
- instead.')
- ctx.stride = _pair(stride)
- ctx.padding = _pair(padding)
- ctx.dilation = _pair(dilation)
- ctx.groups = groups
- ctx.deform_groups = deform_groups
- ctx.with_bias = bias is not None
- if not ctx.with_bias:
- bias = input.new_empty(0) # fake tensor
- ctx.save_for_backward(input, offset, mask, weight, bias)
- output = input.new_empty(
- ModulatedDeformConv2dFunction._output_size(ctx, input, weight))
- ctx._bufs = [input.new_empty(0), input.new_empty(0)]
- ext_module.modulated_deform_conv_forward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- output,
- ctx._bufs[1],
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- input, offset, mask, weight, bias = ctx.saved_tensors
- grad_input = torch.zeros_like(input)
- grad_offset = torch.zeros_like(offset)
- grad_mask = torch.zeros_like(mask)
- grad_weight = torch.zeros_like(weight)
- grad_bias = torch.zeros_like(bias)
- grad_output = grad_output.contiguous()
- ext_module.modulated_deform_conv_backward(
- input,
- weight,
- bias,
- ctx._bufs[0],
- offset,
- mask,
- ctx._bufs[1],
- grad_input,
- grad_weight,
- grad_bias,
- grad_offset,
- grad_mask,
- grad_output,
- kernel_h=weight.size(2),
- kernel_w=weight.size(3),
- stride_h=ctx.stride[0],
- stride_w=ctx.stride[1],
- pad_h=ctx.padding[0],
- pad_w=ctx.padding[1],
- dilation_h=ctx.dilation[0],
- dilation_w=ctx.dilation[1],
- group=ctx.groups,
- deformable_group=ctx.deform_groups,
- with_bias=ctx.with_bias)
- if not ctx.with_bias:
- grad_bias = None
-
- return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias,
- None, None, None, None, None)
-
- @staticmethod
- def _output_size(ctx, input, weight):
- channels = weight.size(0)
- output_size = (input.size(0), channels)
- for d in range(input.dim() - 2):
- in_size = input.size(d + 2)
- pad = ctx.padding[d]
- kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1
- stride_ = ctx.stride[d]
- output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
- if not all(map(lambda s: s > 0, output_size)):
- raise ValueError(
- 'convolution input is too small (output would be ' +
- 'x'.join(map(str, output_size)) + ')')
- return output_size
-
-
- modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply
-
-
- class ModulatedDeformConv2d(nn.Module):
-
- @deprecated_api_warning({'deformable_groups': 'deform_groups'},
- cls_name='ModulatedDeformConv2d')
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- deform_groups=1,
- bias=True):
- super(ModulatedDeformConv2d, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.deform_groups = deform_groups
- # enable compatibility with nn.Conv2d
- self.transposed = False
- self.output_padding = _single(0)
-
- self.weight = nn.Parameter(
- torch.Tensor(out_channels, in_channels // groups,
- *self.kernel_size))
- if bias:
- self.bias = nn.Parameter(torch.Tensor(out_channels))
- else:
- self.register_parameter('bias', None)
- self.init_weights()
-
- def init_weights(self):
- n = self.in_channels
- for k in self.kernel_size:
- n *= k
- stdv = 1. / math.sqrt(n)
- self.weight.data.uniform_(-stdv, stdv)
- if self.bias is not None:
- self.bias.data.zero_()
-
- def forward(self, x, offset, mask):
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
-
- @CONV_LAYERS.register_module('DCNv2')
- class ModulatedDeformConv2dPack(ModulatedDeformConv2d):
- """A ModulatedDeformable Conv Encapsulation that acts as normal Conv
- layers.
-
- Args:
- in_channels (int): Same as nn.Conv2d.
- out_channels (int): Same as nn.Conv2d.
- kernel_size (int or tuple[int]): Same as nn.Conv2d.
- stride (int): Same as nn.Conv2d, while tuple is not supported.
- padding (int): Same as nn.Conv2d, while tuple is not supported.
- dilation (int): Same as nn.Conv2d, while tuple is not supported.
- groups (int): Same as nn.Conv2d.
- bias (bool or str): If specified as `auto`, it will be decided by the
- norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
- False.
- """
-
- _version = 2
-
- def __init__(self, *args, **kwargs):
- super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs)
- self.conv_offset = nn.Conv2d(
- self.in_channels,
- self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
- kernel_size=self.kernel_size,
- stride=self.stride,
- padding=self.padding,
- dilation=self.dilation,
- bias=True)
- self.init_weights()
-
- def init_weights(self):
- super(ModulatedDeformConv2dPack, self).init_weights()
- if hasattr(self, 'conv_offset'):
- self.conv_offset.weight.data.zero_()
- self.conv_offset.bias.data.zero_()
-
- def forward(self, x):
- out = self.conv_offset(x)
- o1, o2, mask = torch.chunk(out, 3, dim=1)
- offset = torch.cat((o1, o2), dim=1)
- mask = torch.sigmoid(mask)
- return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias,
- self.stride, self.padding,
- self.dilation, self.groups,
- self.deform_groups)
-
- def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
- missing_keys, unexpected_keys, error_msgs):
- version = local_metadata.get('version', None)
-
- if version is None or version < 2:
- # the key is different in early versions
- # In version < 2, ModulatedDeformConvPack
- # loads previous benchmark models.
- if (prefix + 'conv_offset.weight' not in state_dict
- and prefix[:-1] + '_offset.weight' in state_dict):
- state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
- prefix[:-1] + '_offset.weight')
- if (prefix + 'conv_offset.bias' not in state_dict
- and prefix[:-1] + '_offset.bias' in state_dict):
- state_dict[prefix +
- 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
- '_offset.bias')
-
- if version is not None and version > 1:
- print_log(
- f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to '
- 'version 2.',
- logger='root')
-
- super()._load_from_state_dict(state_dict, prefix, local_metadata,
- strict, missing_keys, unexpected_keys,
- error_msgs)
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