import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, ModuleList
[文档]class ConvUpsample(BaseModule):
"""ConvUpsample performs 2x upsampling after Conv.
There are several `ConvModule` layers. In the first few layers, upsampling
will be applied after each layer of convolution. The number of upsampling
must be no more than the number of ConvModule layers.
Args:
in_channels (int): Number of channels in the input feature map.
inner_channels (int): Number of channels produced by the convolution.
num_layers (int): Number of convolution layers.
num_upsample (int | optional): Number of upsampling layer. Must be no
more than num_layers. Upsampling will be applied after the first
``num_upsample`` layers of convolution. Default: ``num_layers``.
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer. Default: None.
init_cfg (dict): Config dict for initialization. Default: None.
kwargs (key word augments): Other augments used in ConvModule.
"""
def __init__(self,
in_channels,
inner_channels,
num_layers=1,
num_upsample=None,
conv_cfg=None,
norm_cfg=None,
init_cfg=None,
**kwargs):
super(ConvUpsample, self).__init__(init_cfg)
if num_upsample is None:
num_upsample = num_layers
assert num_upsample <= num_layers, \
f'num_upsample({num_upsample})must be no more than ' \
f'num_layers({num_layers})'
self.num_layers = num_layers
self.num_upsample = num_upsample
self.conv = ModuleList()
for i in range(num_layers):
self.conv.append(
ConvModule(
in_channels,
inner_channels,
3,
padding=1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
in_channels = inner_channels
[文档] def forward(self, x):
num_upsample = self.num_upsample
for i in range(self.num_layers):
x = self.conv[i](x)
if num_upsample > 0:
num_upsample -= 1
x = F.interpolate(
x, scale_factor=2, mode='bilinear', align_corners=False)
return x