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import torch.nn as nn
import torch.nn.functional as F
from annotator.uniformer.mmcv.cnn import ConvModule
from ..builder import NECKS
@NECKS.register_module()
class MultiLevelNeck(nn.Module):
"""MultiLevelNeck.
A neck structure connect vit backbone and decoder_heads.
Args:
in_channels (List[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale).
scales (List[int]): Scale factors for each input feature map.
norm_cfg (dict): Config dict for normalization layer. Default: None.
act_cfg (dict): Config dict for activation layer in ConvModule.
Default: None.
"""
def __init__(self,
in_channels,
out_channels,
scales=[0.5, 1, 2, 4],
norm_cfg=None,
act_cfg=None):
super(MultiLevelNeck, self).__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels
self.scales = scales
self.num_outs = len(scales)
self.lateral_convs = nn.ModuleList()
self.convs = nn.ModuleList()
for in_channel in in_channels:
self.lateral_convs.append(
ConvModule(
in_channel,
out_channels,
kernel_size=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
for _ in range(self.num_outs):
self.convs.append(
ConvModule(
out_channels,
out_channels,
kernel_size=3,
padding=1,
stride=1,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
def forward(self, inputs):
assert len(inputs) == len(self.in_channels)
print(inputs[0].shape)
inputs = [
lateral_conv(inputs[i])
for i, lateral_conv in enumerate(self.lateral_convs)
]
# for len(inputs) not equal to self.num_outs
if len(inputs) == 1:
inputs = [inputs[0] for _ in range(self.num_outs)]
outs = []
for i in range(self.num_outs):
x_resize = F.interpolate(
inputs[i], scale_factor=self.scales[i], mode='bilinear')
outs.append(self.convs[i](x_resize))
return tuple(outs)
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