mirror of
https://github.com/open-mmlab/mmsegmentation.git
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737544f1c5
* add config * add cityscapes config * add default value to docstring * fix lint * add deit-s and deit-b * add readme * add eps at norm_cfg * add drop_path_rate experiment * add deit case at init_weight * add upernet result * update result and add upernet 160k config * update upernet result and fix settings * Update iters number * update result and delete some configs * fix import error * fix drop_path_rate * update result and restore config * update benchmark result * remove cityscapes exp * remove neck * neck exp * add more configs * fix init error * fix ffn setting * update result * update results * update result * update results and fill table * delete or rename configs * fix link delimiter * rename configs and fix link * rename neck to mln
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import ConvModule, xavier_init
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from ..builder import NECKS
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@NECKS.register_module()
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class MultiLevelNeck(nn.Module):
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"""MultiLevelNeck.
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A neck structure connect vit backbone and decoder_heads.
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Args:
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in_channels (List[int]): Number of input channels per scale.
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out_channels (int): Number of output channels (used at each scale).
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scales (List[float]): Scale factors for each input feature map.
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Default: [0.5, 1, 2, 4]
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norm_cfg (dict): Config dict for normalization layer. Default: None.
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act_cfg (dict): Config dict for activation layer in ConvModule.
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Default: None.
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"""
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def __init__(self,
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in_channels,
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out_channels,
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scales=[0.5, 1, 2, 4],
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norm_cfg=None,
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act_cfg=None):
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super(MultiLevelNeck, self).__init__()
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assert isinstance(in_channels, list)
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.scales = scales
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self.num_outs = len(scales)
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self.lateral_convs = nn.ModuleList()
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self.convs = nn.ModuleList()
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for in_channel in in_channels:
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self.lateral_convs.append(
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ConvModule(
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in_channel,
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out_channels,
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kernel_size=1,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg))
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for _ in range(self.num_outs):
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self.convs.append(
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ConvModule(
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out_channels,
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out_channels,
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kernel_size=3,
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padding=1,
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stride=1,
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norm_cfg=norm_cfg,
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act_cfg=act_cfg))
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# default init_weights for conv(msra) and norm in ConvModule
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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xavier_init(m, distribution='uniform')
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def forward(self, inputs):
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assert len(inputs) == len(self.in_channels)
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inputs = [
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lateral_conv(inputs[i])
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for i, lateral_conv in enumerate(self.lateral_convs)
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]
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# for len(inputs) not equal to self.num_outs
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if len(inputs) == 1:
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inputs = [inputs[0] for _ in range(self.num_outs)]
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outs = []
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for i in range(self.num_outs):
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x_resize = F.interpolate(
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inputs[i], scale_factor=self.scales[i], mode='bilinear')
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outs.append(self.convs[i](x_resize))
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return tuple(outs)
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