mirror of
https://github.com/open-mmlab/mmsegmentation.git
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f5eb7f2627
* added cgnet * added testing for cgnet * git test * add cgnet * fix __init__ * rename FGlo with GlobalContextExtractor * add readme.md and rename bn with norm * delete cg_head * fix a language mistake * rename cgnet_m3n21.py to cgnet.py * modify README.md * modify list to tuple * add fcn_head test * add assert to fcn_head * blank * fix fcn_head assert bug * add * add cgnet to README.md and model_zoo.md * modify cgnet README.md Co-authored-by: KID <wps_@mail.nankai.edu.cn>
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import torch
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import torch.nn as nn
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from mmcv.cnn import ConvModule
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from ..builder import HEADS
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from .decode_head import BaseDecodeHead
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@HEADS.register_module()
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class FCNHead(BaseDecodeHead):
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"""Fully Convolution Networks for Semantic Segmentation.
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This head is implemented of `FCNNet <https://arxiv.org/abs/1411.4038>`_.
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Args:
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num_convs (int): Number of convs in the head. Default: 2.
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kernel_size (int): The kernel size for convs in the head. Default: 3.
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concat_input (bool): Whether concat the input and output of convs
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before classification layer.
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"""
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def __init__(self,
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num_convs=2,
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kernel_size=3,
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concat_input=True,
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**kwargs):
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assert num_convs >= 0
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self.num_convs = num_convs
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self.concat_input = concat_input
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self.kernel_size = kernel_size
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super(FCNHead, self).__init__(**kwargs)
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if num_convs == 0:
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assert self.in_channels == self.channels
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convs = []
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convs.append(
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ConvModule(
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self.in_channels,
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self.channels,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg))
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for i in range(num_convs - 1):
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convs.append(
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ConvModule(
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self.channels,
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self.channels,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg))
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if num_convs == 0:
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self.convs = nn.Identity()
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else:
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self.convs = nn.Sequential(*convs)
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if self.concat_input:
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self.conv_cat = ConvModule(
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self.in_channels + self.channels,
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self.channels,
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kernel_size=kernel_size,
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padding=kernel_size // 2,
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conv_cfg=self.conv_cfg,
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norm_cfg=self.norm_cfg,
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act_cfg=self.act_cfg)
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def forward(self, inputs):
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"""Forward function."""
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x = self._transform_inputs(inputs)
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output = self.convs(x)
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if self.concat_input:
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output = self.conv_cat(torch.cat([x, output], dim=1))
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output = self.cls_seg(output)
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return output
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