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- """Fully Convolutional Network with Stride of 8"""
- from __future__ import division
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
- from .segbase import SegBaseModel
-
- __all__ = ['FCN', 'get_fcn', 'get_fcn_resnet50_voc',
- 'get_fcn_resnet101_voc', 'get_fcn_resnet152_voc']
-
-
- class FCN(SegBaseModel):
- def __init__(self, nclass, backbone='resnet50', aux=True, pretrained_base=True, **kwargs):
- super(FCN, self).__init__(nclass, aux, backbone, pretrained_base=pretrained_base, **kwargs)
- self.head = _FCNHead(2048, nclass, **kwargs)
- if aux:
- self.auxlayer = _FCNHead(1024, nclass, **kwargs)
-
- self.__setattr__('exclusive', ['head', 'auxlayer'] if aux else ['head'])
-
- def forward(self, x):
- size = x.size()[2:]
- _, _, c3, c4 = self.base_forward(x)
-
- outputs = []
- x = self.head(c4)
- x = F.interpolate(x, size, mode='bilinear', align_corners=True)
- outputs.append(x)
- if self.aux:
- auxout = self.auxlayer(c3)
- auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True)
- outputs.append(auxout)
- return tuple(outputs)
-
-
- class _FCNHead(nn.Module):
- def __init__(self, in_channels, channels, norm_layer=nn.BatchNorm2d, norm_kwargs=None, **kwargs):
- super(_FCNHead, self).__init__()
- inter_channels = in_channels // 4
- self.block = nn.Sequential(
- nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False),
- norm_layer(inter_channels, **({} if norm_kwargs is None else norm_kwargs)),
- nn.ReLU(True),
- nn.Dropout(0.1),
- nn.Conv2d(inter_channels, channels, 1)
- )
-
- def forward(self, x):
- return self.block(x)
-
-
- def get_fcn(dataset='pascal_voc', backbone='resnet50', pretrained=False, root='~/.torch/models',
- pretrained_base=True, **kwargs):
- acronyms = {
- 'pascal_voc': 'pascal_voc',
- 'pascal_aug': 'pascal_aug',
- 'ade20k': 'ade',
- 'coco': 'coco',
- 'citys': 'citys',
- }
- from ..data.dataloader import datasets
- model = FCN(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base, **kwargs)
- if pretrained:
- from .model_store import get_model_file
- device = torch.device(kwargs['local_rank'])
- model.load_state_dict(torch.load(get_model_file('fcn_%s_%s' % (backbone, acronyms[dataset]), root=root),
- map_location=device))
- return model
-
-
- def get_fcn_resnet50_voc(**kwargs):
- return get_fcn('pascal_voc', 'resnet50', **kwargs)
-
-
- def get_fcn_resnet101_voc(**kwargs):
- return get_fcn('pascal_voc', 'resnet101', **kwargs)
-
-
- def get_fcn_resnet152_voc(**kwargs):
- return get_fcn('pascal_voc', 'resnet152', **kwargs)
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