解析 语义分割域适应的代码
训练模型python adapt_trainer.py gta city --net drn_d_105
adapt_trainer.py文件阅读
- 20-23 解析命令行参数，用到:
segmentation/argmyparse.py
命令行解析 - 选择网络drn_d_105 : 49行
get_models定位到model_util.py；33行参数drn定位到dilated_fcn.py
rom models.dilated_fcn import DRNSegBase, DRNSegPixelClassifier
model_g = DRNSegBase(model_name=net_name, n_class=n_class, input_ch=input_ch)
model_f1 = DRNSegPixelClassifier(n_class=n_class)
model_f2 = DRNSegPixelClassifier(n_class=n_class)
- 定义网络：
dilated_fcn.pydrn_d_105 :drn.py选择参数D
class DRNSegBase(nn.Module):
def __init__(self, model_name, n_class, pretrained_model=None, pretrained=True, input_ch=3):
super(DRNSegBase, self).__init__()
model = drn.__dict__.get(model_name)(
pretrained=pretrained, num_classes=1000, input_ch=input_ch)
pmodel = nn.DataParallel(model)
if pretrained_model is not None:
pmodel.load_state_dict(pretrained_model)
self.base = nn.Sequential(*list(model.children())[:-2])
self.seg = nn.Conv2d(model.out_dim, n_class,
kernel_size=1, bias=True)
m = self.seg
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
m.bias.data.zero_()
def forward(self, x):
x = self.base(x)
x = self.seg(x)
return x
def optim_parameters(self, memo=None):
for param in self.base.parameters():
yield param
for param in self.seg.parameters():
yield param
网络架构
class DRN(nn.Module):
def __init__(self, block, layers, num_classes=1000,
channels=(16, 32, 64, 128, 256, 512, 512, 512),
out_map=False, out_middle=False, pool_size=28, arch='D'):
super(DRN, self).__init__()
self.inplanes = channels[0]
self.out_map = out_map
self.out_dim = channels[-1]
self.out_middle = out_middle
self.arch = arch
self.layer0 = nn.Sequential(
nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3,
bias=False),
nn.BatchNorm2d(channels[0]),
nn.ReLU(inplace=True)
)
self.layer1 = self._make_conv_layers(
channels[0], layers[0], stride=1)
self.layer2 = self._make_conv_layers(
channels[1], layers[1], stride=2)
self.layer3 = self._make_layer(block, channels[2], layers[2], stride=2)
self.layer4 = self._make_layer(block, channels[3], layers[3], stride=2)
self.layer5 = self._make_layer(block, channels[4], layers[4], dilation=2,
new_level=False)
self.layer6 = None if layers[5] == 0 else \
self._make_layer(block, channels[5], layers[5], dilation=4,
new_level=False)
self.layer7 = None if layers[6] == 0 else \
self._make_conv_layers(channels[6], layers[6], dilation=2)
self.layer8 = None if layers[7] == 0 else \
self._make_conv_layers(channels[7], layers[7], dilation=1)
self.avgpool = nn.AvgPool2d(pool_size)
self.fc = nn.Conv2d(self.out_dim, num_classes, kernel_size=1,
stride=1, padding=0, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
测试
>>> import drn
>>> print(drn.__dict__)
>>> print(drn.__dict__).get('drn_d_105')
<function drn_d_105 at 0x7f2a25fe9aa0>
>>> model=drn.__dict__.get('drn_d_105')(pretrained=True,num_classes=1000,input_ch=3)
Downloading: "https://tigress-web.princeton.edu/~fy/drn/models/drn_d_105-12b40979.pth" to /home/名字/.torch/models/drn_d_105-12b40979.pth
100.0%
