release code
This commit is contained in:
miunangel
589
Dassl.ProGrad.pytorch/dassl/modeling/backbone/resnet.py
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589
Dassl.ProGrad.pytorch/dassl/modeling/backbone/resnet.py
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@@ -0,0 +1,589 @@
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import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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from .build import BACKBONE_REGISTRY
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from .backbone import Backbone
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model_urls = {
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"resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
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"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
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"resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
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"resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
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"resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
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}
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(
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in_planes,
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out_planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False
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)
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super().__init__()
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = nn.BatchNorm2d(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super().__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(
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planes,
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planes,
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kernel_size=3,
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stride=stride,
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padding=1,
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bias=False
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)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(
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planes, planes * self.expansion, kernel_size=1, bias=False
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)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNet(Backbone):
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def __init__(
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self,
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block,
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layers,
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ms_class=None,
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ms_layers=[],
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ms_p=0.5,
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ms_a=0.1,
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**kwargs
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):
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self.inplanes = 64
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super().__init__()
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# backbone network
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self.conv1 = nn.Conv2d(
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3, 64, kernel_size=7, stride=2, padding=3, bias=False
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)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self._out_features = 512 * block.expansion
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self.mixstyle = None
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if ms_layers:
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self.mixstyle = ms_class(p=ms_p, alpha=ms_a)
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for layer_name in ms_layers:
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assert layer_name in ["layer1", "layer2", "layer3"]
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print(f"Insert MixStyle after {ms_layers}")
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self.ms_layers = ms_layers
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self._init_params()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(
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self.inplanes,
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planes * block.expansion,
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kernel_size=1,
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stride=stride,
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bias=False,
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),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def _init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(
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m.weight, mode="fan_out", nonlinearity="relu"
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)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def featuremaps(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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if "layer1" in self.ms_layers:
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x = self.mixstyle(x)
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x = self.layer2(x)
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if "layer2" in self.ms_layers:
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x = self.mixstyle(x)
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x = self.layer3(x)
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if "layer3" in self.ms_layers:
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x = self.mixstyle(x)
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return self.layer4(x)
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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return v.view(v.size(0), -1)
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def init_pretrained_weights(model, model_url):
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pretrain_dict = model_zoo.load_url(model_url)
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model.load_state_dict(pretrain_dict, strict=False)
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"""
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Residual network configurations:
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--
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resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
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resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
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resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
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resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
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resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
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"""
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@BACKBONE_REGISTRY.register()
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def resnet18(pretrained=True, **kwargs):
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model = ResNet(block=BasicBlock, layers=[2, 2, 2, 2])
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet34(pretrained=True, **kwargs):
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model = ResNet(block=BasicBlock, layers=[3, 4, 6, 3])
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet34"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50(pretrained=True, **kwargs):
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model = ResNet(block=Bottleneck, layers=[3, 4, 6, 3])
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet50"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet101(pretrained=True, **kwargs):
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model = ResNet(block=Bottleneck, layers=[3, 4, 23, 3])
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet101"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet152(pretrained=True, **kwargs):
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model = ResNet(block=Bottleneck, layers=[3, 8, 36, 3])
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet152"])
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return model
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"""
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Residual networks with mixstyle
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"""
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@BACKBONE_REGISTRY.register()
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def resnet18_ms_l123(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2", "layer3"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet18_ms_l12(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet18_ms_l1(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=MixStyle,
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ms_layers=["layer1"]
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50_ms_l123(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2", "layer3"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet50"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50_ms_l12(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet50"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50_ms_l1(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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ms_class=MixStyle,
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ms_layers=["layer1"]
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet50"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet101_ms_l123(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 23, 3],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2", "layer3"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet101"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet101_ms_l12(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 23, 3],
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ms_class=MixStyle,
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ms_layers=["layer1", "layer2"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet101"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet101_ms_l1(pretrained=True, **kwargs):
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from dassl.modeling.ops import MixStyle
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 23, 3],
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ms_class=MixStyle,
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ms_layers=["layer1"]
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet101"])
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return model
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"""
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Residual networks with efdmix
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"""
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@BACKBONE_REGISTRY.register()
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def resnet18_efdmix_l123(pretrained=True, **kwargs):
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from dassl.modeling.ops import EFDMix
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=EFDMix,
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ms_layers=["layer1", "layer2", "layer3"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet18_efdmix_l12(pretrained=True, **kwargs):
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from dassl.modeling.ops import EFDMix
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=EFDMix,
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ms_layers=["layer1", "layer2"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet18_efdmix_l1(pretrained=True, **kwargs):
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from dassl.modeling.ops import EFDMix
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model = ResNet(
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block=BasicBlock,
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layers=[2, 2, 2, 2],
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ms_class=EFDMix,
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ms_layers=["layer1"]
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet18"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50_efdmix_l123(pretrained=True, **kwargs):
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from dassl.modeling.ops import EFDMix
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model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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ms_class=EFDMix,
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||||
ms_layers=["layer1", "layer2", "layer3"],
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)
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if pretrained:
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init_pretrained_weights(model, model_urls["resnet50"])
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return model
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@BACKBONE_REGISTRY.register()
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def resnet50_efdmix_l12(pretrained=True, **kwargs):
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from dassl.modeling.ops import EFDMix
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||||
model = ResNet(
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block=Bottleneck,
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layers=[3, 4, 6, 3],
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ms_class=EFDMix,
|
||||
ms_layers=["layer1", "layer2"],
|
||||
)
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||||
|
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if pretrained:
|
||||
init_pretrained_weights(model, model_urls["resnet50"])
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@BACKBONE_REGISTRY.register()
|
||||
def resnet50_efdmix_l1(pretrained=True, **kwargs):
|
||||
from dassl.modeling.ops import EFDMix
|
||||
|
||||
model = ResNet(
|
||||
block=Bottleneck,
|
||||
layers=[3, 4, 6, 3],
|
||||
ms_class=EFDMix,
|
||||
ms_layers=["layer1"]
|
||||
)
|
||||
|
||||
if pretrained:
|
||||
init_pretrained_weights(model, model_urls["resnet50"])
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@BACKBONE_REGISTRY.register()
|
||||
def resnet101_efdmix_l123(pretrained=True, **kwargs):
|
||||
from dassl.modeling.ops import EFDMix
|
||||
|
||||
model = ResNet(
|
||||
block=Bottleneck,
|
||||
layers=[3, 4, 23, 3],
|
||||
ms_class=EFDMix,
|
||||
ms_layers=["layer1", "layer2", "layer3"],
|
||||
)
|
||||
|
||||
if pretrained:
|
||||
init_pretrained_weights(model, model_urls["resnet101"])
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@BACKBONE_REGISTRY.register()
|
||||
def resnet101_efdmix_l12(pretrained=True, **kwargs):
|
||||
from dassl.modeling.ops import EFDMix
|
||||
|
||||
model = ResNet(
|
||||
block=Bottleneck,
|
||||
layers=[3, 4, 23, 3],
|
||||
ms_class=EFDMix,
|
||||
ms_layers=["layer1", "layer2"],
|
||||
)
|
||||
|
||||
if pretrained:
|
||||
init_pretrained_weights(model, model_urls["resnet101"])
|
||||
|
||||
return model
|
||||
|
||||
|
||||
@BACKBONE_REGISTRY.register()
|
||||
def resnet101_efdmix_l1(pretrained=True, **kwargs):
|
||||
from dassl.modeling.ops import EFDMix
|
||||
|
||||
model = ResNet(
|
||||
block=Bottleneck,
|
||||
layers=[3, 4, 23, 3],
|
||||
ms_class=EFDMix,
|
||||
ms_layers=["layer1"]
|
||||
)
|
||||
|
||||
if pretrained:
|
||||
init_pretrained_weights(model, model_urls["resnet101"])
|
||||
|
||||
return model
|
||||
Reference in New Issue
Block a user