def se_resnet34(pretrained, num_classes=1000):
"""Constructs a SE-ResNet-34 model."""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
state_dict = torch.load('/home/kg2/se_resnet34_best.pth')['state_dict']
model.load_state_dict(state_dict, strict=False)
return model
def se_resnet34(num_classes=5):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def SE_ResNet101(num_classes=1_000):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet18(num_classes=1_000):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet152(num_classes=5):
"""Constructs a ResNet-152 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def dse_resnet50(num_classes=1000):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(DSEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def feasc18(num_classes=200, nparts=1):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes, nparts=nparts)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def _se_resnet(arch, block, layers, pretrained, progress, **kwargs):
# adapted from the _resnet function in torch vision
model = ResNet(block, layers, **kwargs)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model
def se_resnet50(num_classes=5, pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
model.load_state_dict(load_state_dict_from_url(
"https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl"))
return model
def se_resnet50(num_classes=1000, pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
model.load_state_dict(model_zoo.load_url("https://www.dropbox.com/s/xpq8ne7rwa4kg4c/seresnet50-60a8950a85b2b.pkl"))
return model
def feasc50(num_classes=200, nparts=1, seflag=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if seflag:
rd = [16, 32, 64, 128]
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes, rd=rd, nparts=nparts, seflag=True)
else:
model = ResNet(ResBottleneck, [3, 4, 6, 3], num_classes=num_classes, nparts=nparts, seflag=False)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet34(num_classes=1000, pretrained="/home/ibian/.torch/models/resnet34-333f7ec4.pth"):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
# use pretrained model
if pretrained is not None:
print("=> loading pretrained model '{}'".format(pretrained))
model_dict = model.state_dict()
checkpoint = torch.load(pretrained)
checkpoint = {k: v for k, v in checkpoint.items() if k in model_dict}
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
return model
def Net(num_classes):
model = ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = torch.nn.AdaptiveAvgPool2d(1)
return model
def srm_resnet101(num_classes=1000):
model = ResNet(bottleneck_factory(layer_block=SRMWithCorrMatrix),
[3, 4, 23, 3],
num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def resnet101(num_classes=1000):
model = ResNet(bottleneck_factory(), [3, 4, 23, 3],
num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet50(num_classes=1000):
model = ResNet(bottleneck_factory(layer_block=SELayer), [3, 4, 6, 3],
num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def resnet34(num_classes=1000):
model = ResNet(basic_block_factory(), [3, 4, 6, 3],
num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet34(num_classes=1000):
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBasicBlock, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet50(num_classes=1_000, pretrained=False):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 6, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
if pretrained:
model.load_state_dict(
load_state_dict_from_url(
"https://github.com/moskomule/senet.pytorch/releases/download/archive/seresnet50-60a8950a85b2b.pkl"
))
return model
def se_resnet101(num_classes=1_000):
"""Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
def se_resnet18(num_classes=1000):
"""Constructs a SE-ResNet-18 model."""
model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet101(num_classes=1_000):
model = ResNet(SEBottleneck, [3, 4, 23, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet18(num_classes=1_000):
model = ResNet(SEBasicBlock, [2, 2, 2, 2], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def resnet152(**kwargs):
"""Constructs a ResNet-152 model.
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def resnet18(**kwargs):
"""Constructs a ResNet-18 model.
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def se_resnet152(num_classes=1000):
"""Constructs a SE-ResNet-152 model."""
model = ResNet(SEBottleneck, [3, 8, 36, 3], num_classes=num_classes)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
def resnet34(**kwargs):
"""Constructs a ResNet-34 model.
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
model.avgpool = nn.AdaptiveAvgPool2d(1)
return model
