本文整理汇总了Python中torchvision.models.densenet161方法的典型用法代码示例。如果您正苦于以下问题：Python models.densenet161方法的具体用法？Python models.densenet161怎么用？Python models.densenet161使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torchvision.models的用法示例。
在下文中一共展示了models.densenet161方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: densenet161
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def densenet161(num_classes=1000, pretrained='imagenet'):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet161(num_classes=num_classes, pretrained=False)
if pretrained is not None:
# '.'s are no longer allowed in module names, but pervious _DenseLayer
# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
# They are also in the checkpoints in model_urls. This pattern is used
# to find such keys.
settings = pretrained_settings['densenet161'][pretrained]
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
state_dict = model_zoo.load_url(settings['url'])
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
model = modify_densenets(model)
return model
###############################################################
# InceptionV3 示例2: Dense169
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def Dense169(config):
return models.densenet161(pretrained=True) 示例3: _load_pytorch_model
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def _load_pytorch_model(model_name, summary):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
import torch
if torch.cuda.is_available():
_model = _model.cuda()
from perceptron.models.classification.pytorch import PyTorchModel as ClsPyTorchModel
import numpy as np
mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
pmodel = ClsPyTorchModel(
_model, bounds=(
0, 1), num_classes=1000, preprocessing=(
mean, std))
return pmodel 示例4: load_pytorch_model
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def load_pytorch_model(model_name):
import torchvision.models as models
switcher = {
'alexnet': lambda: models.alexnet(pretrained=True).eval(),
"vgg11": lambda: models.vgg11(pretrained=True).eval(),
"vgg11_bn": lambda: models.vgg11_bn(pretrained=True).eval(),
"vgg13": lambda: models.vgg13(pretrained=True).eval(),
"vgg13_bn": lambda: models.vgg13_bn(pretrained=True).eval(),
"vgg16": lambda: models.vgg16(pretrained=True).eval(),
"vgg16_bn": lambda: models.vgg16_bn(pretrained=True).eval(),
"vgg19": lambda: models.vgg19(pretrained=True).eval(),
"vgg19_bn": lambda: models.vgg19_bn(pretrained=True).eval(),
"resnet18": lambda: models.resnet18(pretrained=True).eval(),
"resnet34": lambda: models.resnet34(pretrained=True).eval(),
"resnet50": lambda: models.resnet50(pretrained=True).eval(),
"resnet101": lambda: models.resnet101(pretrained=True).eval(),
"resnet152": lambda: models.resnet152(pretrained=True).eval(),
"squeezenet1_0": lambda: models.squeezenet1_0(pretrained=True).eval(),
"squeezenet1_1": lambda: models.squeezenet1_1(pretrained=True).eval(),
"densenet121": lambda: models.densenet121(pretrained=True).eval(),
"densenet161": lambda: models.densenet161(pretrained=True).eval(),
"densenet201": lambda: models.densenet201(pretrained=True).eval(),
"inception_v3": lambda: models.inception_v3(pretrained=True).eval(),
}
_load_model = switcher.get(model_name, None)
_model = _load_model()
return _model 示例5: dn161
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def dn161(pre): return children(densenet161(pre))[0] 示例6: densenet161
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def densenet161(num_classes=1000, pretrained='imagenet'):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
"""
model = models.densenet161(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['densenet161'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify_densenets(model)
return model
###############################################################
# InceptionV3 示例7: __init__
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import densenet161 [as 别名]
def __init__(self,option = 'densenet201',pret=True):
super(DenseBase, self).__init__()
self.dim = 2048
if option == 'densenet201':
model_ft = models.densenet201(pretrained=pret)
self.dim = 1920
if option == 'densenet161':
model_ft = models.densenet161(pretrained=pret)
self.dim = 2208
mod = list(model_ft.children())
#mod.pop()
self.features = nn.Sequential(*mod)