本文整理汇总了Python中torchvision.models.vgg11_bn方法的典型用法代码示例。如果您正苦于以下问题：Python models.vgg11_bn方法的具体用法？Python models.vgg11_bn怎么用？Python models.vgg11_bn使用的例子？那么, 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.vgg11_bn方法的5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: __init__
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg11_bn [as 别名]
def __init__(self, p):
super(CAL_network, self).__init__()
self.params = p
# get feature extractor and first FCN layer from vgg
vgg = models.vgg11_bn(pretrained=True)
ls = [l for l in vgg.features]+ [nn.AdaptiveMaxPool2d(1), Flatten()]
self.features = nn.Sequential(*ls)
n_in = 512 # fixed amount of features after feature extractor
# initialize the task blocks
self.red_light = TaskBlock(params=p, n_in=512, n_out=2)
self.hazard_stop = TaskBlock(params=p, n_in=512, n_out=2)
self.speed_sign = TaskBlock(params=p, n_in=512, n_out=4)
self.veh_distance = TaskBlock(params=p, n_in=512, n_out=1)
self.relative_angle = TaskBlock(params=p, n_in=512, n_out=1, cond=True)
self.center_distance = TaskBlock(params=p, n_in=512, n_out=1, cond=True) 示例2: vgg11_bn
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg11_bn [as 别名]
def vgg11_bn(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A") with batch normalization
"""
model = models.vgg11_bn(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11_bn'][pretrained]
model = load_pretrained(model, num_classes, settings)
model = modify_vggs(model)
return model 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 vgg11_bn [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 vgg11_bn [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: vgg11_bn
# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg11_bn [as 别名]
def vgg11_bn(num_classes=1000, pretrained='imagenet'):
"""VGG 11-layer model (configuration "A") with batch normalization
"""
model = models.vgg11_bn(pretrained=False)
if pretrained is not None:
settings = pretrained_settings['vgg11_bn'][pretrained]
model = load_pretrained(model, num_classes, settings)
return model