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Python models.vgg13_bn方法代码示例

本文整理汇总了Python中torchvision.models.vgg13_bn方法的典型用法代码示例。如果您正苦于以下问题：Python models.vgg13_bn方法的具体用法？Python models.vgg13_bn怎么用？Python models.vgg13_bn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.vgg13_bn方法的6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: main

# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg13_bn [as 别名]
def main():
  # model = models.vgg19_bn(pretrained=True)
  # _, summary = weight_watcher.analyze(model, alphas=False)
  # for key, value in summary.items():
  #   print('{:10s} : {:}'.format(key, value))

  _, summary = weight_watcher.analyze(models.vgg13(pretrained=True), alphas=False)
  print('vgg-13 : {:}'.format(summary['lognorm']))
  _, summary = weight_watcher.analyze(models.vgg13_bn(pretrained=True), alphas=False)
  print('vgg-13-BN : {:}'.format(summary['lognorm']))
  _, summary = weight_watcher.analyze(models.vgg16(pretrained=True), alphas=False)
  print('vgg-16 : {:}'.format(summary['lognorm']))
  _, summary = weight_watcher.analyze(models.vgg16_bn(pretrained=True), alphas=False)
  print('vgg-16-BN : {:}'.format(summary['lognorm']))
  _, summary = weight_watcher.analyze(models.vgg19(pretrained=True), alphas=False)
  print('vgg-19 : {:}'.format(summary['lognorm']))
  _, summary = weight_watcher.analyze(models.vgg19_bn(pretrained=True), alphas=False)
  print('vgg-19-BN : {:}'.format(summary['lognorm']))
开发者ID:D-X-Y，项目名称:AutoDL-Projects，代码行数:20，代码来源:test-ww.py

示例2: __init__

# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg13_bn [as 别名]
def __init__(self, model_type='vgg13', layer_type='fc6'):
		super().__init__()
		# get model
		if model_type == 'vgg13':
			self.original_model = models.vgg13_bn(pretrained=True)
		elif model_type == 'vgg16':
			self.original_model = models.vgg16_bn(pretrained=True)
		else:
			raise NameError('Unknown model_type passed')
		self.features = self.original_model.features
		if layer_type == 'fc6':
			self.classifier = nn.Sequential(*list(self.original_model.classifier.children())[:2])
		elif layer_type == 'fc7':
			self.classifier = nn.Sequential(*list(self.original_model.classifier.children())[:-2])
		else:
			raise NameError('Uknown layer_type passed')
开发者ID:JHUVisionLab，项目名称:multi-modal-regression，代码行数:18，代码来源:featureModels.py

示例3: vgg13_bn

# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg13_bn [as 别名]
def vgg13_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 13-layer model (configuration "B") with batch normalization
    """
    model = models.vgg13_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg13_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
开发者ID:alexandonian，项目名称:pretorched-x，代码行数:11，代码来源:torchvision_models.py

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 vgg13_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
开发者ID:advboxes，项目名称:perceptron-benchmark，代码行数:41，代码来源:tools.py

示例5: load_pytorch_model

# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg13_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
开发者ID:advboxes，项目名称:perceptron-benchmark，代码行数:30，代码来源:tools.py

示例6: vgg13_bn

# 需要导入模块: from torchvision import models [as 别名]
# 或者: from torchvision.models import vgg13_bn [as 别名]
def vgg13_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 13-layer model (configuration "B") with batch normalization
    """
    model = models.vgg13_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg13_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    return model
开发者ID:CeLuigi，项目名称:models-comparison.pytorch，代码行数:10，代码来源:torchvision_models.py


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