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Python models.vgg13_bn方法代碼示例

本文整理匯總了Python中torchvision.models.vgg13_bn方法的典型用法代碼示例。如果您正苦於以下問題：Python models.vgg13_bn方法的具體用法？Python models.vgg13_bn怎麽用？Python models.vgg13_bn使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torchvision.models的用法示例。


在下文中一共展示了models.vgg13_bn方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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']))
開發者ID:D-X-Y，項目名稱:AutoDL-Projects，代碼行數:20，代碼來源:test-ww.py

示例2: __init__

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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')
開發者ID:JHUVisionLab，項目名稱:multi-modal-regression，代碼行數:18，代碼來源:featureModels.py

示例3: vgg13_bn

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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
開發者ID:alexandonian，項目名稱:pretorched-x，代碼行數:11，代碼來源:torchvision_models.py

示例4: _load_pytorch_model

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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
開發者ID:advboxes，項目名稱:perceptron-benchmark，代碼行數:41，代碼來源:tools.py

示例5: load_pytorch_model

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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
開發者ID:advboxes，項目名稱:perceptron-benchmark，代碼行數:30，代碼來源:tools.py

示例6: vgg13_bn

# 需要導入模塊: from torchvision import models [as 別名]
# 或者: from torchvision.models import vgg13_bn [as 別名]
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
開發者ID:CeLuigi，項目名稱:models-comparison.pytorch，代碼行數:10，代碼來源:torchvision_models.py


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