本文整理匯總了Python中resnet.ResNet152方法的典型用法代碼示例。如果您正苦於以下問題：Python resnet.ResNet152方法的具體用法？Python resnet.ResNet152怎麽用？Python resnet.ResNet152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resnet的用法示例。
在下文中一共展示了resnet.ResNet152方法的2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: __init__
# 需要導入模塊: import resnet [as 別名]
# 或者: from resnet import ResNet152 [as 別名]
def __init__(self, dropout_rate, feat_length = 512, archi_type='resnet18'):
super(CIFAR10FeatureLayer, self).__init__()
self.archi_type = archi_type
self.feat_length = feat_length
if self.archi_type == 'default':
self.add_module('conv1', nn.Conv2d(3, 32, kernel_size=3, padding=1))
self.add_module('bn1', nn.BatchNorm2d(32))
self.add_module('relu1', nn.ReLU())
self.add_module('pool1', nn.MaxPool2d(kernel_size=2))
#self.add_module('drop1', nn.Dropout(dropout_rate))
self.add_module('conv2', nn.Conv2d(32, 32, kernel_size=3, padding=1))
self.add_module('bn2', nn.BatchNorm2d(32))
self.add_module('relu2', nn.ReLU())
self.add_module('pool2', nn.MaxPool2d(kernel_size=2))
#self.add_module('drop2', nn.Dropout(dropout_rate))
self.add_module('conv3', nn.Conv2d(32, 64, kernel_size=3, padding=1))
self.add_module('bn3', nn.BatchNorm2d(64))
self.add_module('relu3', nn.ReLU())
self.add_module('pool3', nn.MaxPool2d(kernel_size=2))
#self.add_module('drop3', nn.Dropout(dropout_rate))
elif self.archi_type == 'resnet18':
self.add_module('resnet18', resnet.ResNet18(feat_length))
elif self.archi_type == 'resnet50':
self.add_module('resnet50', resnet.ResNet50(feat_length))
elif self.archi_type == 'resnet152':
self.add_module('resnet152', resnet.ResNet152(feat_length))
else:
raise NotImplementedError 示例2: model_factory
# 需要導入模塊: import resnet [as 別名]
# 或者: from resnet import ResNet152 [as 別名]
def model_factory(model_name, **params):
model_dict = {
'densenet121': DenseNet121,
'densenet169': DenseNet169,
'densenet201': DenseNet201,
'densenet161': DenseNet161,
'densenet-cifar': densenet_cifar,
'dual-path-net-26': DPN26,
'dual-path-net-92': DPN92,
'googlenet': GoogLeNet,
'lenet': LeNet,
'mobilenet': MobileNet,
'mobilenetv2': MobileNetV2,
'pnasneta': PNASNetA,
'pnasnetb': PNASNetB,
'preact-resnet18': PreActResNet18,
'preact-resnet34': PreActResNet34,
'preact-resnet50': PreActResNet50,
'preact-resnet101': PreActResNet101,
'preact-resnet152': PreActResNet152,
'resnet18': ResNet18,
'resnet34': ResNet34,
'resnet50': ResNet50,
'resnet101': ResNet101,
'resnet152': ResNet152,
'resnext29_2x64d': ResNeXt29_2x64d,
'resnext29_4x64d': ResNeXt29_4x64d,
'resnext29_8x64d': ResNeXt29_8x64d,
'resnext29_32x64d': ResNeXt29_32x4d,
'senet18': SENet18,
'shufflenetg2': ShuffleNetG2,
'shufflenetg3': ShuffleNetG3,
'shufflenetv2_0.5': ShuffleNetV2,
'shufflenetv2_1.0': ShuffleNetV2,
'shufflenetv2_1.5': ShuffleNetV2,
'shufflenetv2_2.0': ShuffleNetV2,
'vgg11': VGG,
'vgg13': VGG,
'vgg16': VGG,
'vgg19': VGG,
}
if 'vgg' in model_name:
return model_dict[model_name](model_name)
elif 'shufflenetv2' in model_name:
return model_dict[model_name](float(model_name[-3:]))
elif model_name in model_dict.keys():
return model_dict[model_name]()
else:
raise AttributeError('Model doesn\'t exist')