本文整理汇总了Python中resnet.ResNet50方法的典型用法代码示例。如果您正苦于以下问题：Python resnet.ResNet50方法的具体用法？Python resnet.ResNet50怎么用？Python resnet.ResNet50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resnet的用法示例。
在下文中一共展示了resnet.ResNet50方法的3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: run
# 需要导入模块: import resnet [as 别名]
# 或者: from resnet import ResNet50 [as 别名]
def run():
t = time.time()
print('net_cache : ', args.net_cache)
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda()
model = ResNet50()
model = nn.DataParallel(model.cuda())
if os.path.exists(args.net_cache):
print('loading checkpoint {} ..........'.format(args.net_cache))
checkpoint = torch.load(args.net_cache)
best_top1_acc = checkpoint['best_top1_acc']
model.load_state_dict(checkpoint['state_dict'])
#print("loaded checkpoint {} epoch = {}" .format(args.net_cache, checkpoint['epoch']))
else:
print('can not find {} '.format(args.net_cache))
return
num_states = len(stage_repeat) + sum(stage_repeat)
search(model, criterion, num_states)
total_searching_time = time.time() - t
print('total searching time = {:.2f} hours'.format(total_searching_time/3600), flush=True) 示例2: __init__
# 需要导入模块: import resnet [as 别名]
# 或者: from resnet import ResNet50 [as 别名]
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3: model_factory
# 需要导入模块: import resnet [as 别名]
# 或者: from resnet import ResNet50 [as 别名]
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')