本文整理汇总了Python中torchvision.models.densenet.densenet121方法的典型用法代码示例。如果您正苦于以下问题：Python densenet.densenet121方法的具体用法？Python densenet.densenet121怎么用？Python densenet.densenet121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.densenet的用法示例。
在下文中一共展示了densenet.densenet121方法的4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: test_se_densenet
# 需要导入模块: from torchvision.models import densenet [as 别名]
# 或者: from torchvision.models.densenet import densenet121 [as 别名]
def test_se_densenet(pretrained=False):
X = torch.Tensor(32, 3, 224, 224)
if pretrained:
model = se_densenet121(pretrained=pretrained)
net_state_dict = {key: value for key, value in model_zoo.load_url("https://download.pytorch.org/models/densenet121-a639ec97.pth").items()}
model.load_state_dict(net_state_dict, strict=False)
else:
model = se_densenet121(pretrained=pretrained)
# print(model)
if torch.cuda.is_available():
X = X.cuda()
model = model.cuda()
model.eval()
with torch.no_grad():
output = model(X)
print(output.shape) 示例2: test_densenet
# 需要导入模块: from torchvision.models import densenet [as 别名]
# 或者: from torchvision.models.densenet import densenet121 [as 别名]
def test_densenet():
"""create example tensor data for densenet, and print output variable shape"""
X = torch.Tensor(32, 3, 224, 224)
model = densenet121(pretrained=False)
if torch.cuda.is_available():
model = model.cuda()
X = X.cuda()
model.eval()
with torch.no_grad():
output = model(X)
print(output.shape) 示例3: __init__
# 需要导入模块: from torchvision.models import densenet [as 别名]
# 或者: from torchvision.models.densenet import densenet121 [as 别名]
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, pretrained=True):
super(DenseNet, self).__init__()
# First convolution
self.start_features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
# Each denseblock
num_features = num_init_features
init_weights = list(densenet121(pretrained=True).features.children())
start = 0
for i, c in enumerate(self.start_features.children()):
if pretrained:
c.load_state_dict(init_weights[i].state_dict())
start += 1
self.blocks = nn.ModuleList()
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
if pretrained:
block.load_state_dict(init_weights[start].state_dict())
start += 1
self.blocks.append(block)
setattr(self, 'denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
downsample = i < 1
trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2,
downsample=downsample)
if pretrained:
trans.load_state_dict(init_weights[start].state_dict())
start += 1
self.blocks.append(trans)
setattr(self, 'transition%d' % (i + 1), trans)
num_features = num_features // 2 示例4: create_model
# 需要导入模块: from torchvision.models import densenet [as 别名]
# 或者: from torchvision.models.densenet import densenet121 [as 别名]
def create_model(model_name, num_classes=1000, pretrained=False, **kwargs):
if 'test_time_pool' in kwargs:
test_time_pool = kwargs.pop('test_time_pool')
else:
test_time_pool = True
if model_name == 'dpn68':
model = dpn68(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn68b':
model = dpn68b(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn92':
model = dpn92(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn98':
model = dpn98(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn131':
model = dpn131(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'dpn107':
model = dpn107(
pretrained=pretrained, test_time_pool=test_time_pool, num_classes=num_classes)
elif model_name == 'resnet18':
model = resnet18(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet34':
model = resnet34(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet50':
model = resnet50(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet101':
model = resnet101(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'resnet152':
model = resnet152(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet121':
model = densenet121(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet161':
model = densenet161(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet169':
model = densenet169(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'densenet201':
model = densenet201(pretrained=pretrained, num_classes=num_classes, **kwargs)
elif model_name == 'inception_v3':
model = inception_v3(
pretrained=pretrained, num_classes=num_classes, transform_input=False, **kwargs)
else:
assert False, "Unknown model architecture (%s)" % model_name
return model