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python res_Python models.resnet152方法代码示例

發布時間：2023/12/19 python 31 豆豆

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本文整理匯總了Python中torchvision.models.resnet152方法的典型用法代碼示例。如果您正苦于以下問題：Python models.resnet152方法的具體用法？Python models.resnet152怎么用？Python models.resnet152使用的例子？那么恭喜您, 這里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模塊torchvision.models的用法示例。

在下文中一共展示了models.resnet152方法的21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贊，您的評價將有助于我們的系統推薦出更棒的Python代碼示例。

示例1: get_pretrained_resnet

?點贊 9

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def get_pretrained_resnet(new_fc_dim=None):

"""

Fetches a pretrained resnet model (downloading if necessary) and chops off the top linear

layer. If new_fc_dim isn't None, then a new linear layer is added.

:param new_fc_dim:

:return:

"""

resnet152 = models.resnet152(pretrained=True)

del resnet152.fc

if new_fc_dim is not None:

resnet152.fc = nn.Linear(ENCODING_SIZE, new_fc_dim)

_init_fc(resnet152.fc)

else:

resnet152.fc = lambda x: x

return resnet152

開發者ID:uwnlp，項目名稱:verb-attributes，代碼行數:20，

示例2: test_untargeted_resnet152

?點贊 7

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def test_untargeted_resnet152(image, label=None):

import torch

import torchvision.models as models

from perceptron.models.classification import PyTorchModel

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))

model_pyt = models.resnet152(pretrained=True).eval()

if torch.cuda.is_available():

model_pyt = model_pyt.cuda()

model = PyTorchModel(

model_pyt, bounds=(0, 1), num_classes=1000, preprocessing=(mean, std))

print(np.argmax(model.predictions(image)))

attack = Attack(model, criterion=Misclassification())

adversarial_obj = attack(image, label, unpack=False, epsilons=10000)

distance = adversarial_obj.distance

adversarial = adversarial_obj.image

return distance, adversarial

開發者ID:advboxes，項目名稱:perceptron-benchmark，代碼行數:19，

示例3: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, num_layers, pretrained, num_input_images=1):

super(ResnetEncoder, self).__init__()

self.num_ch_enc = np.array([64, 64, 128, 256, 512])

resnets = {18: models.resnet18,

34: models.resnet34,

50: models.resnet50,

101: models.resnet101,

152: models.resnet152}

if num_layers not in resnets:

raise ValueError("{} is not a valid number of resnet layers".format(num_layers))

if num_input_images > 1:

self.encoder = resnet_multiimage_input(num_layers, pretrained, num_input_images)

else:

self.encoder = resnets[num_layers](pretrained)

if num_layers > 34:

self.num_ch_enc[1:] *= 4

開發者ID:TRI-ML，項目名稱:packnet-sfm，代碼行數:23，

示例4: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self,option = 'resnet18',pret=True):

super(ResBase, self).__init__()

self.dim = 2048

if option == 'resnet18':

model_ft = models.resnet18(pretrained=pret)

self.dim = 512

if option == 'resnet50':

model_ft = models.resnet50(pretrained=pret)

if option == 'resnet101':

model_ft = models.resnet101(pretrained=pret)

if option == 'resnet152':

model_ft = models.resnet152(pretrained=pret)

if option == 'resnet200':

model_ft = Res200()

if option == 'resnetnext':

model_ft = ResNeXt(layer_num=101)

mod = list(model_ft.children())

mod.pop()

#self.model_ft =model_ft

self.features = nn.Sequential(*mod)

開發者ID:mil-tokyo，項目名稱:MCD_DA，代碼行數:22，

示例5: get_model

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def get_model(args):

assert args.type in ['2d', '3d']

if args.type == '2d':

print('Loading 2D-ResNet-152 ...')

model = models.resnet152(pretrained=True)

model = nn.Sequential(*list(model.children())[:-2], GlobalAvgPool())

model = model.cuda()

else:

print('Loading 3D-ResneXt-101 ...')

model = resnext.resnet101(

num_classes=400,

shortcut_type='B',

cardinality=32,

sample_size=112,

sample_duration=16,

last_fc=False)

model = model.cuda()

model_data = th.load(args.resnext101_model_path)

model.load_state_dict(model_data)

model.eval()

print('loaded')

return model

開發者ID:antoine77340，項目名稱:video_feature_extractor，代碼行數:25，

示例6: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, requires_grad=False, pretrained=True, num=18):

super(resnet, self).__init__()

if(num==18):

self.net = tv.resnet18(pretrained=pretrained)

elif(num==34):

self.net = tv.resnet34(pretrained=pretrained)

elif(num==50):

self.net = tv.resnet50(pretrained=pretrained)

elif(num==101):

self.net = tv.resnet101(pretrained=pretrained)

elif(num==152):

self.net = tv.resnet152(pretrained=pretrained)

self.N_slices = 5

self.conv1 = self.net.conv1

self.bn1 = self.net.bn1

self.relu = self.net.relu

self.maxpool = self.net.maxpool

self.layer1 = self.net.layer1

self.layer2 = self.net.layer2

self.layer3 = self.net.layer3

self.layer4 = self.net.layer4

開發者ID:richzhang，項目名稱:PerceptualSimilarity，代碼行數:24，

示例7: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, requires_grad=False, pretrained=True, num=18):

super(resnet, self).__init__()

if(num==18):

self.net = models.resnet18(pretrained=pretrained)

elif(num==34):

self.net = models.resnet34(pretrained=pretrained)

elif(num==50):

self.net = models.resnet50(pretrained=pretrained)

elif(num==101):

self.net = models.resnet101(pretrained=pretrained)

elif(num==152):

self.net = models.resnet152(pretrained=pretrained)

self.N_slices = 5

self.conv1 = self.net.conv1

self.bn1 = self.net.bn1

self.relu = self.net.relu

self.maxpool = self.net.maxpool

self.layer1 = self.net.layer1

self.layer2 = self.net.layer2

self.layer3 = self.net.layer3

self.layer4 = self.net.layer4

開發者ID:thunil，項目名稱:TecoGAN，代碼行數:24，

示例8: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, requires_grad=False, pretrained=True, num=18):

super(resnet, self).__init__()

if (num == 18):

self.net = models.resnet18(pretrained=pretrained)

elif (num == 34):

self.net = models.resnet34(pretrained=pretrained)

elif (num == 50):

self.net = models.resnet50(pretrained=pretrained)

elif (num == 101):

self.net = models.resnet101(pretrained=pretrained)

elif (num == 152):

self.net = models.resnet152(pretrained=pretrained)

self.N_slices = 5

self.conv1 = self.net.conv1

self.bn1 = self.net.bn1

self.relu = self.net.relu

self.maxpool = self.net.maxpool

self.layer1 = self.net.layer1

self.layer2 = self.net.layer2

self.layer3 = self.net.layer3

self.layer4 = self.net.layer4

開發者ID:BCV-Uniandes，項目名稱:SMIT，代碼行數:24，

示例9: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, layers, atrous, pretrained=True):

super(ResNet, self).__init__()

self.inner_layer = []

if layers == 18:

self.backbone = models.resnet18(pretrained=pretrained)

elif layers == 34:

self.backbone = models.resnet34(pretrained=pretrained)

elif layers == 50:

self.backbone = models.resnet50(pretrained=pretrained)

elif layers == 101:

self.backbone = models.resnet101(pretrained=pretrained)

elif layers == 152:

self.backbone = models.resnet152(pretrained=pretrained)

else:

raise ValueError('resnet.py: network layers is no support yet')

def hook_func(module, input, output):

self.inner_layer.append(output)

self.backbone.layer1.register_forward_hook(hook_func)

self.backbone.layer2.register_forward_hook(hook_func)

self.backbone.layer3.register_forward_hook(hook_func)

self.backbone.layer4.register_forward_hook(hook_func)

開發者ID:YudeWang，項目名稱:deeplabv3plus-pytorch，代碼行數:25，

示例10: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self):

super(ResNet152_bo,self).__init__()

# 設置網絡名稱,保存模型時命名使用

self.moduel_name=str("ResNet152_bo")

# 加載預訓練好的網絡權重

model = resnet152(pretrained=True)

# 固定權重 nn.Module有成員函數parameters()

if opt.fixed_weight:

for param in model.parameters():

param.requires_grad = False

# 結論：self.model_bo只有修改過的新層(即最后兩層全連接層)的值為True

# 替換最后一層全連接層

# 新層默認requires_grad=True

# resnet152中有self.fc，作為前向過程的最后一層

# (修改輸入圖像大小，可通過報錯信息來調整下面參數)

model.fc = nn.Linear(2048, 2) #420:131072 224:2048

# 此時self.model_bo的權重為預訓練權重，修改的新層(全連接層)權重為自動初始化的

self.model_bo=model

#手動初始化fc層

self._initialize_weights()

開發者ID:bobo0810，項目名稱:XueLangTianchi，代碼行數:24，

示例11: __init__

?點贊 6

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, args, pretrained=True, weldon_pretrained_path=None):

super(ResNet_weldon, self).__init__()

resnet = models.resnet152(pretrained=pretrained)

self.base_layer = nn.Sequential(*list(resnet.children())[:-2])

self.spaConv = nn.Conv2d(2048, 2400, 1,)

# add spatial aggregation layer

self.wldPool = WeldonPooling(15)

# Linear layer for imagenet classification

self.fc = nn.Linear(2400, 1000)

# Loading pretrained weights of resnet weldon on imagenet classification

if pretrained:

try:

state_di = torch.load(

weldon_pretrained_path, map_location=lambda storage, loc: storage)['state_dict']

self.load_state_dict(state_di)

except Exception:

print("Error when loading pretrained resnet weldon")

開發者ID:technicolor-research，項目名稱:dsve-loc，代碼行數:23，

示例12: ImageEncoder

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def ImageEncoder(encoder, load):

encoder = ImageEncoderType.from_string(encoder)

if encoder is ImageEncoderType.RESNET152:

encoder = models.resnet152()

encoder.load_state_dict(torch.load(load))

encoder.fc = Identity()

encoder.cuda()

encoder.eval()

return encoder

開發者ID:ExplorerFreda，項目名稱:VSE-C，代碼行數:11，

示例13: __init__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self):

super(ResNet152Fc, self).__init__()

model_resnet152 = models.resnet152(pretrained=True)

self.conv1 = model_resnet152.conv1

self.bn1 = model_resnet152.bn1

self.relu = model_resnet152.relu

self.maxpool = model_resnet152.maxpool

self.layer1 = model_resnet152.layer1

self.layer2 = model_resnet152.layer2

self.layer3 = model_resnet152.layer3

self.layer4 = model_resnet152.layer4

self.avgpool = model_resnet152.avgpool

self.__in_features = model_resnet152.fc.in_features

開發者ID:jindongwang，項目名稱:transferlearning，代碼行數:15，

示例14: __init__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self):

super(ResNet152Fc, self).__init__()

model_resnet152 = models.resnet152(pretrained=True)

self.conv1 = model_resnet152.conv1

self.bn1 = model_resnet152.bn1

self.relu = model_resnet152.relu

self.maxpool = model_resnet152.maxpool

self.layer1 = model_resnet152.layer1

self.layer2 = model_resnet152.layer2

self.layer3 = model_resnet152.layer3

self.layer4 = model_resnet152.layer4

self.avgpool = model_resnet152.avgpool

開發者ID:jindongwang，項目名稱:transferlearning，代碼行數:14，

示例15: __init__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, zeroshot, embed_dim=None, att_domains=None, num_train_classes=None, l2_weight=None):

"""

:param zeroshot: Whether we're running in zeroshot mode (

can be true or False).

:param embed_dim: Dimension of embeddings (probably 300)

:param att_dims: List of domain sizes per attribute.

:param num_train_classes: If we're doing pretraining, number of classes to use

"""

super(ImsituModel, self).__init__()

self.l2_weight = l2_weight

if zeroshot:

if (embed_dim is not None) and (att_domains is not None):

print("Using embeddings and attributes for zeroshot")

elif embed_dim is not None:

print("Using embeddings for zeroshot")

elif att_domains is not None:

print("using attributes for zeroshot")

else:

raise ValueError("Must supply embeddings or attributes for zeroshot")

self.fc_dim = None

self.att_domains = att_domains if att_domains is not None else []

self.embed_dim = embed_dim

else:

if num_train_classes is None:

raise ValueError("Must supply a # of training classes")

self.fc_dim = num_train_classes

self.att_domains = []

self.embed_dim = None

self.resnet152 = get_pretrained_resnet(self.fc_dim)

if self.embed_dim is not None:

self.embed_linear = nn.Linear(ENCODING_SIZE, self.embed_dim)

_init_fc(self.embed_linear)

if self.att_dim is not None:

self.att_linear = nn.Linear(ENCODING_SIZE, self.att_dim)

_init_fc(self.att_linear)

開發者ID:uwnlp，項目名稱:verb-attributes，代碼行數:41，

示例16: __call__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __call__(self, imgs):

img_feats = self.resnet152(imgs)

if not self.is_zeroshot:

return img_feats

att_res = self.att_linear(img_feats) if self.att_dim is not None else None

embed_res = self.embed_linear(img_feats) if self.embed_dim is not None else None

return ZSResult(att_res, embed_res)

開發者ID:uwnlp，項目名稱:verb-attributes，代碼行數:11，

示例17: resnet152

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def resnet152(num_classes=1000, pretrained='imagenet'):

"""Constructs a ResNet-152 model.

"""

model = models.resnet152(pretrained=False, num_classes=num_classes)

if pretrained is not None:

settings = pretrained_settings['resnet152'][pretrained]

model = load_pretrained(model, num_classes, settings)

model = modify_resnets(model)

return model

###############################################################

#?SqueezeNets

開發者ID:alexandonian，項目名稱:pretorched-x，代碼行數:14，

示例18: __init__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, num_classes, pretrained=True):

super(ResNetDUC, self).__init__()

resnet = models.resnet152()

if pretrained:

resnet.load_state_dict(torch.load(res152_path))

self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool)

self.layer1 = resnet.layer1

self.layer2 = resnet.layer2

self.layer3 = resnet.layer3

self.layer4 = resnet.layer4

for n, m in self.layer3.named_modules():

if 'conv2' in n:

m.dilation = (2, 2)

m.padding = (2, 2)

m.stride = (1, 1)

elif 'downsample.0' in n:

m.stride = (1, 1)

for n, m in self.layer4.named_modules():

if 'conv2' in n:

m.dilation = (4, 4)

m.padding = (4, 4)

m.stride = (1, 1)

elif 'downsample.0' in n:

m.stride = (1, 1)

self.duc = _DenseUpsamplingConvModule(8, 2048, num_classes)

開發者ID:zijundeng，項目名稱:pytorch-semantic-segmentation，代碼行數:29，

示例19: __init__

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def __init__(self, num_classes, input_size, pretrained=True):

super(GCN, self).__init__()

self.input_size = input_size

resnet = models.resnet152()

if pretrained:

resnet.load_state_dict(torch.load(res152_path))

self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu)

self.layer1 = nn.Sequential(resnet.maxpool, resnet.layer1)

self.layer2 = resnet.layer2

self.layer3 = resnet.layer3

self.layer4 = resnet.layer4

self.gcm1 = _GlobalConvModule(2048, num_classes, (7, 7))

self.gcm2 = _GlobalConvModule(1024, num_classes, (7, 7))

self.gcm3 = _GlobalConvModule(512, num_classes, (7, 7))

self.gcm4 = _GlobalConvModule(256, num_classes, (7, 7))

self.brm1 = _BoundaryRefineModule(num_classes)

self.brm2 = _BoundaryRefineModule(num_classes)

self.brm3 = _BoundaryRefineModule(num_classes)

self.brm4 = _BoundaryRefineModule(num_classes)

self.brm5 = _BoundaryRefineModule(num_classes)

self.brm6 = _BoundaryRefineModule(num_classes)

self.brm7 = _BoundaryRefineModule(num_classes)

self.brm8 = _BoundaryRefineModule(num_classes)

self.brm9 = _BoundaryRefineModule(num_classes)

initialize_weights(self.gcm1, self.gcm2, self.gcm3, self.gcm4, self.brm1, self.brm2, self.brm3,

self.brm4, self.brm5, self.brm6, self.brm7, self.brm8, self.brm9)

開發者ID:zijundeng，項目名稱:pytorch-semantic-segmentation，代碼行數:31，

示例20: _load_pytorch_model

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [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，

示例21: load_pytorch_model

?點贊 5

# 需要導入模塊: from torchvision import models [as 別名]

# 或者: from torchvision.models import resnet152 [as 別名]

def load_pytorch_model(model_name):