Python torchvision.models.vgg19_bn() Examples

The following are 11 code examples of torchvision.models.vgg19_bn(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module torchvision.models , or try the search function .
Example #1
Source File: seg_net.py    From pytorch-semantic-segmentation with MIT License 6 votes vote down vote up 
def __init__(self, num_classes, pretrained=True):
        super(SegNet, self).__init__()
        vgg = models.vgg19_bn()
        if pretrained:
            vgg.load_state_dict(torch.load(vgg19_bn_path))
        features = list(vgg.features.children())
        self.enc1 = nn.Sequential(*features[0:7])
        self.enc2 = nn.Sequential(*features[7:14])
        self.enc3 = nn.Sequential(*features[14:27])
        self.enc4 = nn.Sequential(*features[27:40])
        self.enc5 = nn.Sequential(*features[40:])

        self.dec5 = nn.Sequential(
            *([nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2)] +
              [nn.Conv2d(512, 512, kernel_size=3, padding=1),
               nn.BatchNorm2d(512),
               nn.ReLU(inplace=True)] * 4)
        )
        self.dec4 = _DecoderBlock(1024, 256, 4)
        self.dec3 = _DecoderBlock(512, 128, 4)
        self.dec2 = _DecoderBlock(256, 64, 2)
        self.dec1 = _DecoderBlock(128, num_classes, 2)
        initialize_weights(self.dec5, self.dec4, self.dec3, self.dec2, self.dec1)
Example #2
Source File: test-ww.py    From AutoDL-Projects with MIT License 6 votes vote down vote up 
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']))
Example #3
Source File: NetWork.py    From XueLangTianchi with MIT License 6 votes vote down vote up 
def __init__(self):
        super(VGG19_bo,self).__init__()
        model=vgg19_bn(pretrained=True)
        # 设置网络名称
        self.moduel_name=str("VGG19_bo")

        #固定提取特征层，权重为预训练权重
        self.features=model.features
        # 固定权重
        if opt.fixed_weight:
            for param in  self.features.parameters():
                param.requires_grad=False

        # 分类层
        self.classifier=nn.Sequential(
            t.nn.Linear(25088, 4096), #224: 25088     420:86528
            t.nn.ReLU(),
            t.nn.Dropout(p=0.5),
            t.nn.Linear(4096, 4096),
            t.nn.ReLU(),
            t.nn.Dropout(p=0.5),
            t.nn.Linear(4096,2)
        )
        # 仅对分类层初始化
        self._initialize_weights()
Example #4
Source File: torchvision_models.py    From pretorched-x with MIT License 5 votes vote down vote up 
def vgg19_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 19-layer model (configuration 'E') with batch normalization
    """
    model = models.vgg19_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg19_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
Example #5
Source File: tools.py    From perceptron-benchmark with Apache License 2.0 5 votes vote down vote up 
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
Example #6
Source File: tools.py    From perceptron-benchmark with Apache License 2.0 5 votes vote down vote up 
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
Example #7
Source File: torch_imports.py    From pytorch-serverless with MIT License 5 votes vote down vote up 
def vgg19(pre): return children(vgg19_bn(pre))[0]
Example #8
Source File: torchvision_models.py    From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def vgg19_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 19-layer model (configuration 'E') with batch normalization
    """
    model = models.vgg19_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg19_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
Example #9
Source File: vgg.py    From part-affinity with MIT License 5 votes vote down vote up 
def __init__(self, use_bn=True):  # Original implementation doesn't use BN
        super(VGG, self).__init__()
        if use_bn:
            vgg = models.vgg19(pretrained=True)
            layers_to_use = list(list(vgg.children())[0].children())[:23]
        else:
            vgg = models.vgg19_bn(pretrained=True)
            layers_to_use = list(list(vgg.children())[0].children())[:33]
        self.vgg = nn.Sequential(*layers_to_use)
        self.feature_extractor = nn.Sequential(make_standard_block(512, 256, 3),
                                               make_standard_block(256, 128, 3))
        init(self.feature_extractor)
Example #10
Source File: segnet.py    From FCSR-GAN with Apache License 2.0 5 votes vote down vote up 
def __init__(self, num_classes):
        super(SegNet, self).__init__()
        vgg = models.vgg19_bn()
        features = list(vgg.features.children())
        self.enc1 = nn.Sequential(*features[0:7])
        self.enc2 = nn.Sequential(*features[7:14])
        self.enc3 = nn.Sequential(*features[14:27])
        self.enc4 = nn.Sequential(*features[27:40])
        self.enc5 = nn.Sequential(*features[40:])

        self.dec5 = nn.Sequential(
            *([nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2)] +
              [nn.Conv2d(512, 512, kernel_size=3, padding=1),
               nn.BatchNorm2d(512),
               nn.ReLU(inplace=False)] * 4)
        )
        self.dec4 = _DecoderBlock(1024, 256, 4)
        self.dec3 = _DecoderBlock(512, 128, 4)
        self.dec2 = _DecoderBlock(256, 64, 2)
        self.dec1 = _DecoderBlock(128, num_classes, 2)
        # initialize_weights(self.dec5, self.dec4, self.dec3, self.dec2, self.dec1)

        for i in range(5):
            for param in getattr(self, 'enc{:d}'.format(i + 1)).parameters():
                param.requires_grad = False

        for i in range(5):
            for param in getattr(self, 'dec{:d}'.format(i + 1)).parameters():
                param.requires_grad = False
Example #11
Source File: torchvision_models.py    From models-comparison.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def vgg19_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 19-layer model (configuration 'E') with batch normalization
    """
    model = models.vgg19_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg19_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    return model