Python torchvision.models.vgg11_bn() Examples

The following are 7 code examples of torchvision.models.vgg11_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: net.py    From CAL with MIT License 6 votes vote down vote up 
def __init__(self, p):
        super(CAL_network, self).__init__()
        self.params = p

        # get feature extractor and first FCN layer from vgg
        vgg = models.vgg11_bn(pretrained=True)
        ls = [l for l in vgg.features]+ [nn.AdaptiveMaxPool2d(1), Flatten()]
        self.features = nn.Sequential(*ls)
        n_in = 512 # fixed amount of features after feature extractor

        # initialize the task blocks
        self.red_light = TaskBlock(params=p, n_in=512, n_out=2)
        self.hazard_stop = TaskBlock(params=p, n_in=512, n_out=2)
        self.speed_sign = TaskBlock(params=p, n_in=512,  n_out=4)
        self.veh_distance = TaskBlock(params=p, n_in=512, n_out=1)
        self.relative_angle = TaskBlock(params=p, n_in=512, n_out=1, cond=True)
        self.center_distance = TaskBlock(params=p, n_in=512, n_out=1, cond=True)
Example #2
Source File: net.py    From CAL with MIT License 6 votes vote down vote up 
def __init__(self, p):
        super(CAL_network, self).__init__()
        self.params = p

        # get feature extractor and first FCN layer from vgg
        vgg = models.vgg11_bn(pretrained=True)
        ls = [l for l in vgg.features]+ [nn.AdaptiveMaxPool2d(1), Flatten()]
        self.features = nn.Sequential(*ls)
        n_in = 512 # fixed amount of features after feature extractor

        # initialize the task blocks
        self.red_light = TaskBlock(params=p, n_in=512, n_out=2)
        self.hazard_stop = TaskBlock(params=p, n_in=512, n_out=2)
        self.speed_sign = TaskBlock(params=p, n_in=512,  n_out=4)
        self.veh_distance = TaskBlock(params=p, n_in=512, n_out=1)
        self.relative_angle = TaskBlock(params=p, n_in=512, n_out=1, cond=True)
        self.center_distance = TaskBlock(params=p, n_in=512, n_out=1, cond=True)
Example #3
Source File: torchvision_models.py    From pretorched-x with MIT License 5 votes vote down vote up 
def vgg11_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 11-layer model (configuration "A") with batch normalization
    """
    model = models.vgg11_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg11_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
Example #4
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 #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):
    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 #6
Source File: torchvision_models.py    From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def vgg11_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 11-layer model (configuration "A") with batch normalization
    """
    model = models.vgg11_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg11_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_vggs(model)
    return model
Example #7
Source File: torchvision_models.py    From models-comparison.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def vgg11_bn(num_classes=1000, pretrained='imagenet'):
    """VGG 11-layer model (configuration "A") with batch normalization
    """
    model = models.vgg11_bn(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['vgg11_bn'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    return model