Python torchvision.models.densenet201() Examples

The following are 14 code examples of torchvision.models.densenet201(). 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: torchvision_models.py    From pretorched-x with MIT License 6 votes vote down vote up 
def densenet201(num_classes=1000, pretrained='imagenet'):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = models.densenet201(num_classes=num_classes, pretrained=False)
    if pretrained is not None:
       # '.'s are no longer allowed in module names, but pervious _DenseLayer
        # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
        # They are also in the checkpoints in model_urls. This pattern is used
        # to find such keys.
        settings = pretrained_settings['densenet201'][pretrained]
        pattern = re.compile(
            r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
        state_dict = model_zoo.load_url(settings['url'])
        for key in list(state_dict.keys()):
            res = pattern.match(key)
            if res:
                new_key = res.group(1) + res.group(2)
                state_dict[new_key] = state_dict[key]
                del state_dict[key]
        model.load_state_dict(state_dict)
    model = modify_densenets(model)
    return model
Example #2
Source File: test_attack_Gaussian_blur.py    From perceptron-benchmark with Apache License 2.0 6 votes vote down vote up 
def test_untargeted_densenet201(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.densenet201(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
Example #3
Source File: test_attack_AdditiveGaussianNoiseAttack.py    From perceptron-benchmark with Apache License 2.0 6 votes vote down vote up 
def test_untargeted_densenet201(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.densenet201(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
Example #4
Source File: test_attack_AdditiveUniformNoiseAttack.py    From perceptron-benchmark with Apache License 2.0 6 votes vote down vote up 
def test_untargeted_densenet201(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.densenet201(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
Example #5
Source File: test_attack_BlendedUniformNoiseAttack.py    From perceptron-benchmark with Apache License 2.0 6 votes vote down vote up 
def test_untargeted_densenet201(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.densenet201(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
Example #6
Source File: test_attack_MotionBlurAttack.py    From perceptron-benchmark with Apache License 2.0 6 votes vote down vote up 
def test_untargeted_densenet201(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.densenet201(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
Example #7
Source File: models.py    From isic2019 with MIT License 5 votes vote down vote up 
def Dense201(config):
    return models.densenet201(pretrained=True)
Example #8
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 #9
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 #10
Source File: torch_imports.py    From pytorch-serverless with MIT License 5 votes vote down vote up 
def dn201(pre): return children(densenet201(pre))[0]
Example #11
Source File: torchvision_models.py    From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def densenet201(num_classes=1000, pretrained='imagenet'):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = models.densenet201(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['densenet201'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_densenets(model)
    return model
Example #12
Source File: basenet.py    From MCD_DA with MIT License 5 votes vote down vote up 
def __init__(self,option = 'densenet201',pret=True):
        super(DenseBase, self).__init__()
        self.dim = 2048
        if option == 'densenet201':
            model_ft = models.densenet201(pretrained=pret)
            self.dim = 1920
        if option == 'densenet161':
            model_ft = models.densenet161(pretrained=pret)
            self.dim = 2208
        mod = list(model_ft.children())
        #mod.pop()

        self.features = nn.Sequential(*mod)
Example #13
Source File: torchvision_models.py    From models-comparison.pytorch with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def densenet201(num_classes=1000, pretrained='imagenet'):
    r"""Densenet-201 model from
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
    """
    model = models.densenet201(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings['densenet201'][pretrained]
        model = load_pretrained(model, num_classes, settings)
    model = modify_densenets(model)
    return model
Example #14
Source File: cnn_geometric_model.py    From weakalign with MIT License 4 votes vote down vote up 
def __init__(self, train_fe=False, feature_extraction_cnn='vgg', normalization=True, last_layer='', use_cuda=True):
        super(FeatureExtraction, self).__init__()
        self.normalization = normalization
        if feature_extraction_cnn == 'vgg':
            self.model = models.vgg16(pretrained=True)
            # keep feature extraction network up to indicated layer
            vgg_feature_layers=['conv1_1','relu1_1','conv1_2','relu1_2','pool1','conv2_1',
                         'relu2_1','conv2_2','relu2_2','pool2','conv3_1','relu3_1',
                         'conv3_2','relu3_2','conv3_3','relu3_3','pool3','conv4_1',
                         'relu4_1','conv4_2','relu4_2','conv4_3','relu4_3','pool4',
                         'conv5_1','relu5_1','conv5_2','relu5_2','conv5_3','relu5_3','pool5']
            if last_layer=='':
                last_layer = 'pool4'
            last_layer_idx = vgg_feature_layers.index(last_layer)
            self.model = nn.Sequential(*list(self.model.features.children())[:last_layer_idx+1])
        if feature_extraction_cnn == 'resnet101':
            self.model = models.resnet101(pretrained=True)
            resnet_feature_layers = ['conv1',
                                     'bn1',
                                     'relu',
                                     'maxpool',
                                     'layer1',
                                     'layer2',
                                     'layer3',
                                     'layer4']
            if last_layer=='':
                last_layer = 'layer3'
            last_layer_idx = resnet_feature_layers.index(last_layer)
            resnet_module_list = [self.model.conv1,
                                  self.model.bn1,
                                  self.model.relu,
                                  self.model.maxpool,
                                  self.model.layer1,
                                  self.model.layer2,
                                  self.model.layer3,
                                  self.model.layer4]
            
            self.model = nn.Sequential(*resnet_module_list[:last_layer_idx+1])
        if feature_extraction_cnn == 'resnet101_v2':
            self.model = models.resnet101(pretrained=True)
            # keep feature extraction network up to pool4 (last layer - 7)
            self.model = nn.Sequential(*list(self.model.children())[:-3])
        if feature_extraction_cnn == 'densenet201':
            self.model = models.densenet201(pretrained=True)
            # keep feature extraction network up to denseblock3
            # self.model = nn.Sequential(*list(self.model.features.children())[:-3])
            # keep feature extraction network up to transitionlayer2
            self.model = nn.Sequential(*list(self.model.features.children())[:-4])
        if not train_fe:
            # freeze parameters
            for param in self.model.parameters():
                param.requires_grad = False
        # move to GPU
        if use_cuda:
            self.model = self.model.cuda()