Exemple #1
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 def mobilenetv2(self):
     model = MobileNetV2()
     model = ModuleHelper.load_model(model,
                                     pretrained=self.configer.get(
                                         'network', 'pretrained'),
                                     all_match=False)
     return model
Exemple #2
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 def squeezenet(self):
     """Constructs a ResNet-18 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = SqueezeNet()
     model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
     return model
Exemple #3
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def dfnetv2(pretrained=None):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = DFNetV2(num_classes=1000)
    model = ModuleHelper.load_model(model, pretrained=pretrained, all_match=False)
    return model
Exemple #4
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 def vgg(self, vgg_cfg=None):
     """Constructs a ResNet-18 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     backbone = self.configer.get('network', 'backbone')
     model = VGG(cfg_name=backbone, vgg_cfg=vgg_cfg, bn=False)
     model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
     return model
Exemple #5
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 def densenet161(self, pretrained=None, **kwargs):
     r"""Densenet-161 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
     """
     model = DenseNet(num_init_features=96, growth_rate=48,
                      block_config=(6, 12, 36, 24), norm_type=self.configer.get('network', 'norm_type'), **kwargs)
     model = ModuleHelper.load_model(model, pretrained=pretrained)
     return model
Exemple #6
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 def vgg(self, backbone=None, pretrained=None):
     """Constructs a ResNet-18 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = VGG(cfg_name=backbone, bn=False)
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
 def mobilenetv2_dilated8(self, pretrained=None):
     """Constructs a ResNet-18 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = MobileNetV2Dilated8()
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
 def squeezenet(self, pretrained=None):
     """Constructs a ResNet-18 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = SqueezeNet()
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
Exemple #9
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 def deepbase_resnet101(self, **kwargs):
     """Constructs a ResNet-101 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = ResNet(Bottleneck, [3, 4, 23, 3],
                    deep_base=True,
                    norm_type=self.configer.get('network', 'norm_type'),
                    **kwargs)
     model = ModuleHelper.load_model(model,
                                     pretrained=self.configer.get(
                                         'network', 'pretrained'))
     return model
Exemple #10
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 def resnet34(self, **kwargs):
     """Constructs a ResNet-34 model.
     Args:
         pretrained (bool): If True, returns a model pre-trained on Places
     """
     model = ResNet(BasicBlock, [3, 4, 6, 3],
                    deep_base=False,
                    norm_type=self.configer.get('network', 'norm_type'),
                    **kwargs)
     model = ModuleHelper.load_model(model,
                                     pretrained=self.configer.get(
                                         'network', 'pretrained'))
     return model
Exemple #11
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def deepbase_resnet101(num_classes=1000,
                       pretrained=None,
                       norm_type='batchnorm',
                       **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3],
                   num_classes=num_classes,
                   deep_base=True,
                   norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model
Exemple #12
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def resnet34(num_classes=1000,
             pretrained=None,
             norm_type='batchnorm',
             **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3],
                   num_classes=num_classes,
                   deep_base=False,
                   norm_type=norm_type)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model
Exemple #13
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def deepbase_resnet18(num_classes=1000,
                      pretrained=None,
                      norm_type='batchnorm',
                      **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on Places
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2],
                   num_classes=num_classes,
                   deep_base=True,
                   norm_type=norm_type)
    model = ModuleHelper.load_model(model,
                                    all_match=False,
                                    pretrained=pretrained)
    return model
def _shufflenetv2(arch, pretrained, progress, *args, **kwargs):
    model = ShuffleNetV2(*args, **kwargs)
    model = ModuleHelper.load_model(model, pretrained=pretrained)
    return model
 def squeezenet_dilated8(self, pretrained):
     model = DilatedSqueezeNet()
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
 def mobilenetv2(self, pretrained=None):
     model = MobileNetV2()
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
Exemple #17
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 def vgg_bn(self, vgg_cfg=None):
     backbone = self.configer.get('network', 'backbone')
     model = VGG(cfg_name=backbone, vgg_cfg=vgg_cfg, bn=True)
     model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
     return model
Exemple #18
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def dfnetv1(pretrained=None):
    model = DFNetV1(num_classes=1000)
    model = ModuleHelper.load_model(model, pretrained=pretrained, all_match=False)
    return model
 def darknet53(self, pretrained=None):
     """Constructs a darknet-53 model.
     """
     model = DarkNet([1, 2, 8, 8, 4])
     model = ModuleHelper.load_model(model, pretrained=pretrained, all_match=False)
     return model
Exemple #20
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 def darknet53(self):
     """Constructs a darknet-53 model.
     """
     model = DarkNet([1, 2, 8, 8, 4])
     model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
     return model
Exemple #21
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 def vgg_bn(self, backbone=None, pretrained=None):
     model = VGG(cfg_name=backbone, bn=True)
     model = ModuleHelper.load_model(model,
                                     pretrained=pretrained,
                                     all_match=False)
     return model
Exemple #22
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 def squeezenet_dilated8(self):
     model = DilatedSqueezeNet()
     model = ModuleHelper.load_model(model, pretrained=self.configer.get('network', 'pretrained'), all_match=False)
     return model