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def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN MNIST to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_mnist, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN CIFAR to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_cifar, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.5 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_5, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.75 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_75, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.0 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_0, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.3 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_3, MODE, layer_config, pretrained, progress, num_classes)
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def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-18 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet18, MODE, layer_config, pretrained, progress, num_classes)
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def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-20 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet20, MODE, layer_config, pretrained, progress, num_classes)
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def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-32 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet32, MODE, layer_config, pretrained, progress, num_classes)
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def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-34 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet34, MODE, layer_config, pretrained, progress, num_classes)
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def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-44 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet44, MODE, layer_config, pretrained, progress, num_classes)
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def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-50 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet50, MODE, layer_config, pretrained, progress, num_classes)
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def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-56 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet56, MODE, layer_config, pretrained, progress, num_classes)
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def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-101 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet101, MODE, layer_config, pretrained, progress, num_classes)
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def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-110 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet110, MODE, layer_config, pretrained, progress, num_classes)
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def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-152 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet152, MODE, layer_config, pretrained, progress, num_classes)
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def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-1202 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet1202, MODE, layer_config, pretrained, progress, num_classes)
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def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-50 32x4d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext50_32x4d, MODE, layer_config, pretrained, progress, num_classes)
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def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-101 32x8d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext101_32x8d, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-50-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet50_2, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-101-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet101_2, MODE, layer_config, pretrained, progress, num_classes)
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def alexnet(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> AlexNet:
'AlexNet model architecture from the\n `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.\n The required minimum input size of the model is 63x63.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting AlexNet to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.alexnet, MODE, layer_config, pretrained, progress, num_classes)
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def densenet121(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-121 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-121 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet121, MODE, layer_config, pretrained, progress, num_classes)
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def densenet161(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-161 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-161 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet161, MODE, layer_config, pretrained, progress, num_classes)
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def densenet169(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-169 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet169, MODE, layer_config, pretrained, progress, num_classes)
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def densenet201(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-201 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-201 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet201, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN MNIST to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_mnist, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN CIFAR to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_cifar, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.5 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_5, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.75 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_75, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.0 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_0, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.3 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_3, MODE, layer_config, pretrained, progress, num_classes)
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def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-18 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet18, MODE, layer_config, pretrained, progress, num_classes)
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def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-20 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet20, MODE, layer_config, pretrained, progress, num_classes)
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def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-32 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet32, MODE, layer_config, pretrained, progress, num_classes)
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def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-34 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet34, MODE, layer_config, pretrained, progress, num_classes)
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def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-44 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet44, MODE, layer_config, pretrained, progress, num_classes)
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def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-50 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet50, MODE, layer_config, pretrained, progress, num_classes)
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def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-56 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet56, MODE, layer_config, pretrained, progress, num_classes)
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def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-101 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet101, MODE, layer_config, pretrained, progress, num_classes)
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def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-110 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet110, MODE, layer_config, pretrained, progress, num_classes)
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def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-152 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet152, MODE, layer_config, pretrained, progress, num_classes)
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def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-1202 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet1202, MODE, layer_config, pretrained, progress, num_classes)
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def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-50 32x4d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext50_32x4d, MODE, layer_config, pretrained, progress, num_classes)
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def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-101 32x8d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext101_32x8d, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-50-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet50_2, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-101-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet101_2, MODE, layer_config, pretrained, progress, num_classes)
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def alexnet(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> AlexNet:
'AlexNet model architecture from the\n `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.\n The required minimum input size of the model is 63x63.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting AlexNet to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.alexnet, MODE, layer_config, pretrained, progress, num_classes)
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def densenet121(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-121 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-121 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet121, MODE, layer_config, pretrained, progress, num_classes)
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def densenet161(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-161 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-161 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet161, MODE, layer_config, pretrained, progress, num_classes)
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def densenet169(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-169 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet169, MODE, layer_config, pretrained, progress, num_classes)
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def densenet201(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-201 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-201 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet201, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN MNIST to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_mnist, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN CIFAR to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_cifar, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.5 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_5, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.75 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_75, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.0 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_0, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.3 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_3, MODE, layer_config, pretrained, progress, num_classes)
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def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-18 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet18, MODE, layer_config, pretrained, progress, num_classes)
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def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-20 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet20, MODE, layer_config, pretrained, progress, num_classes)
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def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-32 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet32, MODE, layer_config, pretrained, progress, num_classes)
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def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-34 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet34, MODE, layer_config, pretrained, progress, num_classes)
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def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-44 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet44, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-50 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet50, MODE, layer_config, pretrained, progress, num_classes)
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def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-56 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet56, MODE, layer_config, pretrained, progress, num_classes)
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def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-101 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet101, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-110 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet110, MODE, layer_config, pretrained, progress, num_classes)
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def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-152 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet152, MODE, layer_config, pretrained, progress, num_classes)
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def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-1202 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet1202, MODE, layer_config, pretrained, progress, num_classes)
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def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-50 32x4d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext50_32x4d, MODE, layer_config, pretrained, progress, num_classes)
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def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-101 32x8d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext101_32x8d, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-50-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet50_2, MODE, layer_config, pretrained, progress, num_classes)
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def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-101-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet101_2, MODE, layer_config, pretrained, progress, num_classes)
|
def alexnet(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> AlexNet:
'AlexNet model architecture from the\n `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.\n The required minimum input size of the model is 63x63.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting AlexNet to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.alexnet, MODE, layer_config, pretrained, progress, num_classes)
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def densenet121(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-121 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-121 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet121, MODE, layer_config, pretrained, progress, num_classes)
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def densenet161(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-161 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-161 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet161, MODE, layer_config, pretrained, progress, num_classes)
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def densenet169(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-169 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet169, MODE, layer_config, pretrained, progress, num_classes)
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def densenet201(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-201 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Densenet-201 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.densenet201, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN MNIST to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_mnist, MODE, layer_config, pretrained, progress, num_classes)
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def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n '
print('Converting LeNet CNN CIFAR to {} mode'.format(MODE_STRING))
return create_le_net_biomodel(le_net.le_net_cifar, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.5 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_5, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 0.75 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet0_75, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.0 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_0, MODE, layer_config, pretrained, progress, num_classes)
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def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting MNASNet 1.3 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.mnasnet1_3, MODE, layer_config, pretrained, progress, num_classes)
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def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-18 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet18, MODE, layer_config, pretrained, progress, num_classes)
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def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-20 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet20, MODE, layer_config, pretrained, progress, num_classes)
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def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-32 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet32, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-34 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet34, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-44 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet44, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-50 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet50, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-56 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet56, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-101 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet101, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-110 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet110, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-152 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnet152, MODE, layer_config, pretrained, progress, num_classes)
|
def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNet-1202 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(small_resnet.resnet1202, MODE, layer_config, pretrained, progress, num_classes)
|
def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-50 32x4d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext50_32x4d, MODE, layer_config, pretrained, progress, num_classes)
|
def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting ResNext-101 32x8d to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.resnext101_32x8d, MODE, layer_config, pretrained, progress, num_classes)
|
def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-50-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet50_2, MODE, layer_config, pretrained, progress, num_classes)
|
def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n which is twice larger in every block. The number of channels in outer 1x1\n convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048\n channels, and in Wide ResNet-50-2 has 2048-1024-2048.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting Wide ResNet-101-2 to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.wide_resnet101_2, MODE, layer_config, pretrained, progress, num_classes)
|
def alexnet(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> AlexNet:
'AlexNet model architecture from the\n `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.\n The required minimum input size of the model is 63x63.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output dimension of the last linear layer\n layer_config (dict): Custom biologically plausible method layer configuration\n '
print('Converting AlexNet to {} mode'.format(MODE_STRING))
return create_torchvision_biomodel(models.alexnet, MODE, layer_config, pretrained, progress, num_classes)
|
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