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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)
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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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class LeNetMNIST(nn.Module):
def __init__(self):
super(LeNetMNIST, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5, stride=1)
self.relu3 = nn.ReLU()
self.fc1 = nn.Linear(in_features=120, out_features=84)
self.relu4 = nn.ReLU()
self.fc2 = nn.Linear(in_features=84, out_features=10)
def forward(self, x):
out = self.conv1(x)
out = self.relu1(out)
out = self.pool1(out)
out = self.conv2(out)
out = self.relu2(out)
out = self.pool2(out)
out = self.conv3(out)
out = self.relu3(out)
out = torch.flatten(out, 1)
out = self.fc1(out)
out = self.relu4(out)
out = self.fc2(out)
return out
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def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10):
return LeNetMNIST()
|
class LeNetCIFAR(nn.Module):
def __init__(self):
super(LeNetCIFAR, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1)
self.relu3 = nn.ReLU()
self.fc1 = nn.Linear(in_features=128, out_features=256)
self.relu4 = nn.ReLU()
self.fc2 = nn.Linear(in_features=256, out_features=10)
def forward(self, x):
out = self.conv1(x)
out = self.relu1(out)
out = self.pool1(out)
out = self.conv2(out)
out = self.relu2(out)
out = self.pool2(out)
out = self.conv3(out)
out = self.relu3(out)
out = F.avg_pool2d(out, out.size()[3])
out = out.view(out.size(0), (- 1))
out = self.fc1(out)
out = self.relu4(out)
out = self.fc2(out)
return out
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def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10):
return LeNetCIFAR()
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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)
|
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)
|
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)
|
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)
|
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)
|
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 create_torchvision_biomodel(model_architecture, mode, layer_config: dict=None, pretrained: bool=False, progress: bool=True, num_classes: int=1000) -> BioModule:
if (not pretrained):
copy_weights = False
model = model_architecture(pretrained, progress, num_classes=num_classes)
else:
copy_weights = True
model = model_architecture(pretrained, progress, num_classes=1000)
if (num_classes != 1000):
model.fc = nn.Linear(model.fc.in_features, num_classes)
return BioModule(model, mode=mode, copy_weights=copy_weights, layer_config=layer_config, output_dim=num_classes)
|
def create_le_net_biomodel(model_architecture, mode, layer_config: dict=None, pretrained: bool=False, progress: bool=True, num_classes: int=10) -> BioModule:
model = model_architecture(pretrained, progress, num_classes=num_classes)
return BioModule(model, mode=mode, copy_weights=False, layer_config=layer_config, output_dim=num_classes)
|
def apply_xavier_init(module):
if (isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d)):
nn.init.xavier_uniform_(module.weight)
if (module.bias is not None):
nn.init.constant_(module.bias, 0)
|
class BioModule(nn.Module):
def __init__(self, module, mode='fa', copy_weights=True, layer_config=None, output_dim=None):
super(BioModule, self).__init__()
self.module = module
self.mode = mode
self.output_dim = output_dim
self.copy_weights = copy_weights
if (layer_config is None):
layer_config = {'type': mode}
self.layer_config = layer_config
if (self.mode == 'dfa'):
if (self.output_dim is None):
raise ValueError('Model `output_dim` is required for Direct Feedback Alignment (dfa) mode')
module_converter = ModuleConverter(mode=self.mode)
self.module = module_converter.convert(self.module, self.copy_weights, self.layer_config, self.output_dim)
def forward(self, x, targets=None, loss_function=None):
output = self.module(x)
if ((self.mode == 'dfa') and self.module.training):
if (targets is None):
raise ValueError('Targets missing for Direct Feedback Alignment mode')
if (loss_function is None):
raise ValueError('You need to introduce your `loss_function` for Direct Feedback Alignment mode')
loss = loss_function(output, targets)
loss_gradient = grad(loss, output, retain_graph=True)[0]
for layer in self.module.modules():
layer.loss_gradient = loss_gradient
return output
|
class ModuleConverter():
def __init__(self, mode='fa'):
self.mode = mode
def convert(self, module, copy_weights=True, layer_config=None, output_dim=None):
layer_counts = self.count_layers(module)
self.replaced_layers_counts = defaultdict((lambda : 0))
self._replace_layers_recursive(module, self.mode, copy_weights, layer_config, output_dim, self.replaced_layers_counts)
print('Module has been converted to {} mode:\n'.format(self.mode))
if (layer_config is not None):
print('The layer configuration was: ', layer_config)
for (layer, count) in self.replaced_layers_counts.items():
if (layer_counts[layer] != count):
print('- There were originally {} {} layers and {} were converted.'.format(layer_counts[layer], layer, count))
else:
print('- All the {} {} layers were converted successfully.'.format(count, layer))
return module
def _replace_layers_recursive(self, module, mode, copy_weights, layer_config, output_dim, replaced_layers):
for module_name in module._modules.keys():
layer = getattr(module, module_name)
new_layer = convert_layer(layer, mode, copy_weights, layer_config, output_dim)
if (new_layer is not None):
replaced_layers[str(type(layer))] += 1
setattr(module, module_name, new_layer)
for (name, child_module) in module.named_children():
self._replace_layers_recursive(child_module, mode, copy_weights, layer_config, output_dim, replaced_layers)
@staticmethod
def count_layers(module):
layer_counts = defaultdict((lambda : 0))
for layer in module.modules():
layer_counts[str(type(layer))] += 1
return layer_counts
|
def train(model, mode, loss_function, optimizer, train_dataloader, device, epoch, multi_gpu, top_k=5, display_iterations=500):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
topk = AverageMeter(('Acc@' + str(top_k)), ':6.2f')
progress = ProgressMeter(len(train_dataloader), [batch_time, data_time, losses, top1, topk], prefix='Epoch: [{}]'.format(epoch))
model.train()
end = time.time()
for (idx_batch, (inputs, targets)) in enumerate(train_dataloader):
data_time.update((time.time() - end))
(inputs, targets) = (inputs.to(device), targets.to(device))
if (mode == 'dfa'):
outputs = model(inputs, targets, loss_function)
else:
outputs = model(inputs)
outputs = torch.squeeze(outputs)
loss = loss_function(outputs, targets)
(acc1, acck) = accuracy(outputs, targets, topk=(1, top_k))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1[0], inputs.size(0))
topk.update(acck[0], inputs.size(0))
model.zero_grad()
loss.backward()
optimizer.step()
if (mode == 'weight_mirroring'):
if multi_gpu:
model.module.mirror_weights(torch.randn(inputs.size()).to(device), growth_control=True)
else:
model.mirror_weights(torch.randn(inputs.size()).to(device), growth_control=True)
batch_time.update((time.time() - end))
end = time.time()
if ((idx_batch % display_iterations) == 0):
progress.display(idx_batch)
return (top1.avg, losses.avg)
|
def test(model, loss_function, test_dataloader, device, top_k=5):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
topk = AverageMeter(('Acc@' + str(top_k)), ':6.2f')
model.eval()
with torch.no_grad():
end = time.time()
for (idx_batch, (data, target)) in enumerate(test_dataloader):
(inputs, targets) = (data.to(device), target.to(device))
outputs = model(inputs)
outputs = torch.squeeze(outputs)
batch_time.update((time.time() - end))
end = time.time()
loss = loss_function(outputs, targets)
(acc1, acc5) = accuracy(outputs, targets, topk=(1, top_k))
losses.update(loss.item(), inputs.size(0))
top1.update(acc1[0], inputs.size(0))
topk.update(acc5[0], inputs.size(0))
print(' * Acc@1 {top1.avg:.3f} Acc@{top_k} {topk.avg:.3f}'.format(top1=top1, top_k=top_k, topk=topk))
return (top1.avg, losses.avg)
|
def adjust_learning_rate(optimizer, epoch, args):
'Sets the learning rate to the initial LR decayed by 10 every 30 epochs'
lr = (args.lr * (0.1 ** (epoch // 30)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
|
def compute_angles_module(module):
queue = deque()
layers_alignment = OrderedDict()
seen_keys = defaultdict((lambda : 0))
for module_keys in module._modules.keys():
queue.append((module, module_keys))
while (len(queue) > 0):
(module, module_key) = queue.popleft()
layer = getattr(module, module_key)
if ('alignment' in layer.__dict__):
angle = layer.compute_alignment()
key_name = ((module_key + '_') + str(seen_keys[module_key]))
seen_keys[module_key] += 1
layers_alignment[key_name] = angle.item()
if (len(layer._modules.keys()) > 0):
for key in list(layer._modules.keys())[::(- 1)]:
queue.appendleft((layer, key))
return layers_alignment
|
def compute_weight_ratio_module(module, mode):
queue = deque()
weight_diff = OrderedDict()
seen_keys = defaultdict((lambda : 0))
for module_keys in module._modules.keys():
queue.append((module, module_keys))
while (len(queue) > 0):
(module, module_key) = queue.popleft()
layer = getattr(module, module_key)
weight = None
if ((mode == 'backpropagation') and isinstance(layer, (torch.nn.Conv2d, torch.nn.Linear))):
with torch.no_grad():
weight = torch.linalg.norm(layer.weight)
elif ('weight_ratio' in layer.__dict__):
weight = layer.compute_weight_ratio()
if (weight is not None):
key_name = ((module_key + '_') + str(seen_keys[module_key]))
seen_keys[module_key] += 1
weight_diff[key_name] = weight.item()
if (len(layer._modules.keys()) > 0):
for key in list(layer._modules.keys())[::(- 1)]:
queue.appendleft((layer, key))
return weight_diff
|
class AverageMeter(object):
'Computes and stores the average and current value'
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += (val * n)
self.count += n
self.avg = (self.sum / self.count)
def __str__(self):
fmtstr = (((('{name} {val' + self.fmt) + '} ({avg') + self.fmt) + '})')
return fmtstr.format(**self.__dict__)
|
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