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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... |
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 ... |
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 ... |
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, r... |
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,... |
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 ... |
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 ... |
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... |
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 pr... |
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 pr... |
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 pr... |
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 pr... |
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 d... |
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 d... |
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): I... |
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):... |
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): I... |
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): I... |
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... |
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 I... |
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 I... |
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... |
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 I... |
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... |
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 I... |
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 ... |
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... |
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 ... |
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 ... |
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, r... |
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,... |
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 ... |
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 ... |
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... |
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 pr... |
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 pr... |
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 pr... |
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 pr... |
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 d... |
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 d... |
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): I... |
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):... |
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): I... |
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): I... |
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... |
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 I... |
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 I... |
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... |
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 I... |
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... |
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 I... |
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 ... |
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... |
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 ... |
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 ... |
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, r... |
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,... |
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 ... |
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 ... |
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_chan... |
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_ch... |
def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10):
return LeNetCIFAR()
|
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... |
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 pr... |
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 pr... |
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 pr... |
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 pr... |
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 d... |
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 d... |
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): I... |
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):... |
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): I... |
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): I... |
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... |
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 I... |
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 I... |
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... |
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 I... |
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... |
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 I... |
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 ... |
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... |
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 ... |
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 ... |
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, r... |
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,... |
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 ... |
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 ... |
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:
... |
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_conf... |
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... |
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_rec... |
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 = Ave... |
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.tim... |
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 = ge... |
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()
lay... |
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(... |
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