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anomalib.models.reverse_distillation.components.de_resnet

Torch model defining the decoder.

Module Contents

Classes

DecoderBasicBlock	Basic block for decoder ResNet architecture.
DecoderBottleneck	Bottleneck for Decoder.
ResNet	ResNet model for decoder.

Functions

_resnet(block: Type[Union[DecoderBasicBlock, DecoderBottleneck]], layers: List[int], **kwargs: Any) → ResNet
de_resnet18() → ResNet	ResNet-18 model.
de_resnet34() → ResNet	ResNet-34 model.
de_resnet50() → ResNet	ResNet-50 model.
de_resnet101() → ResNet	ResNet-101 model.
de_resnet152() → ResNet	ResNet-152 model.
de_resnext50_32x4d() → ResNet	ResNeXt-50 32x4d model.
de_resnext101_32x8d() → ResNet	ResNeXt-101 32x8d model.
de_wide_resnet50_2() → ResNet	Wide ResNet-50-2 model.
de_wide_resnet101_2() → ResNet	Wide ResNet-101-2 model.
get_decoder(name: str) → ResNet	Get decoder model based on the name of the backbone.

class anomalib.models.reverse_distillation.components.de_resnet.DecoderBasicBlock(inplanes: int, planes: int, stride: int = 1, upsample: Optional[torch.nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[Ellipsis, torch.nn.Module]] = None)

Bases: torch.nn.Module

Basic block for decoder ResNet architecture.

Parameters

• inplanes (int) – Number of input channels.

• planes (int) – Number of output channels.

• stride (int, optional) – Stride for convolution and de-convolution layers. Defaults to 1.

• upsample (Optional[nn.Module], optional) – Module used for upsampling output. Defaults to None.

• groups (int, optional) – Number of blocked connections from input channels to output channels. Defaults to 1.

• base_width (int, optional) – Number of layers in each intermediate convolution layer. Defaults to 64.

• dilation (int, optional) – Spacing between kernel elements. Defaults to 1.

• norm_layer (Optional[Callable[..., nn.Module]], optional) – Batch norm layer to use.Defaults to None.

Raises

• ValueError – If groups are not equal to 1 and base width is not 64.

• NotImplementedError – If dilation is greater than 1.

expansion :int = 1

forward(self, batch: torch.Tensor) → torch.Tensor

Forward-pass of de-resnet block.

class anomalib.models.reverse_distillation.components.de_resnet.DecoderBottleneck(inplanes: int, planes: int, stride: int = 1, upsample: Optional[torch.nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[Ellipsis, torch.nn.Module]] = None)

Bases: torch.nn.Module

Bottleneck for Decoder.

Parameters

• inplanes (int) – Number of input channels.

• planes (int) – Number of output channels.

• stride (int, optional) – Stride for convolution and de-convolution layers. Defaults to 1.

• upsample (Optional[nn.Module], optional) – Module used for upsampling output. Defaults to None.

• groups (int, optional) – Number of blocked connections from input channels to output channels. Defaults to 1.

• base_width (int, optional) – Number of layers in each intermediate convolution layer. Defaults to 64.

• dilation (int, optional) – Spacing between kernel elements. Defaults to 1.

• norm_layer (Optional[Callable[..., nn.Module]], optional) – Batch norm layer to use.Defaults to None.

expansion :int = 4

forward(self, batch: torch.Tensor) → torch.Tensor

Forward-pass of de-resnet bottleneck block.

class anomalib.models.reverse_distillation.components.de_resnet.ResNet(block: Type[Union[DecoderBasicBlock, DecoderBottleneck]], layers: List[int], zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, norm_layer: Optional[Callable[Ellipsis, torch.nn.Module]] = None)

Bases: torch.nn.Module

ResNet model for decoder.

Parameters

• block (Type[Union[DecoderBasicBlock, DecoderBottleneck]]) – Type of block to use in a layer.

• layers (List[int]) – List to specify number for blocks per layer.

• zero_init_residual (bool, optional) – If true, initializes the last batch norm in each layer to zero. Defaults to False.

• groups (int, optional) – Number of blocked connections per layer from input channels to output channels. Defaults to 1.

• width_per_group (int, optional) – Number of layers in each intermediate convolution layer.. Defaults to 64.

• norm_layer (Optional[Callable[..., nn.Module]], optional) – Batch norm layer to use. Defaults to None.

_make_layer(self, block: Type[Union[DecoderBasicBlock, DecoderBottleneck]], planes: int, blocks: int, stride: int = 1) → torch.nn.Sequential

forward(self, batch: torch.Tensor) → List[torch.Tensor]

Forward pass for Decoder ResNet. Returns list of features.

anomalib.models.reverse_distillation.components.de_resnet._resnet(block: Type[Union[DecoderBasicBlock, DecoderBottleneck]], layers: List[int], **kwargs: Any) → ResNet

anomalib.models.reverse_distillation.components.de_resnet.de_resnet18() → ResNet

ResNet-18 model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnet34() → ResNet

ResNet-34 model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnet50() → ResNet

ResNet-50 model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnet101() → ResNet

ResNet-101 model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnet152() → ResNet

ResNet-152 model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnext50_32x4d() → ResNet

ResNeXt-50 32x4d model.

anomalib.models.reverse_distillation.components.de_resnet.de_resnext101_32x8d() → ResNet

ResNeXt-101 32x8d model.

anomalib.models.reverse_distillation.components.de_resnet.de_wide_resnet50_2() → ResNet

Wide ResNet-50-2 model.

anomalib.models.reverse_distillation.components.de_resnet.de_wide_resnet101_2() → ResNet

Wide ResNet-101-2 model.

anomalib.models.reverse_distillation.components.de_resnet.get_decoder(name: str) → ResNet

Get decoder model based on the name of the backbone.

Parameters

name (str) – Name of the backbone.

Returns

Decoder ResNet architecture.

Return type

ResNet