Upsample / Downsample

pydantic model vision_architectures.layers.scale.PixelShuffleScaleConfig

Bases: CNNBlockConfig

Configuration class for scaling using PixelShuffle method.

Show JSON schema

{
   "title": "PixelShuffleScaleConfig",
   "description": "Configuration class for scaling using PixelShuffle method.",
   "type": "object",
   "properties": {
      "in_channels": {
         "description": "Number of input channels",
         "title": "In Channels",
         "type": "integer"
      },
      "out_channels": {
         "description": "Number of output channels",
         "title": "Out Channels",
         "type": "integer"
      },
      "kernel_size": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            }
         ],
         "description": "Kernel size for the convolution",
         "title": "Kernel Size"
      },
      "padding": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "string"
            }
         ],
         "default": "same",
         "description": "Padding for the convolution. Can be 'same' or an integer/tuple of integers.",
         "title": "Padding"
      },
      "stride": {
         "default": 1,
         "description": "Stride for the convolution",
         "title": "Stride",
         "type": "integer"
      },
      "conv_kwargs": {
         "additionalProperties": true,
         "default": {},
         "description": "Additional keyword arguments for the convolution layer",
         "title": "Conv Kwargs",
         "type": "object"
      },
      "transposed": {
         "default": false,
         "description": "Whether to perform ConvTranspose instead of Conv",
         "title": "Transposed",
         "type": "boolean"
      },
      "normalization": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": "batchnorm3d",
         "description": "Normalization layer type.",
         "title": "Normalization"
      },
      "normalization_pre_args": {
         "default": [],
         "description": "Arguments for the normalization layer before providing the dimension. Useful when using GroupNorm layers are being used to specify the number of groups.",
         "items": {},
         "title": "Normalization Pre Args",
         "type": "array"
      },
      "normalization_post_args": {
         "default": [],
         "description": "Arguments for the normalization layer after providing the dimension.",
         "items": {},
         "title": "Normalization Post Args",
         "type": "array"
      },
      "normalization_kwargs": {
         "additionalProperties": true,
         "default": {},
         "description": "Additional keyword arguments for the normalization layer",
         "title": "Normalization Kwargs",
         "type": "object"
      },
      "activation": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": "relu",
         "description": "Activation function type.",
         "title": "Activation"
      },
      "activation_kwargs": {
         "additionalProperties": true,
         "default": {},
         "description": "Additional keyword arguments for the activation function.",
         "title": "Activation Kwargs",
         "type": "object"
      },
      "sequence": {
         "default": "CNA",
         "description": "Sequence of operations in the block.",
         "enum": [
            "C",
            "AC",
            "CA",
            "CD",
            "CN",
            "DC",
            "NC",
            "ACD",
            "ACN",
            "ADC",
            "ANC",
            "CAD",
            "CAN",
            "CDA",
            "CDN",
            "CNA",
            "CND",
            "DAC",
            "DCA",
            "DCN",
            "DNC",
            "NAC",
            "NCA",
            "NCD",
            "NDC",
            "ACDN",
            "ACND",
            "ADCN",
            "ADNC",
            "ANCD",
            "ANDC",
            "CADN",
            "CAND",
            "CDAN",
            "CDNA",
            "CNAD",
            "CNDA",
            "DACN",
            "DANC",
            "DCAN",
            "DCNA",
            "DNAC",
            "DNCA",
            "NACD",
            "NADC",
            "NCAD",
            "NCDA",
            "NDAC",
            "NDCA"
         ],
         "title": "Sequence",
         "type": "string"
      },
      "drop_prob": {
         "default": 0.0,
         "description": "Dropout probability.",
         "title": "Drop Prob",
         "type": "number"
      },
      "scale_factor": {
         "default": 2,
         "description": "Scale factor for upsampling / downsampling.",
         "title": "Scale Factor",
         "type": "integer"
      }
   },
   "required": [
      "in_channels",
      "out_channels",
      "kernel_size"
   ]
}

Config:

• arbitrary_types_allowed: bool = True

• extra: str = ignore

• validate_default: bool = True

• validate_assignment: bool = True

• validate_return: bool = True

Fields:

• scale_factor (int)

Validators:

field scale_factor: int = 2

Scale factor for upsampling / downsampling.

Validated by:

• validate

• validate_before

class vision_architectures.layers.scale.PixelShuffleUpsample3D(config={}, checkpointing_level=0, **kwargs)

Bases: Module

__init__(config={}, checkpointing_level=0, **kwargs)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(*args, **kwargs)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class vision_architectures.layers.scale.PixelShuffleDownsample3D(config={}, checkpointing_level=0, **kwargs)

Bases: Module

__init__(config={}, checkpointing_level=0, **kwargs)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(*args, **kwargs)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.