Embeddings

vision_architectures.layers.embeddings.get_absolute_position_embeddings_3d(dim, grid_size, spacing=(1.0, 1.0, 1.0), crop_offset=None, channels_first=True)

Return type:

Tensor

vision_architectures.layers.embeddings.get_timestep_embeddings_1d(dim, indices)

Return type:

Tensor

vision_architectures.layers.embeddings.get_all_timestep_embeddings_1d(dim, length, device=device(type='cpu'))

Return type:

Tensor

vision_architectures.layers.embeddings.get_absolute_position_embeddings_1d(dim, length, device=device(type='cpu'))

Return type:

Tensor

pydantic model vision_architectures.layers.embeddings.RelativePositionEmbeddings3DConfig

Bases: CustomBaseModel

Show JSON schema

{
   "title": "RelativePositionEmbeddings3DConfig",
   "type": "object",
   "properties": {
      "num_heads": {
         "description": "Number of query attention heads",
         "title": "Num Heads",
         "type": "integer"
      },
      "grid_size": {
         "maxItems": 3,
         "minItems": 3,
         "prefixItems": [
            {
               "type": "integer"
            },
            {
               "type": "integer"
            },
            {
               "type": "integer"
            }
         ],
         "title": "Grid Size",
         "type": "array"
      }
   },
   "required": [
      "num_heads",
      "grid_size"
   ]
}

Config:

• arbitrary_types_allowed: bool = True

• extra: str = ignore

• validate_default: bool = True

• validate_assignment: bool = True

• validate_return: bool = True

Fields:

• grid_size (tuple[int, int, int])

• num_heads (int)

Validators:

• validate » all fields

• validate_before » all fields

field num_heads: int [Required]

Number of query attention heads

Validated by:

• validate

• validate_before

field grid_size: tuple[int, int, int] [Required]

Validated by:

• validate

• validate_before

property num_patches: int

validator validate_before  »  all fields

Base class method for validating data before creating the model.

validator validate  »  all fields

Base method for validating the model after creation.

pydantic model vision_architectures.layers.embeddings.AbsolutePositionEmbeddings3DConfig

Bases: CustomBaseModel

Show JSON schema

{
   "title": "AbsolutePositionEmbeddings3DConfig",
   "type": "object",
   "properties": {
      "dim": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Dim"
      },
      "grid_size": {
         "anyOf": [
            {
               "maxItems": 3,
               "minItems": 3,
               "prefixItems": [
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  }
               ],
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Grid Size"
      },
      "learnable": {
         "default": false,
         "title": "Learnable",
         "type": "boolean"
      }
   }
}

Config:

• arbitrary_types_allowed: bool = True

• extra: str = ignore

• validate_default: bool = True

• validate_assignment: bool = True

• validate_return: bool = True

Fields:

• dim (int | None)

• grid_size (tuple[int, int, int] | None)

• learnable (bool)

Validators:

• validate » all fields

• validate_before » all fields

field dim: int | None = None

Validated by:

• validate

• validate_before

field grid_size: tuple[int, int, int] | None = None

Validated by:

• validate

• validate_before

field learnable: bool = False

Validated by:

• validate

• validate_before

property num_patches: int

validator validate_before  »  all fields

Base class method for validating data before creating the model.

validator validate  »  all fields

Base method for validating the model after creation.

pydantic model vision_architectures.layers.embeddings.AbsolutePositionEmbeddings1DConfig

Bases: CustomBaseModel

Show JSON schema

{
   "title": "AbsolutePositionEmbeddings1DConfig",
   "type": "object",
   "properties": {
      "dim": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Dim"
      },
      "length": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Length"
      },
      "learnable": {
         "default": false,
         "title": "Learnable",
         "type": "boolean"
      }
   }
}

Config:

• arbitrary_types_allowed: bool = True

• extra: str = ignore

• validate_default: bool = True

• validate_assignment: bool = True

• validate_return: bool = True

Fields:

• dim (int | None)

• learnable (bool)

• length (int | None)

Validators:

• validate » all fields

field dim: int | None = None

Validated by:

• validate

• validate_before

field length: int | None = None

Validated by:

• validate

• validate_before

field learnable: bool = False

Validated by:

• validate

• validate_before

validator validate  »  all fields

Base method for validating the model after creation.

pydantic model vision_architectures.layers.embeddings.PatchEmbeddings3DConfig

Bases: CNNBlockConfig

Show JSON schema

{
   "title": "PatchEmbeddings3DConfig",
   "type": "object",
   "properties": {
      "in_channels": {
         "title": "In Channels",
         "type": "integer"
      },
      "out_channels": {
         "default": null,
         "title": "Out Channels",
         "type": "null"
      },
      "kernel_size": {
         "default": null,
         "title": "Kernel Size",
         "type": "null"
      },
      "padding": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "items": {
                  "type": "integer"
               },
               "type": "array"
            },
            {
               "type": "string"
            }
         ],
         "default": "same",
         "title": "Padding"
      },
      "stride": {
         "default": 1,
         "title": "Stride",
         "type": "integer"
      },
      "conv_kwargs": {
         "additionalProperties": true,
         "default": {},
         "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",
         "title": "Normalization"
      },
      "normalization_pre_args": {
         "default": [],
         "items": {},
         "title": "Normalization Pre Args",
         "type": "array"
      },
      "normalization_post_args": {
         "default": [],
         "items": {},
         "title": "Normalization Post Args",
         "type": "array"
      },
      "normalization_kwargs": {
         "additionalProperties": true,
         "default": {},
         "title": "Normalization Kwargs",
         "type": "object"
      },
      "activation": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": "relu",
         "title": "Activation"
      },
      "activation_kwargs": {
         "additionalProperties": true,
         "default": {},
         "title": "Activation Kwargs",
         "type": "object"
      },
      "sequence": {
         "default": "CNA",
         "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,
         "title": "Drop Prob",
         "type": "number"
      },
      "patch_size": {
         "maxItems": 3,
         "minItems": 3,
         "prefixItems": [
            {
               "type": "integer"
            },
            {
               "type": "integer"
            },
            {
               "type": "integer"
            }
         ],
         "title": "Patch Size",
         "type": "array"
      },
      "dim": {
         "title": "Dim",
         "type": "integer"
      },
      "norm_layer": {
         "default": "layernorm",
         "title": "Norm Layer",
         "type": "string"
      }
   },
   "required": [
      "in_channels",
      "patch_size",
      "dim"
   ]
}

Config:

• arbitrary_types_allowed: bool = True

• extra: str = ignore

• validate_default: bool = True

• validate_assignment: bool = True

• validate_return: bool = True

Fields:

• dim (int)

• in_channels (int)

• kernel_size (None)

• norm_layer (str)

• out_channels (None)

• patch_size (tuple[int, int, int])

Validators:

• validate_before » all fields

field patch_size: tuple[int, int, int] [Required]

Validated by:

• validate

• validate_before

field in_channels: int [Required]

Validated by:

• validate

• validate_before

field dim: int [Required]

Validated by:

• validate

• validate_before

field norm_layer: str = 'layernorm'

Validated by:

• validate

• validate_before

field out_channels: None = None

Validated by:

• validate

• validate_before

field kernel_size: None = None

Validated by:

• validate

• validate_before

validator validate_before  »  all fields

Base class method for validating data before creating the model.

vision_architectures.layers.embeddings.get_coords_grid(grid_size)

Return type:

Tensor

class vision_architectures.layers.embeddings.RelativePositionEmbeddings3D(config={}, **kwargs)

Bases: Module

__init__(config={}, **kwargs)

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

forward()

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.embeddings.RelativePositionEmbeddings3DMetaNetwork(config={}, checkpointing_level=0, **kwargs)

Bases: Module

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

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

get_relative_position_embeddings_table()

forward()

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.

vision_architectures.layers.embeddings.get_sinusoidal_embeddings_3d(dim, grid_size, spacing=(1.0, 1.0, 1.0), crop_offset=None, channels_first=True)

Return type:

Tensor

class vision_architectures.layers.embeddings.AbsolutePositionEmbeddings3D(config={}, **kwargs)

Bases: Module

__init__(config={}, **kwargs)

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

forward(x, embedding_type='add', spacings=None, channels_first=True, crop_offsets=None)

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.

vision_architectures.layers.embeddings.get_specific_sinusoidal_embeddings_1d(dim, indices)

Return type:

Tensor

vision_architectures.layers.embeddings.get_sinusoidal_embeddings_1d(dim, length, device=device(type='cpu'))

Return type:

Tensor

class vision_architectures.layers.embeddings.AbsolutePositionEmbeddings1D(config={}, **kwargs)

Bases: Module

__init__(config={}, **kwargs)

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

forward(x, embedding_type='add')

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.embeddings.PatchEmbeddings3D(config={}, checkpointing_level=0, **kwargs)

Bases: CNNBlock3D

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

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