UperNet2D#

pydantic model vision_architectures.nets.upernet_2d.UPerNet2DFusionConfig[source]#

Bases: CNNBlockConfig

Show JSON schema
{
   "title": "UPerNet2DFusionConfig",
   "type": "object",
   "properties": {
      "in_channels": {
         "default": null,
         "description": "Calculated based on other parameters",
         "title": "In Channels",
         "type": "null"
      },
      "out_channels": {
         "default": null,
         "description": "Calculated based on other parameters",
         "title": "Out Channels",
         "type": "null"
      },
      "kernel_size": {
         "default": 3,
         "title": "Kernel Size",
         "type": "integer"
      },
      "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": {
         "default": "batchnorm2d",
         "title": "Normalization",
         "type": "string"
      },
      "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"
      },
      "dim": {
         "title": "Dim",
         "type": "integer"
      },
      "num_features": {
         "title": "Num Features",
         "type": "integer"
      },
      "fused_shape": {
         "anyOf": [
            {
               "maxItems": 2,
               "minItems": 2,
               "prefixItems": [
                  {
                     "type": "integer"
                  },
                  {
                     "type": "integer"
                  }
               ],
               "type": "array"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Shape of the fused feature map. If None, highest input resolution is used.",
         "title": "Fused Shape"
      },
      "interpolation_mode": {
         "default": "bilinear",
         "title": "Interpolation Mode",
         "type": "string"
      }
   },
   "required": [
      "dim",
      "num_features"
   ]
}

Config:

  • arbitrary_types_allowed: bool = True

  • extra: str = ignore

  • validate_default: bool = True

  • validate_assignment: bool = True

  • validate_return: bool = True

Fields:
Validators:

field dim: int [Required]#
Validated by:
field num_features: int [Required]#
Validated by:
field kernel_size: int = 3#
Validated by:
field fused_shape: tuple[int, int] | None = None#

Shape of the fused feature map. If None, highest input resolution is used.

Validated by:
field interpolation_mode: str = 'bilinear'#
Validated by:
field normalization: str = 'batchnorm2d'#
Validated by:
field in_channels: None = None#

Calculated based on other parameters

Validated by:
field out_channels: None = None#

Calculated based on other parameters

Validated by:
pydantic model vision_architectures.nets.upernet_2d.UPerNet2DConfig[source]#

Bases: FPN2DConfig

Show JSON schema
{
   "title": "UPerNet2DConfig",
   "type": "object",
   "properties": {
      "blocks": {
         "items": {
            "$ref": "#/$defs/FPN2DBlockConfig"
         },
         "title": "Blocks",
         "type": "array"
      },
      "fusion": {
         "$ref": "#/$defs/UPerNet2DFusionConfig"
      },
      "enabled_outputs": {
         "default": [
            "object"
         ],
         "items": {
            "enum": [
               "object",
               "part",
               "scene",
               "material",
               "texture"
            ],
            "type": "string"
         },
         "title": "Enabled Outputs",
         "type": "array",
         "uniqueItems": true
      },
      "num_objects": {
         "anyOf": [
            {
               "type": "integer"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "title": "Num Objects"
      }
   },
   "$defs": {
      "FPN2DBlockConfig": {
         "properties": {
            "in_channels": {
               "default": null,
               "description": "Calculated based on other parameters",
               "title": "In Channels",
               "type": "null"
            },
            "out_channels": {
               "default": null,
               "description": "Calculated based on other parameters",
               "title": "Out Channels",
               "type": "null"
            },
            "kernel_size": {
               "default": 3,
               "title": "Kernel Size",
               "type": "integer"
            },
            "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": {
               "default": "batchnorm2d",
               "title": "Normalization",
               "type": "string"
            },
            "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"
            },
            "dim": {
               "title": "Dim",
               "type": "integer"
            },
            "skip_conn_dim": {
               "title": "Skip Conn Dim",
               "type": "integer"
            },
            "is_deepest": {
               "default": false,
               "description": "True if this is the deepest block in the FPN, else False",
               "title": "Is Deepest",
               "type": "boolean"
            },
            "interpolation_mode": {
               "default": "bilinear",
               "title": "Interpolation Mode",
               "type": "string"
            },
            "merge_method": {
               "default": "add",
               "enum": [
                  "add",
                  "concat"
               ],
               "title": "Merge Method",
               "type": "string"
            }
         },
         "required": [
            "dim",
            "skip_conn_dim"
         ],
         "title": "FPN2DBlockConfig",
         "type": "object"
      },
      "UPerNet2DFusionConfig": {
         "properties": {
            "in_channels": {
               "default": null,
               "description": "Calculated based on other parameters",
               "title": "In Channels",
               "type": "null"
            },
            "out_channels": {
               "default": null,
               "description": "Calculated based on other parameters",
               "title": "Out Channels",
               "type": "null"
            },
            "kernel_size": {
               "default": 3,
               "title": "Kernel Size",
               "type": "integer"
            },
            "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": {
               "default": "batchnorm2d",
               "title": "Normalization",
               "type": "string"
            },
            "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"
            },
            "dim": {
               "title": "Dim",
               "type": "integer"
            },
            "num_features": {
               "title": "Num Features",
               "type": "integer"
            },
            "fused_shape": {
               "anyOf": [
                  {
                     "maxItems": 2,
                     "minItems": 2,
                     "prefixItems": [
                        {
                           "type": "integer"
                        },
                        {
                           "type": "integer"
                        }
                     ],
                     "type": "array"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "Shape of the fused feature map. If None, highest input resolution is used.",
               "title": "Fused Shape"
            },
            "interpolation_mode": {
               "default": "bilinear",
               "title": "Interpolation Mode",
               "type": "string"
            }
         },
         "required": [
            "dim",
            "num_features"
         ],
         "title": "UPerNet2DFusionConfig",
         "type": "object"
      }
   },
   "required": [
      "blocks",
      "fusion"
   ]
}

Config:

  • arbitrary_types_allowed: bool = True

  • extra: str = ignore

  • validate_default: bool = True

  • validate_assignment: bool = True

  • validate_return: bool = True

Fields:
Validators:
field fusion: UPerNet2DFusionConfig [Required]#
Validated by:
field enabled_outputs: set[Literal['object', 'part', 'scene', 'material', 'texture']] = {'object'}#
Validated by:
field num_objects: int | None = None#
Validated by:
validator validate_before  »  all fields[source]#

Base class method for validating data before creating the model.

validator validate  »  all fields[source]#

Base method for validating the model after creation.

class vision_architectures.nets.upernet_2d.UPerNet2DFusion(config={}, checkpointing_level=0, **kwargs)[source]#

Bases: Module

__init__(config={}, checkpointing_level=0, **kwargs)[source]#

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

concat_features(features)[source]#
fuse_features(concatenated_features)[source]#
forward(*args, **kwargs)[source]#

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.nets.upernet_2d.UPerNet2D(config={}, checkpointing_level=0, **kwargs)[source]#

Bases: Module, PyTorchModelHubMixin

__init__(config={}, checkpointing_level=0, **kwargs)[source]#

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

forward(features)[source]#

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.