MaxViT3D#
- pydantic model vision_architectures.nets.maxvit_3d.MaxViT3DStem0Config[source]#
Bases:
CNNBlockConfigShow JSON schema
{ "title": "MaxViT3DStem0Config", "type": "object", "properties": { "in_channels": { "title": "In Channels", "type": "integer" }, "out_channels": { "default": null, "description": "This is defined by dim", "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": { "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" }, "dim": { "title": "Dim", "type": "integer" }, "depth": { "default": 2, "title": "Depth", "type": "integer" } }, "required": [ "in_channels", "dim" ] }
- Config:
arbitrary_types_allowed: bool = True
extra: str = ignore
validate_default: bool = True
validate_assignment: bool = True
validate_return: bool = True
- Fields:
- Validators:
-
field in_channels:
int[Required]# - Validated by:
-
field kernel_size:
int= 3# - Validated by:
-
field dim:
int[Required]# - Validated by:
-
field depth:
int= 2# - Validated by:
-
field out_channels:
None= None# This is defined by dim
- Validated by:
- pydantic model vision_architectures.nets.maxvit_3d.MaxViT3DBlockConfig[source]#
Bases:
MBConv3DConfig,Attention3DWithMLPConfigShow JSON schema
{ "title": "MaxViT3DBlockConfig", "type": "object", "properties": { "dim": { "description": "Input channel dimension of the block.", "title": "Dim", "type": "integer" }, "num_heads": { "description": "Number of query heads", "title": "Num Heads", "type": "integer" }, "ratio_q_to_kv_heads": { "default": 1, "title": "Ratio Q To Kv Heads", "type": "integer" }, "logit_scale_learnable": { "default": false, "title": "Logit Scale Learnable", "type": "boolean" }, "attn_drop_prob": { "default": 0.0, "title": "Attn Drop Prob", "type": "number" }, "proj_drop_prob": { "default": 0.0, "title": "Proj Drop Prob", "type": "number" }, "max_attention_batch_size": { "default": -1, "description": "Runs attention by splitting the inputs into chunks of this size. 0 means no chunking. Useful for large inputs during inference.", "title": "Max Attention Batch Size", "type": "integer" }, "mlp_ratio": { "default": 4, "description": "Ratio of the hidden dimension in the MLP to the input dimension.", "title": "Mlp Ratio", "type": "integer" }, "activation": { "default": "relu", "description": "Activation function to use in the block.", "title": "Activation", "type": "string" }, "mlp_drop_prob": { "default": 0.0, "description": "Dropout probability for the MLP.", "title": "Mlp Drop Prob", "type": "number" }, "norm_location": { "default": "post", "description": "Location of the normalization layer in the attention block. Pre-normalization implies normalization before the attention operation, while post-normalization applies it after.", "enum": [ "pre", "post" ], "title": "Norm Location", "type": "string" }, "layer_norm_eps": { "default": 1e-06, "description": "Epsilon value for the layer normalization.", "title": "Layer Norm Eps", "type": "number" }, "in_channels": { "default": null, "description": "Use dim instead", "title": "In Channels", "type": "null" }, "out_channels": { "default": null, "description": "Use expansion_ratio instead", "title": "Out Channels", "type": "null" }, "kernel_size": { "default": 3, "description": "Kernel size for the convolutional layers.", "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": "batchnorm3d", "description": "Normalization layer to use in the block.", "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_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" }, "out_dim": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Output channel dimension of the block. If None, it will be set to `dim`.", "title": "Out Dim" }, "expansion_ratio": { "default": 6.0, "description": "Expansion ratio for the block.", "title": "Expansion Ratio", "type": "number" }, "se_reduction_ratio": { "default": 4.0, "description": "Squeeze-and-excitation reduction ratio.", "title": "Se Reduction Ratio", "type": "number" }, "window_size": { "maxItems": 3, "minItems": 3, "prefixItems": [ { "type": "integer" }, { "type": "integer" }, { "type": "integer" } ], "title": "Window Size", "type": "array" }, "modify_dims": { "default": false, "title": "Modify Dims", "type": "boolean" }, "out_dim_ratio": { "default": 2, "title": "Out Dim Ratio", "type": "integer" } }, "required": [ "dim", "num_heads", "window_size" ] }
- Config:
arbitrary_types_allowed: bool = True
extra: str = ignore
validate_default: bool = True
validate_assignment: bool = True
validate_return: bool = True
- Fields:
- Validators:
-
field window_size:
tuple[int,int,int] [Required]# - Validated by:
-
field modify_dims:
bool= False# - Validated by:
-
field out_dim_ratio:
int= 2# - Validated by:
- pydantic model vision_architectures.nets.maxvit_3d.MaxViT3DStemConfig[source]#
Bases:
MaxViT3DBlockConfigShow JSON schema
{ "title": "MaxViT3DStemConfig", "type": "object", "properties": { "dim": { "description": "Input channel dimension of the block.", "title": "Dim", "type": "integer" }, "num_heads": { "description": "Number of query heads", "title": "Num Heads", "type": "integer" }, "ratio_q_to_kv_heads": { "default": 1, "title": "Ratio Q To Kv Heads", "type": "integer" }, "logit_scale_learnable": { "default": false, "title": "Logit Scale Learnable", "type": "boolean" }, "attn_drop_prob": { "default": 0.0, "title": "Attn Drop Prob", "type": "number" }, "proj_drop_prob": { "default": 0.0, "title": "Proj Drop Prob", "type": "number" }, "max_attention_batch_size": { "default": -1, "description": "Runs attention by splitting the inputs into chunks of this size. 0 means no chunking. Useful for large inputs during inference.", "title": "Max Attention Batch Size", "type": "integer" }, "mlp_ratio": { "default": 4, "description": "Ratio of the hidden dimension in the MLP to the input dimension.", "title": "Mlp Ratio", "type": "integer" }, "activation": { "default": "relu", "description": "Activation function to use in the block.", "title": "Activation", "type": "string" }, "mlp_drop_prob": { "default": 0.0, "description": "Dropout probability for the MLP.", "title": "Mlp Drop Prob", "type": "number" }, "norm_location": { "default": "post", "description": "Location of the normalization layer in the attention block. Pre-normalization implies normalization before the attention operation, while post-normalization applies it after.", "enum": [ "pre", "post" ], "title": "Norm Location", "type": "string" }, "layer_norm_eps": { "default": 1e-06, "description": "Epsilon value for the layer normalization.", "title": "Layer Norm Eps", "type": "number" }, "in_channels": { "default": null, "description": "Use dim instead", "title": "In Channels", "type": "null" }, "out_channels": { "default": null, "description": "Use expansion_ratio instead", "title": "Out Channels", "type": "null" }, "kernel_size": { "default": 3, "description": "Kernel size for the convolutional layers.", "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": "batchnorm3d", "description": "Normalization layer to use in the block.", "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_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" }, "out_dim": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Output channel dimension of the block. If None, it will be set to `dim`.", "title": "Out Dim" }, "expansion_ratio": { "default": 6.0, "description": "Expansion ratio for the block.", "title": "Expansion Ratio", "type": "number" }, "se_reduction_ratio": { "default": 4.0, "description": "Squeeze-and-excitation reduction ratio.", "title": "Se Reduction Ratio", "type": "number" }, "window_size": { "maxItems": 3, "minItems": 3, "prefixItems": [ { "type": "integer" }, { "type": "integer" }, { "type": "integer" } ], "title": "Window Size", "type": "array" }, "modify_dims": { "default": false, "title": "Modify Dims", "type": "boolean" }, "out_dim_ratio": { "default": 2, "title": "Out Dim Ratio", "type": "integer" }, "depth": { "title": "Depth", "type": "integer" } }, "required": [ "dim", "num_heads", "window_size", "depth" ] }
- Config:
arbitrary_types_allowed: bool = True
extra: str = ignore
validate_default: bool = True
validate_assignment: bool = True
validate_return: bool = True
- Fields:
- Validators:
-
field depth:
int[Required]# - Validated by:
- pydantic model vision_architectures.nets.maxvit_3d.MaxViT3DEncoderConfig[source]#
Bases:
CustomBaseModelShow JSON schema
{ "title": "MaxViT3DEncoderConfig", "type": "object", "properties": { "stem0": { "$ref": "#/$defs/MaxViT3DStem0Config" }, "stems": { "items": { "$ref": "#/$defs/MaxViT3DStemConfig" }, "title": "Stems", "type": "array" } }, "$defs": { "MaxViT3DStem0Config": { "properties": { "in_channels": { "title": "In Channels", "type": "integer" }, "out_channels": { "default": null, "description": "This is defined by dim", "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": { "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" }, "dim": { "title": "Dim", "type": "integer" }, "depth": { "default": 2, "title": "Depth", "type": "integer" } }, "required": [ "in_channels", "dim" ], "title": "MaxViT3DStem0Config", "type": "object" }, "MaxViT3DStemConfig": { "properties": { "dim": { "description": "Input channel dimension of the block.", "title": "Dim", "type": "integer" }, "num_heads": { "description": "Number of query heads", "title": "Num Heads", "type": "integer" }, "ratio_q_to_kv_heads": { "default": 1, "title": "Ratio Q To Kv Heads", "type": "integer" }, "logit_scale_learnable": { "default": false, "title": "Logit Scale Learnable", "type": "boolean" }, "attn_drop_prob": { "default": 0.0, "title": "Attn Drop Prob", "type": "number" }, "proj_drop_prob": { "default": 0.0, "title": "Proj Drop Prob", "type": "number" }, "max_attention_batch_size": { "default": -1, "description": "Runs attention by splitting the inputs into chunks of this size. 0 means no chunking. Useful for large inputs during inference.", "title": "Max Attention Batch Size", "type": "integer" }, "mlp_ratio": { "default": 4, "description": "Ratio of the hidden dimension in the MLP to the input dimension.", "title": "Mlp Ratio", "type": "integer" }, "activation": { "default": "relu", "description": "Activation function to use in the block.", "title": "Activation", "type": "string" }, "mlp_drop_prob": { "default": 0.0, "description": "Dropout probability for the MLP.", "title": "Mlp Drop Prob", "type": "number" }, "norm_location": { "default": "post", "description": "Location of the normalization layer in the attention block. Pre-normalization implies normalization before the attention operation, while post-normalization applies it after.", "enum": [ "pre", "post" ], "title": "Norm Location", "type": "string" }, "layer_norm_eps": { "default": 1e-06, "description": "Epsilon value for the layer normalization.", "title": "Layer Norm Eps", "type": "number" }, "in_channels": { "default": null, "description": "Use dim instead", "title": "In Channels", "type": "null" }, "out_channels": { "default": null, "description": "Use expansion_ratio instead", "title": "Out Channels", "type": "null" }, "kernel_size": { "default": 3, "description": "Kernel size for the convolutional layers.", "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": "batchnorm3d", "description": "Normalization layer to use in the block.", "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_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" }, "out_dim": { "anyOf": [ { "type": "integer" }, { "type": "null" } ], "default": null, "description": "Output channel dimension of the block. If None, it will be set to `dim`.", "title": "Out Dim" }, "expansion_ratio": { "default": 6.0, "description": "Expansion ratio for the block.", "title": "Expansion Ratio", "type": "number" }, "se_reduction_ratio": { "default": 4.0, "description": "Squeeze-and-excitation reduction ratio.", "title": "Se Reduction Ratio", "type": "number" }, "window_size": { "maxItems": 3, "minItems": 3, "prefixItems": [ { "type": "integer" }, { "type": "integer" }, { "type": "integer" } ], "title": "Window Size", "type": "array" }, "modify_dims": { "default": false, "title": "Modify Dims", "type": "boolean" }, "out_dim_ratio": { "default": 2, "title": "Out Dim Ratio", "type": "integer" }, "depth": { "title": "Depth", "type": "integer" } }, "required": [ "dim", "num_heads", "window_size", "depth" ], "title": "MaxViT3DStemConfig", "type": "object" } }, "required": [ "stem0", "stems" ] }
- Config:
arbitrary_types_allowed: bool = True
extra: str = ignore
validate_default: bool = True
validate_assignment: bool = True
validate_return: bool = True
- Fields:
- Validators:
validate»all fields
-
field stem0:
MaxViT3DStem0Config[Required]# - Validated by:
-
field stems:
list[MaxViT3DStemConfig] [Required]# - Validated by:
- class vision_architectures.nets.maxvit_3d.MaxViT3DStem0(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.
- 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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class vision_architectures.nets.maxvit_3d.MaxViT3DBlockAttention(config={}, checkpointing_level=0, **kwargs)[source]#
Bases:
SwinV23DLayer
- class vision_architectures.nets.maxvit_3d.MaxViT3DGridAttention(config={}, checkpointing_level=0, **kwargs)[source]#
Bases:
SwinV23DLayerPerform grid attention on the input tensor.
Note that the grid attention implementation differs from the paper where the image is being partitioned based on the window size and not based on the number of windows. For example:
Let us say the input is
A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5 B6 C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6
Let us say the window size is 2x2. The grid attention will be performed on the following 6 windows:
A1 A4 A2 A5 A3 A6 C1 C4 C2 C5 C3 C6 B1 B4 B2 B5 B3 B6 D1 D4 D2 D5 D3 D6
According to the paper, my understanding is that attention should have been applied on the following 4 windows:
A1 A3 A5 A2 A4 A6 B1 B3 B5 B2 B4 B6 C1 C3 C5 C2 C4 C6 D1 D3 D5 D2 D4 D6
i.e. the first token of all 2x2 windows in block attention, the second token of all 2x2 windows in block attention and so on.
This has been implemented different so as to limit the number of tokens to be attended to in a window, as if utilized as per the paper, since 3D inputs are usually very large, the number of total windows in block attention would be very large, leading to a very large number of tokens to attend to in each window in grid attention.
It would also cause problems when estimating the position embeddings as the grid size of the position embeddings would vary very with every input size.
- class vision_architectures.nets.maxvit_3d.MaxViT3DBlock(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.
- 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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class vision_architectures.nets.maxvit_3d.MaxViT3DStem(config={}, checkpointing_level=0, dont_downsample=False, **kwargs)[source]#
Bases:
Module- __init__(config={}, checkpointing_level=0, dont_downsample=False, **kwargs)[source]#
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- 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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- class vision_architectures.nets.maxvit_3d.MaxViT3DEncoder(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.
- 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
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.