ImageEncoderViT

class lightning_pose.models.backbones.vit_img_encoder.ImageEncoderViT(img_size: int = 1024, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, depth: int = 12, num_heads: int = 12, mlp_ratio: float = 4.0, out_chans: int = 256, qkv_bias: bool = True, norm_layer: ~typing.Type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.normalization.LayerNorm'>, act_layer: ~typing.Type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.GELU'>, use_abs_pos: bool = True, use_rel_pos: bool = False, rel_pos_zero_init: bool = True, window_size: int = 0, global_attn_indexes: ~typing.Tuple[int, ...] = ())

Bases: Module

Methods Summary

forward(x)

Define the computation performed at every call.

Methods Documentation

forward(x: Tensor) → Tensor

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.

__init__(img_size: int = 1024, patch_size: int = 16, in_chans: int = 3, embed_dim: int = 768, depth: int = 12, num_heads: int = 12, mlp_ratio: float = 4.0, out_chans: int = 256, qkv_bias: bool = True, norm_layer: ~typing.Type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.normalization.LayerNorm'>, act_layer: ~typing.Type[~torch.nn.modules.module.Module] = <class 'torch.nn.modules.activation.GELU'>, use_abs_pos: bool = True, use_rel_pos: bool = False, rel_pos_zero_init: bool = True, window_size: int = 0, global_attn_indexes: ~typing.Tuple[int, ...] = ()) → None

Parameters:

• img_size (int) – Input image size.

• patch_size (int) – Patch size.

• in_chans (int) – Number of input image channels.

• embed_dim (int) – Patch embedding dimension.

• depth (int) – Depth of ViT.

• num_heads (int) – Number of attention heads in each ViT block.

• mlp_ratio (float) – Ratio of mlp hidden dim to embedding dim.

• qkv_bias (bool) – If True, add a learnable bias to query, key, value.

• norm_layer (nn.Module) – Normalization layer.

• act_layer (nn.Module) – Activation layer.

• use_abs_pos (bool) – If True, use absolute positional embeddings.

• use_rel_pos (bool) – If True, add relative positional embeddings to the attention map.

• rel_pos_zero_init (bool) – If True, zero initialize relative positional parameters.

• window_size (int) – Window size for window attention blocks.

• global_attn_indexes (list) – Indexes for blocks using global attention.