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from functools import partial
from typing import NamedTuple
import numpy as np
import torch
from torch import nn
from .afno import Block
class WMAEOutput(NamedTuple):
loss: torch.Tensor
prediction: torch.Tensor
mask: torch.Tensor
def _sincos_1d(embed_dim: int, positions: np.ndarray) -> np.ndarray:
if embed_dim % 2 != 0:
raise ValueError("The 1D sine-cosine embedding dimension must be even.")
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega = 1.0 / (10000 ** (omega / (embed_dim / 2.0)))
values = np.einsum("m,d->md", positions.reshape(-1), omega)
return np.concatenate((np.sin(values), np.cos(values)), axis=1)
def build_2d_sincos_position_embedding(
embed_dim: int, grid_size: tuple[int, int], include_cls_token: bool
) -> torch.Tensor:
if embed_dim % 4 != 0:
raise ValueError("The 2D sine-cosine embedding dimension must be divisible by four.")
grid_h = np.arange(grid_size[0], dtype=np.float32)
grid_w = np.arange(grid_size[1], dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h)
embedding = np.concatenate(
(_sincos_1d(embed_dim // 2, grid[0]), _sincos_1d(embed_dim // 2, grid[1])), axis=1
)
if include_cls_token:
embedding = np.concatenate((np.zeros((1, embed_dim)), embedding), axis=0)
return torch.from_numpy(embedding).float().unsqueeze(0)
class PatchEmbed(nn.Module):
def __init__(
self,
img_size: tuple[int, int],
patch_size: tuple[int, int],
in_chans: int,
embed_dim: int,
) -> None:
super().__init__()
if img_size[0] % patch_size[0] or img_size[1] % patch_size[1]:
raise ValueError(f"img_size={img_size} must be divisible by patch_size={patch_size}.")
self.img_size = img_size
self.patch_size = patch_size
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.ndim != 4 or tuple(x.shape[-2:]) != self.img_size:
raise ValueError(f"PatchEmbed expects [B,C,{self.img_size[0]},{self.img_size[1]}], got {tuple(x.shape)}.")
return self.proj(x).flatten(2).transpose(1, 2)
class MaskedAutoencoderAFNO(nn.Module):
"""W-MAE pretraining model reconstructed from the official AFNO source."""
def __init__(
self,
img_size: tuple[int, int] = (720, 1440),
patch_size: tuple[int, int] = (8, 8),
in_chans: int = 20,
embed_dim: int = 768,
depth: int = 12,
decoder_embed_dim: int = 512,
decoder_depth: int = 6,
mlp_ratio: float = 4.0,
norm_layer: type[nn.Module] = nn.LayerNorm,
norm_pix_loss: bool = False,
num_blocks: int = 8,
sparsity_threshold: float = 0.01,
hard_thresholding_fraction: float = 1.0,
) -> None:
super().__init__()
self.img_size = tuple(img_size)
self.patch_size = tuple(patch_size)
self.in_chans = in_chans
self.embed_dim = embed_dim
self.decoder_embed_dim = decoder_embed_dim
self.norm_pix_loss = norm_pix_loss
self.patch_embed = PatchEmbed(self.img_size, self.patch_size, in_chans, embed_dim)
num_patches = self.patch_embed.num_patches
# The official AFNO path keeps this checkpoint key but does not prepend
# a class token in forward_encoder, so it must not enter DDP reduction.
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim), requires_grad=False)
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim), requires_grad=False)
block_args = dict(
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
num_blocks=num_blocks,
sparsity_threshold=sparsity_threshold,
hard_thresholding_fraction=hard_thresholding_fraction,
)
self.blocks = nn.ModuleList([Block(dim=embed_dim, **block_args) for _ in range(depth)])
self.norm = norm_layer(embed_dim)
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
self.decoder_pos_embed = nn.Parameter(
torch.zeros(1, num_patches, decoder_embed_dim), requires_grad=False
)
decoder_args = dict(block_args)
decoder_args["norm_layer"] = norm_layer
self.decoder_blocks = nn.ModuleList(
[Block(dim=decoder_embed_dim, **decoder_args) for _ in range(decoder_depth)]
)
self.decoder_norm = norm_layer(decoder_embed_dim)
patch_area = self.patch_size[0] * self.patch_size[1]
self.decoder_pred = nn.Linear(decoder_embed_dim, in_chans * patch_area)
self.initialize_weights()
def initialize_weights(self) -> None:
grid_size = self.patch_embed.grid_size
self.pos_embed.data.copy_(build_2d_sincos_position_embedding(self.embed_dim, grid_size, True))
self.decoder_pos_embed.data.copy_(
build_2d_sincos_position_embedding(self.decoder_embed_dim, grid_size, False)
)
nn.init.xavier_uniform_(self.patch_embed.proj.weight.data.flatten(1))
nn.init.normal_(self.cls_token, std=0.02)
nn.init.normal_(self.mask_token, std=0.02)
self.apply(self._init_weights)
@staticmethod
def _init_weights(module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.zeros_(module.bias)
nn.init.ones_(module.weight)
def patchify(self, images: torch.Tensor) -> torch.Tensor:
if images.ndim != 4 or images.shape[1] != self.in_chans or tuple(images.shape[-2:]) != self.img_size:
raise ValueError(
f"patchify expects [B,{self.in_chans},{self.img_size[0]},{self.img_size[1]}], "
f"got {tuple(images.shape)}."
)
ph, pw = self.patch_size
gh, gw = self.patch_embed.grid_size
x = images.reshape(images.shape[0], self.in_chans, gh, ph, gw, pw)
x = torch.einsum("nchpwq->nhwpqc", x)
return x.reshape(images.shape[0], gh * gw, ph * pw * self.in_chans)
def unpatchify(self, patches: torch.Tensor) -> torch.Tensor:
ph, pw = self.patch_size
gh, gw = self.patch_embed.grid_size
expected_dim = ph * pw * self.in_chans
if patches.ndim != 3 or patches.shape[1:] != (gh * gw, expected_dim):
raise ValueError(f"unpatchify expects [B,{gh * gw},{expected_dim}], got {tuple(patches.shape)}.")
x = patches.reshape(patches.shape[0], gh, gw, ph, pw, self.in_chans)
x = torch.einsum("nhwpqc->nchpwq", x)
return x.reshape(patches.shape[0], self.in_chans, gh * ph, gw * pw)
@staticmethod
def random_masking(
x: torch.Tensor, mask_ratio: float
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if mask_ratio not in {0.0, 0.75}:
raise ValueError("Official W-MAE AFNO grid reshaping supports mask_ratio 0.0 or 0.75 only.")
batch, length, channels = x.shape
len_keep = int(length * (1.0 - mask_ratio))
noise = torch.rand(batch, length, device=x.device)
ids_shuffle = torch.argsort(noise, dim=1)
ids_restore = torch.argsort(ids_shuffle, dim=1)
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, 1, ids_keep.unsqueeze(-1).expand(-1, -1, channels))
mask = torch.ones(batch, length, device=x.device)
mask[:, :len_keep] = 0
return x_masked, torch.gather(mask, 1, ids_restore), ids_restore
def forward_encoder(
self, images: torch.Tensor, mask_ratio: float
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
x = self.patch_embed(images) + self.pos_embed[:, 1:]
x, mask, ids_restore = self.random_masking(x, mask_ratio)
grid_h, grid_w = self.patch_embed.grid_size
divisor = 2 if mask_ratio == 0.75 else 1
expected_tokens = (grid_h // divisor) * (grid_w // divisor)
if x.shape[1] != expected_tokens:
raise ValueError("The selected mask ratio does not form the rectangular AFNO grid expected by W-MAE.")
x = x.reshape(x.shape[0], grid_h // divisor, grid_w // divisor, self.embed_dim)
for block in self.blocks:
x = block(x)
x = self.norm(x)
return x.flatten(1, 2), mask, ids_restore
def forward_decoder(self, latent: torch.Tensor, ids_restore: torch.Tensor) -> torch.Tensor:
x = self.decoder_embed(latent)
missing_tokens = ids_restore.shape[1] - x.shape[1]
if missing_tokens < 0:
raise ValueError("Latent token count exceeds the decoder target token count.")
mask_tokens = self.mask_token.expand(x.shape[0], missing_tokens, -1)
x = torch.cat((x, mask_tokens), dim=1)
x = torch.gather(x, 1, ids_restore.unsqueeze(-1).expand(-1, -1, x.shape[-1]))
x = x + self.decoder_pos_embed
grid_h, grid_w = self.patch_embed.grid_size
x = x.reshape(x.shape[0], grid_h, grid_w, self.decoder_embed_dim)
for block in self.decoder_blocks:
x = block(x)
x = self.decoder_pred(self.decoder_norm(x))
return x.flatten(1, 2)
def forward_loss(
self, images: torch.Tensor, prediction: torch.Tensor, mask: torch.Tensor, mask_ratio: float
) -> torch.Tensor:
target = self.patchify(images)
if self.norm_pix_loss:
mean = target.mean(dim=-1, keepdim=True)
variance = target.var(dim=-1, keepdim=True)
target = (target - mean) / torch.sqrt(variance + 1e-6)
loss = (prediction - target).pow(2).mean(dim=-1)
if mask_ratio == 0.0:
return loss.mean()
masked_count = mask.sum()
if masked_count.item() == 0:
raise ValueError("Masked reconstruction loss requires at least one masked patch.")
return (loss * mask).sum() / masked_count
def forward(self, images: torch.Tensor, mask_ratio: float = 0.75) -> WMAEOutput:
latent, mask, ids_restore = self.forward_encoder(images, mask_ratio)
prediction = self.forward_decoder(latent, ids_restore)
loss = self.forward_loss(images, prediction, mask, mask_ratio)
return WMAEOutput(loss, prediction, mask)
def w_mae_base(
embed_dim: int = 768,
depth: int = 12,
decoder_embed_dim: int = 512,
decoder_depth: int = 6,
mlp_ratio: float = 4.0,
norm_layer: type[nn.Module] = partial(nn.LayerNorm, eps=1e-6),
**kwargs: object,
) -> MaskedAutoencoderAFNO:
return MaskedAutoencoderAFNO(
embed_dim=embed_dim,
depth=depth,
decoder_embed_dim=decoder_embed_dim,
decoder_depth=decoder_depth,
mlp_ratio=mlp_ratio,
norm_layer=norm_layer,
**kwargs,
)
mae_vit_base_patch16 = w_mae_base
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