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# © Recursion Pharmaceuticals 2024
from functools import partial
from typing import Tuple, Union
import torch
import torch.nn as nn
from timm.models.helpers import checkpoint_seq
from timm.models.vision_transformer import Block, Mlp, VisionTransformer
from .masking import transformer_random_masking
from .vit import channel_agnostic_vit
# If interested in training new MAEs, combine an encoder and decoder into a new module, and you should
# leverage the flattening and unflattening utilities as needed from mae_utils.py.
# Be sure to use an encoder-decoder Linear projection layer to match encoder dims with decoder dimensions.
# As described in the paper, images are self-standardized at the start.
class SelfStandardize(nn.Module):
def __init__(self) -> None:
super().__init__()
self.self_standardize = nn.LazyInstanceNorm2d(
affine=False, track_running_stats=False
)
def forward(self, pixels: torch.Tensor) -> torch.Tensor:
x = pixels.float() / 255.0
return self.self_standardize(x)
class MAEEncoder(nn.Module):
def __init__(
self,
vit_backbone: VisionTransformer,
max_in_chans: int = 6,
channel_agnostic: bool = False,
) -> None:
super().__init__()
if channel_agnostic:
self.vit_backbone = channel_agnostic_vit(
vit_backbone, max_in_chans=max_in_chans
)
else:
self.vit_backbone = vit_backbone
self.max_in_chans = max_in_chans
self.channel_agnostic = channel_agnostic
@property
def embed_dim(self) -> int:
return int(self.vit_backbone.embed_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.vit_backbone.forward_features(x)
x = self.vit_backbone.forward_head(x)
return x # type: ignore[no-any-return]
def forward_masked(
self,
x: torch.Tensor,
mask_ratio: float,
constant_noise: Union[torch.Tensor, None] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
x = self.vit_backbone.patch_embed(x)
x = self.vit_backbone._pos_embed(x) # adds class token
x_ = x[:, 1:, :] # no class token
x_, mask, ind_restore = transformer_random_masking(
x_, mask_ratio, constant_noise
)
x = torch.cat([x[:, :1, :], x_], dim=1) # add class token
x = self.vit_backbone.norm_pre(x)
if self.vit_backbone.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.vit_backbone.blocks, x)
else:
x = self.vit_backbone.blocks(x)
x = self.vit_backbone.norm(x)
return x, mask, ind_restore
class MAEDecoder(nn.Module):
def __init__(
self,
embed_dim: int = 512,
depth: int = 8,
num_heads: int = 16,
mlp_ratio: float = 4,
qkv_bias: bool = True,
norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6), # type: ignore[assignment]
) -> None:
super().__init__()
self.embed_dim = embed_dim
self.pos_embeddings = None # to be overwritten by MAE class
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.blocks = nn.Sequential(
*[
Block(
embed_dim,
num_heads,
mlp_ratio,
qkv_bias=qkv_bias,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x + self.pos_embeddings
x = self.blocks(x)
x = self.norm(x)
return x # type: ignore[no-any-return]
def forward_masked(
self, x: torch.Tensor, ind_restore: torch.Tensor
) -> torch.Tensor:
mask_tokens = self.mask_token.repeat(
x.shape[0], ind_restore.shape[1] + 1 - x.shape[1], 1
)
x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # remove class token
x_ = torch.gather(
x_, dim=1, index=ind_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])
) # unshuffle
x = torch.cat([x[:, :1, :], x_], dim=1) # add class token
x = x + self.pos_embeddings
x = self.blocks(x)
x = self.norm(x)
return x # type: ignore[no-any-return]
class CrossAttention(nn.Module):
def __init__(
self, embed_dim, num_heads=8, qkv_bias=False, attn_drop=0.0, proj_drop=0.0
):
super().__init__()
self.num_heads = num_heads
head_dim = embed_dim // num_heads
self.scale = head_dim**-0.5
self.q = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
self.kv = nn.Linear(embed_dim, embed_dim * 2, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(embed_dim, embed_dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, context):
B, N, C = x.shape
_, M, _ = context.shape
q = (
self.q(x)
.reshape(B, N, self.num_heads, C // self.num_heads)
.permute(0, 2, 1, 3)
)
kv = (
self.kv(context)
.reshape(B, M, 2, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
k, v = kv[0], kv[1]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
class CAMAEDecoder(nn.Module):
def __init__(
self,
num_modalities: int = 6,
tokens_per_modality: int = 256,
embed_dim: int = 256,
depth: int = 2,
num_heads: int = 16,
mlp_ratio: float = 4,
qkv_bias: bool = True,
norm_layer: nn.Module = partial(nn.LayerNorm, eps=1e-6), # type: ignore[assignment]
) -> None:
super().__init__()
self.num_modalities = num_modalities
self.tokens_per_modality = tokens_per_modality
self.embed_dim = embed_dim
self.pos_embeddings = None # to be overwritten by MAE class
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.placeholder = nn.Parameter(
torch.zeros(1, 1, embed_dim), requires_grad=False
)
self.modality_tokens = nn.ParameterList(
[
nn.Parameter(torch.zeros(1, 1, self.embed_dim))
for modality in range(self.num_modalities)
]
)
self.cross_attention = CrossAttention(embed_dim=self.embed_dim)
self.mlp = Mlp(self.embed_dim, hidden_features=int(self.embed_dim * mlp_ratio))
self.decoders = nn.ModuleList(
[
nn.Sequential(
*[
Block(
embed_dim,
num_heads,
mlp_ratio,
qkv_bias=qkv_bias,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
for modality in range(self.num_modalities)
]
)
# self.norm = norm_layer(embed_dim) # we decided to drop the last layer norm
self.context_norm = norm_layer(embed_dim)
self.query_norm = norm_layer(embed_dim)
self.out_norm = norm_layer(embed_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_m_s = []
modality_tokens_concat = torch.cat(
[
self.placeholder,
] # placeholder for class token
+ [
m_t.repeat(1, self.tokens_per_modality, 1)
for m_t in self.modality_tokens
],
dim=1,
)
x = (
x + self.pos_embeddings + modality_tokens_concat
) # add pos and tiled modality tokens
x_ = x[:, 1:, :] # no class token
for m, decoder in enumerate(
self.decoders
): # iterate through modalities and decoders
x_m = x_[
:, m * self.tokens_per_modality : (m + 1) * self.tokens_per_modality, :
]
x_m = self.cross_attention(self.query_norm(x_m), self.context_norm(x_))
x_m = x_m + self.mlp(self.out_norm(x_m))
x_m = decoder(x_m)
x_m_s.append(x_m)
x_m_s = torch.cat(x_m_s, dim=1) # concat all tokens
# x_m_s = self.norm(x_m_s) # we decided to drop the last layer norm
x_m_s = torch.cat([x[:, :1, :], x_m_s], dim=1) # add back class token
return x_m_s
def forward_masked(
self, x: torch.Tensor, ind_restore: torch.Tensor
) -> torch.Tensor:
mask_tokens = self.mask_token.repeat(
x.shape[0], ind_restore.shape[1] + 1 - x.shape[1], 1
)
x_ = torch.cat([x[:, 1:, :], mask_tokens], dim=1) # remove class token
x_ = torch.gather(
x_, dim=1, index=ind_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])
) # unshuffle
x = torch.cat([x[:, :1, :], x_], dim=1) # add class token
x = self.forward(x)
return x
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