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from typing import Dict, Tuple, Union
import torch
import torch.nn as nn
from transformers import PretrainedConfig, PreTrainedModel
from loss import FourierLoss
from normalizer import Normalizer
from mae_modules import CAMAEDecoder, MAEDecoder, MAEEncoder
from mae_utils import flatten_images
from vit import (
generate_2d_sincos_pos_embeddings,
sincos_positional_encoding_vit,
vit_small_patch16_256,
)
TensorDict = Dict[str, torch.Tensor]
class MAEConfig(PretrainedConfig):
model_type = "MAE"
def __init__(
self,
mask_ratio=0.75,
encoder=None,
decoder=None,
loss=None,
optimizer=None,
input_norm=None,
fourier_loss=None,
fourier_loss_weight=0.0,
lr_scheduler=None,
use_MAE_weight_init=False,
crop_size=-1,
mask_fourier_loss=True,
return_channelwise_embeddings=False,
**kwargs,
):
super().__init__(**kwargs)
self.mask_ratio = mask_ratio
self.encoder = encoder
self.decoder = decoder
self.loss = loss
self.optimizer = optimizer
self.input_norm = input_norm
self.fourier_loss = fourier_loss
self.fourier_loss_weight = fourier_loss_weight
self.lr_scheduler = lr_scheduler
self.use_MAE_weight_init = use_MAE_weight_init
self.crop_size = crop_size
self.mask_fourier_loss = mask_fourier_loss
self.return_channelwise_embeddings = return_channelwise_embeddings
class MAEModel(PreTrainedModel):
config_class = MAEConfig
# Loss metrics
TOTAL_LOSS = "loss"
RECON_LOSS = "reconstruction_loss"
FOURIER_LOSS = "fourier_loss"
def __init__(self, config: MAEConfig):
super().__init__(config)
self.mask_ratio = config.mask_ratio
# Could use Hydra to instantiate instead
self.encoder = MAEEncoder(
vit_backbone=sincos_positional_encoding_vit(
vit_backbone=vit_small_patch16_256(global_pool="avg")
),
max_in_chans=11, # upper limit on number of input channels
channel_agnostic=True,
)
self.decoder = CAMAEDecoder(
depth=8,
embed_dim=512,
mlp_ratio=4,
norm_layer=nn.LayerNorm,
num_heads=16,
num_modalities=6,
qkv_bias=True,
tokens_per_modality=256,
)
self.input_norm = torch.nn.Sequential(
Normalizer(),
nn.InstanceNorm2d(None, affine=False, track_running_stats=False),
)
self.fourier_loss_weight = config.fourier_loss_weight
self.mask_fourier_loss = config.mask_fourier_loss
self.return_channelwise_embeddings = config.return_channelwise_embeddings
self.tokens_per_channel = 256 # hardcode the number of tokens per channel since we are patch16 crop 256
# loss stuff
self.loss = torch.nn.MSELoss(reduction="none")
self.fourier_loss = FourierLoss(num_multimodal_modalities=6)
if self.fourier_loss_weight > 0 and self.fourier_loss is None:
raise ValueError(
"FourierLoss weight is activated but no fourier_loss was defined in constructor"
)
elif self.fourier_loss_weight >= 1:
raise ValueError(
"FourierLoss weight is too large to do mixing factor, weight should be < 1"
)
self.patch_size = int(self.encoder.vit_backbone.patch_embed.patch_size[0])
# projection layer between the encoder and decoder
self.encoder_decoder_proj = nn.Linear(
self.encoder.embed_dim, self.decoder.embed_dim, bias=True
)
self.decoder_pred = nn.Linear(
self.decoder.embed_dim,
self.patch_size**2
* (1 if self.encoder.channel_agnostic else self.in_chans),
bias=True,
) # linear layer from decoder embedding to input dims
# overwrite decoder pos embeddings based on encoder params
self.decoder.pos_embeddings = generate_2d_sincos_pos_embeddings( # type: ignore[assignment]
self.decoder.embed_dim,
length=self.encoder.vit_backbone.patch_embed.grid_size[0],
use_class_token=self.encoder.vit_backbone.cls_token is not None,
num_modality=(
self.decoder.num_modalities if self.encoder.channel_agnostic else 1
),
)
if config.use_MAE_weight_init:
w = self.encoder.vit_backbone.patch_embed.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
torch.nn.init.normal_(self.encoder.vit_backbone.cls_token, std=0.02)
torch.nn.init.normal_(self.decoder.mask_token, std=0.02)
self.apply(self._MAE_init_weights)
def setup(self, stage: str) -> None:
super().setup(stage)
def _MAE_init_weights(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
@staticmethod
def decode_to_reconstruction(
encoder_latent: torch.Tensor,
ind_restore: torch.Tensor,
proj: torch.nn.Module,
decoder: MAEDecoder | CAMAEDecoder,
pred: torch.nn.Module,
) -> torch.Tensor:
"""Feed forward the encoder latent through the decoders necessary projections and transformations."""
decoder_latent_projection = proj(
encoder_latent
) # projection from encoder.embed_dim to decoder.embed_dim
decoder_tokens = decoder.forward_masked(
decoder_latent_projection, ind_restore
) # decoder.embed_dim output
predicted_reconstruction = pred(
decoder_tokens
) # linear projection to input dim
return predicted_reconstruction[:, 1:, :] # drop class token
def forward(
self, imgs: torch.Tensor, constant_noise: Union[torch.Tensor, None] = None
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
imgs = self.input_norm(imgs)
latent, mask, ind_restore = self.encoder.forward_masked(
imgs, self.mask_ratio, constant_noise
) # encoder blocks
reconstruction = self.decode_to_reconstruction(
latent,
ind_restore,
self.encoder_decoder_proj,
self.decoder,
self.decoder_pred,
)
return latent, reconstruction, mask
def compute_MAE_loss(
self,
reconstruction: torch.Tensor,
img: torch.Tensor,
mask: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, float]]:
"""Computes final loss and returns specific values of component losses for metric reporting."""
loss_dict = {}
img = self.input_norm(img)
target_flattened = flatten_images(
img,
patch_size=self.patch_size,
channel_agnostic=self.encoder.channel_agnostic,
)
loss: torch.Tensor = self.loss(
reconstruction, target_flattened
) # should be with MSE or MAE (L1) with reduction='none'
loss = loss.mean(
dim=-1
) # average over embedding dim -> mean loss per patch (N,L)
loss = (loss * mask).sum() / mask.sum() # mean loss on masked patches only
loss_dict[self.RECON_LOSS] = loss.item()
# compute fourier loss
if self.fourier_loss_weight > 0:
floss: torch.Tensor = self.fourier_loss(reconstruction, target_flattened)
if not self.mask_fourier_loss:
floss = floss.mean()
else:
floss = floss.mean(dim=-1)
floss = (floss * mask).sum() / mask.sum()
loss_dict[self.FOURIER_LOSS] = floss.item()
# here we use a mixing factor to keep the loss magnitude appropriate with fourier
if self.fourier_loss_weight > 0:
loss = (1 - self.fourier_loss_weight) * loss + (
self.fourier_loss_weight * floss
)
return loss, loss_dict
def training_step(self, batch: TensorDict, batch_idx: int) -> TensorDict:
img = batch["pixels"]
latent, reconstruction, mask = self(img.clone())
full_loss, loss_dict = self.compute_MAE_loss(reconstruction, img.float(), mask)
return {
"loss": full_loss,
**loss_dict, # type: ignore[dict-item]
}
def validation_step(self, batch: TensorDict, batch_idx: int) -> TensorDict:
return self.training_step(batch, batch_idx)
def update_metrics(self, outputs: TensorDict, batch: TensorDict) -> None:
self.metrics["lr"].update(value=self.lr_scheduler.get_last_lr())
for key, value in outputs.items():
if key.endswith("loss"):
self.metrics[key].update(value)
def on_validation_batch_end( # type: ignore[override]
self,
outputs: TensorDict,
batch: TensorDict,
batch_idx: int,
dataloader_idx: int = 0,
) -> None:
super().on_validation_batch_end(outputs, batch, batch_idx, dataloader_idx)
def predict(self, imgs: torch.Tensor) -> torch.Tensor:
imgs = self.input_norm(imgs)
X = self.encoder.vit_backbone.forward_features(
imgs
) # 3d tensor N x num_tokens x dim
if self.return_channelwise_embeddings:
N, _, d = X.shape
num_channels = imgs.shape[1]
X_reshaped = X[:, 1:, :].view(N, num_channels, self.tokens_per_channel, d)
pooled_segments = X_reshaped.mean(
dim=2
) # Resulting shape: (N, num_channels, d)
latent = pooled_segments.view(N, num_channels * d).contiguous()
else:
latent = X[:, 1:, :].mean(dim=1) # 1 + 256 * C tokens
return latent
def save_pretrained(self, save_directory: str, **kwargs):
filename = kwargs.pop("filename", "model.safetensors")
modelpath = f"{save_directory}/{filename}"
self.config.save_pretrained(save_directory)
torch.save({"state_dict": self.state_dict()}, modelpath)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
filename = kwargs.pop("filename", "model.safetensors")
modelpath = f"{pretrained_model_name_or_path}/{filename}"
config = MAEConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
state_dict = torch.load(modelpath, map_location="cpu")
model = cls(config)
model.load_state_dict(state_dict["state_dict"])
return model
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