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2D model for multiple instance learning (MIL)
Performs attention over bag of features (i.e., attention-weighted mean of features)
Option to add LSTM or Transformer before attention aggregation
Uses timm backbones
"""
import re
import torch
import torch.nn as nn
from einops import rearrange
from timm import create_model
from typing import Dict, Optional, Tuple
from skp.configs.base import Config
from skp.models.modules import FeatureReduction
from skp.models.pooling import get_pool_layer
class Attention(nn.Module):
"""
Given a batch containing bags of features (B, N, D),
generate attention scores over the features in a bag, N,
and perform an attention-weighted mean of the features (B, D)
"""
def __init__(self, embed_dim: int, dropout: float = 0.0, version: str = "v1"):
super().__init__()
version = version.lower()
if version == "v1":
self.mlp = nn.Sequential(
nn.Tanh(), nn.Dropout(dropout), nn.Linear(embed_dim, 1)
)
elif version == "v2":
self.mlp = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.Tanh(),
nn.Dropout(dropout),
nn.Linear(embed_dim, 1),
)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor]:
a = self.mlp(x)
a = a.softmax(dim=1)
x = (x * a).sum(dim=1)
return x, a
class BiLSTM(nn.Module):
def __init__(self, embed_dim: int, dropout: float = 0.0, num_layers: int = 1):
super().__init__()
self.lstm = nn.LSTM(
input_size=embed_dim,
hidden_size=embed_dim // 2,
num_layers=num_layers,
bias=True,
batch_first=True,
dropout=dropout,
bidirectional=True,
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x, _ = self.lstm(x)
return x
class Transformer(nn.Module):
def __init__(
self,
embed_dim: int,
dropout: float = 0.0,
num_layers: int = 1,
nhead: int = 16,
activation: str = "gelu",
):
super().__init__()
encoder_layer = nn.TransformerEncoderLayer(
d_model=embed_dim,
nhead=nhead,
dim_feedforward=embed_dim,
dropout=dropout,
activation=activation,
batch_first=True,
norm_first=False,
bias=True,
)
self.T = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
def forward(
self, x: torch.Tensor, mask: Optional[torch.Tensor] = None
) -> torch.Tensor:
return self.T(x, mask=mask)
class Net(nn.Module):
def __init__(self, cfg: Config):
super().__init__()
self.cfg = cfg
backbone_args = {
"pretrained": self.cfg.pretrained,
"num_classes": 0,
"global_pool": "",
"features_only": self.cfg.features_only,
"in_chans": self.cfg.num_input_channels,
}
if self.cfg.backbone_img_size:
# some models require specifying image size (e.g., coatnet)
if "efficientvit" in self.cfg.backbone:
backbone_args["img_size"] = self.cfg.image_height
else:
backbone_args["img_size"] = (
self.cfg.image_height,
self.cfg.image_width,
)
self.backbone = create_model(self.cfg.backbone, **backbone_args)
# get feature dim by passing sample through net
self.feature_dim = self.backbone(
torch.randn(
(
2,
self.cfg.num_input_channels,
self.cfg.image_height,
self.cfg.image_width,
)
)
).size(
-1 if "xcit" in self.cfg.backbone else 1
) # xcit models are channels-last
self.feature_dim = self.feature_dim * (2 if self.cfg.pool == "catavgmax" else 1)
self.pooling = get_pool_layer(self.cfg, dim=2)
if isinstance(self.cfg.reduce_feature_dim, int):
self.backbone = nn.Sequential(
self.backbone,
FeatureReduction(self.feature_dim, self.cfg.reduce_feature_dim),
)
self.feature_dim = self.cfg.reduce_feature_dim
if self.cfg.add_lstm:
self.pre_attn = BiLSTM(
embed_dim=self.feature_dim,
dropout=self.cfg.lstm_dropout or 0.0,
num_layers=self.cfg.lstm_num_layers or 1,
)
elif self.cfg.add_transformer:
self.pre_attn = Transformer(
embed_dim=self.feature_dim,
dropout=self.cfg.transformer_dropout or 0.0,
num_layers=self.cfg.transformer_num_layers or 1,
nhead=self.cfg.transformer_nhead or 16,
activation=self.cfg.transformer_act or "gelu",
)
else:
self.pre_attn = nn.Identity()
self.attn = Attention(
self.feature_dim,
dropout=self.cfg.attn_dropout,
version=self.cfg.attn_version or "v1",
)
self.dropout = nn.Dropout(p=self.cfg.dropout)
self.linear = nn.Linear(self.feature_dim, self.cfg.num_classes)
if self.cfg.load_pretrained_backbone:
print(
f"Loading pretrained backbone from {self.cfg.load_pretrained_backbone} ..."
)
weights = torch.load(
self.cfg.load_pretrained_backbone,
map_location=lambda storage, loc: storage,
)["state_dict"]
# Replace model prefix as this does not exist in Net
weights = {re.sub(r"^model.", "", k): v for k, v in weights.items()}
# Get backbone only
weights = {
re.sub(r"^backbone.", "", k): v
for k, v in weights.items()
if "backbone" in k
}
self.backbone.load_state_dict(weights)
self.criterion = None
self.backbone_frozen = False
if self.cfg.freeze_backbone:
self.freeze_backbone()
def normalize(self, x: torch.Tensor) -> torch.Tensor:
if self.cfg.normalization == "-1_1":
mini, maxi = (
self.cfg.normalization_params["min"],
self.cfg.normalization_params["max"],
)
x = x - mini
x = x / (maxi - mini)
x = x - 0.5
x = x * 2.0
elif self.cfg.normalization == "0_1":
mini, maxi = (
self.cfg.normalization_params["min"],
self.cfg.normalization_params["max"],
)
x = x - mini
x = x / (maxi - mini)
elif self.cfg.normalization == "mean_sd":
mean, sd = (
self.cfg.normalization_params["mean"],
self.cfg.normalization_params["sd"],
)
x = (x - mean) / sd
elif self.cfg.normalization == "per_channel_mean_sd":
mean, sd = (
self.cfg.normalization_params["mean"],
self.cfg.normalization_params["sd"],
)
assert len(mean) == len(sd) == x.size(1)
mean, sd = torch.tensor(mean).unsqueeze(0), torch.tensor(sd).unsqueeze(0)
for i in range(x.ndim - 2):
mean, sd = mean.unsqueeze(-1), sd.unsqueeze(-1)
x = (x - mean) / sd
elif self.cfg.normalization == "none":
x = x
return x
def forward(
self,
batch: Dict,
return_loss: bool = False,
return_features: bool = False,
return_attn_scores: bool = False,
) -> Dict[str, torch.Tensor]:
x = batch["x"]
y = batch.get("y", None)
if return_loss:
assert y is not None
b, n = x.shape[:2]
x = rearrange(x, "b n c h w -> (b n) c h w")
features = self.extract_features(x, normalize=True)
features = rearrange(features, "(b n) d -> b n d", b=b, n=n)
if isinstance(self.pre_attn, Transformer):
features = self.pre_attn(features, mask=batch.get("mask", None))
else:
features = self.pre_attn(features)
features, attn_scores = self.attn(features)
if self.cfg.multisample_dropout:
logits = torch.stack(
[self.linear(self.dropout(features)) for _ in range(5)]
).mean(0)
else:
logits = self.linear(self.dropout(features))
if self.cfg.model_activation_fn == "sigmoid":
logits = logits.sigmoid()
elif self.cfg.model_activation_fn == "softmax":
logits = logits.softmax(dim=1)
out = {"logits": logits}
if return_features:
out["features"] = features
if return_attn_scores:
out["attn_scores"] = attn_scores
if return_loss:
loss = self.criterion(out, batch)
if isinstance(loss, dict):
out.update(loss)
else:
out["loss"] = loss
return out
def extract_features(self, x: torch.Tensor, normalize: bool = True) -> torch.Tensor:
x = self.normalize(x) if normalize else x
return self.pooling(self.backbone(x))
def freeze_backbone(self) -> None:
for param in self.backbone.parameters():
param.requires_grad = False
self.backbone_frozen = True
def set_criterion(self, loss: nn.Module) -> None:
self.criterion = loss
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