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import torch
import torch.nn.functional as F
from torch import nn, optim
import lightning.pytorch as pl
import torchvision.models.video as tvmv
import sklearn.metrics as skm
"""Head model for SYNTAX prediction."""
class SyntaxLightningModule(pl.LightningModule):
def __init__(
self,
num_classes,
lr: float,
variant: str,
weight_decay: float = 0,
max_epochs: int = None,
weight_path: str = None,
save_path: str = None,
pl_weight_path: str = None,
pt_weights_format: bool = False,
sigma_a: float = 0,
sigma_b: float = 1,
**kwargs,
):
self.save_hyperparameters()
super().__init__()
self.num_classes = num_classes
self.save_path = save_path
self.weight_path = weight_path
self.variant = variant
self.sigma_a = sigma_a
self.sigma_b = sigma_b
self.model = tvmv.r3d_18(weights=tvmv.R3D_18_Weights.DEFAULT)
self.lr = lr
self.loss_clf = nn.BCEWithLogitsLoss(reduction="none")
self.loss_reg = nn.MSELoss(reduction="none")
in_features = self.model.fc.in_features
self.model.fc = nn.Linear(in_features=in_features, out_features=2, bias=True)
if weight_path is not None:
print("Load model weights (backbone)")
self.load_weights_backbone(weight_path, self.model)
if self.variant != "mean_out":
self.model.fc = nn.Identity()
if self.variant == "mean_out":
pass
elif self.variant in ("gru_mean", "gru_last"):
self.rnn = nn.GRU(in_features, in_features // 4, batch_first=True)
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(in_features=in_features // 4, out_features=num_classes, bias=True)
elif self.variant in ("lstm_mean", "lstm_last"):
self.lstm = nn.LSTM(
input_size=in_features,
hidden_size=in_features // 4,
proj_size=num_classes,
batch_first=True,
)
elif self.variant == "mean":
self.fc = nn.Linear(in_features=in_features, out_features=num_classes, bias=True)
elif self.variant in ("bert_mean", "bert_cls", "bert_cls2"):
encoder_layer = nn.TransformerEncoderLayer(
d_model=in_features,
nhead=4,
batch_first=True,
dim_feedforward=in_features // 4,
)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=1)
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(in_features=in_features, out_features=num_classes, bias=True)
if self.variant == "bert_cls2":
self.cls = nn.Parameter(torch.randn(1, 1, in_features))
else:
raise ValueError(f"Unknown model variant {self.variant}")
if pl_weight_path is not None:
print(f"Load LightningModule weights from {pl_weight_path}")
if pt_weights_format:
pl_state_dict = torch.load(pl_weight_path, weights_only=False)
else:
pl_state_dict = torch.load(pl_weight_path, weights_only=False)["state_dict"]
self.load_weights(pl_state_dict, self.model, "model")
if self.variant == "mean_out":
pass
elif self.variant in ("gru_mean", "gru_last"):
self.load_weights(pl_state_dict, self.rnn, "rnn")
self.load_weights(pl_state_dict, self.fc, "fc")
elif self.variant in ("lstm_mean", "lstm_last"):
self.load_weights(pl_state_dict, self.lstm, "lstm")
elif self.variant == "mean":
self.load_weights(pl_state_dict, self.fc, "fc")
elif self.variant in ("bert_mean", "bert_cls", "bert_cls2"):
self.load_weights(pl_state_dict, self.encoder, "encoder")
self.load_weights(pl_state_dict, self.fc, "fc")
if self.variant == "bert_cls2":
old_shape = self.cls.shape
self.cls = nn.Parameter(pl_state_dict["cls"])
assert old_shape == self.cls.shape
else:
raise ValueError(f"Unknown model variant {self.variant}")
self.max_epochs = max_epochs
self.weight_decay = weight_decay
self.y_val = []
self.p_val = []
self.r_val = []
self.ty_val = []
self.tp_val = []
def load_weights_backbone(self, weight_path: str, model: nn.Module) -> None:
"""
Universal loader for backbone weights (r3d_18).
- If the file is a Lightning checkpoint (a dict with 'state_dict'),
extract state_dict['state_dict'] and strip the 'model.' prefix.
- If the file is a raw state_dict (.pt/.pth) saved via model.state_dict(),
load it directly.
Before loading, drop any keys whose tensor shapes do not match the module.
"""
obj = torch.load(weight_path, weights_only=False, map_location="cpu")
if isinstance(obj, dict) and "state_dict" in obj:
raw_state = obj["state_dict"]
state_dict = {k.replace("model.", ""): v for k, v in raw_state.items()}
src_type = "lightning_checkpoint"
else:
state_dict = obj
src_type = "raw_state_dict"
current_state = model.state_dict()
filtered_state = {}
mismatched_keys = []
for k, v in state_dict.items():
if k in current_state and current_state[k].shape == v.shape:
filtered_state[k] = v
else:
mismatched_keys.append(k)
incompatible = model.load_state_dict(filtered_state, strict=False)
loaded_keys = [k for k in filtered_state.keys() if k not in incompatible.missing_keys]
print(
f"[Backbone] Loaded weights from '{weight_path}' "
f"(type={src_type}): {len(loaded_keys)} params, "
f"missing={len(incompatible.missing_keys)}, "
f"unexpected={len(incompatible.unexpected_keys)}, "
f"skipped_mismatched={len(mismatched_keys)}"
)
if mismatched_keys:
print(f"[Backbone] Size‑mismatched keys (skipped), example: {mismatched_keys[:5]}")
if incompatible.missing_keys:
print(f"[Backbone] Missing keys after filtering, example: {incompatible.missing_keys[:5]}")
if incompatible.unexpected_keys:
print(f"[Backbone] Unexpected keys after filtering, example: {incompatible.unexpected_keys[:5]}")
def load_weights(self, state_dict, module, prefix: str):
"""Filter and load only the weights that belong to the given module."""
module_state = {
k.replace(f"{prefix}.", ""): v
for k, v in state_dict.items()
if k.startswith(prefix)
}
missing, unexpected = module.load_state_dict(module_state, strict=False)
if missing:
print(f"Missing keys for {prefix}: {missing}")
if unexpected:
print(f"Unexpected keys for {prefix}: {unexpected}")
def forward(self, x):
batch_seq_shape = x.shape[0:2]
x = torch.flatten(x, start_dim=0, end_dim=1)
x = self.model(x)
x = torch.unflatten(x, 0, batch_seq_shape)
if self.variant == "mean_out":
x = torch.mean(x, dim=1)
elif self.variant in ("gru_mean", "gru_last"):
_all_outs_, [_last_out_] = self.rnn(x)
if self.variant == "gru_mean":
x = torch.mean(_all_outs_, dim=1)
else:
x = _last_out_
x = self.dropout(x)
x = self.fc(x)
elif self.variant in ("lstm_mean", "lstm_last"):
_all_outs_, (_last_out_, _last_state_) = self.lstm(x)
if self.variant == "lstm_mean":
x = torch.mean(_all_outs_, dim=1)
else:
x = _last_out_
elif self.variant == "mean":
x = torch.mean(x, dim=1)
x = self.fc(x)
elif self.variant in ("bert_mean", "bert_cls", "bert_cls2"):
if self.variant == "bert_cls":
x = F.pad(x, (0, 0, 1, 0), "constant", 0)
elif self.variant == "bert_cls2":
bs = x.size(0)
x = torch.cat([self.cls.expand(bs, -1, -1), x], dim=1)
x = self.encoder(x)
if self.variant == "bert_mean":
x = torch.mean(x, dim=1)
else:
x = x[:, 0, :]
x = self.dropout(x)
x = self.fc(x)
else:
raise ValueError(f"Unknown model variant {self.variant}")
return x
def training_step(self, batch, batch_idx):
x, y, target, path = batch
y_hat = self(x)
yp_clf = y_hat[:, 0:1]
yp_reg = y_hat[:, 1:]
weights_clf = torch.where(y > 0, 1.0, 0.2)
clf_loss = self.loss_clf(yp_clf, y)
clf_loss = (clf_loss * weights_clf).mean()
reg_loss_raw = self.loss_reg(yp_reg, target)
sigma = self.sigma_a * target + self.sigma_b
reg_loss = (reg_loss_raw / (sigma ** 2)).mean()
loss = clf_loss + 0.5 * reg_loss
y_pred = torch.sigmoid(yp_clf)
y_bin = torch.round(y.cpu().detach()).int()
y_pred_bin = torch.round(y_pred.cpu().detach()).int()
self.log("train_clf_loss", clf_loss, prog_bar=True, sync_dist=True)
self.log("train_val_loss", reg_loss, prog_bar=True, sync_dist=True)
self.log("train_full_loss", loss, prog_bar=True, sync_dist=True)
self.log("train_f1", skm.f1_score(y_bin, y_pred_bin, zero_division=0),
prog_bar=True, sync_dist=True)
self.log("train_acc", skm.accuracy_score(y_bin, y_pred_bin),
prog_bar=True, sync_dist=True)
return loss
def validation_step(self, batch, batch_idx):
x, y, target, path = batch
y_hat = self(x)
yp_clf = y_hat[:, 0:1]
yp_reg = y_hat[:, 1:]
loss = self.loss_clf(yp_clf, y)
reg_loss_raw = self.loss_reg(yp_reg, target)
loss = loss.mean()
y_pred = torch.sigmoid(yp_clf)
self.y_val.append(int(y[..., 0].cpu()))
self.p_val.append(float(y_pred[..., 0].cpu()))
self.r_val.append(round(float(y_pred[..., 0].cpu())))
self.ty_val.append(float(target[..., 0].cpu()))
self.tp_val.append(float(yp_reg[..., 0].cpu()))
clf_loss = self.loss_clf(yp_clf, y)
reg_loss_raw = self.loss_reg(yp_reg, target)
sigma = self.sigma_a * target + self.sigma_b
reg_loss = (reg_loss_raw / (sigma ** 2)).mean()
loss = clf_loss + 0.5 * reg_loss
return loss
def on_validation_epoch_end(self):
try:
auc = skm.roc_auc_score(self.y_val, self.p_val)
f1 = skm.f1_score(self.y_val, self.r_val, zero_division=0)
acc = skm.accuracy_score(self.y_val, self.r_val)
mae = skm.mean_absolute_error(self.y_val, self.r_val)
self.log("val_auc", auc, prog_bar=True, sync_dist=True)
self.log("val_f1", f1, prog_bar=True, sync_dist=True)
self.log("val_acc", acc, prog_bar=True, sync_dist=True)
self.log("val_mae", mae, prog_bar=True, sync_dist=True)
rmse = skm.root_mean_squared_error(self.ty_val, self.tp_val)
self.log("val_rmse", rmse, prog_bar=True, sync_dist=True)
except ValueError as err:
print(err)
print("Y_VAL", self.y_val)
print("P_VAL", self.p_val)
self.y_val.clear()
self.p_val.clear()
self.r_val.clear()
self.ty_val.clear()
self.tp_val.clear()
def on_train_epoch_end(self) -> None:
self.log(
"lr",
self.optimizers().optimizer.param_groups[0]["lr"],
on_step=False,
on_epoch=True,
sync_dist=True,
)
def configure_optimizers(self):
if self.weight_path:
if self.variant == "mean_out":
trainable_modules = [self.model.fc]
elif self.variant in ("gru_mean", "gru_last"):
trainable_modules = [self.rnn, self.fc]
elif self.variant in ("lstm_mean", "lstm_last"):
trainable_modules = [self.lstm]
elif self.variant == "mean":
trainable_modules = [self.fc]
elif self.variant in ("bert_mean", "bert_cls", "bert_cls2"):
trainable_modules = [self.encoder, self.fc]
if self.variant == "bert_cls2":
trainable_modules.append(self.cls)
else:
trainable_modules = []
for param in self.parameters():
param.requires_grad = False
for m in trainable_modules:
for p in m.parameters():
p.requires_grad = True
params = [p for m in trainable_modules for p in m.parameters()]
else:
for param in self.parameters():
param.requires_grad = True
params = self.parameters()
optimizer = optim.Adam(params, lr=self.lr, weight_decay=self.weight_decay)
if self.max_epochs is not None:
lr_scheduler = optim.lr_scheduler.OneCycleLR(
optimizer=optimizer, max_lr=self.lr, total_steps=self.max_epochs
)
return [optimizer], [lr_scheduler]
else:
return optimizer
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
"""Inference step."""
x, y, target, path = batch
y_hat = self(x)
yp_clf = y_hat[:, 0:1]
yp_reg = y_hat[:, 1:]
y_pred = torch.sigmoid(yp_clf)
return {
"y": y,
"y_pred": torch.round(y_pred),
"y_prob": y_pred,
"y_reg": yp_reg,
"target": target,
}
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