3 files changed, 54 insertions, 34 deletions

diff --git a/training/functional.py b/training/functional.py
index 695a24f..3036ed9 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -461,6 +461,10 @@ def train_loop(
     num_epochs: int = 100,
     gradient_accumulation_steps: int = 1,
     group_labels: list[str] = [],
+    avg_loss: AverageMeter = AverageMeter(),
+    avg_acc: AverageMeter = AverageMeter(),
+    avg_loss_val: AverageMeter = AverageMeter(),
+    avg_acc_val: AverageMeter = AverageMeter(),
     callbacks: TrainingCallbacks = TrainingCallbacks(),
 ):
     num_training_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
@@ -472,14 +476,8 @@ def train_loop(
     global_step = 0
     cache = {}
 
-    avg_loss = AverageMeter()
-    avg_acc = AverageMeter()
-
-    avg_loss_val = AverageMeter()
-    avg_acc_val = AverageMeter()
-
-    best_acc = 0.0
-    best_acc_val = 0.0
+    best_acc = avg_acc.avg
+    best_acc_val = avg_acc_val.avg
 
     local_progress_bar = tqdm(
         range(num_training_steps_per_epoch + num_val_steps_per_epoch),
@@ -544,12 +542,12 @@ def train_loop(
 
                     accelerator.backward(loss)
 
-                    avg_loss.update(loss.detach_(), bsz)
-                    avg_acc.update(acc.detach_(), bsz)
+                    avg_loss.update(loss.item(), bsz)
+                    avg_acc.update(acc.item(), bsz)
 
                     logs = {
-                        "train/loss": avg_loss.avg.item(),
-                        "train/acc": avg_acc.avg.item(),
+                        "train/loss": avg_loss.avg,
+                        "train/acc": avg_acc.avg,
                         "train/cur_loss": loss.item(),
                         "train/cur_acc": acc.item(),
                     }
@@ -603,47 +601,47 @@ def train_loop(
                         loss = loss.detach_()
                         acc = acc.detach_()
 
-                        cur_loss_val.update(loss, bsz)
-                        cur_acc_val.update(acc, bsz)
+                        cur_loss_val.update(loss.item(), bsz)
+                        cur_acc_val.update(acc.item(), bsz)
 
-                        avg_loss_val.update(loss, bsz)
-                        avg_acc_val.update(acc, bsz)
+                        avg_loss_val.update(loss.item(), bsz)
+                        avg_acc_val.update(acc.item(), bsz)
 
                         local_progress_bar.update(1)
                         global_progress_bar.update(1)
 
                         logs = {
-                            "val/loss": avg_loss_val.avg.item(),
-                            "val/acc": avg_acc_val.avg.item(),
+                            "val/loss": avg_loss_val.avg,
+                            "val/acc": avg_acc_val.avg,
                             "val/cur_loss": loss.item(),
                             "val/cur_acc": acc.item(),
                         }
                         local_progress_bar.set_postfix(**logs)
 
-                logs["val/cur_loss"] = cur_loss_val.avg.item()
-                logs["val/cur_acc"] = cur_acc_val.avg.item()
+                logs["val/cur_loss"] = cur_loss_val.avg
+                logs["val/cur_acc"] = cur_acc_val.avg
 
                 accelerator.log(logs, step=global_step)
 
                 if accelerator.is_main_process:
-                    if avg_acc_val.avg.item() > best_acc_val and milestone_checkpoints:
+                    if avg_acc_val.avg > best_acc_val and milestone_checkpoints:
                         local_progress_bar.clear()
                         global_progress_bar.clear()
 
                         accelerator.print(
-                            f"Global step {global_step}: Validation accuracy reached new maximum: {best_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
+                            f"Global step {global_step}: Validation accuracy reached new maximum: {best_acc_val:.2e} -> {avg_acc_val.avg:.2e}")
                         on_checkpoint(global_step, "milestone")
-                        best_acc_val = avg_acc_val.avg.item()
+                        best_acc_val = avg_acc_val.avg
             else:
                 if accelerator.is_main_process:
-                    if avg_acc.avg.item() > best_acc and milestone_checkpoints:
+                    if avg_acc.avg > best_acc and milestone_checkpoints:
                         local_progress_bar.clear()
                         global_progress_bar.clear()
 
                         accelerator.print(
-                            f"Global step {global_step}: Training accuracy reached new maximum: {best_acc:.2e} -> {avg_acc.avg.item():.2e}")
+                            f"Global step {global_step}: Training accuracy reached new maximum: {best_acc:.2e} -> {avg_acc.avg:.2e}")
                         on_checkpoint(global_step, "milestone")
-                        best_acc = avg_acc.avg.item()
+                        best_acc = avg_acc.avg
 
         # Create the pipeline using using the trained modules and save it.
         if accelerator.is_main_process:
@@ -688,6 +686,10 @@ def train(
     offset_noise_strength: float = 0.15,
     disc: Optional[ConvNeXtDiscriminator] = None,
     min_snr_gamma: int = 5,
+    avg_loss: AverageMeter = AverageMeter(),
+    avg_acc: AverageMeter = AverageMeter(),
+    avg_loss_val: AverageMeter = AverageMeter(),
+    avg_acc_val: AverageMeter = AverageMeter(),
     **kwargs,
 ):
     text_encoder, unet, optimizer, train_dataloader, val_dataloader, lr_scheduler = strategy.prepare(
@@ -737,6 +739,10 @@ def train(
         num_epochs=num_train_epochs,
         gradient_accumulation_steps=gradient_accumulation_steps,
         group_labels=group_labels,
+        avg_loss=avg_loss,
+        avg_acc=avg_acc,
+        avg_loss_val=avg_loss_val,
+        avg_acc_val=avg_acc_val,
         callbacks=callbacks,
     )
 
diff --git a/training/strategy/lora.py b/training/strategy/lora.py
index 1f0a117..3f4dbbc 100644
--- a/training/strategy/lora.py
+++ b/training/strategy/lora.py
@@ -138,6 +138,14 @@ def lora_strategy_callbacks(
             state_dict.update(text_encoder_state_dict)
             lora_config["text_encoder_peft_config"] = text_encoder_.get_peft_config_as_dict(inference=True)
 
+            if len(placeholder_tokens) != 0:
+                ti_state_dict = {
+                    f"ti_${token}": text_encoder.text_model.embeddings.get_embed(ids)
+                    for (token, ids)
+                    in zip(placeholder_tokens, placeholder_token_ids)
+                }
+                state_dict.update(ti_state_dict)
+
             save_file(state_dict, checkpoint_output_dir / f"{step}_{postfix}.safetensors")
             with open(checkpoint_output_dir / "lora_config.json", "w") as f:
                 json.dump(lora_config, f)
diff --git a/training/util.py b/training/util.py
index 8bd8a83..61f1533 100644
--- a/training/util.py
+++ b/training/util.py
@@ -16,19 +16,25 @@ def save_args(basepath: Path, args, extra={}):
 
 
 class AverageMeter:
-    avg: Any
-
-    def __init__(self, name=None):
-        self.name = name
+    def __init__(self, inv_gamma=1.0, power=2 / 3):
+        self.inv_gamma = inv_gamma
+        self.power = power
         self.reset()
 
     def reset(self):
-        self.sum = self.count = self.avg = 0
+        self.step = 0
+        self.avg = 0
+
+    def get_decay(self):
+        if self.step <= 0:
+            return 1
+
+        return (self.step / self.inv_gamma) ** -self.power
 
     def update(self, val, n=1):
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
+        for _ in range(n):
+            self.step += n
+            self.avg += (val - self.avg) * self.get_decay()
 
 
 class EMAModel(EMAModel_):