From b0d0b419441ef1ceadc95bbbcd26cf115f62c8bb Mon Sep 17 00:00:00 2001 From: Falko Habel Date: Sun, 20 Apr 2025 22:28:30 +0200 Subject: [PATCH 1/3] updated trainer to save checkpooints after n hours and at 22 o'clock with the mission to safe energy --- src/aiunn/finetune/trainer.py | 225 +++++++++++++++++++++++----------- 1 file changed, 152 insertions(+), 73 deletions(-) diff --git a/src/aiunn/finetune/trainer.py b/src/aiunn/finetune/trainer.py index b94d57d..33b2533 100644 --- a/src/aiunn/finetune/trainer.py +++ b/src/aiunn/finetune/trainer.py @@ -10,6 +10,7 @@ from torch.utils.checkpoint import checkpoint import gc import time import shutil +import datetime class EarlyStopping: @@ -50,10 +51,16 @@ class aiuNNTrainer: self.optimizer = None self.scaler = GradScaler() self.best_loss = float('inf') - self.use_checkpointing = True + self.csv_path = None + self.checkpoint_dir = None self.data_loader = None self.validation_loader = None - self.log_dir = None + self.last_checkpoint_time = time.time() + self.checkpoint_interval = 2 * 60 * 60 # 2 hours + self.last_22_date = None + self.recent_checkpoints = [] + self.current_epoch = 0 + def load_data(self, dataset_params=None, batch_size=1, validation_split=0.2, custom_train_dataset=None, custom_val_dataset=None): """ @@ -110,23 +117,19 @@ class aiuNNTrainer: return self.data_loader, self.validation_loader def _setup_logging(self, output_path): - """Set up directory structure for logging and model checkpoints""" - timestamp = time.strftime("%Y%m%d-%H%M%S") - self.log_dir = os.path.join(output_path, f"training_run_{timestamp}") - os.makedirs(self.log_dir, exist_ok=True) - + """Set up basic logging and checkpoint directory""" # Create checkpoint directory - self.checkpoint_dir = os.path.join(self.log_dir, "checkpoints") + self.checkpoint_dir = os.path.join(output_path, "checkpoints") os.makedirs(self.checkpoint_dir, exist_ok=True) # Set up CSV logging - self.csv_path = os.path.join(self.log_dir, 'training_log.csv') + self.csv_path = os.path.join(output_path, 'training_log.csv') with open(self.csv_path, mode='w', newline='') as file: writer = csv.writer(file) if self.validation_loader: - writer.writerow(['Epoch', 'Train Loss', 'Validation Loss', 'Improved']) + writer.writerow(['Epoch', 'Train Loss', 'Validation Loss']) else: - writer.writerow(['Epoch', 'Train Loss', 'Improved']) + writer.writerow(['Epoch', 'Train Loss']) def _evaluate(self): """Evaluate the model on validation data""" @@ -152,64 +155,100 @@ class aiuNNTrainer: self.model.train() return val_loss - def _save_checkpoint(self, epoch, is_best=False): - """Save model checkpoint""" - checkpoint_path = os.path.join(self.checkpoint_dir, f"epoch_{epoch}.pt") - best_model_path = os.path.join(self.log_dir, "best_model") + def _save_checkpoint(self, epoch, batch_count, is_best=False, is_22=False): + """Save checkpoint with support for regular, best, and 22:00 saves""" + if is_22: + today = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=2))).date() + checkpoint_name = f"checkpoint_22h_{today.strftime('%Y%m%d')}.pt" + else: + checkpoint_name = f"checkpoint_epoch{epoch}_batch{batch_count}.pt" + + checkpoint_path = os.path.join(self.checkpoint_dir, checkpoint_name) - # Save the model checkpoint - self.model.save(checkpoint_path) + checkpoint_data = { + 'epoch': epoch, + 'batch': batch_count, + 'model_state_dict': self.model.state_dict(), + 'optimizer_state_dict': self.optimizer.state_dict(), + 'best_loss': self.best_loss, + 'scaler_state_dict': self.scaler.state_dict() + } - # If this is the best model so far, copy it to best_model + torch.save(checkpoint_data, checkpoint_path) + + # Save best model separately if is_best: - if os.path.exists(best_model_path): - shutil.rmtree(best_model_path) - self.model.save(best_model_path) - print(f"Saved new best model with loss: {self.best_loss:.6f}") - - def finetune(self, output_path, epochs=10, lr=1e-4, patience=3, min_delta=0.001): - """ - Finetune the upscaler model + best_model_path = os.path.join(os.path.dirname(self.checkpoint_dir), "best_model") + self.model.save_pretrained(best_model_path) + + return checkpoint_path + + def _handle_checkpoints(self, epoch, batch_count, is_improved): + """Handle periodic and 22:00 checkpoint saving""" + current_time = time.time() + current_dt = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=2))) - Args: - output_path (str): Directory to save models and logs - epochs (int): Maximum number of training epochs - lr (float): Learning rate - patience (int): Early stopping patience - min_delta (float): Minimum improvement for early stopping - """ - # Check if data is loaded + # Regular interval checkpoint + if (current_time - self.last_checkpoint_time) >= self.checkpoint_interval: + self._save_checkpoint(epoch, batch_count, is_improved) + self.last_checkpoint_time = current_time + + # Special 22:00 checkpoint + is_22_oclock = current_dt.hour == 22 and current_dt.minute < 15 + if is_22_oclock and self.last_22_date != current_dt.date(): + self._save_checkpoint(epoch, batch_count, is_improved, is_22=True) + self.last_22_date = current_dt.date() + + def finetune(self, output_path, epochs=10, lr=1e-4, patience=3, min_delta=0.001): + """Finetune the upscaler model""" if self.data_loader is None: raise ValueError("Data not loaded. Call load_data first.") - # Setup optimizer + # Setup optimizer and directories self.optimizer = optim.Adam(self.model.parameters(), lr=lr) + self.checkpoint_dir = os.path.join(output_path, "checkpoints") + os.makedirs(self.checkpoint_dir, exist_ok=True) - # Set up logging - self._setup_logging(output_path) + # Setup CSV logging + self.csv_path = os.path.join(output_path, 'training_log.csv') + with open(self.csv_path, mode='w', newline='') as file: + writer = csv.writer(file) + header = ['Epoch', 'Train Loss', 'Validation Loss'] if self.validation_loader else ['Epoch', 'Train Loss'] + writer.writerow(header) + + # Load existing checkpoint if available + checkpoint_info = self.load_checkpoint() + start_epoch = checkpoint_info[0] if checkpoint_info else 0 + start_batch = checkpoint_info[1] if checkpoint_info else 0 # Setup early stopping early_stopping = EarlyStopping(patience=patience, min_delta=min_delta) + self.best_loss = float('inf') # Training loop self.model.train() - - for epoch in range(epochs): - # Training phase + for epoch in range(start_epoch, epochs): + self.current_epoch = epoch epoch_loss = 0.0 - progress_bar = tqdm(self.data_loader, desc=f"Epoch {epoch + 1}/{epochs}") - for low_res, high_res in progress_bar: - # Move data to GPU with channels_last format where possible + train_batches = list(enumerate(self.data_loader)) + start_idx = start_batch if epoch == start_epoch else 0 + + progress_bar = tqdm(train_batches[start_idx:], + initial=start_idx, + total=len(train_batches), + desc=f"Epoch {epoch + 1}/{epochs}") + + for batch_idx, (low_res, high_res) in progress_bar: + # Training step low_res = low_res.to(self.device, non_blocking=True).to(memory_format=torch.channels_last) high_res = high_res.to(self.device, non_blocking=True) self.optimizer.zero_grad() with autocast(device_type=self.device.type): - if self.use_checkpointing: - # Ensure the input tensor requires gradient so that checkpointing records the computation graph - low_res.requires_grad_() + if hasattr(self, 'use_checkpointing') and self.use_checkpointing: + low_res.requires_grad_() outputs = checkpoint(self.model, low_res) else: outputs = self.model(low_res) @@ -222,69 +261,109 @@ class aiuNNTrainer: epoch_loss += loss.item() progress_bar.set_postfix({'loss': loss.item()}) - # Optionally delete variables to free memory + # Handle checkpoints + self._handle_checkpoints(epoch + 1, batch_idx + 1, loss.item() < self.best_loss) + del low_res, high_res, outputs, loss - # Calculate average epoch loss + # End of epoch processing avg_train_loss = epoch_loss / len(self.data_loader) - # Validation phase (if validation loader exists) + # Validation phase if self.validation_loader: val_loss = self._evaluate() / len(self.validation_loader) is_improved = val_loss < self.best_loss if is_improved: self.best_loss = val_loss - # Log results - print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}, Val Loss: {val_loss:.6f}") + # Log to CSV with open(self.csv_path, mode='a', newline='') as file: writer = csv.writer(file) - writer.writerow([epoch + 1, avg_train_loss, val_loss, "Yes" if is_improved else "No"]) + writer.writerow([epoch + 1, avg_train_loss, val_loss]) + + print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}, Val Loss: {val_loss:.6f}") else: - # If no validation, use training loss for improvement tracking is_improved = avg_train_loss < self.best_loss if is_improved: self.best_loss = avg_train_loss - # Log results - print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}") + # Log to CSV with open(self.csv_path, mode='a', newline='') as file: writer = csv.writer(file) - writer.writerow([epoch + 1, avg_train_loss, "Yes" if is_improved else "No"]) + writer.writerow([epoch + 1, avg_train_loss]) + + print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {avg_train_loss:.6f}") - # Save checkpoint - self._save_checkpoint(epoch + 1, is_best=is_improved) - - # Perform garbage collection and clear GPU cache after each epoch - gc.collect() - torch.cuda.empty_cache() + # Save best model if improved + if is_improved: + best_model_path = os.path.join(output_path, "best_model") + self.model.save_pretrained(best_model_path) # Check early stopping - early_stopping(val_loss if self.validation_loader else avg_train_loss) - if early_stopping.early_stop: + if early_stopping(val_loss if self.validation_loader else avg_train_loss): print(f"Early stopping triggered at epoch {epoch + 1}") break + + # Cleanup + gc.collect() + torch.cuda.empty_cache() return self.best_loss - + + def load_checkpoint(self, specific_checkpoint=None): + """Enhanced checkpoint loading with specific checkpoint support""" + if specific_checkpoint: + checkpoint_path = os.path.join(self.checkpoint_dir, specific_checkpoint) + else: + checkpoint_files = [f for f in os.listdir(self.checkpoint_dir) + if f.startswith("checkpoint_") and f.endswith(".pt")] + if not checkpoint_files: + return None + + checkpoint_files.sort(key=lambda x: os.path.getmtime( + os.path.join(self.checkpoint_dir, x)), reverse=True) + checkpoint_path = os.path.join(self.checkpoint_dir, checkpoint_files[0]) + + if not os.path.exists(checkpoint_path): + return None + + checkpoint = torch.load(checkpoint_path, map_location=self.device) + self.model.load_state_dict(checkpoint['model_state_dict']) + self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + self.scaler.load_state_dict(checkpoint['scaler_state_dict']) + self.best_loss = checkpoint['best_loss'] + + print(f"Loaded checkpoint from {checkpoint_path}") + return checkpoint['epoch'], checkpoint['batch'] + def save(self, output_path=None): """ Save the best model to the specified path Args: - output_path (str, optional): Path to save the model. If None, uses the best model from training. + output_path (str, optional): Path to save the model. If None, tries to use the checkpoint directory from training. + + Returns: + str: Path where the model was saved + + Raises: + ValueError: If no output path is specified and no checkpoint directory exists """ - if output_path is None and self.log_dir is not None: - best_model_path = os.path.join(self.log_dir, "best_model") + if output_path is None and self.checkpoint_dir is not None: + # First try to copy the best model if it exists + best_model_path = os.path.join(os.path.dirname(self.checkpoint_dir), "best_model") if os.path.exists(best_model_path): - print(f"Best model already saved at {best_model_path}") - return best_model_path + output_path = os.path.join(os.path.dirname(self.checkpoint_dir), "final_model") + shutil.copytree(best_model_path, output_path, dirs_exist_ok=True) + print(f"Copied best model to {output_path}") + return output_path else: - output_path = os.path.join(self.log_dir, "final_model") + # If no best model exists, save current model state + output_path = os.path.join(os.path.dirname(self.checkpoint_dir), "final_model") if output_path is None: - raise ValueError("No output path specified and no training has been done yet.") + raise ValueError("No output path specified and no checkpoint directory exists from training.") - self.model.save(output_path) + self.model.save_pretrained(output_path) print(f"Model saved to {output_path}") return output_path \ No newline at end of file -- 2.34.1 From 38530d5d441109e0bd798a3916f8cf024ecd67e3 Mon Sep 17 00:00:00 2001 From: Falko Habel Date: Sun, 20 Apr 2025 22:29:08 +0200 Subject: [PATCH 2/3] increased version number --- setup.py | 2 +- src/aiunn/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 769f912..e934f29 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ from setuptools import setup, find_packages setup( name="aiunn", - version="0.2.0", + version="0.2.1", packages=find_packages(where="src"), package_dir={"": "src"}, install_requires=[ diff --git a/src/aiunn/__init__.py b/src/aiunn/__init__.py index 4a57351..03bd20e 100644 --- a/src/aiunn/__init__.py +++ b/src/aiunn/__init__.py @@ -3,4 +3,4 @@ from .upsampler.aiunn import aiuNN from .upsampler.config import aiuNNConfig from .inference.inference import aiuNNInference -__version__ = "0.2.0" \ No newline at end of file +__version__ = "0.2.1" \ No newline at end of file -- 2.34.1 From f3e59a65860c483000d4026edc69152b762a218e Mon Sep 17 00:00:00 2001 From: Falko Habel Date: Sun, 20 Apr 2025 22:36:43 +0200 Subject: [PATCH 3/3] updated readme to feature tf support --- README.md | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index aeb0837..802ba14 100644 --- a/README.md +++ b/README.md @@ -26,15 +26,22 @@ pip install git+https://gitea.fabelous.app/Machine-Learning/aiuNN.git Here's a basic example of how to use `aiuNN` for image upscaling: ```python src/main.py -from aiia import AIIABase +from aiia import AIIABase, AIIAConfig from aiunn import aiuNN, aiuNNTrainer import pandas as pd from torchvision import transforms +# Create a configuration and build a base model. +config = AIIAConfig() +ai_config = aiuNNConfig() + +base_model = AIIABase(config) +upscaler = aiuNN(config=ai_config) + # Load your base model and upscaler pretrained_model_path = "path/to/aiia/model" -base_model = AIIABase.load(pretrained_model_path, precision="bf16") -upscaler = aiuNN(base_model) +base_model = AIIABase.from_pretrained(pretrained_model_path) +upscaler.load_base_model(base_model) # Create trainer with your dataset class trainer = aiuNNTrainer(upscaler, dataset_class=UpscaleDataset) @@ -105,19 +112,19 @@ class UpscaleDataset(Dataset): # Open image bytes with Pillow and convert to RGBA first low_res_rgba = Image.open(io.BytesIO(low_res_bytes)).convert('RGBA') high_res_rgba = Image.open(io.BytesIO(high_res_bytes)).convert('RGBA') - + # Create a new RGB image with black background low_res_rgb = Image.new("RGB", low_res_rgba.size, (0, 0, 0)) high_res_rgb = Image.new("RGB", high_res_rgba.size, (0, 0, 0)) - + # Composite the original image over the black background low_res_rgb.paste(low_res_rgba, mask=low_res_rgba.split()[3]) high_res_rgb.paste(high_res_rgba, mask=high_res_rgba.split()[3]) - + # Now we have true 3-channel RGB images with transparent areas converted to black low_res = low_res_rgb high_res = high_res_rgb - + # If a transform is provided (e.g. conversion to Tensor), apply it if self.transform: low_res = self.transform(low_res) @@ -127,4 +134,4 @@ class UpscaleDataset(Dataset): print(f"\nError at index {idx}: {str(e)}") self.failed_indices.add(idx) return self[(idx + 1) % len(self)] -``` +``` \ No newline at end of file -- 2.34.1