develop #41
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@ -1,9 +1,11 @@
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import torch
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from torch import nn
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import csv
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import datetime
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import time
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import pandas as pd
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from tqdm import tqdm
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from transformers import PreTrainedModel
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from ..model.Model import AIIA
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from ..model.config import AIIAConfig
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from ..data.DataLoader import AIIADataLoader
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import os
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@ -21,12 +23,12 @@ class ProjectionHead(nn.Module):
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return self.conv_rotate(x).mean(dim=(2, 3)) # Global average pooling for rotation task
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class Pretrainer:
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def __init__(self, model: PreTrainedModel, learning_rate=1e-4, config: AIIAConfig=None):
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def __init__(self, model: AIIA, learning_rate=1e-4, config: AIIAConfig=None):
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"""
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Initialize the pretrainer with a model.
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Args:
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model (PreTrainedModel): The model instance to pretrain
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model (AIIA): The model instance to pretrain
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learning_rate (float): Learning rate for optimization
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config (dict): Model configuration containing hidden_size
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"""
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@ -112,20 +114,169 @@ class Pretrainer:
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return batch_loss
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def train(self, dataset_paths,output_path:str="AIIA", column="image_bytes", num_epochs=3, batch_size=2, sample_size=10000):
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"""
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Train the model using multiple specified datasets.
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def _save_checkpoint(self, checkpoint_dir, epoch, batch_count, checkpoint_name):
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"""Save a model checkpoint.
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Args:
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dataset_paths (list): List of paths to parquet datasets
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num_epochs (int): Number of training epochs
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batch_size (int): Batch size for training
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sample_size (int): Number of samples to use from each dataset
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checkpoint_dir (str): Directory to save the checkpoint
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epoch (int): Current epoch number
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batch_count (int): Current batch count
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checkpoint_name (str): Name for the checkpoint file
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Returns:
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str: Path to the saved checkpoint
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"""
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checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name)
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checkpoint_data = {
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'epoch': epoch + 1,
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'batch': batch_count,
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'model_state_dict': self.model.state_dict(),
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'projection_head_state_dict': self.projection_head.state_dict(),
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'optimizer_state_dict': self.optimizer.state_dict(),
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'train_losses': self.train_losses,
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'val_losses': self.val_losses,
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}
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torch.save(checkpoint_data, checkpoint_path)
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return checkpoint_path
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def load_checkpoint(self, checkpoint_dir, specific_checkpoint=None):
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"""
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Check for checkpoints and load if available.
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Args:
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checkpoint_dir (str): Directory where checkpoints are stored
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specific_checkpoint (str, optional): Specific checkpoint file to load. If None, loads the most recent.
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Returns:
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tuple: (loaded_epoch, loaded_batch) if checkpoint was loaded, None otherwise
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"""
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# Create checkpoint directory if it doesn't exist
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os.makedirs(checkpoint_dir, exist_ok=True)
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# If a specific checkpoint is requested
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if specific_checkpoint:
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checkpoint_path = os.path.join(checkpoint_dir, specific_checkpoint)
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if os.path.exists(checkpoint_path):
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return self._load_checkpoint_file(checkpoint_path)
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else:
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print(f"Specified checkpoint {specific_checkpoint} not found.")
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return None
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# Find all checkpoint files
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checkpoint_files = [f for f in os.listdir(checkpoint_dir) if f.startswith("checkpoint_") and f.endswith(".pt")]
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if not checkpoint_files:
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print("No checkpoints found in directory.")
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return None
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# Find the most recent checkpoint
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checkpoint_files.sort(key=lambda x: os.path.getmtime(os.path.join(checkpoint_dir, x)), reverse=True)
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most_recent = checkpoint_files[0]
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checkpoint_path = os.path.join(checkpoint_dir, most_recent)
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return self._load_checkpoint_file(checkpoint_path)
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def _load_checkpoint_file(self, checkpoint_path):
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"""
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Load a specific checkpoint file.
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Args:
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checkpoint_path (str): Path to the checkpoint file
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Returns:
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tuple: (loaded_epoch, loaded_batch) if checkpoint was loaded, None otherwise
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"""
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try:
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checkpoint = torch.load(checkpoint_path, map_location=self.device)
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# Load model state
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self.model.load_state_dict(checkpoint['model_state_dict'])
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# Load projection head state
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self.projection_head.load_state_dict(checkpoint['projection_head_state_dict'])
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# Load optimizer state
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self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
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# Load loss history
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self.train_losses = checkpoint.get('train_losses', [])
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self.val_losses = checkpoint.get('val_losses', [])
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loaded_epoch = checkpoint['epoch']
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loaded_batch = checkpoint['batch']
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print(f"Checkpoint loaded from {checkpoint_path}")
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print(f"Resuming from epoch {loaded_epoch}, batch {loaded_batch}")
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return loaded_epoch, loaded_batch
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except Exception as e:
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print(f"Error loading checkpoint: {e}")
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return None
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def train(self, dataset_paths, output_path="AIIA", column="image_bytes",
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num_epochs=3, batch_size=2, sample_size=10000, checkpoint_dir=None):
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"""Train the model using multiple specified datasets with checkpoint resumption support."""
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if not dataset_paths:
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raise ValueError("No dataset paths provided")
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# Read and merge all datasets
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self._initialize_checkpoint_variables()
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start_epoch, start_batch, resume_training = self._load_checkpoints(checkpoint_dir)
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dataframes = self._load_and_merge_datasets(dataset_paths, sample_size)
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aiia_loader = self._initialize_data_loader(dataframes, column, batch_size)
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criterion_denoise, criterion_rotate, best_val_loss = self._initialize_loss_functions()
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for epoch in range(start_epoch, num_epochs):
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print(f"\nEpoch {epoch+1}/{num_epochs}")
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print("-" * 20)
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total_train_loss, batch_count = self._training_phase(aiia_loader.train_loader,
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start_batch if (epoch == start_epoch and resume_training) else 0,
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criterion_denoise,
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criterion_rotate)
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avg_train_loss = total_train_loss / max(batch_count, 1)
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self.train_losses.append(avg_train_loss)
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print(f"Training Loss: {avg_train_loss:.4f}")
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val_loss = self._validation_phase(aiia_loader.val_loader, criterion_denoise, criterion_rotate)
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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self.model.save(output_path)
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print("Best model saved!")
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losses_path = os.path.join(os.path.dirname(output_path), 'losses.csv')
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self.save_losses(losses_path)
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def _initialize_checkpoint_variables(self):
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"""Initialize checkpoint tracking variables."""
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self.last_checkpoint_time = time.time()
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self.checkpoint_interval = 2 * 60 * 60 # 2 hours in seconds
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self.last_22_date = None
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self.recent_checkpoints = []
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def _load_checkpoints(self, checkpoint_dir):
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"""Load checkpoints and return start epoch, batch, and resumption flag."""
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start_epoch = 0
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start_batch = 0
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resume_training = False
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if checkpoint_dir is not None:
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os.makedirs(checkpoint_dir, exist_ok=True)
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checkpoint_info = self.load_checkpoint(checkpoint_dir)
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if checkpoint_info:
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start_epoch, start_batch = checkpoint_info
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resume_training = True
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# Adjust epoch to be 0-indexed for the loop
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start_epoch -= 1
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return start_epoch, start_batch, resume_training
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def _load_and_merge_datasets(self, dataset_paths, sample_size):
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"""Load and merge datasets."""
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dataframes = []
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for path in dataset_paths:
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try:
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@ -137,10 +288,11 @@ class Pretrainer:
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if not dataframes:
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raise ValueError("No valid datasets could be loaded")
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merged_df = pd.concat(dataframes, ignore_index=True)
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return pd.concat(dataframes, ignore_index=True)
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# Initialize data loader
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aiia_loader = AIIADataLoader(
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def _initialize_data_loader(self, merged_df, column, batch_size):
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"""Initialize the data loader."""
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return AIIADataLoader(
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merged_df,
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column=column,
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batch_size=batch_size,
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@ -148,24 +300,30 @@ class Pretrainer:
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collate_fn=self.safe_collate
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)
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def _initialize_loss_functions(self):
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"""Initialize loss functions and tracking variables."""
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criterion_denoise = nn.MSELoss()
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criterion_rotate = nn.CrossEntropyLoss()
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best_val_loss = float('inf')
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return criterion_denoise, criterion_rotate, best_val_loss
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for epoch in range(num_epochs):
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print(f"\nEpoch {epoch+1}/{num_epochs}")
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print("-" * 20)
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# Training phase
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def _training_phase(self, train_loader, skip_batches, criterion_denoise, criterion_rotate):
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"""Handle the training phase."""
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self.model.train()
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self.projection_head.train()
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total_train_loss = 0.0
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batch_count = 0
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for batch_data in tqdm(aiia_loader.train_loader):
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train_batches = list(enumerate(train_loader))
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for i, batch_data in tqdm(train_batches[skip_batches:],
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initial=skip_batches,
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total=len(train_batches)):
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if batch_data is None:
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continue
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current_batch = i + 1
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self._handle_checkpoints(current_batch)
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self.optimizer.zero_grad()
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batch_loss = self._process_batch(batch_data, criterion_denoise, criterion_rotate)
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@ -175,22 +333,42 @@ class Pretrainer:
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total_train_loss += batch_loss.item()
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batch_count += 1
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avg_train_loss = total_train_loss / max(batch_count, 1)
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self.train_losses.append(avg_train_loss)
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print(f"Training Loss: {avg_train_loss:.4f}")
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return total_train_loss, batch_count
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# Validation phase
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def _handle_checkpoints(self, current_batch):
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"""Handle checkpoint saving logic."""
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current_time = time.time()
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current_dt = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=2))) # German time
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today = current_dt.date()
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if self.checkpoint_dir and (current_time - self.last_checkpoint_time) >= self.checkpoint_interval:
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checkpoint_name = f"checkpoint_epoch{self.current_epoch+1}_batch{current_batch}.pt"
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checkpoint_path = self._save_checkpoint(self.checkpoint_dir, self.current_epoch, current_batch, checkpoint_name)
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# Track and maintain only 3 recent checkpoints
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self.recent_checkpoints.append(checkpoint_path)
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if len(self.recent_checkpoints) > 3:
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oldest = self.recent_checkpoints.pop(0)
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if os.path.exists(oldest):
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os.remove(oldest)
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self.last_checkpoint_time = current_time
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print(f"Checkpoint saved at {checkpoint_path}")
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# Special 22:00 checkpoint (considering it's currently 10:15 PM)
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is_22_oclock = current_dt.hour == 22 and current_dt.minute < 15
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if self.checkpoint_dir and is_22_oclock and self.last_22_date != today:
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checkpoint_name = f"checkpoint_22h_{today.strftime('%Y%m%d')}.pt"
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checkpoint_path = self._save_checkpoint(self.checkpoint_dir, self.current_epoch, current_batch, checkpoint_name)
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self.last_22_date = today
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print(f"22:00 Checkpoint saved at {checkpoint_path}")
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def _validation_phase(self, val_loader, criterion_denoise, criterion_rotate):
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"""Handle the validation phase."""
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self.model.eval()
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self.projection_head.eval()
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val_loss = self._validate(aiia_loader.val_loader, criterion_denoise, criterion_rotate)
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if val_loss < best_val_loss:
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best_val_loss = val_loss
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self.model.save_pretrained(output_path)
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print("Best model save_pretrainedd!")
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losses_path = os.path.join(os.path.dirname(output_path), 'losses.csv')
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self.save_pretrained_losses(losses_path)
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return self._validate(val_loader, criterion_denoise, criterion_rotate)
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def _validate(self, val_loader, criterion_denoise, criterion_rotate):
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"""Perform validation and return average validation loss."""
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@ -216,8 +394,8 @@ class Pretrainer:
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return avg_val_loss
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def save_pretrained_losses(self, csv_file):
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"""save_pretrained training and validation losses to a CSV file."""
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def save_losses(self, csv_file):
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"""Save training and validation losses to a CSV file."""
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data = list(zip(
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range(1, len(self.train_losses) + 1),
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self.train_losses,
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