curriculum_implementation.py

# curriculum_implementation.py

import json
import torch
import torch.optim as optim
import torch.nn as nn
from torch.utils.data import DataLoader

import importlib

def setup_training_framework(config_path):
    """
    Initialize the training framework using a provided configuration file.

    Parameters:
    - config_path: str, path to the configuration file for the training framework.

    Returns:
    - framework: dict, initialized framework with necessary settings.
    """
    try:
        # Load configuration settings
        with open(config_path, 'r') as config_file:
            framework_config = json.load(config_file)

        # Dynamically import model class
        model_class = framework_config.get("model_class")
        if isinstance(model_class, str):
            module_name, class_name = model_class.rsplit('.', 1)
            model_class = getattr(importlib.import_module(module_name), class_name)

        # Initialize model
        model = model_class(**framework_config.get("model_params", {}))

        # Initialize optimizer
        optimizer_class = getattr(optim, framework_config["optimizer"])
        optimizer = optimizer_class(model.parameters(), **framework_config.get("optimizer_params", {}))

        # Initialize loss function
        loss_function_class = getattr(nn, framework_config["loss_function"])
        loss_function = loss_function_class(**framework_config.get("loss_params", {}))

        # Initialize dataset and dataloader
        dataset_class = framework_config["dataset"]
        if isinstance(dataset_class, str):
            if '.' in dataset_class:
                module_name, class_name = dataset_class.rsplit('.', 1)
                dataset_class = getattr(importlib.import_module(module_name), class_name)
            else:
                raise ValueError(f"Invalid dataset class format: {dataset_class}. Must be fully qualified.")

        dataset = dataset_class(**framework_config.get("dataset_params", {}))
        dataloader = DataLoader(dataset, **framework_config.get("dataloader_params", {}))

        framework = {
            "model": model,
            "optimizer": optimizer,
            "loss_function": loss_function,
            "dataloader": dataloader,
            "metrics": framework_config.get("metrics"),
        }

        print("Training framework initialized successfully.")
        return framework

    except Exception as e:
        print(f"Error setting up training framework: {e}")
        raise


def train_with_curriculum(framework, curriculum):
    """
    Train a model using the provided curriculum.

    Parameters:
    - framework: dict, initialized training framework.
    - curriculum: list of dicts, stages of the curriculum with settings like noise levels, epochs, etc.

    Returns:
    - results: dict, training results including metrics and performance summaries.
    """
    try:
        model = framework["model"]
        optimizer = framework["optimizer"]
        loss_function = framework["loss_function"]
        dataloader = framework["dataloader"]
        results = {
            "stages": [],
            "overall_metrics": {}
        }

        # Training loop over curriculum stages
        for stage in curriculum:
            print(f"Starting training stage {stage['stage_id']}...")
            noise_level = stage["noise_level"]
            epochs = stage["epochs"]

            # Adjust dataset or dataloader if required by noise_level
            if hasattr(dataloader.dataset, 'set_noise_level'):
                dataloader.dataset.set_noise_level(noise_level)

            # Training for the current stage
            for epoch in range(epochs):
                model.train()
                epoch_loss = 0.0
                correct = 0
                total = 0

                for inputs, targets in dataloader:
                    optimizer.zero_grad()
                    outputs = model(inputs)
                    loss = loss_function(outputs, targets)
                    loss.backward()
                    optimizer.step()

                    epoch_loss += loss.item() * inputs.size(0)
                    if outputs.dim() == 1:  # Regression outputs
                        predicted = outputs
                    else:  # Classification outputs
                        _, predicted = outputs.max(1)

                    total += targets.size(0)
                    correct += predicted.eq(targets).sum().item()

                print(f"Epoch {epoch + 1}/{epochs}: Loss = {epoch_loss / total}, Accuracy = {correct / total}")

            # Record stage results
            stage_result = {
                "stage_id": stage["stage_id"],
                "noise_level": noise_level,
                "epochs": epochs,
                "metrics": {
                    "loss": epoch_loss / total,
                    "accuracy": correct / total,
                }
            }

            print(f"Completed training stage {stage['stage_id']} with metrics: {stage_result['metrics']}")
            results["stages"].append(stage_result)

        # Aggregate overall metrics
        results["overall_metrics"] = {
            "average_loss": sum(stage["metrics"]["loss"] for stage in results["stages"]) / len(results["stages"]),
            "average_accuracy": sum(stage["metrics"]["accuracy"] for stage in results["stages"]) / len(results["stages"]),
        }

        print("Training completed successfully.")
        return results

    except Exception as e:
        print(f"Error during training with curriculum: {e}")
        raise