443 lines
18 KiB
Python
443 lines
18 KiB
Python
import argparse
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import importlib.util
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import logging
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import os
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import random
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import sys
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import time # Import time for timing
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import numpy as np
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import torch
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import torch.utils.data
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# Project specific imports
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from models.detection import get_maskrcnn_model
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from utils.data_utils import PennFudanDataset, collate_fn, get_transform
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from utils.eval_utils import evaluate # Import evaluate function
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from utils.log_utils import setup_logging
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def main(args):
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# --- Configuration Loading ---
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try:
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config_path = os.path.abspath(args.config)
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if not os.path.exists(config_path):
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print(f"Error: Config file not found at {config_path}")
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sys.exit(1)
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# Derive module path from file path relative to workspace root
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workspace_root = os.path.abspath(
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os.getcwd()
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) # Assuming script is run from root
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relative_path = os.path.relpath(config_path, workspace_root)
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if relative_path.startswith(".."):
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print(f"Error: Config file {args.config} is outside the project directory.")
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sys.exit(1)
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module_path_no_ext, _ = os.path.splitext(relative_path)
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module_path_str = module_path_no_ext.replace(os.sep, ".")
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print(f"Attempting to import config module: {module_path_str}")
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config_module = importlib.import_module(module_path_str)
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config = config_module.config
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print(
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f"Loaded configuration from: {config_path} (via module {module_path_str})"
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)
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except ImportError as e:
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print(f"Error importing config module '{module_path_str}': {e}")
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print(
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"Ensure the config file path is correct and relative imports within it are valid."
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)
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import traceback
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traceback.print_exc()
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sys.exit(1)
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except AttributeError as e:
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print(
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f"Error: Could not find 'config' dictionary in module {module_path_str}. {e}"
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)
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sys.exit(1)
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except Exception as e:
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print(f"Error loading configuration file {args.config}: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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# --- Output Directory Setup ---
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output_dir = config.get("output_dir", "outputs")
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config_name = config.get("config_name", "default_run")
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output_path = os.path.join(output_dir, config_name)
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checkpoint_path = os.path.join(output_path, "checkpoints")
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os.makedirs(output_path, exist_ok=True)
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os.makedirs(checkpoint_path, exist_ok=True)
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print(f"Output will be saved to: {output_path}")
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# --- Logging Setup (Prompt 9) ---
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setup_logging(output_path, config_name)
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logging.info("--- Training Script Started ---")
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logging.info(f"Loaded configuration from: {args.config}")
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logging.info(f"Loaded configuration dictionary: {config}")
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logging.info(f"Output will be saved to: {output_path}")
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# --- Reproducibility ---
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seed = config.get("seed", 42)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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# Consider adding these for more determinism, but they might impact performance
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# torch.backends.cudnn.deterministic = True
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# torch.backends.cudnn.benchmark = False
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logging.info(f"Set random seed to: {seed}")
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# --- Device Setup ---
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device_name = config.get("device", "cuda")
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if device_name == "cuda" and not torch.cuda.is_available():
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logging.warning("CUDA requested but not available, falling back to CPU.")
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device_name = "cpu"
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device = torch.device(device_name)
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logging.info(f"Using device: {device}")
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# --- Dataset and DataLoader ---
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data_root = config.get("data_root")
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if not data_root or not os.path.isdir(data_root):
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logging.error(f"Data root directory not found or not specified: {data_root}")
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sys.exit(1)
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try:
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# Create the full training dataset instance first
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dataset_full = PennFudanDataset(
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root=data_root, transforms=get_transform(train=True)
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)
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logging.info(f"Full dataset size: {len(dataset_full)}")
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# Create validation dataset instance with eval transforms
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dataset_val_instance = PennFudanDataset(
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root=data_root, transforms=get_transform(train=False)
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)
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# Split the dataset indices
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torch.manual_seed(
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config.get("seed", 42)
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) # Use the same seed for consistent splits
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indices = torch.randperm(len(dataset_full)).tolist()
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val_split_ratio = config.get(
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"val_split_ratio", 0.1
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) # Default to 10% validation
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val_split_count = int(val_split_ratio * len(dataset_full))
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if val_split_count == 0 and len(dataset_full) > 0:
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logging.warning(
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f"Validation split resulted in 0 samples (ratio={val_split_ratio}, total={len(dataset_full)}). Using 1 sample for validation."
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)
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val_split_count = 1
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elif val_split_count >= len(dataset_full):
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logging.error(
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f"Validation split ratio ({val_split_ratio}) too high, results in no training samples."
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)
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sys.exit(1)
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train_indices = indices[:-val_split_count]
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val_indices = indices[-val_split_count:]
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# Create Subset datasets
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dataset_train = torch.utils.data.Subset(dataset_full, train_indices)
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dataset_val = torch.utils.data.Subset(dataset_val_instance, val_indices)
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logging.info(
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f"Using {len(train_indices)} samples for training and {len(val_indices)} for validation."
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)
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# Create DataLoaders
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data_loader_train = torch.utils.data.DataLoader(
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dataset_train,
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batch_size=config.get("batch_size", 2),
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# Shuffle should be true for the training subset loader
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shuffle=True,
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num_workers=config.get("num_workers", 4),
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collate_fn=collate_fn,
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pin_memory=config.get("pin_memory", True),
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)
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data_loader_val = torch.utils.data.DataLoader(
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dataset_val,
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batch_size=config.get(
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"batch_size", 2
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), # Often use same or larger batch size for validation
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shuffle=False, # No need to shuffle validation data
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num_workers=config.get("num_workers", 4),
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collate_fn=collate_fn,
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pin_memory=config.get("pin_memory", True),
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)
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logging.info(
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f"Training dataloader configured. Est. batches: {len(data_loader_train)}"
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)
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logging.info(
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f"Validation dataloader configured. Est. batches: {len(data_loader_val)}"
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)
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except Exception as e:
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logging.error(f"Error setting up dataset/dataloader: {e}", exc_info=True)
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sys.exit(1)
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# --- Model Instantiation ---
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num_classes = config.get("num_classes")
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if num_classes is None:
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logging.error("'num_classes' not specified in configuration.")
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sys.exit(1)
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try:
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model = get_maskrcnn_model(
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num_classes=num_classes,
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pretrained=config.get("pretrained", True),
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pretrained_backbone=config.get("pretrained_backbone", True),
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)
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model.to(device)
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logging.info("Model loaded successfully.")
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except Exception as e:
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logging.error(f"Error loading model: {e}", exc_info=True)
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sys.exit(1)
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# --- Optimizer ---
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# Filter parameters that require gradients
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params = [p for p in model.parameters() if p.requires_grad]
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try:
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optimizer = torch.optim.SGD(
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params,
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lr=config.get("lr", 0.005),
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momentum=config.get("momentum", 0.9),
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weight_decay=config.get("weight_decay", 0.0005),
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)
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logging.info(
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f"Optimizer SGD configured with lr={config.get('lr', 0.005)}, momentum={config.get('momentum', 0.9)}, weight_decay={config.get('weight_decay', 0.0005)}"
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)
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except Exception as e:
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logging.error(f"Error creating optimizer: {e}", exc_info=True)
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sys.exit(1)
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# --- LR Scheduler (Prompt 10) ---
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try:
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lr_scheduler = torch.optim.lr_scheduler.StepLR(
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optimizer,
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step_size=config.get("lr_step_size", 3),
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gamma=config.get("lr_gamma", 0.1),
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)
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logging.info(
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f"LR scheduler StepLR configured with step_size={config.get('lr_step_size', 3)}, gamma={config.get('lr_gamma', 0.1)}"
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)
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except Exception as e:
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logging.error(f"Error creating LR scheduler: {e}", exc_info=True)
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sys.exit(1)
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# --- Resume Logic (Prompt 11) ---
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start_epoch = 0
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latest_checkpoint_path = None
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if os.path.isdir(checkpoint_path):
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checkpoints = sorted(
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[f for f in os.listdir(checkpoint_path) if f.endswith(".pth")]
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)
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if checkpoints: # Check if list is not empty
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latest_checkpoint_file = checkpoints[
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-1
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] # Get the last one (assuming naming convention like epoch_N.pth)
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latest_checkpoint_path = os.path.join(
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checkpoint_path, latest_checkpoint_file
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)
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logging.info(f"Found latest checkpoint: {latest_checkpoint_path}")
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else:
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logging.info("No checkpoints found in directory. Starting from scratch.")
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else:
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logging.info("Checkpoint directory not found. Starting from scratch.")
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if latest_checkpoint_path:
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try:
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logging.info(f"Loading checkpoint '{latest_checkpoint_path}'")
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# Ensure loading happens on the correct device
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checkpoint = torch.load(latest_checkpoint_path, map_location=device)
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# Load model state - handle potential 'module.' prefix if saved with DataParallel
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model_state_dict = checkpoint["model_state_dict"]
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# Simple check and correction for DataParallel prefix
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if all(key.startswith("module.") for key in model_state_dict.keys()):
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logging.info("Removing 'module.' prefix from checkpoint keys.")
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model_state_dict = {
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k.replace("module.", ""): v for k, v in model_state_dict.items()
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}
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model.load_state_dict(model_state_dict)
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# Load optimizer state
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optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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# Load LR scheduler state
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lr_scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
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# Load starting epoch (epoch saved is the one *completed*, so start from next)
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start_epoch = checkpoint["epoch"]
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logging.info(f"Resuming training from epoch {start_epoch + 1}")
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# Optionally load and verify config consistency
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# loaded_config = checkpoint.get('config')
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# if loaded_config:
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# # Perform checks if necessary
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# pass
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except Exception as e:
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logging.error(
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f"Error loading checkpoint: {e}. Starting training from scratch.",
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exc_info=True,
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)
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start_epoch = 0 # Reset start_epoch if loading fails
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# --- Training Loop (Prompt 10, modified for Prompt 11) ---
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logging.info("--- Starting Training Loop --- ")
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start_time = time.time()
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num_epochs = config.get("num_epochs", 10)
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# Modify loop to start from start_epoch
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for epoch in range(start_epoch, num_epochs):
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model.train() # Set model to training mode for each epoch
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epoch_start_time = time.time()
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logging.info(f"--- Epoch {epoch + 1}/{num_epochs} --- ")
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# Variables for tracking epoch progress (optional)
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epoch_loss_sum = 0.0
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num_batches = len(data_loader_train)
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for i, (images, targets) in enumerate(data_loader_train):
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batch_start_time = time.time() # Optional: time each batch
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try:
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# Move data to the device
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images = list(image.to(device) for image in images)
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targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
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# Perform forward pass
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loss_dict = model(images, targets)
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losses = sum(loss for loss in loss_dict.values())
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loss_value = losses.item()
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epoch_loss_sum += loss_value
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# Perform backward pass
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optimizer.zero_grad()
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losses.backward()
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optimizer.step()
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# Log batch loss periodically
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if (i + 1) % config.get("log_freq", 10) == 0:
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batch_time = time.time() - batch_start_time
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# Include individual losses if desired
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loss_dict_items = {
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k: f"{v.item():.4f}" for k, v in loss_dict.items()
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}
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logging.info(
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f" Epoch {epoch + 1}, Iter {i + 1}/{num_batches}, Loss: {loss_value:.4f}, Batch Time: {batch_time:.2f}s"
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)
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logging.debug(
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f" Loss Dict: {loss_dict_items}"
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) # Log individual losses at DEBUG level
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except Exception as e:
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logging.error(
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f"Error during training epoch {epoch+1}, batch {i+1}: {e}",
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exc_info=True,
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)
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# Decide if you want to stop training or continue to next batch/epoch
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logging.warning("Skipping rest of epoch due to error.")
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break # Exit the inner loop for this epoch
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# --- End of Epoch --- #
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# Step the learning rate scheduler
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lr_scheduler.step()
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# Log epoch summary
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epoch_end_time = time.time()
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epoch_duration = epoch_end_time - epoch_start_time
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avg_epoch_loss = epoch_loss_sum / num_batches if num_batches > 0 else 0
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current_lr = optimizer.param_groups[0]["lr"] # Get current learning rate
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logging.info(f"--- Epoch {epoch + 1} Summary --- ")
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logging.info(f" Average Loss: {avg_epoch_loss:.4f}")
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logging.info(f" Learning Rate: {current_lr:.6f}")
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logging.info(f" Epoch Duration: {epoch_duration:.2f}s")
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# --- Checkpointing (Prompt 11) --- #
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# Save checkpoint periodically or at the end
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save_checkpoint = False
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if (epoch + 1) % config.get("checkpoint_freq", 1) == 0:
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save_checkpoint = True
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logging.info(f"Checkpoint frequency met (epoch {epoch + 1})")
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elif (epoch + 1) == num_epochs:
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save_checkpoint = True
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logging.info(f"Final epoch ({epoch + 1}) reached, saving checkpoint.")
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if save_checkpoint:
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checkpoint_filename = f"checkpoint_epoch_{epoch + 1}.pth"
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save_path = os.path.join(checkpoint_path, checkpoint_filename)
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try:
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checkpoint_data = {
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"epoch": epoch + 1,
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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"scheduler_state_dict": lr_scheduler.state_dict(),
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"config": config, # Save config for reference
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}
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torch.save(checkpoint_data, save_path)
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logging.info(f"Checkpoint saved to {save_path}")
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except Exception as e:
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logging.error(
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f"Failed to save checkpoint for epoch {epoch + 1} to {save_path}: {e}",
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exc_info=True,
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)
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# --- Evaluation (Prompt 12) --- #
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if data_loader_val:
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logging.info(f"Starting evaluation for epoch {epoch + 1}...")
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try:
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val_metrics = evaluate(model, data_loader_val, device)
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logging.info(f"Epoch {epoch + 1} Validation Metrics: {val_metrics}")
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# --- Best Model Checkpoint Logic (Optional Add-on) ---
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# Add logic here to track the best metric (e.g., val_metrics['average_loss'])
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# and save a separate 'best_model.pth' checkpoint if the current epoch is better.
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# Example:
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# if 'average_loss' in val_metrics:
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# current_val_loss = val_metrics['average_loss']
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# if best_val_loss is None or current_val_loss < best_val_loss:
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# best_val_loss = current_val_loss
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# best_model_path = os.path.join(output_path, 'best_model.pth')
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# try:
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# # Save only the model state_dict for the best model
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# torch.save(model.state_dict(), best_model_path)
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# logging.info(f"Saved NEW BEST model checkpoint to {best_model_path} (Val Loss: {best_val_loss:.4f})")
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# except Exception as e:
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# logging.error(f"Failed to save best model checkpoint: {e}", exc_info=True)
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except Exception as e:
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logging.error(
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f"Error during evaluation for epoch {epoch + 1}: {e}", exc_info=True
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)
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# Decide if this error should stop the entire training process
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# --- End of Training --- #
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total_training_time = time.time() - start_time
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logging.info("--- Training Finished --- ")
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logging.info(
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f"Total Training Time: {total_training_time:.2f}s ({total_training_time / 3600:.2f} hours)"
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Train Mask R-CNN on Penn-Fudan dataset."
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)
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parser.add_argument(
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"--config",
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type=str,
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required=True,
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help="Path to the Python configuration file (e.g., configs/pennfudan_maskrcnn_config.py)",
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)
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args = parser.parse_args()
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main(args)
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