Update: Refactor train.py format.

tools/train.py

@@ -41,15 +41,31 @@ def train(rank=None, args=None):
     :param args: Input parameters
     :return: None
     """
+    # =================================Before training=================================
+    # Output params to console
     logger.info(msg=f"[{rank}]: Input params: {args}")
+    # Step1: Set path and create log
+    # Saving path
+    result_path = args.result_path
+    # Run name
+    run_name = args.run_name
+    # Create data logging path
+    results_logging = setup_logging(save_path=result_path, run_name=run_name)
+    results_dir = results_logging[1]
+    results_vis_dir = results_logging[2]
+    results_tb_dir = results_logging[3]
+    # Tensorboard
+    tb_logger = SummaryWriter(log_dir=results_tb_dir)
+    # Train log
+    save_train_logging(arg=args, save_path=results_dir)
+
+    # Step2: Get the parameters of the initializer and args
     # Initialize the seed
     seed_initializer(seed_id=args.seed)
     # Sample type
     sample = args.sample
     # Network
     network = args.network
-    # Run name
-    run_name = args.run_name
     # Input image size
     image_size = args.image_size
     # Select optimizer
@@ -116,20 +132,13 @@ def train(rank=None, args=None):
     image_format = args.image_format
     # Noise schedule
     noise_schedule = args.noise_schedule
-    # Saving path
-    result_path = args.result_path
-    # Create data logging path
-    results_logging = setup_logging(save_path=result_path, run_name=run_name)
-    results_dir = results_logging[1]
-    results_vis_dir = results_logging[2]
-    results_tb_dir = results_logging[3]
-    # Dataloader
-    dataloader = get_dataset(image_size=image_size, dataset_path=dataset_path, batch_size=batch_size,
-                             num_workers=num_workers, distributed=distributed)
     # Resume training
     resume = args.resume
     # Pretrain
     pretrain = args.pretrain
+
+    # =================================About model initializer=================================
+    # Step3: Init model
     # Network
     Network = network_initializer(network=network, device=device)
     # Model
@@ -170,17 +179,21 @@ def train(rank=None, args=None):
     mse = nn.MSELoss()
     # Initialize the diffusion model
     diffusion = sample_initializer(sample=sample, image_size=image_size, device=device, schedule_name=noise_schedule)
-    # Tensorboard
-    tb_logger = SummaryWriter(log_dir=results_tb_dir)
-    # Train log
-    save_train_logging(args, results_dir)
-    # Number of dataset batches in the dataloader
-    len_dataloader = len(dataloader)
     # Exponential Moving Average (EMA) may not be as dominant for single class as for multi class
     ema = EMA(beta=0.995)
     # EMA model
     ema_model = copy.deepcopy(model).eval().requires_grad_(False)
 
+    # =================================About data=================================
+    # Step4: Set data
+    # Dataloader
+    dataloader = get_dataset(image_size=image_size, dataset_path=dataset_path, batch_size=batch_size,
+                             num_workers=num_workers, distributed=distributed)
+    # Number of dataset batches in the dataloader
+    len_dataloader = len(dataloader)
+
+    # =================================Training=================================
+    # Step5: Training
     logger.info(msg=f"[{device}]: Start training.")
     # Start iterating
     for epoch in range(start_epoch, args.epochs):
@@ -281,6 +294,7 @@ def train(rank=None, args=None):
             dist.barrier()
 
     logger.info(msg=f"[{device}]: Finish training.")
+    logger.info(msg="[Note]: If you want to evaluate model quality, use 'FID_calculator.py' to evaluate.")
 
     # Clean up the distributed environment
     if distributed: