Update: Remove the num classes parameter; replace manual num classes …

…with the auto acquisition method; modify the get_dataset method.

tools/train.py

@@ -26,7 +26,7 @@
     image_format_choices, noise_schedule_choices
 from model.modules.ema import EMA
 from utils.initializer import device_initializer, seed_initializer, network_initializer, optimizer_initializer, \
-    sample_initializer, lr_initializer, amp_initializer
+    sample_initializer, lr_initializer, amp_initializer, classes_initializer
 from utils.utils import plot_images, save_images, get_dataset, setup_logging, save_train_logging
 from utils.checkpoint import load_ckpt, save_ckpt
 
@@ -60,8 +60,14 @@ def train(rank=None, args=None):
     init_lr = args.lr
     # Learning rate function
     lr_func = args.lr_func
+    # Batch size
+    batch_size = args.batch_size
+    # Number of workers
+    num_workers = args.num_workers
+    # Dataset path
+    dataset_path = args.dataset_path
     # Number of classes
-    num_classes = args.num_classes
+    num_classes = classes_initializer(dataset_path=dataset_path)
     # classifier-free guidance interpolation weight, users can better generate model effect
     cfg_scale = args.cfg_scale
     # Whether to enable conditional training
@@ -118,7 +124,8 @@ def train(rank=None, args=None):
     results_vis_dir = results_logging[2]
     results_tb_dir = results_logging[3]
     # Dataloader
-    dataloader = get_dataset(args=args, distributed=distributed)
+    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
@@ -396,9 +403,6 @@ def main(args):
     parser.add_argument("--world_size", type=int, default=2)
 
     # =====================Enable the conditional training (if '--conditional' is set to 'True')=====================
-    # Number of classes (required)
-    # [Note] The classes settings are consistent with the loaded datasets settings.
-    parser.add_argument("--num_classes", type=int, default=10)
     # classifier-free guidance interpolation weight, users can better generate model effect (recommend)
     parser.add_argument("--cfg_scale", type=int, default=3)