train.py

train.py

@@ -0,0 +1,63 @@
+'''
+@copyright ziqi-jin
+'''
+import argparse
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from datasets import get_dataset,get_lettuce_dataset
+from losses import get_losses
+from extend_sam import get_model, get_optimizer, get_scheduler, get_opt_pamams, get_runner
+import random
+import numpy as np
+import torch
+def set_seed(seed: int):
+    """
+    固定训练过程中的随机种子，保证结果相对可复现。
+    """
+    random.seed(seed)                 # Python 内置的 random
+    np.random.seed(seed)              # NumPy
+    torch.manual_seed(seed)           # PyTorch CPU
+    torch.cuda.manual_seed(seed)      # PyTorch当前 GPU
+    torch.cuda.manual_seed_all(seed)  # PyTorch所有 GPU
+    
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+set_seed(42)
+
+supported_tasks = ['detection', 'semantic_seg', 'instance_seg']
+parser = argparse.ArgumentParser()
+parser.add_argument('--task_name', default='semantic_seg', type=str)
+parser.add_argument('--cfg', default=None, type=str)
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    task_name = args.task_name
+    if args.cfg is not None:
+        config = OmegaConf.load(args.cfg)
+    else:
+        assert task_name in supported_tasks, "Please input the supported task name."
+        config = OmegaConf.load("./config/{task_name}.yaml".format(task_name=args.task_name))
+
+    train_cfg = config.train
+    val_cfg = config.val
+    test_cfg = config.test
+
+    train_dataset,val_dataset = get_lettuce_dataset()
+    train_loader = DataLoader(train_dataset, batch_size=train_cfg.bs, shuffle=True, num_workers=train_cfg.num_workers,
+                              drop_last=train_cfg.drop_last)
+    val_loader = DataLoader(val_dataset, batch_size=val_cfg.bs, shuffle=False, num_workers=val_cfg.num_workers,
+                            drop_last=val_cfg.drop_last)
+    losses = get_losses(losses=train_cfg.losses)
+    # according the model name to get the adapted model
+    model = get_model(model_name=train_cfg.model.sam_name, **train_cfg.model.params)
+    opt_params = get_opt_pamams(model, lr_list=train_cfg.opt_params.lr_list, group_keys=train_cfg.opt_params.group_keys,
+                                wd_list=train_cfg.opt_params.wd_list)
+    optimizer = get_optimizer(opt_name=train_cfg.opt_name, params=opt_params, lr=train_cfg.opt_params.lr_default,
+                              momentum=train_cfg.opt_params.momentum, weight_decay=train_cfg.opt_params.wd_default)
+    scheduler = get_scheduler(optimizer=optimizer, lr_scheduler=train_cfg.scheduler_name)
+    runner = get_runner(train_cfg.runner_name)(model, optimizer, losses, train_loader, val_loader, scheduler)
+    # train_step
+    runner.train(train_cfg)
+    if test_cfg.need_test:
+        runner.test(test_cfg)
+