Added save_checkpoint

train.py

@@ -18,6 +18,7 @@ from arguments import args, ret_args
 import dataset
 from dataset import *
 from model.transcrowd_gap import base_patch16_384_gap, stn_patch16_384_gap
+from checkpoint import save_checkpoint
 
 logger = logging.getLogger("train")
 
@@ -44,14 +45,14 @@ def setup_process_group(
 def build_train_loader(data_keys, args):
     train_dataset = ListDataset(
         data_keys,
-        shuffle = True, 
+        shuffle = True,
         transform = transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
-        ]), 
-        train = True, 
-        batch_size = args.batch_size, 
-        nr_workers = args.workers, 
+        ]),
+        train = True,
+        batch_size = args.batch_size,
+        nr_workers = args.workers,
         args = args
     )
     if args.use_ddp:
@@ -61,9 +62,9 @@ def build_train_loader(data_keys, args):
     else:
         train_dist_sampler = None
     train_loader = DataLoader(
-        dataset=train_dataset, 
-        sampler=train_dist_sampler, 
-        batch_size=args.batch_size, 
+        dataset=train_dataset,
+        sampler=train_dist_sampler,
+        batch_size=args.batch_size,
         drop_last=False
     )
     return train_loader
@@ -71,13 +72,13 @@ def build_train_loader(data_keys, args):
 
 def build_test_loader(data_keys, args):
     test_dataset = ListDataset(
-        data_keys, 
-        shuffle=False, 
+        data_keys,
+        shuffle=False,
         transform = transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
-        ]), 
-        args=args, 
+        ]),
+        args=args,
         train=False
     )
     if args.use_ddp:
@@ -87,8 +88,8 @@ def build_test_loader(data_keys, args):
     else:
         test_dist_sampler = None
     test_loader = DataLoader(
-        dataset=test_dataset, 
-        sampler=test_dist_sampler, 
+        dataset=test_dataset,
+        sampler=test_dist_sampler,
         batch_size=1
     )
     return test_loader
@@ -114,27 +115,27 @@ def worker(rank: int, args: Namespace):
     test_data = convert_data(test_list, args, train=False)
     train_loader = build_train_loader(train_data, args)
     test_loader = build_test_loader(test_data, args)
-    
+
 
     # Instantiate model
     if args.model == "stn":
         model = stn_patch16_384_gap(args.pth_tar).to(device)
     else:
         model = base_patch16_384_gap(args.pth_tar).to(device)
-    
+
     if args.use_ddp:
         model = nn.parallel.DistributedDataParallel(
-            model, 
-            device_ids=[rank], output_device=rank, 
-            find_unused_parameters=True, 
+            model,
+            device_ids=[rank], output_device=rank,
+            find_unused_parameters=True,
             gradient_as_bucket_view=True    # XXX: vital, otherwise OOM
         )
 
     # criterion, optimizer, scheduler
     criterion = nn.L1Loss(size_average=False).to(device)
     optimizer = torch.optim.Adam(
-        [{"params": model.parameters(), "lr": args.lr}], 
-        lr=args.lr, 
+        [{"params": model.parameters(), "lr": args.lr}],
+        lr=args.lr,
         weight_decay=args.weight_decay
     )
     scheduler = torch.optim.lr_scheduler.MultiStepLR(
@@ -147,7 +148,7 @@ def worker(rank: int, args: Namespace):
         if not os.path.exists(args.save_path):
             os.makedirs(args.save_path)
 
-    if args.progress: 
+    if args.progress:
         if os.path.isfile(args.progress):
             print("=> Loading checkpoint \'{}\'".format(args.progress))
             checkpoint = torch.load(args.progress)
@@ -161,7 +162,7 @@ def worker(rank: int, args: Namespace):
         rank, args.start_epoch, args.best_pred
     ))
 
-    # For each epoch: 
+    # For each epoch:
     for epoch in range(args.start_epoch, args.epochs):
         # Tell sampler which epoch it is
         if args.use_ddp:
@@ -183,11 +184,19 @@ def worker(rank: int, args: Namespace):
             print("* best MAE {mae:.3f} *".format(mae=args.best_pred))
 
             # Save checkpoint
-            # if not args.use_ddp or torch.distributed.get_rank() == 0:
-                
+            if not args.use_ddp or torch.distributed.get_rank() == 0:
+                save_checkpoint({
+                    "epoch": epoch + 1,
+                    "arch": args.progress,
+                    "state_dict": model.state_dict(),
+                    "best_prec1": args.best_pred,
+                    "optimizer": optimizer.state_dict(),
+                }, is_best, args.save_path)
+
 
     # cleanup
-    torch.distributed.destroy_process_group()
+    if args.use_ddp:
+        torch.distributed.destroy_process_group()
 
 
 def train_one_epoch(
@@ -197,7 +206,7 @@ def train_one_epoch(
         optimizer,
         scheduler,
         epoch: int,
-        device, 
+        device,
         args: Namespace
 ):
     # Get learning rate
@@ -256,7 +265,7 @@ def valid_one_epoch(test_loader, model, device, args):
         with torch.no_grad():
             out = model(img)
             count = torch.sum(out).item()
-        
+
         gt_count = torch.sum(gt_count).item()
         mae += abs(gt_count - count)
         mse += abs(gt_count - count) ** 2