Load variables when --resume /path/to/checkpoint --test-only (#3285)

Co-authored-by: Francisco Massa <fvsmassa@gmail.com>

references/segmentation/train.py

@@ -133,11 +133,6 @@ def main(args):
         model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
         model_without_ddp = model.module
 
-    if args.test_only:
-        confmat = evaluate(model, data_loader_test, device=device, num_classes=num_classes)
-        print(confmat)
-        return
-
     params_to_optimize = [
         {"params": [p for p in model_without_ddp.backbone.parameters() if p.requires_grad]},
         {"params": [p for p in model_without_ddp.classifier.parameters() if p.requires_grad]},
@@ -155,10 +150,16 @@ def main(args):
 
     if args.resume:
         checkpoint = torch.load(args.resume, map_location='cpu')
-        model_without_ddp.load_state_dict(checkpoint['model'])
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
-        args.start_epoch = checkpoint['epoch'] + 1
+        model_without_ddp.load_state_dict(checkpoint['model'], strict=not args.test_only)
+        if not args.test_only:
+            optimizer.load_state_dict(checkpoint['optimizer'])
+            lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
+            args.start_epoch = checkpoint['epoch'] + 1
+
+    if args.test_only:
+        confmat = evaluate(model, data_loader_test, device=device, num_classes=num_classes)
+        print(confmat)
+        return
 
     start_time = time.time()
     for epoch in range(args.start_epoch, args.epochs):