Add resume training from checkpoint (#1381)

benchmarks/imagenet/resnet50/main.py

@@ -39,6 +39,7 @@
 parser.add_argument("--accelerator", type=str, default="gpu")
 parser.add_argument("--devices", type=int, default=1)
 parser.add_argument("--precision", type=str, default="16-mixed")
+parser.add_argument("--ckpt-path", type=Path, default=None)
 parser.add_argument("--compile-model", action="store_true")
 parser.add_argument("--methods", type=str, nargs="+")
 parser.add_argument("--num-classes", type=int, default=1000)
@@ -76,6 +77,7 @@ def main(
     skip_knn_eval: bool,
     skip_linear_eval: bool,
     skip_finetune_eval: bool,
+    ckpt_path: Union[Path, None],
 ) -> None:
     torch.set_float32_matmul_precision("high")
 
@@ -96,6 +98,8 @@ def main(
 
         if epochs <= 0:
             print_rank_zero("Epochs <= 0, skipping pretraining.")
+            if ckpt_path is not None:
+                model.load_state_dict(torch.load(ckpt_path)["state_dict"])
         else:
             pretrain(
                 model=model,
@@ -109,6 +113,7 @@ def main(
                 accelerator=accelerator,
                 devices=devices,
                 precision=precision,
+                ckpt_path=ckpt_path,
             )
 
         if skip_knn_eval:
@@ -171,6 +176,7 @@ def pretrain(
     accelerator: str,
     devices: int,
     precision: str,
+    ckpt_path: Union[Path, None],
 ) -> None:
     print_rank_zero(f"Running pretraining for {method}...")
 
@@ -222,10 +228,12 @@ def pretrain(
         strategy="ddp_find_unused_parameters_true",
         sync_batchnorm=True,
     )
+
     trainer.fit(
         model=model,
         train_dataloaders=train_dataloader,
         val_dataloaders=val_dataloader,
+        ckpt_path=ckpt_path,
     )
     for metric in ["val_online_cls_top1", "val_online_cls_top5"]:
         print_rank_zero(f"max {metric}: {max(metric_callback.val_metrics[metric])}")