[Fix] CheckpointHook behavior incorrect if given filename_tmpl argument (#518)

mmengine/hooks/checkpoint_hook.py

@@ -74,6 +74,12 @@ class CheckpointHook(Hook):
         file_client_args (dict, optional): Arguments to instantiate a
             FileClient. See :class:`mmcv.fileio.FileClient` for details.
             Defaults to None.
+        filename_tmpl (str, optional): String template to indicate checkpoint
+            name. If specified, must contain one and only one "{}", which will
+            be replaced with ``epoch + 1`` if ``by_epoch=True`` else
+            ``iteration + 1``.
+            Defaults to None, which means "epoch_{}.pth" or "iter_{}.pth"
+            accordingly.
 
     Examples:
         >>> # Save best based on single metric
@@ -116,6 +122,7 @@ class CheckpointHook(Hook):
                  greater_keys: Optional[Sequence[str]] = None,
                  less_keys: Optional[Sequence[str]] = None,
                  file_client_args: Optional[dict] = None,
+                 filename_tmpl: Optional[str] = None,
                  **kwargs) -> None:
         self.interval = interval
         self.by_epoch = by_epoch
@@ -124,8 +131,15 @@ class CheckpointHook(Hook):
         self.out_dir = out_dir  # type: ignore
         self.max_keep_ckpts = max_keep_ckpts
         self.save_last = save_last
-        self.args = kwargs
         self.file_client_args = file_client_args
+        if filename_tmpl is None:
+            if self.by_epoch:
+                self.filename_tmpl = 'epoch_{}.pth'
+            else:
+                self.filename_tmpl = 'iter_{}.pth'
+        else:
+            self.filename_tmpl = filename_tmpl
+        self.args = kwargs
 
         # save best logic
         assert (isinstance(save_best, str) or is_list_of(save_best, str)
@@ -277,11 +291,9 @@ class CheckpointHook(Hook):
             runner (Runner): The runner of the training process.
         """
         if self.by_epoch:
-            ckpt_filename = self.args.get(
-                'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
+            ckpt_filename = self.filename_tmpl.format(runner.epoch + 1)
         else:
-            ckpt_filename = self.args.get(
-                'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
+            ckpt_filename = self.filename_tmpl.format(runner.iter + 1)
 
         runner.save_checkpoint(
             self.out_dir,
@@ -299,18 +311,15 @@ class CheckpointHook(Hook):
         # remove other checkpoints
         if self.max_keep_ckpts > 0:
             if self.by_epoch:
-                name = 'epoch_{}.pth'
                 current_ckpt = runner.epoch + 1
             else:
-                name = 'iter_{}.pth'
                 current_ckpt = runner.iter + 1
             redundant_ckpts = range(
                 current_ckpt - self.max_keep_ckpts * self.interval, 0,
                 -self.interval)
-            filename_tmpl = self.args.get('filename_tmpl', name)
             for _step in redundant_ckpts:
                 ckpt_path = self.file_client.join_path(
-                    self.out_dir, filename_tmpl.format(_step))
+                    self.out_dir, self.filename_tmpl.format(_step))
                 if self.file_client.isfile(ckpt_path):
                     self.file_client.remove(ckpt_path)
                 else:
@@ -334,12 +343,10 @@ class CheckpointHook(Hook):
             return
 
         if self.by_epoch:
-            ckpt_filename = self.args.get('filename_tmpl',
-                                          'epoch_{}.pth').format(runner.epoch)
+            ckpt_filename = self.filename_tmpl.format(runner.epoch)
             cur_type, cur_time = 'epoch', runner.epoch
         else:
-            ckpt_filename = self.args.get('filename_tmpl',
-                                          'iter_{}.pth').format(runner.iter)
+            ckpt_filename = self.filename_tmpl.format(runner.iter)
             cur_type, cur_time = 'iter', runner.iter
 
         # handle auto in self.key_indicators and self.rules before the loop

tests/test_hooks/test_checkpoint_hook.py

@@ -4,9 +4,71 @@ import os.path as osp
 from unittest.mock import Mock, patch
 
 import pytest
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset
 
+from mmengine.evaluator import BaseMetric
 from mmengine.hooks import CheckpointHook
 from mmengine.logging import MessageHub
+from mmengine.model import BaseModel
+from mmengine.optim import OptimWrapper
+from mmengine.runner import Runner
+
+
+class ToyModel(BaseModel):
+
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(2, 1)
+
+    def forward(self, inputs, data_sample, mode='tensor'):
+        labels = torch.stack(data_sample)
+        inputs = torch.stack(inputs)
+        outputs = self.linear(inputs)
+        if mode == 'tensor':
+            return outputs
+        elif mode == 'loss':
+            loss = (labels - outputs).sum()
+            outputs = dict(loss=loss)
+            return outputs
+        else:
+            return outputs
+
+
+class DummyDataset(Dataset):
+    METAINFO = dict()  # type: ignore
+    data = torch.randn(12, 2)
+    label = torch.ones(12)
+
+    @property
+    def metainfo(self):
+        return self.METAINFO
+
+    def __len__(self):
+        return self.data.size(0)
+
+    def __getitem__(self, index):
+        return dict(inputs=self.data[index], data_sample=self.label[index])
+
+
+class TriangleMetric(BaseMetric):
+
+    default_prefix: str = 'test'
+
+    def __init__(self, length):
+        super().__init__()
+        self.length = length
+        self.best_idx = length // 2
+        self.cur_idx = 0
+
+    def process(self, *args, **kwargs):
+        self.results.append(0)
+
+    def compute_metrics(self, *args, **kwargs):
+        self.cur_idx += 1
+        acc = 1.0 - abs(self.cur_idx - self.best_idx) / self.length
+        return dict(acc=acc)
 
 
 class MockPetrel:
@@ -370,3 +432,40 @@ class TestCheckpointHook:
         checkpoint_hook.before_train(runner)
         checkpoint_hook.after_train_iter(runner, batch_idx=batch_idx)
         assert not os.path.exists(f'{work_dir}/iter_8.pth')
+
+    def test_with_runner(self, tmp_path):
+        max_epoch = 10
+        work_dir = osp.join(str(tmp_path), 'runner_test')
+        tmpl = '{}.pth'
+        save_interval = 2
+        checkpoint_cfg = dict(
+            type='CheckpointHook',
+            interval=save_interval,
+            filename_tmpl=tmpl,
+            by_epoch=True)
+        runner = Runner(
+            model=ToyModel(),
+            work_dir=work_dir,
+            train_dataloader=dict(
+                dataset=DummyDataset(),
+                sampler=dict(type='DefaultSampler', shuffle=True),
+                batch_size=3,
+                num_workers=0),
+            val_dataloader=dict(
+                dataset=DummyDataset(),
+                sampler=dict(type='DefaultSampler', shuffle=False),
+                batch_size=3,
+                num_workers=0),
+            val_evaluator=dict(type=TriangleMetric, length=max_epoch),
+            optim_wrapper=OptimWrapper(
+                torch.optim.Adam(ToyModel().parameters())),
+            train_cfg=dict(
+                by_epoch=True, max_epochs=max_epoch, val_interval=1),
+            val_cfg=dict(),
+            default_hooks=dict(checkpoint=checkpoint_cfg))
+        runner.train()
+        for epoch in range(max_epoch):
+            if epoch % save_interval != 0 or epoch == 0:
+                continue
+            path = osp.join(work_dir, tmpl.format(epoch))
+            assert osp.isfile(path=path)