[feature]: Able to use save_best option

mmseg/core/evaluation/eval_hooks.py

@@ -1,7 +1,9 @@
 import os.path as osp
 
+import torch.distributed as dist
 from mmcv.runner import DistEvalHook as _DistEvalHook
 from mmcv.runner import EvalHook as _EvalHook
+from torch.nn.modules.batchnorm import _BatchNorm
 
 
 class EvalHook(_EvalHook):
@@ -23,33 +25,17 @@ def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
         super().__init__(*args, by_epoch=by_epoch, **kwargs)
         self.efficient_test = efficient_test
 
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
+    def _do_evaluate(self, runner):
+        """perform evaluation and save ckpt."""
+        if not self._should_evaluate(runner):
             return
-        from mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
-        results = single_gpu_test(
-            runner.model,
-            self.dataloader,
-            show=False,
-            efficient_test=self.efficient_test)
-        self.evaluate(runner, results)
 
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
-
-        Override default ``single_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
         from mmseg.apis import single_gpu_test
-        runner.log_buffer.clear()
         results = single_gpu_test(runner.model, self.dataloader, show=False)
-        self.evaluate(runner, results)
+        runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+        key_score = self.evaluate(runner, results)
+        if self.save_best:
+            self._save_ckpt(runner, key_score)
 
 
 class DistEvalHook(_DistEvalHook):
@@ -71,39 +57,38 @@ def __init__(self, *args, by_epoch=False, efficient_test=False, **kwargs):
         super().__init__(*args, by_epoch=by_epoch, **kwargs)
         self.efficient_test = efficient_test
 
-    def after_train_iter(self, runner):
-        """After train epoch hook.
-
-        Override default ``multi_gpu_test``.
-        """
-        if self.by_epoch or not self.every_n_iters(runner, self.interval):
+    def _do_evaluate(self, runner):
+        """perform evaluation and save ckpt."""
+        # Synchronization of BatchNorm's buffer (running_mean
+        # and running_var) is not supported in the DDP of pytorch,
+        # which may cause the inconsistent performance of models in
+        # different ranks, so we broadcast BatchNorm's buffers
+        # of rank 0 to other ranks to avoid this.
+        if self.broadcast_bn_buffer:
+            model = runner.model
+            for name, module in model.named_modules():
+                if isinstance(module,
+                              _BatchNorm) and module.track_running_stats:
+                    dist.broadcast(module.running_var, 0)
+                    dist.broadcast(module.running_mean, 0)
+
+        if not self._should_evaluate(runner):
             return
-        from mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
-        results = multi_gpu_test(
-            runner.model,
-            self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
-            gpu_collect=self.gpu_collect,
-            efficient_test=self.efficient_test)
-        if runner.rank == 0:
-            print('\n')
-            self.evaluate(runner, results)
 
-    def after_train_epoch(self, runner):
-        """After train epoch hook.
+        tmpdir = self.tmpdir
+        if tmpdir is None:
+            tmpdir = osp.join(runner.work_dir, '.eval_hook')
 
-        Override default ``multi_gpu_test``.
-        """
-        if not self.by_epoch or not self.every_n_epochs(runner, self.interval):
-            return
         from mmseg.apis import multi_gpu_test
-        runner.log_buffer.clear()
         results = multi_gpu_test(
             runner.model,
             self.dataloader,
-            tmpdir=osp.join(runner.work_dir, '.eval_hook'),
+            tmpdir=tmpdir,
             gpu_collect=self.gpu_collect)
         if runner.rank == 0:
             print('\n')
-            self.evaluate(runner, results)
+            runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+            key_score = self.evaluate(runner, results)
+
+            if self.save_best:
+                self._save_ckpt(runner, key_score)

tools/test.py

@@ -122,8 +122,16 @@ def main():
     if fp16_cfg is not None:
         wrap_fp16_model(model)
     checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
-    model.CLASSES = checkpoint['meta']['CLASSES']
-    model.PALETTE = checkpoint['meta']['PALETTE']
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        print('"CLASSES" not found in meta, use dataset.CLASSES instead')
+        model.CLASSES = dataset.CLASSES
+    if 'PALETTE' in checkpoint.get('meta', {}):
+        model.PALETTE = checkpoint['meta']['PALETTE']
+    else:
+        print('"PALETTE" not found in meta, use dataset.PALETTE instead')
+        model.PALETTE = dataset.PALETTE
 
     efficient_test = False
     if args.eval_options is not None:

tools/train.py

@@ -149,6 +149,8 @@ def main():
             PALETTE=datasets[0].PALETTE)
     # add an attribute for visualization convenience
     model.CLASSES = datasets[0].CLASSES
+    # passing checkpoint meta for saving best checkpoint
+    meta.update(cfg.checkpoint_config.meta)
     train_segmentor(
         model,
         datasets,