EvalHook uses case-insensitive key indicator matching and configurabl…

…e test functions

mmcv/runner/hooks/evaluation.py

@@ -41,6 +41,10 @@ class EvalHook(Hook):
             .etc will be inferred by 'greater' rule. Keys contain 'loss' will
             be inferred by 'less' rule. Options are 'greater', 'less', None.
             Default: None.
+        test_fn (callable, optional): test a model with samples from a
+            dataloader, and return the test results. If ``None``, the default
+            test function ``mmcv.engine.single_gpu_test`` will be used.
+            (default: ``None``)
         **eval_kwargs: Evaluation arguments fed into the evaluate function of
             the dataset.
 
@@ -68,6 +72,7 @@ def __init__(self,
                  by_epoch=True,
                  save_best=None,
                  rule=None,
+                 test_fn=None,
                  **eval_kwargs):
         if not isinstance(dataloader, DataLoader):
             raise TypeError(f'dataloader must be a pytorch DataLoader, '
@@ -99,11 +104,14 @@ def __init__(self,
             self.best_ckpt_path = None
             self._init_rule(rule, self.save_best)
 
+        self.test_fn = test_fn
+
     def _init_rule(self, rule, key_indicator):
         """Initialize rule, key_indicator, comparison_func, and best score.
 
         Here is the rule to determine which rule is used for key indicator
-        when the rule is not specific:
+        when the rule is not specific (note that the key indicator matching
+        is case-insensitive):
         1. If the key indicator is in ``self.greater_keys``, the rule will be
            specified as 'greater'.
         2. Or if the key indicator is in ``self.less_keys``, the rule will be
@@ -124,13 +132,18 @@ def _init_rule(self, rule, key_indicator):
 
         if rule is None:
             if key_indicator != 'auto':
-                if key_indicator in self.greater_keys:
+                # use case-insensitive key indicator to infer the rule
+                key_indicator_lc = key_indicator.lower()
+                greater_keys = [key.lower() for key in self.greater_keys]
+                less_keys = [key.lower() for key in self.less_keys]
+
+                if key_indicator_lc in greater_keys:
                     rule = 'greater'
-                elif key_indicator in self.less_keys:
+                elif key_indicator_lc in less_keys:
                     rule = 'less'
-                elif any(key in key_indicator for key in self.greater_keys):
+                elif any(key in key_indicator_lc for key in greater_keys):
                     rule = 'greater'
-                elif any(key in key_indicator for key in self.less_keys):
+                elif any(key in key_indicator_lc for key in less_keys):
                     rule = 'less'
                 else:
                     raise ValueError(f'Cannot infer the rule for key '
@@ -181,8 +194,13 @@ def _do_evaluate(self, runner):
         if not self._should_evaluate(runner):
             return
 
-        from mmcv.engine import single_gpu_test
-        results = single_gpu_test(runner.model, self.dataloader)
+        if self.test_fn is None:
+            from mmcv.engine import single_gpu_test
+            test_fn = single_gpu_test
+        else:
+            test_fn = self.test_fn
+
+        results = test_fn(runner.model, self.dataloader)
         runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
         key_score = self.evaluate(runner, results)
         if self.save_best:
@@ -311,13 +329,18 @@ class DistEvalHook(EvalHook):
             .etc will be inferred by 'greater' rule. Keys contain 'loss' will
             be inferred by 'less' rule. Options are 'greater', 'less', None.
             Default: None.
+        test_fn (callable, optional): test a model with samples from a
+            dataloader in a multi-gpu manner, and return the test results. If
+            ``None``, the default test function ``mmcv.engine.multi_gpu_test``
+            will be used. (default: ``None``)
         tmpdir (str | None): Temporary directory to save the results of all
             processes. Default: None.
         gpu_collect (bool): Whether to use gpu or cpu to collect results.
             Default: False.
         broadcast_bn_buffer (bool): Whether to broadcast the
             buffer(running_mean and running_var) of rank 0 to other rank
             before evaluation. Default: True.
+        multi_
         **eval_kwargs: Evaluation arguments fed into the evaluate function of
             the dataset.
     """
@@ -329,6 +352,7 @@ def __init__(self,
                  by_epoch=True,
                  save_best=None,
                  rule=None,
+                 test_fn=None,
                  broadcast_bn_buffer=True,
                  tmpdir=None,
                  gpu_collect=False,
@@ -340,6 +364,7 @@ def __init__(self,
             by_epoch=by_epoch,
             save_best=save_best,
             rule=rule,
+            test_fn=test_fn,
             **eval_kwargs)
         self.broadcast_bn_buffer = broadcast_bn_buffer
         self.tmpdir = tmpdir
@@ -367,8 +392,13 @@ def _do_evaluate(self, runner):
         if tmpdir is None:
             tmpdir = osp.join(runner.work_dir, '.eval_hook')
 
-        from mmcv.engine import multi_gpu_test
-        results = multi_gpu_test(
+        if self.test_fn is None:
+            from mmcv.engine import multi_gpu_test
+            test_fn = multi_gpu_test
+        else:
+            test_fn = self.test_fn
+
+        results = test_fn(
             runner.model,
             self.dataloader,
             tmpdir=tmpdir,