[Feature] Support eval concate dataset and add tool to show dataset

mmseg/core/evaluation/metrics.py

@@ -112,8 +112,6 @@ def total_intersect_and_union(results,
          ndarray: The prediction histogram on all classes.
          ndarray: The ground truth histogram on all classes.
     """
-    num_imgs = len(results)
-    assert len(list(gt_seg_maps)) == num_imgs
     total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
     total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)

mmseg/datasets/builder.py

@@ -30,6 +30,7 @@ def _concat_dataset(cfg, default_args=None):
     img_dir = cfg['img_dir']
     ann_dir = cfg.get('ann_dir', None)
     split = cfg.get('split', None)
+    separate_eval = cfg.get('separate_eval', True)
     num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
     if ann_dir is not None:
         num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
@@ -49,6 +50,9 @@ def _concat_dataset(cfg, default_args=None):
     datasets = []
     for i in range(num_dset):
         data_cfg = copy.deepcopy(cfg)
+        # pop 'separate_eval' since it is not a valid key for common datasets.
+        if 'separate_eval' in data_cfg:
+            data_cfg.pop('separate_eval')
         if isinstance(img_dir, (list, tuple)):
             data_cfg['img_dir'] = img_dir[i]
         if isinstance(ann_dir, (list, tuple)):
@@ -57,7 +61,7 @@ def _concat_dataset(cfg, default_args=None):
             data_cfg['split'] = split[i]
         datasets.append(build_dataset(data_cfg, default_args))
 
-    return ConcatDataset(datasets)
+    return ConcatDataset(datasets, separate_eval)
 
 
 def build_dataset(cfg, default_args=None):

mmseg/datasets/custom.py

@@ -99,6 +99,9 @@ def __init__(self,
         self.label_map = None
         self.CLASSES, self.PALETTE = self.get_classes_and_palette(
             classes, palette)
+        if test_mode:
+            assert self.CLASSES is not None, \
+                '`cls.CLASSES` or `classes` should be specified when testing'
 
         # join paths if data_root is specified
         if self.data_root is not None:
@@ -339,7 +342,12 @@ def get_palette_for_custom_classes(self, class_names, palette=None):
 
         return palette
 
-    def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
+    def evaluate(self,
+                 results,
+                 metric='mIoU',
+                 logger=None,
+                 gt_seg_maps=None,
+                 **kwargs):
         """Evaluate the dataset.
 
         Args:
@@ -350,6 +358,8 @@ def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
                 'mDice' and 'mFscore' are supported.
             logger (logging.Logger | None | str): Logger used for printing
                 related information during evaluation. Default: None.
+            gt_seg_maps (generator[ndarray]): Custom gt seg maps as input,
+                used in ConcatDataset
 
         Returns:
             dict[str, float]: Default metrics.
@@ -364,14 +374,9 @@ def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
         # test a list of files
         if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
                 results, str):
-            gt_seg_maps = self.get_gt_seg_maps()
-            if self.CLASSES is None:
-                num_classes = len(
-                    reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
-            else:
-                num_classes = len(self.CLASSES)
-            # reset generator
-            gt_seg_maps = self.get_gt_seg_maps()
+            if gt_seg_maps is None:
+                gt_seg_maps = self.get_gt_seg_maps()
+            num_classes = len(self.CLASSES)
             ret_metrics = eval_metrics(
                 results,
                 gt_seg_maps,

mmseg/datasets/dataset_wrappers.py

@@ -1,24 +1,159 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+from itertools import chain
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
 from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
 
 from .builder import DATASETS
+from .cityscapes import CityscapesDataset
 
 
 @DATASETS.register_module()
 class ConcatDataset(_ConcatDataset):
     """A wrapper of concatenated dataset.
 
     Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
-    concat the group flag for image aspect ratio.
+    support evaluation and formatting results
 
     Args:
         datasets (list[:obj:`Dataset`]): A list of datasets.
+        separate_eval (bool): Whether to evaluate the concatenated
+            dataset results separately, Defaults to True.
     """
 
-    def __init__(self, datasets):
+    def __init__(self, datasets, separate_eval=True):
         super(ConcatDataset, self).__init__(datasets)
         self.CLASSES = datasets[0].CLASSES
         self.PALETTE = datasets[0].PALETTE
+        self.separate_eval = separate_eval
+        if any([isinstance(ds, CityscapesDataset) for ds in datasets]):
+            raise NotImplementedError(
+                'Evaluating ConcatDataset containing CityscapesDataset is not supported!'
+            )
+
+    def evaluate(self, results, logger=None, **kwargs):
+        """Evaluate the results.
+
+        Args:
+            results (list[tuple[torch.Tensor]] | list[str]]): per image pre_eval
+                 results or predict segmentation map for computing evaluation
+                 metric.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+
+        Returns:
+            dict[str: float]: evaluate results of the total dataset or each separate
+            dataset if `self.separate_eval=True`.
+        """
+        assert len(results) == self.cumulative_sizes[-1], \
+            ('Dataset and results have different sizes: '
+             f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
+
+        # Check whether all the datasets support evaluation
+        for dataset in self.datasets:
+            assert hasattr(dataset, 'evaluate'), \
+                f'{type(dataset)} does not implement evaluate function'
+
+        if self.separate_eval:
+            dataset_idx = -1
+            total_eval_results = dict()
+            for size, dataset in zip(self.cumulative_sizes, self.datasets):
+                start_idx = 0 if dataset_idx == -1 else \
+                    self.cumulative_sizes[dataset_idx]
+                end_idx = self.cumulative_sizes[dataset_idx + 1]
+
+                results_per_dataset = results[start_idx:end_idx]
+                print_log(
+                    f'\nEvaluateing {dataset.img_dir} with '
+                    f'{len(results_per_dataset)} images now',
+                    logger=logger)
+
+                eval_results_per_dataset = dataset.evaluate(
+                    results_per_dataset, logger=logger, **kwargs)
+                dataset_idx += 1
+                for k, v in eval_results_per_dataset.items():
+                    total_eval_results.update({f'{dataset_idx}_{k}': v})
+
+            return total_eval_results
+
+        if len(set([type(ds) for ds in self.datasets])) != 1:
+            raise NotImplementedError(
+                'All the datasets should have same types when self.separate_eval=False'
+            )
+        else:
+            if mmcv.is_list_of(results, np.ndarray) or mmcv.is_list_of(
+                    results, str):
+                # merge the generators of gt_seg_maps
+                gt_seg_maps = chain(
+                    *[dataset.get_gt_seg_maps() for dataset in self.datasets])
+            else:
+                # if the results are `pre_eval` results,
+                # we do not need gt_seg_maps to evaluate
+                gt_seg_maps = None
+            eval_results = self.datasets[0].evaluate(
+                results, gt_seg_maps=gt_seg_maps, logger=logger, **kwargs)
+            return eval_results
+
+    def get_dataset_idx_and_sample_idx(self, indice):
+        """Return dataset and sample index when given an indice of
+        ConcatDataset.
+
+        Args:
+            indice (int): indice of sample in ConcatDataset
+
+        Returns:
+            int: the index of sub dataset the sample belong to
+            int: the index of sample in its corresponding subset
+        """
+        if indice < 0:
+            if -indice > len(self):
+                raise ValueError(
+                    'absolute value of index should not exceed dataset length')
+            indice = len(self) + indice
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, indice)
+        if dataset_idx == 0:
+            sample_idx = indice
+        else:
+            sample_idx = indice - self.cumulative_sizes[dataset_idx - 1]
+        return dataset_idx, sample_idx
+
+    def format_results(self, results, imgfile_prefix, indices=None, **kwargs):
+        """format result for every sample of ConcatDataset."""
+        if indices is None:
+            indices = list(range(len(self)))
+
+        assert isinstance(results, list), 'results must be a list.'
+        assert isinstance(indices, list), 'indices must be a list.'
+
+        ret_res = []
+        for i, indice in enumerate(indices):
+            dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
+                indice)
+            res = self.datasets[dataset_idx].format_results(
+                [results[i]],
+                imgfile_prefix + f'/{dataset_idx}',
+                indices=[sample_idx],
+                **kwargs)
+            ret_res.append(res)
+        return sum(ret_res, [])
+
+    def pre_eval(self, preds, indices):
+        """do pre eval for every sample of ConcatDataset."""
+        # In order to compat with batch inference
+        if not isinstance(indices, list):
+            indices = [indices]
+        if not isinstance(preds, list):
+            preds = [preds]
+        ret_res = []
+        for i, indice in enumerate(indices):
+            dataset_idx, sample_idx = self.get_dataset_idx_and_sample_idx(
+                indice)
+            res = self.datasets[dataset_idx].pre_eval(preds[i], sample_idx)
+            ret_res.append(res)
+        return sum(ret_res, [])
 
 
 @DATASETS.register_module()