pytorch metrics implementation

mmseg/core/evaluation/metrics.py

@@ -1,5 +1,6 @@
 import mmcv
 import numpy as np
+import torch
 
 
 def intersect_and_union(pred_label,
@@ -11,8 +12,10 @@ def intersect_and_union(pred_label,
     """Calculate intersection and Union.
 
     Args:
-        pred_label (ndarray): Prediction segmentation map.
-        label (ndarray): Ground truth segmentation map.
+        pred_label (ndarray | str): Prediction segmentation map
+            or predict result filename.
+        label (ndarray | str): Ground truth segmentation map
+            or label filename.
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. The parameter will
@@ -21,25 +24,29 @@ def intersect_and_union(pred_label,
             work only when label is str. Default: False.
 
      Returns:
-         ndarray: The intersection of prediction and ground truth histogram
-             on all classes.
-         ndarray: The union of prediction and ground truth histogram on all
-             classes.
-         ndarray: The prediction histogram on all classes.
-         ndarray: The ground truth histogram on all classes.
+         torch.Tensor: The intersection of prediction and ground truth
+            histogram on all classes.
+         torch.Tensor: The union of prediction and ground truth histogram on
+            all classes.
+         torch.Tensor: The prediction histogram on all classes.
+         torch.Tensor: The ground truth histogram on all classes.
     """
 
     if isinstance(pred_label, str):
-        pred_label = np.load(pred_label)
+        pred_label = torch.from_numpy(np.load(pred_label))
+    else:
+        pred_label = torch.from_numpy((pred_label))
 
     if isinstance(label, str):
-        label = mmcv.imread(label, flag='unchanged', backend='pillow')
-    # modify if custom classes
+        label = torch.from_numpy(
+            mmcv.imread(label, flag='unchanged', backend='pillow'))
+    else:
+        label = torch.from_numpy(label)
+
     if label_map is not None:
         for old_id, new_id in label_map.items():
             label[label == old_id] = new_id
     if reduce_zero_label:
-        # avoid using underflow conversion
         label[label == 0] = 255
         label = label - 1
         label[label == 254] = 255
@@ -49,13 +56,13 @@ def intersect_and_union(pred_label,
     label = label[mask]
 
     intersect = pred_label[pred_label == label]
-    area_intersect, _ = np.histogram(
-        intersect, bins=np.arange(num_classes + 1))
-    area_pred_label, _ = np.histogram(
-        pred_label, bins=np.arange(num_classes + 1))
-    area_label, _ = np.histogram(label, bins=np.arange(num_classes + 1))
+    area_intersect = torch.histc(
+        intersect.float(), bins=(num_classes), min=0, max=num_classes)
+    area_pred_label = torch.histc(
+        pred_label.float(), bins=(num_classes), min=0, max=num_classes)
+    area_label = torch.histc(
+        label.float(), bins=(num_classes), min=0, max=num_classes)
     area_union = area_pred_label + area_label - area_intersect
-
     return area_intersect, area_union, area_pred_label, area_label
 
 
@@ -68,8 +75,10 @@ def total_intersect_and_union(results,
     """Calculate Total Intersection and Union.
 
     Args:
-        results (list[ndarray]): List of prediction segmentation maps.
-        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        results (list[ndarray] | list[str]): List of prediction segmentation
+            maps or list of prediction result filenames.
+        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
+            segmentation maps or list of label filenames.
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. Default: dict().
@@ -83,23 +92,23 @@ 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(gt_seg_maps) == num_imgs
-    total_area_intersect = np.zeros((num_classes, ), dtype=np.float)
-    total_area_union = np.zeros((num_classes, ), dtype=np.float)
-    total_area_pred_label = np.zeros((num_classes, ), dtype=np.float)
-    total_area_label = np.zeros((num_classes, ), dtype=np.float)
+    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)
+    total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
     for i in range(num_imgs):
         area_intersect, area_union, area_pred_label, area_label = \
-            intersect_and_union(results[i], gt_seg_maps[i], num_classes,
-                                ignore_index, label_map, reduce_zero_label)
+            intersect_and_union(
+                results[i], gt_seg_maps[i], num_classes, ignore_index,
+                label_map, reduce_zero_label)
         total_area_intersect += area_intersect
         total_area_union += area_union
         total_area_pred_label += area_pred_label
         total_area_label += area_label
-    return total_area_intersect, total_area_union, \
-        total_area_pred_label, total_area_label
+    return total_area_intersect, total_area_union, total_area_pred_label, \
+        total_area_label
 
 
 def mean_iou(results,
@@ -112,8 +121,10 @@ def mean_iou(results,
     """Calculate Mean Intersection and Union (mIoU)
 
     Args:
-        results (list[ndarray]): List of prediction segmentation maps.
-        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        results (list[ndarray] | list[str]): List of prediction segmentation
+            maps or list of prediction result filenames.
+        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
+            segmentation maps or list of label filenames.
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         nan_to_num (int, optional): If specified, NaN values will be replaced
@@ -126,7 +137,6 @@ def mean_iou(results,
          ndarray: Per category accuracy, shape (num_classes, ).
          ndarray: Per category IoU, shape (num_classes, ).
     """
-
     all_acc, acc, iou = eval_metrics(
         results=results,
         gt_seg_maps=gt_seg_maps,
@@ -149,8 +159,10 @@ def mean_dice(results,
     """Calculate Mean Dice (mDice)
 
     Args:
-        results (list[ndarray]): List of prediction segmentation maps.
-        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        results (list[ndarray] | list[str]): List of prediction segmentation
+            maps or list of prediction result filenames.
+        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
+            segmentation maps or list of label filenames.
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         nan_to_num (int, optional): If specified, NaN values will be replaced
@@ -186,8 +198,10 @@ def eval_metrics(results,
                  reduce_zero_label=False):
     """Calculate evaluation metrics
     Args:
-        results (list[ndarray]): List of prediction segmentation maps.
-        gt_seg_maps (list[ndarray]): list of ground truth segmentation maps.
+        results (list[ndarray] | list[str]): List of prediction segmentation
+            maps or list of prediction result filenames.
+        gt_seg_maps (list[ndarray] | list[str]): list of ground truth
+            segmentation maps or list of label filenames.
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         metrics (list[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'.
@@ -200,17 +214,16 @@ def eval_metrics(results,
          ndarray: Per category accuracy, shape (num_classes, ).
          ndarray: Per category evalution metrics, shape (num_classes, ).
     """
-
     if isinstance(metrics, str):
         metrics = [metrics]
     allowed_metrics = ['mIoU', 'mDice']
     if not set(metrics).issubset(set(allowed_metrics)):
         raise KeyError('metrics {} is not supported'.format(metrics))
+
     total_area_intersect, total_area_union, total_area_pred_label, \
-        total_area_label = total_intersect_and_union(results, gt_seg_maps,
-                                                     num_classes, ignore_index,
-                                                     label_map,
-                                                     reduce_zero_label)
+        total_area_label = total_intersect_and_union(
+            results, gt_seg_maps, num_classes, ignore_index, label_map,
+            reduce_zero_label)
     all_acc = total_area_intersect.sum() / total_area_label.sum()
     acc = total_area_intersect / total_area_label
     ret_metrics = [all_acc, acc]
@@ -222,6 +235,7 @@ def eval_metrics(results,
             dice = 2 * total_area_intersect / (
                 total_area_pred_label + total_area_label)
             ret_metrics.append(dice)
+    ret_metrics = [metric.numpy() for metric in ret_metrics]
     if nan_to_num is not None:
         ret_metrics = [
             np.nan_to_num(metric, nan=nan_to_num) for metric in ret_metrics

tests/test_metrics.py

@@ -164,3 +164,45 @@ def test_mean_dice():
         results, label, num_classes, ignore_index=255, nan_to_num=-1)
     assert acc[-1] == -1
     assert iou[-1] == -1
+
+
+def test_filename_inputs():
+    import cv2
+    import tempfile
+
+    def save_arr(input_arrays: list, title: str, is_image: bool, dir: str):
+        filenames = []
+        SUFFIX = '.png' if is_image else '.npy'
+        for idx, arr in enumerate(input_arrays):
+            filename = '{}/{}-{}{}'.format(dir, title, idx, SUFFIX)
+            if is_image:
+                cv2.imwrite(filename, arr)
+            else:
+                np.save(filename, arr)
+            filenames.append(filename)
+        return filenames
+
+    pred_size = (10, 512, 1024)
+    num_classes = 19
+    ignore_index = 255
+    results = np.random.randint(0, num_classes, size=pred_size)
+    labels = np.random.randint(0, num_classes, size=pred_size)
+    labels[:, 2, 5:10] = ignore_index
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+
+        result_files = save_arr(results, 'pred', False, temp_dir)
+        label_files = save_arr(labels, 'label', True, temp_dir)
+
+        all_acc, acc, iou = eval_metrics(
+            result_files,
+            label_files,
+            num_classes,
+            ignore_index,
+            metrics='mIoU')
+
+        all_acc_l, acc_l, iou_l = legacy_mean_iou(results, labels, num_classes,
+                                                  ignore_index)
+        assert all_acc == all_acc_l
+        assert np.allclose(acc, acc_l)
+        assert np.allclose(iou, iou_l)