add shape checking for MS-SSIM input and revise docstring as comment

mmeval/metrics/ms_ssim.py

@@ -1,16 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
-from typing import TYPE_CHECKING, Dict, List, Sequence, Tuple
+from scipy import signal
+from typing import Dict, List, Sequence, Tuple
 
 from mmeval.core import BaseMetric
-from mmeval.utils import try_import
 from .utils.image_transforms import reorder_image
 
-if TYPE_CHECKING:
-    from scipy import signal
-else:
-    signal = try_import('scipy.signal')
-
 
 class MultiScaleStructureSimilarity(BaseMetric):
     """MS-SSIM (Multi-Scale Structure Similarity) metric.
@@ -34,13 +29,13 @@ class MultiScaleStructureSimilarity(BaseMetric):
             between the maximum the and minimum allowed values).
             Defaults to 255.
         filter_size (int): Size of blur kernel to use (will be reduced for
-            small images). Default to 11.
+            small images). Defaults to 11.
         filter_sigma (float): Standard deviation for Gaussian blur kernel (will
-            be reduced for small images). Default to 1.5.
+            be reduced for small images). Defaults to 1.5.
         k1 (float): Constant used to maintain stability in the SSIM calculation
-            (0.01 in the original paper). Default to 0.01.
+            (0.01 in the original paper). Defaults to 0.01.
         k2 (float): Constant used to maintain stability in the SSIM calculation
-            (0.03 in the original paper). Default to 0.03.
+            (0.03 in the original paper). Defaults to 0.03.
         weights (List[float]): List of weights for each level. Defaults to
             [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]. Noted that the default
             weights don't sum to 1.0 but do match the paper / matlab code.
@@ -84,13 +79,15 @@ def __init__(self,
         self.weights = np.array(weights)
 
     def add(self, predictions: Sequence[np.ndarray]) -> None:  # type: ignore # yapf: disable # noqa: E501
-        """Add PSNR score of batch to ``self._results``
+        """Add a bunch of images to calculate metric result.
 
         Args:
             predictions (Sequence[np.ndarray]): Predictions of the model. The
-                number of elements in the Sequence must be divisible by 2.
-                The channel order of each element should align with
-                `self.input_order` and the range should be [0, 255].
+                number of elements in the Sequence must be divisible by 2, and
+                the width and height of each element must be divisible by 2 **
+                num_scale (`self.weights.size`). The channel order of each
+                element should align with `self.input_order` and the range
+                should be [0, 255].
         """
 
         num_samples = len(predictions)
@@ -103,6 +100,16 @@ def add(self, predictions: Sequence[np.ndarray]) -> None:  # type: ignore # yapf
             reorder_image(pred, self.input_order) for pred in predictions[1::2]
         ]
 
+        least_size = 2**self.weights.size
+        assert all([
+            sample.shape[0] % least_size == 0 for sample in half1
+        ]), ('The height and width of each sample must be divisible by '
+             f'{least_size} (2 ** len(self.weights.size)).')
+        assert all([
+            sample.shape[0] % least_size == 0 for sample in half2
+        ]), ('The height and width of each sample must be divisible by '
+             f'{least_size} (2 ** self.weights.size).')
+
         half1 = np.stack(half1, axis=0).astype(np.uint8)
         half2 = np.stack(half2, axis=0).astype(np.uint8)
 
@@ -131,7 +138,7 @@ def compute_ms_ssim(self, img1: np.array, img2: np.array) -> List[float]:
             img2 (ndarray): Images with range [0, 255] and order "NHWC".
 
         Returns:
-            np.ndarray: MS-SSIM score between `img1` and `img2`.
+            np.ndarray: MS-SSIM score between `img1` and `img2` of shape (N, ).
         """
         if img1.shape != img2.shape:
             raise RuntimeError(
@@ -227,15 +234,15 @@ def _ssim_for_multi_scale(
             img2 (np.ndarray): Images with range [0, 255] and order "NHWC".
             max_val (int): the dynamic range of the images (i.e., the
                 difference between the maximum the and minimum allowed
-                values). Default to 255.
+                values). Defaults to 255.
             filter_size (int): Size of blur kernel to use (will be reduced for
-                small images). Default to 11.
+                small images). Defaults to 11.
             filter_sigma (float): Standard deviation for Gaussian blur kernel (
-                will be reduced for small images). Default to 1.5.
+                will be reduced for small images). Defaults to 1.5.
             k1 (float): Constant used to maintain stability in the SSIM
-                calculation (0.01 in the original paper). Default to 0.01.
+                calculation (0.01 in the original paper). Defaults to 0.01.
             k2 (float): Constant used to maintain stability in the SSIM
-                calculation (0.03 in the original paper). Default to 0.03.
+                calculation (0.03 in the original paper). Defaults to 0.03.
 
         Returns:
             tuple: Pair containing the mean SSIM and contrast sensitivity

mmeval/metrics/swd.py

@@ -1,14 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
-from typing import TYPE_CHECKING, Any, Dict, List, Sequence
+from scipy import ndimage
+from typing import Any, Dict, List, Sequence
 
 from mmeval.core import BaseMetric
-from mmeval.utils import try_import
-
-if TYPE_CHECKING:
-    from scipy import ndimage
-else:
-    ndimage = try_import('scipy.ndimage')
 
 
 class SlicedWassersteinDistance(BaseMetric):

tests/test_metrics/test_ms_ssim.py

@@ -58,3 +58,7 @@ def test_raise_error():
             np.random.randint(0, 255, (64, 64, 3)),
             np.random.randint(0, 255, (64, 64, 3))
         )
+
+    with pytest.raises(AssertionError):
+        inputs = [np.random.randint(0, 255, (3, 32, 32))] * 3
+        ms_ssim(inputs)