Fix pytrans validation issue

mindspore/dataset/vision/py_transforms_util.py

@@ -167,6 +167,8 @@ def to_pil(img):
         img (PIL image), Converted image.
     """
     if not is_pil(img):
+        if not isinstance(img, np.ndarray):
+            raise TypeError("The input of ToPIL should be ndarray. Got {}".format(type(img)))
         return Image.fromarray(img)
     return img
 
@@ -1063,7 +1065,10 @@ def linear_transform(np_img, transformation_matrix, mean_vector):
         raise ValueError("mean_vector length {0} should match either one dimension of the square "
                          "transformation_matrix {1}.".format(mean_vector.shape[0], transformation_matrix.shape))
     zero_centered_img = np_img.reshape(1, -1) - mean_vector
-    transformed_img = np.dot(zero_centered_img, transformation_matrix).reshape(np_img.shape)
+    transformed_img = np.dot(zero_centered_img, transformation_matrix)
+    if transformed_img.size != np_img.size:
+        raise ValueError("Linear transform failed, input shape should match with transformation_matrix.")
+    transformed_img = transformed_img.reshape(np_img.shape)
     return transformed_img
 
 
@@ -1265,8 +1270,8 @@ def rgb_to_hsvs(np_rgb_imgs, is_hwc):
     shape_size = len(np_rgb_imgs.shape)
 
     if not shape_size in (3, 4):
-        raise TypeError("img shape should be (H, W, C)/(N, H, W, C)/(C ,H, W)/(N, C, H, W). \
-                         Got {}.".format(np_rgb_imgs.shape))
+        raise TypeError("img shape should be (H, W, C)/(N, H, W, C)/(C ,H, W)/(N, C, H, W). "
+                        "Got {}.".format(np_rgb_imgs.shape))
 
     if shape_size == 3:
         batch_size = 0
@@ -1336,8 +1341,8 @@ def hsv_to_rgbs(np_hsv_imgs, is_hwc):
     shape_size = len(np_hsv_imgs.shape)
 
     if not shape_size in (3, 4):
-        raise TypeError("img shape should be (H, W, C)/(N, H, W, C)/(C, H, W)/(N, C, H, W). \
-                         Got {}.".format(np_hsv_imgs.shape))
+        raise TypeError("img shape should be (H, W, C)/(N, H, W, C)/(C, H, W)/(N, C, H, W). "
+                        "Got {}.".format(np_hsv_imgs.shape))
 
     if shape_size == 3:
         batch_size = 0