[Fix] Fix some inferencer bugs (#1706)

* [Fix] Fix some inferencer bugs

* fix

configs/kie/sdmgr/_base_sdmgr_unet16.py

@@ -24,5 +24,5 @@ test_pipeline = [
     dict(type='LoadImageFromFile'),
     dict(type='LoadKIEAnnotations'),
     dict(type='Resize', scale=(1024, 512), keep_ratio=True),
-    dict(type='PackKIEInputs'),
+    dict(type='PackKIEInputs', meta_keys=('img_path', )),
 ]

mmocr/apis/inferencers/base_mmocr_inferencer.py

@@ -80,6 +80,7 @@ class BaseMMOCRInferencer(BaseInferencer):
         Args:
             inputs (InputsType): Inputs for the inferencer. It can be a path
                 to image / image directory, or an array, or a list of these.
+                Note: If it's an numpy array, it should be in BGR order.
             return_datasamples (bool): Whether to return results as
                 :obj:`BaseDataElement`. Defaults to False.
             batch_size (int): Inference batch size. Defaults to 1.
@@ -206,7 +207,7 @@ class BaseMMOCRInferencer(BaseInferencer):
                 img = mmcv.imfrombytes(img_bytes, channel_order='rgb')
                 img_name = osp.basename(single_input)
             elif isinstance(single_input, np.ndarray):
-                img = single_input.copy()
+                img = single_input.copy()[:, :, ::-1]  # to RGB
                 img_num = str(self.num_visualized_imgs).zfill(8)
                 img_name = f'{img_num}.jpg'
             else:

mmocr/apis/inferencers/kie_inferencer.py

@@ -77,6 +77,13 @@ class KIEInferencer(BaseMMOCRInferencer):
         self.novisual = all(
             self._get_transform_idx(pipeline_cfg, t) == -1
             for t in self.loading_transforms)
+        # Remove Resize from test_pipeline, since SDMGR requires bbox
+        # annotations to be resized together with pictures, but visualization
+        # loads the original image from the disk.
+        # TODO: find a more elegant way to fix this
+        idx = self._get_transform_idx(pipeline_cfg, 'Resize')
+        if idx != -1:
+            pipeline_cfg.pop(idx)
         # If it's in non-visual mode, self.pipeline will be specified.
         # Otherwise, file_pipeline and ndarray_pipeline will be specified.
         if self.novisual:
@@ -93,6 +100,7 @@ class KIEInferencer(BaseMMOCRInferencer):
 
         - img (str or ndarray): Path to the image or the image itself. If KIE
           Inferencer is used in no-visual mode, this key is not required.
+          Note: If it's an numpy array, it should be in BGR order.
         - img_shape (tuple(int, int)): Image shape in (H, W). In
         - instances (list[dict]): A list of instances.
             - bbox (ndarray(dtype=np.float32)): Shape (4, ). Bounding box.
@@ -182,6 +190,7 @@ class KIEInferencer(BaseMMOCRInferencer):
 
         - img (str or ndarray): Path to the image or the image itself. If KIE
           Inferencer is used in no-visual mode, this key is not required.
+          Note: If it's an numpy array, it should be in BGR order.
         - img_shape (tuple(int, int)): Image shape in (H, W). In
         - instances (list[dict]): A list of instances.
             - bbox (ndarray(dtype=np.float32)): Shape (4, ). Bounding box.
@@ -286,10 +295,10 @@ class KIEInferencer(BaseMMOCRInferencer):
             assert 'img' in single_input or 'img_shape' in single_input
             if 'img' in single_input:
                 if isinstance(single_input['img'], str):
-                    img = mmcv.imread(single_input['img'])
+                    img = mmcv.imread(single_input['img'], channel_order='rgb')
                     img_name = osp.basename(single_input['img'])
                 elif isinstance(single_input['img'], np.ndarray):
-                    img = single_input['img'].copy()
+                    img = single_input['img'].copy()[:, :, ::-1]  # To RGB
                     img_name = f'{img_num}.jpg'
             elif 'img_shape' in single_input:
                 img = np.zeros(single_input['img_shape'], dtype=np.uint8)

mmocr/ocr.py

@@ -46,7 +46,7 @@ def parse_args():
         help='Pretrained key information extraction algorithm. It\'s the path'
         'to the config file or the model name defined in metafile.')
     parser.add_argument(
-        '--kie-ckpt',
+        '--kie-weights',
         type=str,
         default=None,
         help='Path to the custom checkpoint file of the selected kie model. '
@@ -77,7 +77,8 @@ def parse_args():
     call_args = vars(parser.parse_args())
 
     init_kws = [
-        'det', 'det_weights', 'rec', 'rec_weights', 'kie', 'kie_ckpt', 'device'
+        'det', 'det_weights', 'rec', 'rec_weights', 'kie', 'kie_weights',
+        'device'
     ]
     init_args = {}
     for init_kw in init_kws:

tests/test_apis/test_inferencers/test_textdet_inferencer.py

@@ -54,8 +54,9 @@ class TestTextDetinferencer(TestCase):
         res_ndarray = self.inferencer(img, return_vis=True)
         self.assert_predictions_equal(res_path['predictions'],
                                       res_ndarray['predictions'])
-        self.assertIn('visualization', res_path)
+        self.assertTrue(
-        self.assertIn('visualization', res_ndarray)
+            np.allclose(res_path['visualization'],
+                        res_ndarray['visualization']))
 
         # multiple images
         img_paths = [
@@ -68,8 +69,10 @@ class TestTextDetinferencer(TestCase):
         res_ndarray = self.inferencer(imgs, return_vis=True)
         self.assert_predictions_equal(res_path['predictions'],
                                       res_ndarray['predictions'])
-        self.assertIn('visualization', res_path)
+        for i in range(len(img_paths)):
-        self.assertIn('visualization', res_ndarray)
+            self.assertTrue(
+                np.allclose(res_path['visualization'][i],
+                            res_ndarray['visualization'][i]))
 
         # img dir, test different batch sizes
         img_dir = 'tests/data/det_toy_dataset/imgs/test/'

tests/test_apis/test_inferencers/test_textrec_inferencer.py

@@ -52,8 +52,9 @@ class TestTextRecinferencer(TestCase):
         res_ndarray = self.inferencer(img, return_vis=True)
         self.assert_predictions_equal(res_path['predictions'],
                                       res_ndarray['predictions'])
-        self.assertIn('visualization', res_path)
+        self.assertTrue(
-        self.assertIn('visualization', res_ndarray)
+            np.allclose(res_path['visualization'],
+                        res_ndarray['visualization']))
 
         # multiple images
         img_paths = [
@@ -66,8 +67,10 @@ class TestTextRecinferencer(TestCase):
         res_ndarray = self.inferencer(imgs, return_vis=True)
         self.assert_predictions_equal(res_path['predictions'],
                                       res_ndarray['predictions'])
-        self.assertIn('visualization', res_path)
+        for i in range(len(img_paths)):
-        self.assertIn('visualization', res_ndarray)
+            self.assertTrue(
+                np.allclose(res_path['visualization'][i],
+                            res_ndarray['visualization'][i]))
 
         # img dir, test different batch sizes
         img_dir = 'tests/data/rec_toy_dataset/imgs'