feat: init_segmentor_kn, inference_segmentor_kn, show_result

mmseg/apis/__init__.py

@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .inference import (
     inference_segmentor,
+    inference_segmentor_kn,
     init_segmentor,
     init_segmentor_kn,
     show_result_pyplot,
@@ -10,7 +11,8 @@ from .train import (get_root_logger, init_random_seed, set_random_seed,
                     train_segmentor)
 
 __all__ = [
-    'get_root_logger', 'set_random_seed', 'train_segmentor', 'init_segmentor',
-    'init_segmentor_kn', 'inference_segmentor', 'multi_gpu_test',
-    'single_gpu_test', 'show_result_pyplot', 'init_random_seed'
+    'get_root_logger', 'set_random_seed', 'train_segmentor',
+    'init_segmentor', 'init_segmentor_kn', 'inference_segmentor',
+    'inference_segmentor_kn', 'multi_gpu_test', 'single_gpu_test',
+    'show_result_pyplot', 'init_random_seed'
 ]

mmseg/apis/inference.py

@@ -128,14 +128,13 @@ def inference_segmentor(model, img):
 
 @torch.no_grad()
 def inference_segmentor_kn(model, img):
-    if model.endswith(".onnx"):
+    if isinstance(model, ONNXRuntimeSegmentorKN):
         cfg = model.cfg
         test_pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
         test_pipeline = Compose(test_pipeline)
         data = dict(img=img)
         data = test_pipeline(data)
         data = collate([data], samples_per_gpu=1)
-        data['img_metas'] = [i.data[0] for i in data['img_metas']]
         return model(return_loss=False, rescale=True, **data)
     else:
         return inference_segmentor(model, img)

mmseg/models/segmentors/base.py

@@ -411,6 +411,10 @@ class ONNXRuntimeSegmentorKN(BaseSegmentor):
         preds /= self.count_mat
         return preds
 
+    @property
+    def module(self):
+        return self
+
     @torch.no_grad()
     def simple_test(
             self,
@@ -426,7 +430,6 @@ class ONNXRuntimeSegmentorKN(BaseSegmentor):
             seg_pred = self.sess.run(
                 self.output_name_list, {self.input_name: img}
             )[0]
-        print(img.shape, seg_pred.shape)
         if img_meta is not None:
             ori_shape = img_meta[0]['ori_shape']
             if not (ori_shape[0] == seg_pred.shape[-2]