rename init/inference_segmentor to init/inference_model

.dev/benchmark_inference.py

@@ -9,7 +9,7 @@ from argparse import ArgumentParser
 import requests
 from mmcv import Config
 
-from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
+from mmseg.apis import inference_model, init_model, show_result_pyplot
 from mmseg.utils import get_root_logger
 
 # ignore warnings when segmentors inference
@@ -57,7 +57,7 @@ def parse_args():
     return args
 
 
-def inference_model(config_name, checkpoint, args, logger=None):
+def inference(config_name, checkpoint, args, logger=None):
     cfg = Config.fromfile(config_name)
     if args.aug:
         if 'flip' in cfg.data.test.pipeline[
@@ -72,9 +72,9 @@ def inference_model(config_name, checkpoint, args, logger=None):
             else:
                 print(f'{config_name}: unable to start aug test', flush=True)
 
-    model = init_segmentor(cfg, checkpoint, device=args.device)
+    model = init_model(cfg, checkpoint, device=args.device)
     # test a single image
-    result = inference_segmentor(model, args.img)
+    result = inference_model(model, args.img)
 
     # show the results
     if args.show:
@@ -102,7 +102,7 @@ def main(args):
                                       model_info['checkpoint'].strip())
                 try:
                     # build the model from a config file and a checkpoint file
-                    inference_model(config_name, checkpoint, args)
+                    inference(config_name, checkpoint, args)
                 except Exception:
                     print(f'{config_name} test failed!')
                     continue
@@ -139,7 +139,7 @@ def main(args):
             # test model inference with checkpoint
             try:
                 # build the model from a config file and a checkpoint file
-                inference_model(config_path, checkpoint, args, logger)
+                inference(config_path, checkpoint, args, logger)
             except Exception as e:
                 logger.error(f'{config_path} " : {repr(e)}')
 

demo/MMSegmentation_Tutorial.ipynb

@@ -144,7 +144,7 @@
    },
    "outputs": [],
    "source": [
-    "from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot\n",
+    "from mmseg.apis import inference_model, init_model, show_result_pyplot\n",
     "from mmseg.core.evaluation import get_palette"
    ]
   },
@@ -173,7 +173,7 @@
    "outputs": [],
    "source": [
     "# build the model from a config file and a checkpoint file\n",
-    "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
+    "model = init_model(config_file, checkpoint_file, device='cuda:0')"
    ]
   },
   {
@@ -186,7 +186,7 @@
    "source": [
     "# test a single image\n",
     "img = 'demo/demo.png'\n",
-    "result = inference_segmentor(model, img)"
+    "result = inference_model(model, img)"
    ]
   },
   {
@@ -596,7 +596,7 @@
     "img = mmcv.imread('iccv09Data/images/6000124.jpg')\n",
     "\n",
     "model.cfg = cfg\n",
-    "result = inference_segmentor(model, img)\n",
+    "result = inference_model(model, img)\n",
     "plt.figure(figsize=(8, 6))\n",
     "show_result_pyplot(model, img, result, palette)"
    ]

demo/image_demo.py

@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from argparse import ArgumentParser
 
-from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot
+from mmseg.apis import inference_model, init_model, show_result_pyplot
 from mmseg.core.evaluation import get_palette
 
 
@@ -24,9 +24,9 @@ def main():
     args = parser.parse_args()
 
     # build the model from a config file and a checkpoint file
-    model = init_segmentor(args.config, args.checkpoint, device=args.device)
+    model = init_model(args.config, args.checkpoint, device=args.device)
     # test a single image
-    result = inference_segmentor(model, args.img)
+    result = inference_model(model, args.img)
     # show the results
     show_result_pyplot(
         model,

demo/inference_demo.ipynb

@@ -20,7 +20,7 @@
    },
    "outputs": [],
    "source": [
-    "from mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot\n",
+    "from mmseg.apis import init_model, inference_model, show_result_pyplot\n",
     "from mmseg.core.evaluation import get_palette"
    ]
   },
@@ -45,7 +45,7 @@
    "outputs": [],
    "source": [
     "# build the model from a config file and a checkpoint file\n",
-    "model = init_segmentor(config_file, checkpoint_file, device='cuda:0')"
+    "model = init_model(config_file, checkpoint_file, device='cuda:0')"
    ]
   },
   {
@@ -56,7 +56,7 @@
    "source": [
     "# test a single image\n",
     "img = 'demo.png'\n",
-    "result = inference_segmentor(model, img)"
+    "result = inference_model(model, img)"
    ]
   },
   {

demo/video_demo.py

@@ -3,7 +3,7 @@ from argparse import ArgumentParser
 
 import cv2
 
-from mmseg.apis import inference_segmentor, init_segmentor
+from mmseg.apis import inference_model, init_model
 from mmseg.core.evaluation import get_palette
 
 
@@ -52,7 +52,7 @@ def main():
         'At least one output should be enabled.'
 
     # build the model from a config file and a checkpoint file
-    model = init_segmentor(args.config, args.checkpoint, device=args.device)
+    model = init_model(args.config, args.checkpoint, device=args.device)
 
     # build input video
     cap = cv2.VideoCapture(args.video)
@@ -83,7 +83,7 @@ def main():
                 break
 
             # test a single image
-            result = inference_segmentor(model, frame)
+            result = inference_model(model, frame)
 
             # blend raw image and prediction
             draw_img = model.show_result(

docs/en/get_started.md

@@ -203,18 +203,18 @@ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
 To verify whether MMSegmentation and the required environment are installed correctly, we can run sample python codes to initialize a segmentor and inference a demo image:
 
 ```python
-from mmseg.apis import inference_segmentor, init_segmentor
+from mmseg.apis import inference_model, init_model
 import mmcv
 
 config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
 checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 
 # build the model from a config file and a checkpoint file
-model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
+model = init_model(config_file, checkpoint_file, device='cuda:0')
 
 # test a single image and show the results
 img = 'test.jpg'  # or img = mmcv.imread(img), which will only load it once
-result = inference_segmentor(model, img)
+result = inference_model(model, img)
 # visualize the results in a new window
 model.show_result(img, result, show=True)
 # or save the visualization results to image files
@@ -224,7 +224,7 @@ model.show_result(img, result, out_file='result.jpg', opacity=0.5)
 # test a video and show the results
 video = mmcv.VideoReader('video.mp4')
 for frame in video:
-   result = inference_segmentor(model, frame)
+   result = inference_model(model, frame)
    model.show_result(frame, result, wait_time=1)
 ```
 

docs/zh_cn/get_started.md

@@ -203,18 +203,18 @@ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
 为了验证 MMSegmentation 和它所需要的环境是否正确安装，我们可以使用样例 python 代码来初始化一个 segmentor 并推理一张 demo 图像。
 
 ```python
-from mmseg.apis import inference_segmentor, init_segmentor
+from mmseg.apis import inference_model, init_model
 import mmcv
 
 config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py'
 checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'
 
 # 从一个 config 配置文件和 checkpoint 文件里创建分割模型
-model = init_segmentor(config_file, checkpoint_file, device='cuda:0')
+model = init_model(config_file, checkpoint_file, device='cuda:0')
 
 # 测试一张样例图片并得到结果
 img = 'test.jpg'  # 或者 img = mmcv.imread(img), 这将只加载图像一次．
-result = inference_segmentor(model, img)
+result = inference_model(model, img)
 # 在新的窗口里可视化结果
 model.show_result(img, result, show=True)
 # 或者保存图片文件的可视化结果
@@ -224,7 +224,7 @@ model.show_result(img, result, out_file='result.jpg', opacity=0.5)
 # 测试一个视频并得到分割结果
 video = mmcv.VideoReader('video.mp4')
 for frame in video:
-   result = inference_segmentor(model, frame)
+   result = inference_model(model, frame)
    model.show_result(frame, result, wait_time=1)
 ```
 

mmseg/apis/__init__.py

@@ -1,4 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .inference import inference_segmentor, init_segmentor, show_result_pyplot
+from .inference import inference_model, init_model, show_result_pyplot
 
-__all__ = ['init_segmentor', 'inference_segmentor', 'show_result_pyplot']
+__all__ = ['init_model', 'inference_model', 'show_result_pyplot']

mmseg/apis/inference.py

@@ -9,7 +9,7 @@ from mmseg.datasets.pipelines import Compose
 from mmseg.models import build_segmentor
 
 
-def init_segmentor(config, checkpoint=None, device='cuda:0'):
+def init_model(config, checkpoint=None, device='cuda:0'):
     """Initialize a segmentor from config file.
 
     Args:
@@ -67,7 +67,7 @@ class LoadImage:
         return results
 
 
-def inference_segmentor(model, img):
+def inference_model(model, img):
     """Inference image(s) with the segmentor.
 
     Args:

tools/print_config.py

@@ -4,7 +4,7 @@ import warnings
 
 from mmcv import Config, DictAction
 
-from mmseg.apis import init_segmentor
+from mmseg.apis import init_model
 
 
 def parse_args():
@@ -59,7 +59,7 @@ def main():
     cfg.dump('example.py')
     # dump models graph
     if args.graph:
-        model = init_segmentor(args.config, device='cpu')
+        model = init_model(args.config, device='cpu')
         print(f'Model graph:\n{str(model)}')
         with open('example-graph.txt', 'w') as f:
             f.writelines(str(model))

tools/torchserve/mmseg_handler.py

@@ -8,7 +8,7 @@ import torch
 from mmcv.cnn.utils.sync_bn import revert_sync_batchnorm
 from ts.torch_handler.base_handler import BaseHandler
 
-from mmseg.apis import inference_segmentor, init_segmentor
+from mmseg.apis import inference_model, init_model
 
 
 class MMsegHandler(BaseHandler):
@@ -26,7 +26,7 @@ class MMsegHandler(BaseHandler):
         checkpoint = os.path.join(model_dir, serialized_file)
         self.config_file = os.path.join(model_dir, 'config.py')
 
-        self.model = init_segmentor(self.config_file, checkpoint, self.device)
+        self.model = init_model(self.config_file, checkpoint, self.device)
         self.model = revert_sync_batchnorm(self.model)
         self.initialized = True
 
@@ -43,7 +43,7 @@ class MMsegHandler(BaseHandler):
         return images
 
     def inference(self, data, *args, **kwargs):
-        results = [inference_segmentor(self.model, img) for img in data]
+        results = [inference_model(self.model, img) for img in data]
         return results
 
     def postprocess(self, data):

tools/torchserve/test_torchserve.py

@@ -6,7 +6,7 @@ import matplotlib.pyplot as plt
 import mmcv
 import requests
 
-from mmseg.apis import inference_segmentor, init_segmentor
+from mmseg.apis import inference_model, init_model
 
 
 def parse_args():
@@ -46,9 +46,9 @@ def main(args):
     else:
         plt.imshow(plt.imread(BytesIO(content)))
         plt.show()
-    model = init_segmentor(args.config, args.checkpoint, args.device)
+    model = init_model(args.config, args.checkpoint, args.device)
     image = mmcv.imread(args.img)
-    result = inference_segmentor(model, image)
+    result = inference_model(model, image)
     plt.imshow(result[0])
     plt.show()