[Feature] remote sensing inference (#3131)

## Motivation

Supports inference for ultra-large-scale remote sensing images.

## Modification

Add RSImageInference.py in demo.

## Use cases

Taking the inference of Vaihingen dataset images using PSPNet as an
example, the following settings are required:

**img**: Specify the path of the image.
**model**: Provide the configuration file for the model.
**checkpoint**: Specify the weight file for the model.
**out**: Set the output path for the results.
**batch_size**: Determine the batch size used during inference.
**win_size**: Specify the width and height(512x512) of the sliding
window.
**stride**: Set the stride(400x400) for sliding the window.
**thread(default: 1)**: Specify the number of threads to be used for
inference.
**Inference device (default: cuda:0)**: Specify the device for inference
(e.g., cuda:0 for CPU).

```shell
python demo/rs_image_inference.py demo/demo.png projects/pp_mobileseg/configs/pp_mobileseg/pp_mobileseg_mobilenetv3_2x16_80k_ade20k_512x512_tiny.py pp_mobileseg_mobilenetv3_2xb16_3rdparty-tiny_512x512-ade20k-a351ebf5.pth --batch-size 8 --device cpu --thread 2
```

---------

Co-authored-by: xiexinch <[email protected]>

.circleci/test.yml

@@ -73,7 +73,7 @@ jobs:
       - run:
           name: Skip timm unittests and generate coverage report
           command: |
-            python -m coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
+            python -m coverage run --branch --source mmseg -m pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py --ignore tests/test_apis/test_rs_inferencer.py
             python -m coverage xml
             python -m coverage report -m
   build_cuda:
@@ -119,7 +119,7 @@ jobs:
       - run:
           name: Run unittests but skip timm unittests
           command: |
-            docker exec mmseg pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py
+            docker exec mmseg pytest tests/ --ignore tests/test_models/test_backbones/test_timm_backbone.py --ignore tests/test_models/test_backbones/test_timm_backbone.py --ignore tests/test_apis/test_rs_inferencer.py
 workflows:
   pr_stage_lint:
     when: << pipeline.parameters.lint_only >>

demo/rs_image_inference.py

@@ -0,0 +1,50 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from argparse import ArgumentParser
+
+from mmseg.apis import RSImage, RSInferencer
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument('image', help='Image file path')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--output-path',
+        help='Path to save result image',
+        default='result.png')
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=1,
+        help='maximum number of windows inferred simultaneously')
+    parser.add_argument(
+        '--window-size',
+        help='window xsize,ysize',
+        default=(224, 224),
+        type=int,
+        nargs=2)
+    parser.add_argument(
+        '--stride',
+        help='window xstride,ystride',
+        default=(224, 224),
+        type=int,
+        nargs=2)
+    parser.add_argument(
+        '--thread', default=1, type=int, help='number of inference threads')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    args = parser.parse_args()
+    inferencer = RSInferencer.from_config_path(
+        args.config,
+        args.checkpoint,
+        batch_size=args.batch_size,
+        thread=args.thread,
+        device=args.device)
+    image = RSImage(args.image)
+
+    inferencer.run(image, args.window_size, args.stride, args.output_path)
+
+
+if __name__ == '__main__':
+    main()

mmseg/apis/__init__.py

@@ -1,7 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .inference import inference_model, init_model, show_result_pyplot
 from .mmseg_inferencer import MMSegInferencer
+from .remote_sense_inferencer import RSImage, RSInferencer
 
 __all__ = [
-    'init_model', 'inference_model', 'show_result_pyplot', 'MMSegInferencer'
+    'init_model', 'inference_model', 'show_result_pyplot', 'MMSegInferencer',
+    'RSInferencer', 'RSImage'
 ]

mmseg/apis/inference.py

@@ -1,14 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import warnings
-from collections import defaultdict
 from pathlib import Path
-from typing import Optional, Sequence, Union
+from typing import Optional, Union
 
 import mmcv
 import numpy as np
 import torch
 from mmengine import Config
-from mmengine.dataset import Compose
 from mmengine.registry import init_default_scope
 from mmengine.runner import load_checkpoint
 from mmengine.utils import mkdir_or_exist
@@ -18,6 +16,7 @@
 from mmseg.structures import SegDataSample
 from mmseg.utils import SampleList, dataset_aliases, get_classes, get_palette
 from mmseg.visualization import SegLocalVisualizer
+from .utils import ImageType, _preprare_data
 
 
 def init_model(config: Union[str, Path, Config],
@@ -90,41 +89,6 @@ def init_model(config: Union[str, Path, Config],
     return model
 
 
-ImageType = Union[str, np.ndarray, Sequence[str], Sequence[np.ndarray]]
-
-
-def _preprare_data(imgs: ImageType, model: BaseSegmentor):
-
-    cfg = model.cfg
-    for t in cfg.test_pipeline:
-        if t.get('type') == 'LoadAnnotations':
-            cfg.test_pipeline.remove(t)
-
-    is_batch = True
-    if not isinstance(imgs, (list, tuple)):
-        imgs = [imgs]
-        is_batch = False
-
-    if isinstance(imgs[0], np.ndarray):
-        cfg.test_pipeline[0]['type'] = 'LoadImageFromNDArray'
-
-    # TODO: Consider using the singleton pattern to avoid building
-    # a pipeline for each inference
-    pipeline = Compose(cfg.test_pipeline)
-
-    data = defaultdict(list)
-    for img in imgs:
-        if isinstance(img, np.ndarray):
-            data_ = dict(img=img)
-        else:
-            data_ = dict(img_path=img)
-        data_ = pipeline(data_)
-        data['inputs'].append(data_['inputs'])
-        data['data_samples'].append(data_['data_samples'])
-
-    return data, is_batch
-
-
 def inference_model(model: BaseSegmentor,
                     img: ImageType) -> Union[SegDataSample, SampleList]:
     """Inference image(s) with the segmentor.