Feature/seginference dir (#213)

* fix installation procedure explanation of segmentation. This is mainly cuda related issues.

* let it runnable segmentation inference for image directory too.

* update segmentation readme for image directory processing.

* add new line

.gitignore

@@ -4,3 +4,5 @@
 __pycache__/
 classification/convertor/
 segmentation/convertor/
+checkpoint_dir/
+demo/

segmentation/README.md

@@ -26,23 +26,28 @@ conda activate internimage
   the [official installation instructions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
 - Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
 
-For examples, to install torch==1.11 with CUDA==11.3:
+For examples, to install torch==1.11 with CUDA==11.3 and nvcc:
 ```bash
-pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
+conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch -y
+conda install -c conda-forge cudatoolkit-dev=11.3 -y # to install nvcc
 ```
 
-- Install `timm==0.6.11` and `mmcv-full==1.5.0`:
+- Install other requirements:
+
+  note: conda opencv will break torchvision as not to support GPU, so we need to install opencv using pip. 	  
 
 ```bash
-pip install -U openmim
-mim install mmcv-full==1.5.0
-pip install timm==0.6.11 mmdet==2.28.1
+conda install -c conda-forge termcolor yacs pyyaml scipy pip -y
+pip install opencv-python
 ```
 
-- Install other requirements:
+- Install `timm` and `mmcv-full` and `mmsegmentation':
 
 ```bash
-pip install opencv-python termcolor yacs pyyaml scipy
+pip install -U openmim
+mim install mmcv-full==1.5.0
+mim install mmsegmentation==0.27.0
+pip install timm==0.6.11 mmdet==2.28.1
 ```
 
 - Compile CUDA operators
@@ -67,6 +72,7 @@ To evaluate our `InternImage` on ADE20K val, run:
 ```bash
 sh dist_test.sh <config-file> <checkpoint> <gpu-num> --eval mIoU
 ```
+You can download checkpoint files from [here](https://huggingface.co/OpenGVLab/InternImage/tree/fc1e4e7e01c3e7a39a3875bdebb6577a7256ff91). Then place it to segmentation/checkpoint_dir/seg.
 
 For example, to evaluate the `InternImage-T` with a single GPU:
 
@@ -103,7 +109,8 @@ GPUS=8 sh slurm_train.sh <partition> <job-name> configs/ade20k/upernet_internima
 ```
 
 ### Image Demo
-To inference a single image like this:
+To inference a single/multiple image like this.
+If you specify image containing directory instead of a single image, it will process all the images in the directory.:
 ```
 CUDA_VISIBLE_DEVICES=0 python image_demo.py \
   data/ade/ADEChallengeData2016/images/validation/ADE_val_00000591.jpg \

segmentation/image_demo.py

@@ -11,11 +11,29 @@ from mmcv.runner import load_checkpoint
 from mmseg.core import get_classes
 import cv2
 import os.path as osp
+import os
+
+
+def test_single_image(model, img_name, out_dir, color_palette, opacity):
+    result = inference_segmentor(model, img_name)
+    
+    # show the results
+    if hasattr(model, 'module'):
+        model = model.module
+    img = model.show_result(img_name, result,
+                            palette=color_palette,
+                            show=False, opacity=opacity)
+    
+    # save the results
+    mmcv.mkdir_or_exist(out_dir)
+    out_path = osp.join(out_dir, osp.basename(img_name))
+    cv2.imwrite(out_path, img)
+    print(f"Result is save at {out_path}")
 
 
 def main():
     parser = ArgumentParser()
-    parser.add_argument('img', help='Image file')
+    parser.add_argument('img', help='Image file or a directory contains images')
     parser.add_argument('config', help='Config file')
     parser.add_argument('checkpoint', help='Checkpoint file')
     parser.add_argument('--out', type=str, default="demo", help='out dir')
@@ -34,7 +52,6 @@ def main():
     args = parser.parse_args()
 
     # build the model from a config file and a checkpoint file
-    
     model = init_segmentor(args.config, checkpoint=None, device=args.device)
     checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
     if 'CLASSES' in checkpoint.get('meta', {}):
@@ -42,18 +59,12 @@ def main():
     else:
         model.CLASSES = get_classes(args.palette)
         
-    # test a single image
-    result = inference_segmentor(model, args.img)
-    # show the results
-    if hasattr(model, 'module'):
-        model = model.module
-    img = model.show_result(args.img, result,
-                            palette=get_palette(args.palette),
-                            show=False, opacity=args.opacity)
-    mmcv.mkdir_or_exist(args.out)
-    out_path = osp.join(args.out, osp.basename(args.img))
-    cv2.imwrite(out_path, img)
-    print(f"Result is save at {out_path}")
+    # check arg.img is directory of a single image.
+    if osp.isdir(args.img):
+        for img in os.listdir(args.img):
+            test_single_image(model, osp.join(args.img, img), args.out, get_palette(args.palette), args.opacity)
+    else:
+        test_single_image(model, args.img, args.out, get_palette(args.palette), args.opacity)
 
 if __name__ == '__main__':
     main()
\ No newline at end of file