Fix typos (#208)

Signed-off-by: lizz <[email protected]>
open-mmlab · Feb 25, 2021 · 0a32934 · 0a32934
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diff --git a/docs/install.md b/docs/install.md
@@ -40,7 +40,7 @@ PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.
 conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
 ```
 
-If you build PyTorch from source instead of installing the prebuilt pacakge,
+If you build PyTorch from source instead of installing the prebuilt package,
 you can use more CUDA versions such as 9.0.
 
 c. Clone the mmediting repository.

diff --git a/tools/data/matting/bgm/preprocess_bgm_dataset.py b/tools/data/matting/bgm/preprocess_bgm_dataset.py
@@ -7,7 +7,7 @@
 
 
 def generate_json(data_root, seg_root, bg_root, all_data):
-    """Generate training json list for Backgroung Matting video dataset.
+    """Generate training json list for Background Matting video dataset.
 
     Args:
         data_root (str): Background Matting video data root.

diff --git a/tools/data/matting/comp1k/README.md b/tools/data/matting/comp1k/README.md
@@ -86,7 +86,7 @@ mmediting
 ## Prepare the dataset for FBA
 
 FBA adopts dynamic dataset augmentation proposed in [Learning-base Sampling for Natural Image Matting](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning-Based_Sampling_for_Natural_Image_Matting_CVPR_2019_paper.pdf).
-In addition, to reduce artifacts during augmentation, it uses the extened version of foreground as foreground.
+In addition, to reduce artifacts during augmentation, it uses the extended version of foreground as foreground.
 We provide scripts to estimate foregrounds.
 
 Prepare the test set as follows:

diff --git a/tools/data/matting/comp1k/extend_fg.py b/tools/data/matting/comp1k/extend_fg.py
@@ -81,7 +81,7 @@ def parse_args():
         formatter_class=argparse.ArgumentDefaultsHelpFormatter)
     parser.add_argument('data_root', help='Adobe composition 1k dataset root')
     parser.add_argument(
-        '--nproc', type=int, default=4, help='number of processer')
+        '--nproc', type=int, default=4, help='number of processor')
     args = parser.parse_args()
     return args
 

diff --git a/tools/pytorch2onnx.py b/tools/pytorch2onnx.py
@@ -127,7 +127,7 @@ def parse_args():
     data = dict(merged_path=args.img_path, trimap_path=args.trimap_path)
     data = test_pipeline(data)
 
-    # conver model to onnx file
+    # convert model to onnx file
     pytorch2onnx(
         model,
         data,

diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh
diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh
@@ -1,7 +1,6 @@
 #!/usr/bin/env bash
 export MASTER_PORT=$((12000 + $RANDOM % 20000))
 
-
 set -x
 
 PARTITION=$1