fix conflicts

open-mmlab · Feb 13, 2022 · bc93c51 · bc93c51
2 parents 31f1ab6 + 0934a57
commit bc93c51
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diff --git a/demo/MMSegmentation_Tutorial.ipynb b/demo/MMSegmentation_Tutorial.ipynb
@@ -214,7 +214,7 @@
    "source": [
     "## Train a semantic segmentation model on a new dataset\n",
     "\n",
-    "To train on a customized dataset, the following steps are neccessary. \n",
+    "To train on a customized dataset, the following steps are necessary. \n",
     "1. Add a new dataset class. \n",
     "2. Create a config file accordingly. \n",
     "3. Perform training and evaluation. "
@@ -228,11 +228,11 @@
    "source": [
     "### Add a new dataset\n",
     "\n",
-    "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same perfix. To support a new dataset, we may need to modify the original file structure. \n",
+    "Datasets in MMSegmentation require image and semantic segmentation maps to be placed in folders with the same prefix. To support a new dataset, we may need to modify the original file structure. \n",
     "\n",
     "In this tutorial, we give an example of converting the dataset. You may refer to [docs](https://github.com/open-mmlab/mmsegmentation/docs/en/tutorials/new_dataset.md) for details about dataset reorganization. \n",
     "\n",
-    "We use [Standord Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
+    "We use [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) as an example. The dataset contains 715 images chosen from existing public datasets [LabelMe](http://labelme.csail.mit.edu), [MSRC](http://research.microsoft.com/en-us/projects/objectclassrecognition), [PASCAL VOC](http://pascallin.ecs.soton.ac.uk/challenges/VOC) and [Geometric Context](http://www.cs.illinois.edu/homes/dhoiem/). Images from these datasets are mainly outdoor scenes, each containing approximately 320-by-240 pixels. \n",
     "In this tutorial, we use the region annotations as labels. There are 8 classes in total, i.e. sky, tree, road, grass, water, building, mountain, and foreground object. "
    ]
   },
@@ -249,8 +249,8 @@
    "outputs": [],
    "source": [
     "# download and unzip\n",
-    "!wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O standford_background.tar.gz\n",
-    "!tar xf standford_background.tar.gz"
+    "!wget http://dags.stanford.edu/data/iccv09Data.tar.gz -O stanford_background.tar.gz\n",
+    "!tar xf stanford_background.tar.gz"
    ]
   },
   {
@@ -423,7 +423,7 @@
     "id": "1y2oV5w97jQo"
    },
    "source": [
-    "Since the given config is used to train PSPNet on cityscapes dataset, we need to modify it accordingly for our new dataset.  "
+    "Since the given config is used to train PSPNet on the cityscapes dataset, we need to modify it accordingly for our new dataset.  "
    ]
   },
   {

diff --git a/tools/convert_datasets/potsdam.py b/tools/convert_datasets/potsdam.py
@@ -126,8 +126,8 @@ def main():
     zipp_list = glob.glob(os.path.join(dataset_path, '*.zip'))
     print('Find the data', zipp_list)
 
-    with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
-        for zipp in zipp_list:
+    for zipp in zipp_list:
+        with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
             zip_file = zipfile.ZipFile(zipp)
             zip_file.extractall(tmp_dir)
             src_path_list = glob.glob(os.path.join(tmp_dir, '*.tif'))