Merge branch 'polish_docs' into 'master'

Fix typo in docs

See merge request open-mmlab/mmaction-lite!386

docs/config_recognition.md

@@ -34,9 +34,9 @@ test_cfg = dict(average_clips=None) # Config for testing hyperparameters for TSN
 dataset_type = 'RawframeDataset'  # Type of dataset for training, valiation and testing
 data_root = 'data/kinetics400/rawframes_train/'  # Root path to data for training
 data_root_val = 'data/kinetics400/rawframes_val/'  # Root path to data for validation and testing
-ann_file_train = 'data/kinetics400/kinetics_train_list.txt'  # Path to the annotation file for training
-ann_file_val = 'data/kinetics400/kinetics_val_list.txt'  # Path to the annotation file for validation
-ann_file_test = 'data/kinetics400/kinetics_val_list.txt'  # Path to the annotation file for testing
+ann_file_train = 'data/kinetics400/kinetics400_train_list_rawframes.txt'  # Path to the annotation file for training
+ann_file_val = 'data/kinetics400/kinetics400_val_list_rawframes.txt'  # Path to the annotation file for validation
+ann_file_test = 'data/kinetics400/kinetics400_val_list_rawframes.txt'  # Path to the annotation file for testing
 img_norm_cfg = dict(  # Config of image normalition used in data pipeline
     mean=[123.675, 116.28, 103.53],  # Mean values of different channels to normalize
     std=[58.395, 57.12, 57.375],  # Std values of different channels to normalize

docs/getting_started.md

@@ -223,7 +223,7 @@ We adopt distributed training for both single machine and multiple machines.
 Supposing that the server has 8 GPUs, 8 processes will be started and each process runs on a single GPU.
 
 Each process keeps an isolated model, data loader, and optimizer.
-Model parameters are only synchronized once at the begining.
+Model parameters are only synchronized once at the beginning.
 After a forward and backward pass, gradients will be allreduced among all GPUs,
 and the optimizer will update model parameters.
 Since the gradients are allreduced, the model parameter stays the same for all processes after the iteration.
@@ -294,7 +294,7 @@ You can check [slurm_train.sh](/tools/slurm_train.sh) for full arguments and env
 
 If you have just multiple machines connected with ethernet, you can refer to
 pytorch [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
-Usually it is slow if you do not have high speed networking like infiniband.
+Usually it is slow if you do not have high speed networking like InfiniBand.
 
 ### Launch multiple jobs on a single machine
 

docs/install.md

@@ -55,7 +55,7 @@ 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, you can use more CUDA versions such as 9.0.
+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 mmaction repository
 

docs/tutorials/finetune.md

@@ -37,7 +37,7 @@ model = dict(
         init_std=0.01))
 ```
 
-Note that the `pretrained='torchvision://resnet50'` setting is used for initilizing backbone.
+Note that the `pretrained='torchvision://resnet50'` setting is used for initializing backbone.
 If you are training a new model from ImageNet-pretrained weights, this is for you.
 However, this setting is not related to our task at hand.
 What we need is `load_from`, which will be discussed later.

docs/tutorials/new_dataset.md

@@ -94,7 +94,7 @@ There are two ways to work with custom datasets.
   a pickle or json file, then you can simply use `RawframeDataset`, `VideoDataset` or `ActivityNetDataset`.
 
 After the data pre-processing, the users need to further modify the config files to use the dataset.
-Here is an example of using a custom dataset in Rawframe format.
+Here is an example of using a custom dataset in rawframe format.
 
 In `configs/task/method/my_custom_config.py`:
 

tools/data/kinetics400/preparing_kinetics400.md

@@ -51,7 +51,7 @@ mkdir /mnt/SSD/kinetics400_extracted_val/
 ln -s /mnt/SSD/kinetics400_extracted_val/ ../../../data/kinetics400/rawframes_val/
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 bash extract_rgb_frames.sh

tools/data/mit/preparing_mit.md

@@ -18,7 +18,7 @@ This part is **optional** if you only want to use the video loader.
 Before extracting, please refer to [install.md](/docs/install.md) for installing [dense_flow](https://github.com/open-mmlab/denseflow).
 
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 Fist, You can run the following script to soft link the extracted frames.
 

tools/data/mmit/preparing_mmit.md

@@ -25,7 +25,7 @@ mkdir /mnt/SSD/mmit_extracted/
 ln -s /mnt/SSD/mmit_extracted/ ../../../data/mmit/rawframes
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 bash extract_rgb_frames.sh

tools/data/sthv1/preparing_sthv1.md

@@ -33,7 +33,7 @@ mkdir /mnt/SSD/sthv1_extracted/
 ln -s /mnt/SSD/sthv1_extracted/ ../../../data/sthv1/rawframes
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 cd $MMACTION/tools/data/sthv1/

tools/data/sthv2/preparing_sthv2.md

@@ -32,7 +32,7 @@ mkdir /mnt/SSD/sthv2_extracted/
 ln -s /mnt/SSD/sthv2_extracted/ ../../../data/sthv2/rawframes
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 cd $MMACTION/tools/data/sthv2/

tools/data/thumos14/preparing_thumos14.md

-# Preparing THUMOS-14
+# Preparing THUMOS'14
 
 For basic dataset information, you can refer to the dataset [website](https://www.crcv.ucf.edu/THUMOS14/download.html).
 Before we start, please make sure that the directory is located at `$MMACTION/tools/data/thumos14/`.
@@ -37,7 +37,7 @@ mkdir /mnt/SSD/thumos14_extracted/
 ln -s /mnt/SSD/thumos14_extracted/ ../data/thumos14/rawframes/
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 cd $MMACTION/tools/data/thumos14/
@@ -62,10 +62,10 @@ bash fetch_tag_proposals.sh
 
 ## Step 5. Check Directory Structure
 
-After the whole data process for THUMOS-14 preparation,
-you will get the rawframes (RGB + Flow), videos and annotation files for THUMOS-14.
+After the whole data process for THUMOS'14 preparation,
+you will get the rawframes (RGB + Flow), videos and annotation files for THUMOS'14.
 
-In the context of the whole project (for THUMOS-14 only), the folder structure will look like:
+In the context of the whole project (for THUMOS'14 only), the folder structure will look like:
 
 ```
 mmaction
@@ -103,4 +103,4 @@ mmaction
 │   │   │   |   ├── video_test_0000001
 ```
 
-For training and evaluating on THUMOS-14, please refer to [getting_started.md](/docs/getting_started.md).
+For training and evaluating on THUMOS'14, please refer to [getting_started.md](/docs/getting_started.md).

tools/data/ucf101/preparing_ucf101.md

@@ -35,7 +35,7 @@ mkdir /mnt/SSD/ucf101_extracted/
 ln -s /mnt/SSD/ucf101_extracted/ ../../../data/ucf101/rawframes
 ```
 
-If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-comsuming), consider running the following script to extract **RGB-only** frames.
+If you didn't install dense_flow in the installation or only want to play with RGB frames (since extracting optical flow can be time-consuming), consider running the following script to extract **RGB-only** frames.
 
 ```shell
 bash extract_rgb_frames.sh