Rename sth to mmaction2 (#10)

.github/ISSUE_TEMPLATE/reimplementation_questions.md

@@ -12,7 +12,7 @@ assignees: ''
 There are several common situations in the reimplementation issues as below
 1. Reimplement a model in the model zoo using the provided configs
 2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets)
-3. Reimplement a custom model but all the components are implemented in MMAction
+3. Reimplement a custom model but all the components are implemented in MMAction2
 4. Reimplement a custom model with new modules implemented by yourself
 
 There are several things to do for different cases as below.

demo/demo.py

@@ -6,7 +6,7 @@ from mmaction.apis import inference_recognizer, init_recognizer
 
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='MMAction demo')
+    parser = argparse.ArgumentParser(description='MMAction2 demo')
     parser.add_argument('config', help='test config file path')
     parser.add_argument('checkpoint', help='checkpoint file')
     parser.add_argument('video', help='video file')

docker/Dockerfile

@@ -12,10 +12,10 @@ RUN apt-get update && apt-get install -y git ninja-build libglib2.0-0 libsm6 lib
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
-# Install mmaction
+# Install mmaction2
 RUN conda clean --all
-RUN git clone https://github.com/open-mmlab/mmaction2.git /mmaction
-WORKDIR /mmaction
+RUN git clone https://github.com/open-mmlab/mmaction2.git /mmaction2
+WORKDIR /mmaction2
 ENV FORCE_CUDA="1"
 RUN pip install cython --no-cache-dir
 RUN pip install --no-cache-dir -e .

docs/conf.py

@@ -18,9 +18,9 @@ sys.path.insert(0, os.path.abspath('..'))
 
 # -- Project information -----------------------------------------------------
 
-project = 'MMAction'
+project = 'MMAction2'
 copyright = '2020, OpenMMLab'
-author = 'MMAction Authors'
+author = 'MMAction2 Authors'
 
 # The full version, including alpha/beta/rc tags
 with open('../mmaction/VERSION', 'r') as f:

docs/config.md

@@ -13,7 +13,7 @@
 
 <!-- /TOC -->
 
-We use python files as our config system. You can find all the provided configs under `$MMAction/configs`.
+We use python files as our config system. You can find all the provided configs under `$MMAction2/configs`.
 
 ## Config File Naming Convention
 

docs/getting_started.md

@@ -139,7 +139,7 @@ A notebook demo can be found in [demo/demo.ipynb](/demo/demo.ipynb)
 
 ### Build a model with basic components
 
-In MMAction, model components are basically categorized as 4 types.
+In MMAction2, model components are basically categorized as 4 types.
 
 - recognizer: the whole recognizer model pipeline, usually contains a backbone and cls_head.
 - backbone: usually an FCN network to extract feature maps, e.g., ResNet, BNInception.
@@ -216,7 +216,7 @@ are good examples which show how to do that.
 
 ### Iteration pipeline
 
-MMAction implements distributed training and non-distributed training,
+MMAction2 implements distributed training and non-distributed training,
 which uses `MMDistributedDataParallel` and `MMDataParallel` respectively.
 
 We adopt distributed training for both single machine and multiple machines.
@@ -278,7 +278,7 @@ Here is an example of using 8 GPUs to load TSN checkpoint.
 
 ### Train with multiple machines
 
-If you can run MMAction on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
+If you can run MMAction2 on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.)
 
 ```shell
 [GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}

mmaction/utils/collect_env.py

@@ -51,7 +51,7 @@ def collect_env():
     env_info['OpenCV'] = cv2.__version__
 
     env_info['MMCV'] = mmcv.__version__
-    env_info['MMAction'] = mmaction.__version__
+    env_info['MMAction2'] = mmaction.__version__
     return env_info
 
 

requirements/build.txt

-# These must be installed before building mmaction
+# These must be installed before building mmaction2
 numpy
 torch>=1.3

tests/test_utils.py

@@ -18,7 +18,7 @@ def test_collect_env():
     target_keys = [
         'sys.platform', 'Python', 'CUDA available', 'GCC', 'PyTorch',
         'PyTorch compiling details', 'TorchVision', 'OpenCV', 'MMCV',
-        'MMAction'
+        'MMAction2'
     ]
     cuda_available = torch.cuda.is_available()
     if cuda_available:
@@ -63,4 +63,4 @@ def test_collect_env():
     assert env_info['OpenCV'] == cv2.__version__
 
     assert env_info['MMCV'] == mmcv.__version__
-    assert env_info['MMAction'] == mmaction.__version__
+    assert env_info['MMAction2'] == mmaction.__version__

tools/bench_processing.py

@@ -27,7 +27,7 @@ def main():
 
     # init logger before other steps
     logger = get_root_logger()
-    logger.info(f'MMAction Version: {__version__}')
+    logger.info(f'MMAction2 Version: {__version__}')
     logger.info(f'Config: {cfg.text}')
 
     # create bench data list

tools/benchmark.py

@@ -13,7 +13,7 @@ from mmaction.models import build_model
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description='MMAction benchmark a recognizer')
+        description='MMAction2 benchmark a recognizer')
     parser.add_argument('config', help='test config file path')
     parser.add_argument(
         '--log-interval', default=10, help='interval of logging')

tools/data/activitynet/preparing_activitynet.md

@@ -32,7 +32,7 @@ you will get the features and annotation files.
 In the context of the whole project (for ActivityNet only), the folder structure will look like:
 
 ```
-mmaction
+mmaction2
 ├── mmaction
 ├── tools
 ├── configs

tools/data/kinetics400/preparing_kinetics400.md

@@ -86,7 +86,7 @@ In the context of the whole project (for Kinetics-400 only), the *minimal* folde
 (*minimal* means that some data are not necessary: for example, you may want to evaluate kinetics-400 using the original video format.)
 
 ```
-mmaction
+mmaction2
 ├── mmaction
 ├── tools
 ├── configs

tools/data/mit/preparing_mit.md

@@ -54,7 +54,7 @@ you will get the rawframes (RGB + Flow), videos and annotation files for Moments
 In the context of the whole project (for Moments in Time only), the folder structure will look like:
 
 ```
-mmaction
+mmaction2
 ├── data
 │   └── mit
 │       ├── annotations

tools/data/mmit/preparing_mmit.md

@@ -53,7 +53,7 @@ you will get the rawframes (RGB + Flow), videos and annotation files for Multi-M
 In the context of the whole project (for Multi-Moments in Time only), the folder structure will look like:
 
 ```
-mmaction/
+mmaction2/
 └── data
     └── mmit
         ├── annotations

tools/data/sthv2/preparing_sthv2.md

@@ -63,7 +63,7 @@ you will get the rawframes (RGB + Flow), videos and annotation files for Somethi
 In the context of the whole project (for Something-Something V2 only), the folder structure will look like:
 
 ```
-mmaction
+mmaction2
 ├── mmaction
 ├── tools
 ├── configs

tools/data/thumos14/preparing_thumos14.md

@@ -68,7 +68,7 @@ you will get the rawframes (RGB + Flow), videos and annotation files for THUMOS'
 In the context of the whole project (for THUMOS'14 only), the folder structure will look like:
 
 ```
-mmaction
+mmaction2
 ├── mmaction
 ├── tools
 ├── configs

tools/data/ucf101/preparing_ucf101.md

@@ -63,7 +63,7 @@ you will get the rawframes (RGB + Flow), videos and annotation files for UCF-101
 In the context of the whole project (for UCF-101 only), the folder structure will look like:
 
 ```
-mmaction
+mmaction2
 ├── mmaction
 ├── tools
 ├── configs

tools/test.py

@@ -13,7 +13,7 @@ from mmaction.models import build_model
 
 def parse_args():
     parser = argparse.ArgumentParser(
-        description='MMAction test (and eval) a model')
+        description='MMAction2 test (and eval) a model')
     parser.add_argument('config', help='test config file path')
     parser.add_argument('checkpoint', help='checkpoint file')
     parser.add_argument(