-`EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `top_k_accuracy`, `mean_class_accuracy` are available for all datasets in recognition, `mean_average_precision` for Multi-Moments in Time, `AR@AN` for ActivityNet, etc.
-`--gpu-collect`: If specified, recognition results will be collected using gpu communication. Otherwise, it will save the results on different gpus to `TMPDIR` and collect them by the rank 0 worker.
-`TMPDIR`: Temporary directory used for collecting results from multiple workers, available when `--gpu-collect` is not specified.
-`OPTIONS`: Custom options used for evaluation. Allowed values depend on the arguments of the `evaluate` function in dataset.
-`AVG_TYPE`: Items to average the test clips. If set to `prob`, it will apply softmax before averaging the clip scores. Otherwise, it will directly average the clip scores.
-`JOB_LAUNCHER`: Items for distributed job initialization launcher. Allowed choices are `none`, `pytorch`, `slurm`, `mpi`. Especially, if set to none, it will test in a non-distributed mode.
-`LOCAL_RANK`: ID for local rank. If not specified, it will be set to 0.
...
...
@@ -80,7 +81,7 @@ Assume that you have already downloaded the checkpoints to the directory `checkp
2. Test TSN on Something-Something V1 with 8 GPUS, and evaluate the top-k accuracy.
The annotation of a rawframe dataset is a text file with multiple lines,
and each line indicates `frame_directory` (relative path) of a video,
`total_frames` of a video and the `label` of a video, which are split with a whitespace.
Here is an example.
```
some/directory-1 163 1
some/directory-2 122 1
some/directory-3 258 2
some/directory-4 234 2
some/directory-5 295 3
some/directory-6 121 3
```
- video annotation
The annotation of a video dataset is a text file with multiple lines,
and each line indicates a sample video with the `filepath` (relative path) and `label`,
which are split with a whitespace.
Here is an example.
```
some/path/000.mp4 1
some/path/001.mp4 1
some/path/002.mp4 2
some/path/003.mp4 2
some/path/004.mp4 3
some/path/005.mp4 3
```
- ActivityNet annotation
The annotation of ActivityNet dataset is a json file. Each key is a video name
and the corresponding value is the meta data and annotation for the video.
Here is an example.
```
{
"video1": {
"duration_second": 211.53,
"duration_frame": 6337,
"annotations": [
{
"segment": [
30.025882995319815,
205.2318595943838
],
"label": "Rock climbing"
}
],
"feature_frame": 6336,
"fps": 30.0,
"rfps": 29.9579255898
},
"video2": {
"duration_second": 26.75,
"duration_frame": 647,
"annotations": [
{
"segment": [
2.578755070202808,
24.914101404056165
],
"label": "Drinking beer"
}
],
"feature_frame": 624,
"fps": 24.0,
"rfps": 24.1869158879
}
}
```
There are two ways to work with custom datasets.
- online conversion
You can write a new Dataset class inherited from [BaseDataset](/mmaction/datasets/base.py), and overwrite two methods
`load_annotations(self)` and `evaluate(self, results, metrics, logger)`,
like [RawframeDataset](/mmaction/datasets/rawframe_dataset.py), [VideoDataset](/mmaction/datasets/video_dataset.py) or [ActivityNetDataset](/mmaction/datasets/activitynet_dataset.py).
- offline conversion
You can convert the annotation format to the expected format above and save it to
a pickle or json file, then you can simply use `RawframeDataset`, `VideoDataset` or `ActivityNetDataset`.
### Customize optimizer
An example of customized optimizer is [CopyOfSGD](/mmaction/core/optimizer/copy_of_sgd.py).
More generally, a customized optimizer could be defined as following.
In `mmaction/core/optimizer/my_optimizer.py`:
```python
from.registryimportOPTIMIZERS
fromtorch.optimimportOptimizer
@OPTIMIZERS.register_module()
classMyOptimizer(Optimizer):
```
In `mmaction/core/optimizer/__init__.py`:
```python
from.my_optimizerimportMyOptimizer
```
Then you can use `MyOptimizer` in `optimizer` field of config files.
Especially, If you want to construct a optimizer based on a specified model and param-wise config,
You can write a new optimizer constructor inherit from [DefaultOptimizerConstructor](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/optimizer/default_constructor.py)
and overwrite the `add_params(self, params, module)` method.
An example of customized optimizer constructor is [TSMOptimizerConstructor](/mmaction/core/optimizer/tsm_optimizer_constructor.py).
More generally, a customized optimizer constructor could be defined as following.
In `mmaction/core/optimizer/my_optimizer_constructor.py`:
