[Docs] Train spatio temporal stream on custom coco dataset

[gguzzy]

The doc issue

I wanted to know how we can actually use an annotated dataset in coco format, containing specific keypoints annotations, to train it on a custom 2s-agcn both bone-motion and/or joint-motion. The issue I do not get is why we should use train_test_split to create train, test and val datasets and how I would change this
`dataset_type = 'CocoDataset'
ann_file_train = 'train.json'
ann_file_val = 'test.json'
ann_file_test = 'val.json'
classes = ('Drunk', 'Not drunk')
train_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['bm']),
dict(type='UniformSampleFrames', clip_len=100),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]
val_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['bm']),
dict(
type='UniformSampleFrames', clip_len=100, num_clips=1, test_mode=True),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]
test_pipeline = [
dict(type='PreNormalize2D'),
dict(type='GenSkeFeat', dataset='coco', feats=['bm']),
dict(
type='UniformSampleFrames', clip_len=100, num_clips=10,
test_mode=True),
dict(type='PoseDecode'),
dict(type='FormatGCNInput', num_person=2),
dict(type='PackActionInputs')
]

train_dataloader = dict(
classes=classes,
batch_size=16,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=5,
dataset=dict(
type=dataset_type,
ann_file=ann_file_train,
pipeline=train_pipeline,
classes=classes,
# split='xsub_train'`

Considering that this code is not working since mmaction2 cannot find:

1. CocoDataset raising: not in the mmaction::dataset registry. Please check whether the value of CocoDataset is correct or it was registered as expected.

Is there any available script to autoconvert my annotations or should I do anything else to accomplish a custom training? I do not want to lose my annotations.

Thanks

Suggest a potential alternative/fix

Write a real notebook showing how to train a real custom dataset!!! All documentation on website is obsolete, ambiguous and not working. Many key components of training are missing, and makes no sense to create a custom class for a well-known annotations as Coco are.

Thanks.