GitLab

Summary:
Evaluate the predictor generated by previous step.
This diff modify the lightning_train_net to reuse the evaluation logic by adding a `predictor_path` param.
This diff also makes Lightning training backend depends on `cfg.MODEL.DEVICE` so that in evaluate_predictor step, user could set backend by changing model device. This is useful for evaluating int8 quantized model.

Reviewed By: newstzpz

Differential Revision: D27150609

fbshipit-source-id: fb72da3e81db932c0fa479350150720143e09a3e

Summary:
Given that the way to create D2 (https://github.com/facebookresearch/d2go/commit/465cdb842513eb910aa20fcedea1d2edd15dc7b7)go runner and Lightning task are different, get_class was introduced so that in application we could do:
```
if is Lightning:
    task_cls = get_class(classname)
    task = task_cls(cfg)
else:
    runner = create_runner(classname)
```
It turns out that we could need to do that in many places: workflow, binaries.
This diff revert `get_class` and return class in `create_runner` if the class is a Lightning module.

Reviewed By: newstzpz

Differential Revision: D26676595

fbshipit-source-id: c3ce2016d09fe073af4c2dd9f98eea4e59ca621b

Summary:
As titled. The OSS version only use PyTorch Lightning while internal version leverages some features(e.g. Manifold integration, every_n_step checkpointing).
This diff splits train_net.main into smaller functions so that they could be shared across OSS and internal versions.

Reviewed By: zhanghang1989

Differential Revision: D26752701

fbshipit-source-id: 7f68e2a81e78193e117517a0ff668ab14b76ea65

fbshipit-source-id: f4a8ba78691d8cf46e003ef0bd2e95f170932778