Replace ImageNet ensemble baseline with Robustness Metrics.

[copybara-service]

Replace ImageNet ensemble baseline with Robustness Metrics.

There are two approaches to implement ensembles.

1. Load all SavedModels into a single model.

• Pro: Simple to compute results.
• Con: All models must fit in memory and compute can't parallelize across models.

2. Eval each model in parallel, saving predictions. Then load predictions and compute metrics. (approach in Uncertainty Baselines)

• Pro: Scales with compute and memory.
• Con: Requires two stages (the first uses accelerators, the second is CPU-only). We're already doing the first stage to report non-ensemble results. So two stages is not that inconvenient.

This CL does #2.

Fixes google/uncertainty-baselines#63, google/uncertainty-baselines#71.

Note: I added 'ece' back to the imagenet_variants report.

TODOs in later PRs

• Loading predictions is slow. Each file is at most 200MB with 50K predictions of 1000 float32 values, and read_predictions shouldn't take this long. np.load gets, say, read speeds of 200 MB/s (https://stackoverflow.com/a/30332316). It may be because we're loading batch_size=1?
• Replace het_ensemble.py and sngp_ensemble.py.