Removed datamodule from an input parameter

[deng-cy]

Before submitting

• Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
• Did you read the contributor guideline, Pull Request section?
• Did you make sure to update the docs?
• Did you write any new necessary tests?

What does this PR do?

Removed datamodule as an input parameter of MoCo model. There are two reasons:

1. Datamodule is preferably passed into Train.fit() function.
2. In the original code, datamodule will be saved as a hyperparameter, which makes the yaml difficult to read.

Besides, logging code was reformatted to match Lightning 1.0.0.

PR review

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[pep8speaks]

Hello @deng-cy! Thanks for updating this PR.

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2020-10-15 12:34:15 UTC

[codecov]

Codecov Report

Merging #270 into master will increase coverage by 3.44%.
The diff coverage is 90.90%.

@@            Coverage Diff             @@
##           master     #270      +/-   ##
==========================================
+ Coverage   80.41%   83.86%   +3.44%     
==========================================
  Files          91       91              
  Lines        4840     4858      +18     
==========================================
+ Hits         3892     4074     +182     
+ Misses        948      784     -164     

Flag	Coverage Δ
#cpu	23.91% <0.00%> (+<0.01%)	⬆️
#pytest	23.91% <0.00%> (+<0.01%)	⬆️
#unittests	83.32% <90.90%> (+3.45%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
..._bolts/models/self_supervised/moco/moco2_module.py	79.23% <87.50%> (+35.13%)	⬆️
pl_bolts/models/vision/image_gpt/igpt_module.py	96.15% <100.00%> (+49.90%)	⬆️
pl_bolts/utils/arguments.py	96.15% <0.00%> (+0.04%)	⬆️
pl_bolts/losses/self_supervised_learning.py	77.70% <0.00%> (+0.14%)	⬆️
pl_bolts/models/rl/common/gym_wrappers.py	89.91% <0.00%> (+0.26%)	⬆️
pl_bolts/datasets/imagenet_dataset.py	19.16% <0.00%> (+0.37%)	⬆️
pl_bolts/optimizers/lars_scheduling.py	95.74% <0.00%> (+0.50%)	⬆️
pl_bolts/datasets/ssl_amdim_datasets.py	74.32% <0.00%> (+1.08%)	⬆️
pl_bolts/models/self_supervised/resnets.py	92.78% <0.00%> (+2.57%)	⬆️
...l_bolts/models/rl/vanilla_policy_gradient_model.py	94.64% <0.00%> (+2.67%)	⬆️
... and 9 more

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[Borda]

@deng-cy great! can we do the same also for iGPT :]