Added nn.Module support for FasterRCNN backbone

[abhayraw1]

What does this PR do?

Fixes #660

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Did you have fun?

Sure! 🙃

[abhayraw1]

@Borda, I made the changes. Let me know what you think!

PS: Tests had failed due to the issue below, which I see has been fixed now...

This change is causing tests to fail in master. Please check!

3bf65da#diff-81b21ad681f00e8e80279aaf4d5e2c3a304b5a587b79850cff8d4472147f7f0aL92

[codecov]

Codecov Report

Merging #661 (164d368) into master (4046278) will decrease coverage by 48.44%.
The diff coverage is 14.28%.

❗ Current head 164d368 differs from pull request most recent head 68cc487. Consider uploading reports for the commit 68cc487 to get more accurate results

@@             Coverage Diff             @@
##           master     #661       +/-   ##
===========================================
- Coverage   72.82%   24.37%   -48.45%     
===========================================
  Files         121      121               
  Lines        7550     7520       -30     
===========================================
- Hits         5498     1833     -3665     
- Misses       2052     5687     +3635     

Flag	Coverage Δ
cpu	24.37% <14.28%> (-48.45%)	⬇️
pytest	24.37% <14.28%> (-48.45%)	⬇️

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

Impacted Files	Coverage Δ
...models/detection/faster_rcnn/faster_rcnn_module.py	23.45% <14.28%> (-45.38%)	⬇️
pl_bolts/models/rl/__init__.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/common/agents.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/dueling_dqn_model.py	0.00% <0.00%> (-100.00%)	⬇️
pl_bolts/models/rl/advantage_actor_critic_model.py	0.00% <0.00%> (-97.71%)	⬇️
pl_bolts/models/rl/double_dqn_model.py	0.00% <0.00%> (-95.84%)	⬇️
...l_bolts/models/rl/vanilla_policy_gradient_model.py	0.00% <0.00%> (-93.45%)	⬇️
pl_bolts/models/rl/common/networks.py	0.00% <0.00%> (-92.31%)	⬇️
pl_bolts/models/rl/reinforce_model.py	0.00% <0.00%> (-89.40%)	⬇️
pl_bolts/models/rl/per_dqn_model.py	0.00% <0.00%> (-86.89%)	⬇️
... and 68 more

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[Borda]

@abhayraw1 could you pls check the failing test (which are passing on master)? 🐰

[abhayraw1]

Sure @Borda

[SeanNaren]

Hey @abhayraw1 seems there is one failing test left, do you mind checking it out! thanks for your work so far!!

[akihironitta]

@abhayraw1 LGTM!

[Borda]

Not sure what is wrong here:

                boxes = target["boxes"]
                degenerate_boxes = boxes[:, 2:] <= boxes[:, :2]
                if degenerate_boxes.any():
                    # print the first degenerate box
                    bb_idx = torch.where(degenerate_boxes.any(dim=1))[0][0]
                    degen_bb: List[float] = boxes[bb_idx].tolist()
>                   raise ValueError("All bounding boxes should have positive height and width."
                                     " Found invalid box {} for target at index {}."
                                     .format(degen_bb, target_idx))
E                   ValueError: All bounding boxes should have positive height and width. Found invalid box [137.5, 556.25, 743.75, 556.25] for target at index 0.

as I see the [137.5, 556.25] <= [743.75, 556.25] shall be true, but it fails...

[ethanwharris]

LGTM 😃