Implements Feature Pyramid Network

[daniel-j-h]

For #60.

This changeset implements a Feature Pyramid Network (FPN) on top of a (potentially pre-trained) ResNet.

• Feature Pyramid Networks for Object Detection
• A Unified Architecture for Instance and Semantic Segmentation

The implementation tries to follow these two resources carefully.

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from http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf

Here is the overall design for the full architecture:

• The left-most bottom-up pathway is the ResNet with its layers. Every time it is downsampling the spatial resolution by two it is doubling the number of feature maps.
• The lateral pathways are using 1x1 convolutions to transform the ResNet feature maps (of sizes 256, 512, 1024, 2048) into a fixed number of feature maps (configurable, 256 by default).
• The top-down pathways are then upsampling the feature maps by a factor of two again to get the spatial resolutions in sync for merging (adding) the lateral and top-down feature maps.
• For segmentation we then add 3x3 convolutions on top of the FPN feature maps, concatenate their outputs, and add a final convolution with number of classes in its output.
• We need to upsample the final output by a factor of four since we are starting with the ResNet features which are already downsampled in resolution by a factor of four.

[maning]

Got this error:

./rs weights --dataset config/dataset-building.toml
Traceback (most recent call last):
  File "/usr/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/data/robosat/robosat/tools/__main__.py", line 5, in <module>
    from robosat.tools import (
  File "/data/robosat/robosat/tools/predict.py", line 17, in <module>
    from robosat.fpn import FPNSegmenation
ImportError: cannot import name 'FPNSegmenation'

Typo? There are 4 lines having FPNSegmenation in this PR.

[daniel-j-h]

Ah, that's what you get from a quick Friday evening refactor: silly mistakes 😅

I just fixed it, give it another go! Sorry for the noise here.

[bkowshik]

Per #104 (review)

• Assert image resolution has to be divisible by 32 for resnet in fpn
• Rebase branch with master

[daniel-j-h]

Next actions here

• benchmark training
• benchmark prediction
• look into spatial dropout
• change classifier from a simple conv to e.g. conv1x1, bn, relu, dropout2d, conv1x1
• format with black
• merge into master
• tag new major release

[daniel-j-h]

By now there are pre-trained resnet50-fpns in torchvision. If we want to stay with semantic segmentation we should try them and later potentially extend to instance segmentation on top.