Object detection workflow

[gheinrich]

Planning to split this into small pull requests. This is the complete object detection work flow.

This includes frameworks for data and view extensions.

The object detection data extension creates datasets from object detection datasets. On the main page, select New dataset > Images > Object Detection

The dataset is configured with a form:

To create the model: on the main page, select New Model > Images > Object Detection.

After the model is trained, on the model page, select the bounding box view extension:

Then proceed to inference as usual. If you do Test Many, you may see something like:

This includes as a bonus:
- an image gradient work flow (the data extension creates datasets as in the image regression example) and the view extension displays arrows that point to the "light" in the images,
- an image processing work flow (the data extension creates datasets with images as inputs/outputs - useful for FCNs)

Example of gradient visualization:

[lukeyeager]

datum_bbox = caffe.io.bbox_to_datum(label,
AttributeError: 'module' object has no attribute 'bbox_to_datum'

I removed bbox_to_datum from caffe.io because I figured we could just include it in our code rather than checking it into Caffe.

[lukeyeager]

Error handling for creating KITTI datasets is not good. Exceptions raised while the task is running should be caught and logged as errors, like so:
https://github.com/NVIDIA/DIGITS/blob/v3.3.0/tools/create_db.py#L779-L781
That will allow DIGITS to display the proper error message.

Also, I can't see any results on the dataset#show page, including the task log file. This is super helpful for debugging other failed tasks.

[gheinrich]

Exceptions raised while the task is running should be caught and logged as errors, like so:
https://github.com/NVIDIA/DIGITS/blob/v3.3.0/tools/create_db.py#L779-L781

I actually had that but the problem was I was missing something like this:
https://github.com/NVIDIA/DIGITS/blob/v3.3.0/digits/dataset/tasks/create_db.py#L218-L221

I have updated the "data extension" framework with better error handling (including those that occur in reader threads). So you would now get something like:

[gheinrich]

Updated to remove dependency on OpenCV bindings for Python.

[lukeyeager]

Is this script really executable?

[gheinrich]

Whoops. I'll remove that line.

[lukeyeager]

Woohoo I just successfully completed the whole object detection workflow!

[gheinrich]

Woohoo I just successfully completed the whole object detection workflow!

Excellent! Which SHA1 did you use (there's been quite a few revisions so I'd like to make sure you've been using one of the last ones).

[lukeyeager]

The latest for this branch.

I think we need to figure out how to add DetectNet as a "default/standard/zoo" network before this makes sense to merge, maybe? Also, maybe we should enable the gradient thing? It could be a cool walkthrough demo for DIGITS.