how to visualize a detection model?

❓ Questions and Help

I was trying to visualize a detection model built in maskrcnn_benchmark, for deeper analytics. I have tried two ways:

1. export to onnx format model, then use Netron for visualize.
2. use pytorchviz for visualize.

But they all raise a error like Auto nesting doesn't know how to process an input object of type maskrcnn_benchmark.structures.image_list.ImageList. Accepted types: Tensors, or lists/tuples of them

Obviously, there are two mountains stopped me, ImageList and BoxList, it seems like I should wait until ONNX standard supports these operators.

I am here looking for some help. Is there any solutions or tricks to visualize a model?

[veraposeidon]

@qizhuli It works, thanks a lot! And instead of using FileIO to save a graph infomation, I found a more convenient way to do this. Just use:

graph.format = format
graph.reder(filename)

where:

• graph is graph representation returned by funtion make_dot.
• ‘format’ is the file format you want to save. For your infomation, here is a list of formats that graphviz support. https://www.graphviz.org/doc/info/output.html
• ‘filename’ is the path you want to save.

for example. Instead of:

graph = make_dot(loss, params=dict(list(model.named_parameters()) + [('dummy_image',dummy_image)]))
with open(args.output_path, 'w') as f:
    f.write(str(graph))
f.close()

Use:

graph = make_dot(loss, params=dict(list(model.named_parameters()) + [('dummy_image',dummy_image)]))
graph.format = 'pdf'
graph.render("visPDF")

then, this will generate two files: visPDF and visPDF.pdf. visPDF is text file same as FileIO generated, and visPDF.pdf is the visualized file.

by the way, do find a good format after tries. png format generated blurred image, and jpg report bugs. pdf works fine.

[qizhuli]

Sure! Here is the output of my script for ResNet50-FPN Mask-RCNN network ($root/configs/e2e_mask_rcnn_R_50_FPN_1x.yaml). Simply save it at $file_path and run xdot $file_path to visualise it.

Edit: I rendered it into a pdf file for your viewing convenience 😃