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c2board

A hacked-up visualization tool for caffe2. Specifically, it dumps the computation graph and the training statistics of caffe2 into a tensorboard compatible format. Once it starts dumping, you can use tensorboard to visualize the results.

Prerequisites

  • Caffe2.
  • Tensorboard. The code is meant to be standalone, so that you do not need to import both tensorflow and caffe2 at the same time -- unexpected behaviors can occur. However, tensorboard needs to be installed somewhere (like in another conda environment) for visualization, otherwise the dumped information is not useful by itself.
  • Python libraries:
    # for conda users
    conda install protobuf
    # or for ubuntu: sudo apt-get install protobuf-compiler libprotobuf-dev
    pip install json numpy Pillow six threading

Installation

  1. Clone the repository
git clone https://github.com/endernewton/c2board.git
  1. Make and install it locally
cd c2board
make

Usage

  • For graph visualization, you can follow demo_graph.py. The main function to call is writer.write_graph, which accepts either a CNNModelHelper, or Net, or NetDef object to visualize.
  • Training statistics can be divided into two types:
    • First, for scalars, they are usually loss, current iteration pre-computed, therefore we can directly store them without any additional effort. The relevant function to call is writer.write_scalars(dict, iter) where dict is the dictionary of the scalars, and iter the current iteration.
    • Second, for histograms and images, we need to call the workspace.FetchBlob function to fetch the values. For those, we first use writer.append_histogram(name) or writer.append_image(name) to build the list of blobs we are interested in when building up the graph. Then, during training we only need to call writer.write_summaries(iter) and the underlying method will take care of fetching blobs, computing histograms, etc.
  • When it starts dumping, you can just use the normal tensorboard command line to visualize things.

Screen shots

These screen shots are taken when training a detector with detectron.

References