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Visualization Tools for PySlowFast

This document provides a brief intro for running various visualization tools provided with PySlowFast. Before launching any job, make sure you have properly installed the PySlowFast following the instruction in README.md and you have prepared the dataset following DATASET.md with the correct format.

Tensorboard Support for Train/Eval/Test

We provide Tensorboard support during the train/eval/test pipeline to assist live monitoring various metrics, and class-level performance with loss/error graphs, confusion matrices and histograms. Enable Tensorboard support by adding the following to your yaml config file:

TENSORBOARD:
  ENABLE: True
  LOG_DIR: # Leave empty to use cfg.OUTPUT_DIR/runs-{cfg.TRAIN.DATASET} as path.
  CLASS_NAMES_PATH: # Path to json file providing class_name - id mapping.
  CONFUSION_MATRIX:
    ENABLE: True
    SUBSET_PATH: # Path to txt file contains class names separated by newline characters.
                 # Only classes in this file will be visualized in the confusion matrix.
  HISTOGRAM:
    ENABLE: True
    TOP_K: 10   # Top-k most frequently predicted classes for each class in the dataset.
    SUBSET_PATH: # Path to txt file contains class names separated by newline characters.
                 # Only classes in this file will be visualized with histograms.

More details can be found at defaults.py

Loss & Error Graphs on Tensorboard:

Confusion matrices:

To enable this mode, set:

TENSORBOARD:
  ENABLE: True
  CATEGORIES_PATH: # Path to a json file for categories -> classes mapping
                   # in the format {"parent_class": ["child_class1", "child_class2",...], ...}.
  CONFUSION_MATRIX:
    ENABLE: True

Histograms of top-k most frequent predictions:

Model Analysis

In addition, we provide tools to help with understanding your trained model(s), more options at defaults.py

Adding the following to your yaml config file:

TENSORBOARD:
  ENABLE: True
  MODEL_VIS:
    ENABLE: True
    MODEL_WEIGHTS: # Set to True to visualize model weights.
    ACTIVATIONS: # Set to True to visualize feature maps.
    INPUT_VIDEO: # Set to True to visualize the input video(s) for the corresponding feature maps.
    LAYER_LIST: # List of layer names to visualize weights and activations for.
    GRAD_CAM:
      ENABLE: True
      LAYER_LIST: # List of CNN layers to use for Grad-CAM visualization method.
                  # The number of layer must be equal to the number of pathway(s).

Weights Visualization on Tensorboard:

Feature Maps & Inputs Visualization:

Input Videos Visualization with Grad-CAM:

Run the Demo on Videos/Camera

To run inference with PySlowFast model(s) on wild video(s), add the following to your yaml config file:

DEMO:
  ENABLE: True
  LABEL_FILE_PATH: # Path to json file providing class_name - id mapping.
  INPUT_VIDEO: # Path to input video file.
  OUTPUT_FILE: # Path to output video file to write results to.
               # Leave an empty string if you would like to display results to a window.
  THREAD_ENABLE: # Run video reader/writer in the background with multi-threading.
  NUM_VIS_INSTANCES: # Number of CPU(s)/processes use to run video visualizer.
  NUM_CLIPS_SKIP: # Number of clips to skip prediction/visualization
                  # (mostly to smoothen/improve display quality with wecam input).

If you would like to use webcam as an input, in place of DEMO.INPUT_VIDEO, set DEMO.WEBCAM to the index of the webcam for input. Please check for more options at defaults.py

Action Recognition Demo:

Action Detection Demo:

Demo with AVA video(s):

We also offer an option to use trained models to create and visualize prediction results and ground-truth labels on AVA-format videos and metadata. An example config is:

DEMO:
  ENABLE: True
  OUTPUT_FILE: yourPath/output.mp4
  LABEL_FILE_PATH:  yourPath/ava_classnames.json
  INPUT_VIDEO: yourPath/frames/HVAmkvLrthQ  # Path to a video file or image folder
  PREDS_BOXES: yourPath/ava_detection_train_boxes_and_labels_include_negative.csv # Path to pre-computed bouding boxes in AVA format.
  GT_BOXES: yourPath/ava_train_v2.2.csv # Path to ground-truth boxes and labels in AVA format (optional).

Run command

python \tools\run_net.py --cfg path/to/<pretrained_model_config_file>.yaml

Download class name files