Practical Tools and Scripts

Apart from training and evaluation scripts, detrex also provides lots of practical tools under tools/ directory or useful scripts.

Tensorboard Log Analysis

detrex automatically saves tensorboard logs in cfg.train.output_dir, users can directly analyze the training logs using:

tensorboard --logdir /path/to/cfg.train.output_dir

Model Analysis

Analysis tool for FLOPs, parameters, activations of detrex models.

• Analyze FLOPs

cd detrex
python tools/analyze_model.py --num-inputs 100 \
                              --tasks flop \
                              --config-file /path/to/config.py \
                              train.init_checkpoint=/path/to/model.pkl

• Analyze parameters

cd detrex
python tools/analyze_model.py --tasks parameter \
                              --config-file /path/to/config.py \

• Analyze activations

cd detrex
python tools/analyze_model.py --num-inputs 100 \
                              --tasks activation \
                              --config-file /path/to/config.py \
                              train.init_checkpoint=/path/to/model.pkl

• Analyze model structure

cd detrex
python tools/analyze_model.py --tasks structure \
                              --config-file /path/to/config.py \

Visualization

Here are some useful tools for visualizing the model predictions or dataset.

Visualize Predictions

To visualize the json instance detection/segmentation results dumped by COCOEvaluator, you should firstly specify the output_dir args for Evaluator in your config files, default to None in detrex.

# your config.py
dataloader = get_config("common/data/coco_detr.py").dataloader

# dump the testing results into output_dir for visualization
# save in the same directory as training logs
dataloader.evaluator.output_dir = /path/to/dir

Then run the following scripts:

cd detrex
python tools/visualize_json_results.py --input /path/to/x.json \  # path to the saved testing results
                                       --output dir/ \
                                       --dataset coco_2017_val

Note: the visualization results will be saved in dir/, here’s the example of the visualization for prediction results:

Visualize Datasets

Visualize ground truth raw annotations or training data (after preprocessing/augmentations).

• Visualize raw annotations

cd detrex
python tools/visualize_data.py --config-file /path/to/config.py \
                               --source annotation
                               --output_dir dir/
                               [--show]

• Visualize training data

cd detrex
python tools/visualize_data.py --config-file /path/to/config.py \
                               --source dataloader
                               --output_dir dir/
                               [--show]

Note: The visualization results will be saved in dir/, here’s the example of the visualization for annotations: