This repository contains the code necessary to run the inference part of the ADAE algorithm. It was created by Qishen Ha, Bo Liu and colleagues from NVIDIA as their submission for the SIIM/ISIC 2020 Grand Challenge hosted by Kaggle.
The original repository of the submission can be found here: https://github.com/haqishen/SIIM-ISIC-Melanoma-Classification-1st-Place-Solution
The code was edited to allow running the prediction on NVIDIA (CUDA) GPUs, as well as CPU.
To run the GPU version, please ensure that all necessary drivers for Torch are installed (those are not part of the requirements!).
Data and model setup (assumes the Kaggle API is installed)
By default, the prediction code (in predict.py) uses the ./data folder as input and the ./weights folder for the model files.
Before using the code, please issue a call of
predict.py --download-models
to download the necessary PTH model files. For this to work, you must already have an account at Kaggle, and have generated an API token for the python API to be able to download files!
To run inference, use the predict.py script. If no CSV file is given as
argument, the code continually scans the folder given in --data-dir for new
CSV files, which will then be processed, and then renamed (with an extension
of "*.done"). If a CSV file is given, the file will not be renamed, and the
program will exit after processing the referenced images.
Should the 3 resized versions of an image not exist, they will be created (takes additional time).
usage: predict.py [-h] [-b BATCH_SIZE] [-c CSV_FILE] [-C CUDA_VISIBLE_DEVICES]
[-d DATA_DIR] [-D DEVICE] [--download-models] [-e ENSEMBLE]
[-f FOLDS] [-g GAUSS_KERNEL] [-H HEATMAP_DIR] [-l]
[-L LOG_ODDS_DIR] [-m MODELS] [-M MODEL_DIR] [-n N_TEST]
[-o OUTPUTS] [-O OVERLAYS] [-p] [-P PROBMAP_DIR] [-s]
[-S SUB_DIR] [-t TARGET] [-T LOG_ODDS_TERMS]
[--thresh-heat-mean THRESH_HEAT_MEAN]
[--thresh-heat-t THRESH_HEAT_T]
[--thresh-logodds-mean THRESH_LOGODDS_MEAN]
[--thresh-logodds-t THRESH_LOGODDS_T]
[--thresh-prob-mean THRESH_PROB_MEAN]
[--thresh-prob-t THRESH_PROB_T] [-w NUM_WORKERS]
[-z MAP_SIZE]
optional arguments:
-h, --help show this help message and exit
-b BATCH_SIZE, --batch-size BATCH_SIZE
Set batch size (images run at once, default=16)
-c CSV_FILE, --csv-file CSV_FILE
CSV file (default: scans for *.csv, then renames)
-C CUDA_VISIBLE_DEVICES, --CUDA_VISIBLE_DEVICES CUDA_VISIBLE_DEVICES
CUDA_VISIBLE_DEVICES setting (default="0")
-d DATA_DIR, --data-dir DATA_DIR
Directory for images (and CSVs, default=./data)
-D DEVICE, --device DEVICE
Torch device to use (default=auto, use GPU if present)
--download-models Download models from kaggle (requires user account !)
-e ENSEMBLE, --ensemble ENSEMBLE
Average created CSV files (default=90, 0 to disable)
-f FOLDS, --folds FOLDS
Folds to run (default=0,1,2,3,4 ; -f list to show)
-g GAUSS_KERNEL, --gauss-kernel GAUSS_KERNEL
Gaussian smoothing kernel for maps (default=1.2)
-H HEATMAP_DIR, --heatmap-dir HEATMAP_DIR
Stores heatmaps in given folder (default:None)
-l, --log-avg Average n-test passes as log-probs (default=True)
-L LOG_ODDS_DIR, --log-odds-dir LOG_ODDS_DIR
Stores log-odds maps in given folder (default:None)
-m MODELS, --models MODELS
Models to evaluate (default:all ; -m list to show)
-M MODEL_DIR, --model-dir MODEL_DIR
Folder with model (*.pth) files
(default=/home/jochenw/test/weights)
-n N_TEST, --n-test N_TEST
Evaluate flipped/rotated versions (default=8)
-o OUTPUTS, --outputs OUTPUTS
Targets to output (default=melanoma, -o list to show)
-O OVERLAYS, --overlays OVERLAYS
Generate overlays (default=None, -O list to show)
-p, --preload-models Preload models (only in scanning mode, default=False)
-P PROBMAP_DIR, --probmap-dir PROBMAP_DIR
Stores probmaps in given folder (default:None)
-s, --silent Silent mode (do not print anything, default=False)
-S SUB_DIR, --sub-dir SUB_DIR
Folder to store submission CSVs in (default=./subs)
-t TARGET, --target TARGET
Output that is the target (default=melanoma)
-T LOG_ODDS_TERMS, --log-odds-terms LOG_ODDS_TERMS
Log-odds map terms (default:outputs, -o list to show)
--thresh-heat-mean THRESH_HEAT_MEAN
Threshold for heat (mean) maps (default=5.0)
--thresh-heat-t THRESH_HEAT_T
Threshold for heat (mean) maps (default=3.0)
--thresh-logodds-mean THRESH_LOGODDS_MEAN
Threshold for heat (mean) maps (default=2.0)
--thresh-logodds-t THRESH_LOGODDS_T
Threshold for heat (mean) maps (default=3.0)
--thresh-prob-mean THRESH_PROB_MEAN
Threshold for heat (mean) maps (default=0.2)
--thresh-prob-t THRESH_PROB_T
Threshold for heat (mean) maps (default=3.0)
-w NUM_WORKERS, --num-workers NUM_WORKERS
Number of worker threads (default=16)
-z MAP_SIZE, --map-size MAP_SIZE
Output map size, X-by-X pixels (default=84 [56..512])
This repository contains one example image, ISIC_0074542, together with the
metadata. It is the first image of the challenge dataset coming from Memorial Sloan
Kettering Cancer Center.
The resulting files were generated with this command line
./predict.py -d ./data -H ./heatmaps -L ./logodds -P ./probmaps -S ./subs -c ./data/ADAE_example.csv -w 0
If you are doing inference using the CPU as the device, you may have to set the number of workers to 0, to prevent a Torch issue from stopping program execution: