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Mask R-CNN

This document has instructions for how to run Mask R-CNN for the following modes/precisions:

Benchmarking instructions and scripts for model training and inference.

FP32 Inference Instructions

  1. Download the MS COCO 2014 dataset.

  2. Clone the Mask R-CNN model repository. It is used as external model directory for dependencies.

$ https://github.com/matterport/Mask_RCNN.git
$ cd Mask_RCNN
  1. Download pre-trained COCO weights mask_rcnn_coco.h5) from the Mask R-CNN repository release page, and place it in the MaskRCNN directory (from step 2) .
$ wget -q https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5
  1. Clone this intelai/models repository:
$ https://github.com/IntelAI/models.git

This repository includes launch scripts for running benchmarks and the an optimized version of the Mask R-CNN model code.

  1. Navigate to the benchmarks directory in your local clone of the intelai/models repo from step 4. The launch_benchmark.py script in the benchmarks directory is used for starting a benchmarking run in a optimized TensorFlow docker container. It has arguments to specify which model, framework, mode, precision, and docker image to use, along with your path to the external model directory for --model-source-dir (from step 2) and --data-location (from step 1).

Run benchmarking for throughput and latency with --batch-size=1 :

$ cd /home/<user>/models/benchmarks

$ python launch_benchmark.py \
    --model-source-dir /home/<user>/Mask_RCNN \
    --model-name maskrcnn \
    --framework tensorflow \
    --precision fp32 \
    --mode inference \
    --batch-size 1 \
    --socket-id 0 \
    --data-location /home/<user>/COCO2014 \
    --docker-image intelaipg/intel-optimized-tensorflow:1.15.2
  1. Log files are located at: intelai/models/benchmarks/common/tensorflow/logs, or you can provide specific location using --output-dir

Below is a sample log file tail when running benchmarking for throughput and latency:

Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=0.19s).
Accumulating evaluation results...
DONE (t=0.16s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.442
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.612
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.483
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.621
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.373
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.461
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.473
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.500
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.654
Batch size: 1
Time spent per BATCH: 734.5615 ms
Total samples/sec: 1.3614 samples/s
Total time:  43.79594111442566
Ran inference with batch size 1
Log file location: {--output-dir value}/benchmark_maskrcnn_inference_fp32_20200917_164707.log