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Faster R-CNN (ResNet50)

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

Instructions and scripts for the Faster R-CNN ResNet50 model training and inference for other precisions are coming later.

FP32 Inference Instructions

  1. Clone the tensorflow/models at the specified tag. Scripts from this repo will be used for running inference.

    git clone https://github.com/tensorflow/models.git
pushd models
git checkout tags/v1.12.0
popd

  2. Download and preprocess the COCO validation images using the instructions here. Be sure to export the $DATASET_DIR and $OUTPUT_DIR environment variables. The coco_val.record file will be used in the following inference examples.

  3. Download and extract the pre-trained model.

    wget https://storage.googleapis.com/intel-optimized-tensorflow/models/faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz
tar -xzvf faster_rcnn_resnet50_fp32_coco_pretrained_model.tar.gz

  4. Clone the intelai/models repo. This repo has the launch script for running the model.

    git clone https://github.com/IntelAI/models.git

  5. Run the launch_benchmark.py script from the intelai/models repo, with the appropriate parameters including: the coco_val.record data location (from step 2), the pre-trained model pipeline.config file and the checkpoint location (from step 3), and the location of your tensorflow/models clone (from step 1).

    Run batch and online inference using the following command:

    cd /home/<user>/models/benchmarks

python launch_benchmark.py \
  --data-location $OUTPUT_DIR \
  --model-source-dir /home/<user>/tensorflow/models \
  --model-name faster_rcnn \
  --framework tensorflow \
  --precision fp32 \
  --mode inference \
  --socket-id 0 \
  --checkpoint /home/<user>/faster_rcnn_resnet50_fp32_coco \
  --docker-image intelaipg/intel-optimized-tensorflow:1.15.2 \
  -- config_file=pipeline.config

    Or test accuracy where the --data-location is the path the directory where your coco_val.record file is located and the --in-graph is the pre-trained graph located in the pre-trained model directory (from step 3):

    python launch_benchmark.py \
  --model-name faster_rcnn \
  --mode inference \
  --precision fp32 \
  --framework tensorflow \
  --docker-image intelaipg/intel-optimized-tensorflow:1.15.2 \
  --model-source-dir /home/<user>/tensorflow/models \
  --data-location $OUTPUT_DIR \
  --in-graph /home/<user>/faster_rcnn_resnet50_fp32_coco/frozen_inference_graph.pb \
  --accuracy-only

  6. The log file is saved to the value of --output-dir. Below is a sample log file tail when running for batch and online inference:

    I0923 18:13:53.033420 140562203281216 eval_util.py:72] Metrics written to tf summary.
I0923 18:13:53.033456 140562203281216 eval_util.py:463] Finished evaluation!
Time spent : ... seconds.
Time spent per BATCH: ... seconds.
Log file location: {--output-dir value}/benchmark_faster_rcnn_inference_fp32_20200923_181013.log

    And here is a sample log file tail when running for accuracy:

    DONE (t=1.35s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.316
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.489
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.355
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.316
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.271
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.380
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.383
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.383
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Ran inference with batch size 1
Log location outside container: {--output-dir value}/benchmark_faster_rcnn_inference_fp32_20190114_205714.log

Int8 Inference Instructions

These instructions use the TCMalloc memory allocator, which produces better performance results for Int8 precision models with smaller batch sizes. If you want to disable the use of TCMalloc, set --disable-tcmalloc=True when calling launch_benchmark.py and the script will run without TCMalloc.

  1. Please follow the steps from the Faster R-CNN FP32 instructions written above for cloning dependecy repositories and getting the coco dataset (steps 1 and 2). Note that the performance test uses the raw coco images, but the accuracy test uses the TF records file.

  2. Download the pre-trained model.

    wget https://storage.googleapis.com/intel-optimized-tensorflow/models/faster_rcnn_int8_pretrained_model.pb

  3. Clone the intelai/models repo. This repo has the launch script for running the model.

    git clone https://github.com/IntelAI/models.git

  4. Run the launch_benchmark.py script from the intelai/models repo, with the appropriate parameters. To run on single socket use --socket_id switch. By default, it will be using all available sockets. The optional parameter number_of_steps (default value = 5000) can be added at the end of command after -- as shown below:

    Run batch and online inference using the following command. The --data-location is the path to the directory that contains the raw coco dataset validation images which you downloaded and unzipped:

    cd /home/<user>/models/benchmarks

python launch_benchmark.py \
  --data-location ${DATASET_DIR} \
  --model-source-dir /home/<user>/tensorflow/models \
  --model-name faster_rcnn \
  --framework tensorflow \
  --precision int8 \
  --mode inference \
  --socket-id 0 \
  --in-graph /home/<user>/faster_rcnn_int8_pretrained_model.pb \
  --docker-image intelaipg/intel-optimized-tensorflow:1.15.2 \
  --benchmark-only \
  -- number_of_steps=5000

    Test accuracy where the --data-location is the path the directory where your coco_val.record file is located and the --in-graph is the pre-trained graph model:

    python launch_benchmark.py \
  --model-name faster_rcnn \
  --mode inference \
  --precision int8 \
  --framework tensorflow \
  --socket-id 0 \
  --docker-image intelaipg/intel-optimized-tensorflow:1.15.2 \
  --model-source-dir /home/<user>/tensorflow/models \
  --data-location ${OUTPUT_DIR}/coco_val.record \
  --in-graph /home/<user>/faster_rcnn_int8_pretrained_model.pb  \
  --accuracy-only

  5. The log file is saved to the value of --output-dir. Below is a sample log file tail when running for batch and online inference:

    Step 4970: ... seconds
Step 4980: ... seconds
Step 4990: ... seconds
Avg. Duration per Step: ...
Log location outside container: {--output-dir value}/benchmark_faster_rcnn_inference_int8_20190117_232539.log

    And here is a sample log file tail when running for accuracy:

    Accumulating evaluation results...
DONE (t=1.34s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.310
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.479
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.351
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.310
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.267
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.372
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.375
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.375
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = -1.000
Ran inference with batch size -1
Log location outside container: {--output-dir value}/benchmark_faster_rcnn_inference_int8_20190117_231937.log