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SSD-ResNet34 FP32 inference - Advanced Instructions

This document has advanced instructions for running SSD-ResNet34 FP32 inference, which provides more control over the individual parameters that are used. For more information on using /benchmarks/launch_benchmark.py, see the launch benchmark documentation.

Prior to using these instructions, please follow the setup instructions from the model's README and/or the AI Kit documentation to get your environment setup (if running on bare metal) and download the dataset, pretrained model, etc. If you are using AI Kit, please exclude the --docker-image flag from the commands below, since you will be running the the TensorFlow conda environment instead of docker.

Any of the launch_benchmark.py commands below can be run on bare metal by removing the --docker-image arg. Ensure that you have all of the required prerequisites installed in your environment before running without the docker container.

If you are new to docker and are running into issues with the container, see this document for troubleshooting tips.

Once your environment is setup, navigate to the benchmarks directory of the model zoo and set environment variables pointing to the directory for the coco validation dataset, TensorFlow models repo, pretrained model frozen graph, and an output directory where log files will be written.

# cd to the benchmarks directory in the model zoo
cd benchmarks

export DATASET_DIR=<directory with the validation-*-of-* files (for accuracy testing only)>
export TF_MODELS_DIR=<path to the TensorFlow Models repo>
export PRETRAINED_MODEL=<path to the 300x300 or 1200x1200 pretrained model pb file>
export OUTPUT_DIR=<directory where log files will be written>

SSD-ResNet34 can be run for batch and online inference, or accuracy.

To run for batch and online inference, use the command below. If you are running using docker, you will need to provide the ssd-resnet-benchmarks path for volume flag. To run without docker, omit the --docker-image and --volume flags. By default it runs with input size 300x300, you may add -- input-size=1200 flag to run benchmark with input size 1200x1200. Use the 300x300 or 1200x1200 pretrained model, depending on the input size. Optionally, you can also specify the number of warmup-steps and steps as shown in the example below, the default values are warmup-steps=200 and steps=800.

# benchmarks with input size 1200x1200
python launch_benchmark.py \
    --in-graph ${PRETRAINED_MODEL} \
    --model-source-dir ${TF_MODELS_DIR} \
    --model-name ssd-resnet34 \
    --framework tensorflow \
    --precision fp32 \
    --mode inference \
    --socket-id 0 \
    --batch-size 1 \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --volume /home/<user>/ssd-resnet-benchmarks:/workspace/ssd-resnet-benchmarks \
    --output-dir ${OUTPUT_DIR} \
    --benchmark-only \
    -- input-size=1200 warmup-steps=50 steps=200

To run the accuracy test, use the command below. By default it runs with input size 300x300, you may add -- input-size=1200 flag last to run the test with input size 1200x1200. Use the 300x300 or 1200x1200 pretrained model, depending on the input size. To run without docker, omit the --docker-image and --volume flags.

# accuracy test with input size 300x300
python launch_benchmark.py \
    --data-location ${DATASET_DIR} \
    --in-graph ${PRETRAINED_MODEL} \
    --model-source-dir ${TF_MODELS_DIR} \
    --model-name ssd-resnet34 \
    --framework tensorflow \
    --precision fp32 \
    --mode inference \
    --socket-id 0 \
    --batch-size 1 \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --volume /home/<user>/ssd-resnet-benchmarks:/workspace/ssd-resnet-benchmarks \
    --output-dir ${OUTPUT_DIR} \
    --accuracy-only

The log file is saved to the value of ${OUTPUT_DIR}.

Below is a sample log file tail when running for performance:

Batchsize: 1
Time spent per BATCH:    ... ms
Total samples/sec:    ... samples/s

Below is a sample log file tail when testing accuracy:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.211
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.366
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.216
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.057
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.220
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.344
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.216
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.302
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.310
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.091
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.494
Current AP: 0.21082