Advanced.md

Inception V4 FP32 inference - Advanced Instructions

This document has advanced instructions for running Inception V4 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 dataset, 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=<path to the dataset (only required for accuracy)>
export OUTPUT_DIR=<directory where log files will be written>
export PRETRAINED_MODEL=<path to the frozen graph that you downloaded>

Inception V4 can be run to test accuracy, batch inference, or online inference. Use one of the following examples below, depending on your use case.

• For accuracy run the following command that uses the DATASET_DIR, a batch size of 100, and the --accuracy-only flag:

  python launch_benchmark.py \
    --model-name inceptionv4 \
    --precision fp32 \
    --mode inference \
    --framework tensorflow \
    --accuracy-only \
    --batch-size 100 \
    --socket-id 0 \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --in-graph ${PRETRAINED_MODEL} \
    --data-location ${DATASET_DIR} \
    --output-dir ${OUTPUT_DIR}
  

• For batch inference, use the command below that has a batch size of 240 and the --benchmark-only flag. Synthetic data will be used, since no dataset is being provided.

  python launch_benchmark.py \
    --model-name inceptionv4 \
    --precision fp32 \
    --mode inference \
    --framework tensorflow \
    --benchmark-only \
    --batch-size 240 \
    --socket-id 0 \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --in-graph ${PRETRAINED_MODEL} \
    --output-dir ${OUTPUT_DIR}
  

• For online inference use the command below that has a batch size of 1 and the --benchmark-only flag. Again, synthetic data is being used in this example.

  python launch_benchmark.py \
    --model-name inceptionv4 \
    --precision fp32 \
    --mode inference \
    --framework tensorflow \
    --benchmark-only \
    --batch-size 1 \
    --socket-id 0 \
    --docker-image intel/intel-optimized-tensorflow:latest \
    --in-graph ${PRETRAINED_MODEL} \
    --output-dir ${OUTPUT_DIR}
  

Note that the --verbose flag can be added to any of the above commands to get additional debug output.

The log file is saved to the OUTPUT_DIR directory. Below are examples of what the tail of your log file should look like for the different configs.

Example log tail when running for accuracy:

...
Iteration time: 1337.8728 ms
Processed 49600 images. (Top1 accuracy, Top5 accuracy) = (0.8015, 0.9517)
Iteration time: 1331.8253 ms
Processed 49700 images. (Top1 accuracy, Top5 accuracy) = (0.8017, 0.9518)
Iteration time: 1339.1553 ms
Processed 49800 images. (Top1 accuracy, Top5 accuracy) = (0.8017, 0.9518)
Iteration time: 1334.5991 ms
Processed 49900 images. (Top1 accuracy, Top5 accuracy) = (0.8018, 0.9519)
Iteration time: 1336.1905 ms
Processed 50000 images. (Top1 accuracy, Top5 accuracy) = (0.8018, 0.9519)
Ran inference with batch size 100
Log location outside container: ${OUTPUT_DIR}/benchmark_inceptionv4_inference_fp32_20190308_182729.log

Example log tail when running for batch inference:

[Running warmup steps...]
steps = 10, 91.4372832625 images/sec
[Running benchmark steps...]
steps = 10, 91.0217283977 images/sec
steps = 20, 90.8331507586 images/sec
steps = 30, 91.1284943026 images/sec
steps = 40, 91.1885998597 images/sec
steps = 50, 91.1905741783 images/sec
Ran inference with batch size 240
Log location outside container: ${OUTPUT_DIR}/benchmark_inceptionv4_inference_fp32_20190308_184431.log

Example log tail when running for online inference:

[Running warmup steps...]
steps = 10, 15.6993019295 images/sec
[Running benchmark steps...]
steps = 10, 16.3553780883 images/sec
steps = 20, 15.771143231 images/sec
steps = 30, 15.7133587586 images/sec
steps = 40, 16.0477494988 images/sec
steps = 50, 15.483992912 images/sec
Latency: 63.534 ms
Ran inference with batch size 1
Log location outside container: ${OUTPUT_DIR}/benchmark_inceptionv4_inference_fp32_20190307_221954.log