STT-tensorflow/tensorflow/lite/java/ovic

OVIC Benchmarker for LPCV 2020

This folder contains the SDK for track one of the Low Power Computer Vision workshop at CVPR 2020.

Pre-requisite

Follow the steps here to install Tensorflow, Bazel, and the Android NDK and SDK.

Test the benchmarker:

The testing utilities helps the developers (you) to make sure that your submissions in TfLite format will be processed as expected in the competition's benchmarking system.

Note: for now the tests only provides correctness checks, i.e. classifier predicts the correct category on the test image, but no on-device latency measurements. To test the latency measurement functionality, the tests will print the latency running on a desktop computer, which is not indicative of the on-device run-time. We are releasing an benchmarker Apk that would allow developers to measure latency on their own devices.

Obtain the sample models

The test data (models and images) should be downloaded automatically for you by Bazel. In case they are not, you can manually install them as below.

Note: all commands should be called from your tensorflow installation folder (under this folder you should find tensorflow/lite).

• Download the testdata package:

curl -L https://storage.googleapis.com/download.tensorflow.org/data/ovic_2019_04_30.zip -o /tmp/ovic.zip

• Unzip the package into the testdata folder:

unzip -j /tmp/ovic.zip -d tensorflow/lite/java/ovic/src/testdata/

Run tests

You can run test with Bazel as below. This helps to ensure that the installation is correct.

bazel test //tensorflow/lite/java/ovic:OvicClassifierTest --cxxopt=-Wno-all --test_output=all

bazel test //tensorflow/lite/java/ovic:OvicDetectorTest --cxxopt=-Wno-all --test_output=all

Test your submissions

Once you have a submission that follows the instructions from the competition site, you can verify it in two ways:

Validate using randomly generated images

You can call the validator binary below to verify that your model fits the format requirements. This often helps you to catch size mismatches (e.g. output for classification should be [1, 1001] instead of [1,1,1,1001]). Let say the submission file is located at /path/to/my_model.lite, then call:

bazel build //tensorflow/lite/java/ovic:ovic_validator --cxxopt=-Wno-all
bazel-bin/tensorflow/lite/java/ovic/ovic_validator /path/to/my_model.lite classify

Successful validation should print the following message to terminal:

Successfully validated /path/to/my_model.lite.

To validate detection models, use the same command but provide "detect" as the second argument instead of "classify".

Test that the model produces sensible outcomes

You can go a step further to verify that the model produces results as expected. This helps you catch bugs during TFLite conversion (e.g. using the wrong mean and std values).

• Move your submission to the testdata folder:

cp /path/to/my_model.lite tensorflow/lite/java/ovic/src/testdata/

• Resize the test image to the resolutions that are expected by your submission:

The test images can be found at tensorflow/lite/java/ovic/src/testdata/test_image_*.jpg. You may reuse these images if your image resolutions are 128x128 or 224x224.

• Add your model and test image to the BUILD rule at tensorflow/lite/java/ovic/src/testdata/BUILD:

filegroup(
    name = "ovic_testdata",
    srcs = [
        "@tflite_ovic_testdata//:detect.lite",
        "@tflite_ovic_testdata//:float_model.lite",
        "@tflite_ovic_testdata//:low_res_model.lite",
        "@tflite_ovic_testdata//:quantized_model.lite",
        "@tflite_ovic_testdata//:test_image_128.jpg",
        "@tflite_ovic_testdata//:test_image_224.jpg"
        "my_model.lite",        # <--- Your submission.
        "my_test_image.jpg",    # <--- Your test image.
    ],
    ...

• For classification models, modify OvicClassifierTest.java:

  • change TEST_IMAGE_PATH to my_test_image.jpg.

  • change either FLOAT_MODEL_PATH or QUANTIZED_MODEL_PATH to my_model.lite depending on whether your model runs inference in float or 8-bit.

  • change TEST_IMAGE_GROUNDTRUTH (ImageNet class ID) to be consistent with your test image.

• For detection models, modify OvicDetectorTest.java:

  • change TEST_IMAGE_PATH to my_test_image.jpg.
  • change MODEL_PATH to my_model.lite.
  • change GROUNDTRUTH (COCO class ID) to be consistent with your test image.

Now you can run the bazel tests to catch any runtime issues with the submission.

Note: Please make sure that your submission passes the test. If a submission fails to pass the test it will not be processed by the submission server.

Measure on-device latency

We provide two ways to measure the on-device latency of your submission. The first is through our competition server, which is reliable and repeatable, but is limited to a few trials per day. The second is through the benchmarker Apk, which requires a device and may not be as accurate as the server, but has a fast turn-around and no access limitations. We recommend that the participants use the benchmarker apk for early development, and reserve the competition server for evaluating promising submissions.

Running the benchmarker app

Make sure that you have followed instructions in Test your submissions to add your model to the testdata folder and to the corresponding build rules.

Modify tensorflow/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java:

• Add your model to the benchmarker apk by changing modelPath and testImagePath to your submission and test image.

  if (benchmarkClassification) {
    ...
    testImagePath = "my_test_image.jpg";
    modelPath = "my_model.lite";
  } else {  // Benchmarking detection.
  ...

If you are adding a detection model, simply modify modelPath and testImagePath in the else block above.

• Adjust the benchmark parameters when needed:

You can change the length of each experiment, and the processor affinity below. BIG_CORE_MASK is an integer whose binary encoding represents the set of used cores. This number is phone-specific. For example, Pixel 4 has 8 cores: the 4 little cores are represented by the 4 less significant bits, and the 4 big cores by the 4 more significant bits. Therefore a mask value of 16, or in binary 00010000, represents using only the first big core. The mask 32, or in binary 00100000 uses the second big core and should deliver identical results as the mask 16 because the big cores are interchangeable.

  /** Wall time for each benchmarking experiment. */
  private static final double WALL_TIME = 3000;
  /** Maximum number of iterations in each benchmarking experiment. */
  private static final int MAX_ITERATIONS = 100;
  /** Mask for binding to a single big core. Pixel 1 (4), Pixel 4 (16). */
  private static final int BIG_CORE_MASK = 16;

Note: You'll need ROOT access to the phone to change processor affinity.

• Build and install the app.

bazel build -c opt --cxxopt=-Wno-all //tensorflow/lite/java/ovic/demo/app:ovic_benchmarker_binary
adb install -r bazel-bin/tensorflow/lite/java/ovic/demo/app/ovic_benchmarker_binary.apk

Start the app and pick a task by clicking either the CLF button for classification or the DET button for detection. The button should turn bright green, signaling that the experiment is running. The benchmarking results will be displayed after about the WALL_TIME you specified above. For example:

my_model.lite: Average latency=158.6ms after 20 runs.

Sample latencies

Note: the benchmarking results can be quite different depending on the background processes running on the phone. A few things that help stabilize the app's readings are placing the phone on a cooling plate, restarting the phone, and shutting down internet access.

Classification Model	Pixel 1	Pixel 2	Pixel 4
float_model.lite	97	113	37
quantized_model.lite	73	61	13
low_res_model.lite	3	3	1

Detection Model	Pixel 2	Pixel 4
detect.lite	248	82
quantized_detect.lite	59	17
quantized_fpnlite.lite	96	29

All latency numbers are in milliseconds. The Pixel 1 and Pixel 2 latency numbers are measured on Oct 17 2019 (Github commit hash I05def66f58fa8f2161522f318e00c1b520cf0606)

The Pixel 4 latency numbers are measured on Apr 14 2020 (Github commit hash 4b2cb67756009dda843c6b56a8b320c8a54373e0).

Since Pixel 4 has excellent support for 8-bit quantized models, we strongly recommend you to check out the Post-Training Quantization tutorial.

The detection models above are both single-shot models (i.e. no object proposal generation) using TfLite's fast version of Non-Max-Suppression (NMS). The fast NMS is significant faster than the regular NMS (used by the ObjectDetectionAPI in training) at the expense of about 1% mAP for the listed models.

Latency table

We have compiled a latency table for common neural network operators such as convolutions, separable convolutions, and matrix multiplications. The table of results is available here:

• https://storage.cloud.google.com/ovic-data/

The results were generated by creating a small network containing a single operation, and running the op under the test harness. For more details see the NetAdapt paper¹. We plan to expand table regularly as we test with newer OS releases and updates to Tensorflow Lite.

Sample benchmarks

Below are the baseline models (MobileNetV2, MnasNet, and MobileNetV3) used to compute the reference accuracy for ImageNet classification. The naming convention of the models are [precision]_[model class]_[resolution]_[multiplier]. Pixel 2 Latency (ms) is measured on a single Pixel 2 big core using the competition server on Oct 17 2019, while Pixel 4 latency (ms) is measured on a single Pixel 4 big core using the competition server on Apr 14 2020. You can find these models on TFLite's hosted model page.

Model	Pixel 2	Pixel 4	Top-1 Accuracy
quant_mobilenetv2_96_35	4	1	0.420
quant_mobilenetv2_96_50	5	1	0.478
quant_mobilenetv2_128_35	6	2	0.474
quant_mobilenetv2_128_50	8	2	0.546
quant_mobilenetv2_160_35	9	2	0.534
quant_mobilenetv2_96_75	8	2	0.560
quant_mobilenetv2_96_100	10	3	0.579
quant_mobilenetv2_160_50	12	3	0.583
quant_mobilenetv2_192_35	12	3	0.557
quant_mobilenetv2_128_75	13	3	0.611
quant_mobilenetv2_192_50	16	4	0.616
quant_mobilenetv2_128_100	16	4	0.629
quant_mobilenetv2_224_35	17	5	0.581
quant_mobilenetv2_160_75	20	5	0.646
float_mnasnet_96_100	21	7	0.625
quant_mobilenetv2_224_50	22	6	0.637
quant_mobilenetv2_160_100	25	6	0.674
quant_mobilenetv2_192_75	29	7	0.674
quant_mobilenetv2_192_100	35	9	0.695
float_mnasnet_224_50	35	12	0.679
quant_mobilenetv2_224_75	39	10	0.684
float_mnasnet_160_100	45	15	0.706
quant_mobilenetv2_224_100	48	12	0.704
float_mnasnet_224_75	55	18	0.718
float_mnasnet_192_100	62	20	0.724
float_mnasnet_224_100	84	27	0.742
float_mnasnet_224_130	126	40	0.758
float_v3-small-minimalistic_224_100	-	5	0.620
quant_v3-small_224_100	-	5	0.641
float_v3-small_224_75	-	5	0.656
float_v3-small_224_100	-	7	0.677
quant_v3-large_224_100	-	12	0.728
float_v3-large_224_75	-	15	0.735
float_v3-large-minimalistic_224_100	-	17	0.722
float_v3-large_224_100	-	20	0.753

References

1. NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
   Yang, Tien-Ju, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, and Hartwig Adam. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 285-300. 2018
   [link] arXiv:1804.03230, 2018.