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我有一个来自darkflow的张量流图,我在 android 设备上运行推理(在 CPU Snapdragon 820 上)。我发现这个图形转换工具可以优化部署模型。所以我优化了我的图表,预计它会比以前更快,但它变慢了大约 10%。

什么会导致这种情况?我究竟做错了什么?

以下是详细信息:

$ ./flow --model cfg/tiny-yolo-voc.cfg --load bin/tiny-yolo-voc.weights --savepb --verbalise

  • 我使用以下命令优化了图表:

$ bazel-bin/tensorflow/tools/graph_transforms/transform_graph /
--in_graph=../darkflow/darkflow/built_graph/tiny-yolo-voc.pb /
--out_graph=../darkflow/darkflow/built_graph/optimized-tiny -yolo-voc.pb /
--inputs='input' --outputs='output' /
--transforms='strip_unused_nodes(type=float, shape="1,299,299,3") fold_constants(ignore_errors=true) fold_batch_norms fold_old_batch_norms'

  • 我的代码:

InfrerenceRunner.java:

public class InferenceRunner {

    private static final String INPUT_NODE = "input";
    private static final String OUTPUT_NODE = "output";
    protected final TensorFlowInferenceInterface mInferenceInterface;
    private final int mGridSize;
    private final int mNumOfLabels;
    private int mInputSize;

    public InferenceRunner(Context context, String modelFile, int inputSize, int gridSize, int numOfLabels) {
        this.mInputSize = inputSize;
        this.mGridSize = gridSize;
        this.mNumOfLabels = numOfLabels;
        mInferenceInterface = new TensorFlowInferenceInterface(context.getAssets(), modelFile);
    }

    public synchronized void runInference(Bitmap image) {
        Trace.beginSection("imageTransform");
        Bitmap bitmap = Bitmap.createScaledBitmap(image, mInputSize, mInputSize, false);
        int[] intValues = new int[mInputSize * mInputSize];
        float[] floatValues = new float[mInputSize * mInputSize * 3];
        bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());

        for (int i = 0; i < intValues.length; ++i) {
            floatValues[i * 3 + 0] = ((intValues[i] >> 16) & 0xFF) / 255.0f;
            floatValues[i * 3 + 1] = ((intValues[i] >> 8) & 0xFF) / 255.0f;
            floatValues[i * 3 + 2] = (intValues[i] & 0xFF) / 255.0f;
        }
        Trace.endSection();

        Trace.beginSection("inferenceFeed");
        mInferenceInterface.feed(INPUT_NODE, floatValues, 1, mInputSize, mInputSize, 3);
        Trace.endSection();

        Trace.beginSection("inferenceRun");
        mInferenceInterface.run(new String[]{OUTPUT_NODE});
        Trace.endSection();

        final float[] resu =
                new float[mGridSize * mGridSize * (mNumOfLabels + 5) * 5];
        Trace.beginSection("inferenceFetch");
        mInferenceInterface.fetch(OUTPUT_NODE, resu);
        Trace.endSection();
    }
}

MainActivity:onCreate():

...
tinyYolo = new InferenceRunner(getApplicationContext(), TINY_YOLO_MODEL_FILE, TINY_YOLO_INPUT_SIZE, 13, 20);
optimizedTinyYolo = new InferenceRunner(getApplicationContext(), OPTIMIZED_TINY_YOLO_MODEL_FILE, TINY_YOLO_INPUT_SIZE, 13, 20);
...

MainActivity:onResume():

...
mHandler.post(new Runnable() {
        @Override
        public void run() {
            Trace.beginSection("TinyYoloModel");
            for (int i = 0; i < 5; i++) {
                tinyYolo.runInference(b);
            }
            Trace.endSection();

            Log.d(TAG, "run: optimized");
            Trace.beginSection("OptimizedModel");
            for (int i = 0; i < 5; i++) {
                optimizedTinyYolo.runInference(b);
            }
            Trace.endSection();
        }
    });
...
  • 我的 Systrace 输出: 系统跟踪

TinyYoloModel 墙持续时间为 5,525ms
OptimizedModel 持续时间为 6,043ms
TinyYoloModel inferenceRun avg: 1051ms
OptimizedModel inferenceRun avg: 1158ms

你知道为什么优化模型变慢了吗?

如果您需要更多信息,请随时发表评论!谢谢你的帮助。

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