зеркало из https://github.com/microsoft/archai.git
Fix test_snpe so it works with new olive commands.
This commit is contained in:
Chris Lovett
Родитель
3a115cd048
Коммит
b6ee9af9db
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@ -4,10 +4,7 @@
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script_dir="$(dirname ${BASH_SOURCE})"
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source ~/anaconda3/etc/profile.d/conda.sh
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conda activate snap-37
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# pushd $SNPE_ROOT
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# source bin/envsetup.sh -o ~/anaconda3/envs/snap/lib/python3.6/site-packages/onnx
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# popd
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conda activate snap37
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while true
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do
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@ -71,52 +71,23 @@ you need to double check something.
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1. **Prepare data**. Run `python create_data.py --help`, this scripts creates data for both quantization and test and puts it in your local experiment folder under `data/test` and `data/quant`.
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For example:
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```
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python create_data.py --input d:\datasets\FaceSynthetics --count 100 --dim 256
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python create_data.py --input ~/datasets/FaceSynthetics --count 1000 --dim 256
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```
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1. **Copy your tensorflow model**. In your experiment folder create a folder named `model` and copy your
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trained tensorflow model into this folder. You should have something like:
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1. **Convert and quantize model**. You can use `test_snpe.py` to convert a .onnx model to .dlc and
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quantize it. For example:
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``
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python test_snpe.py --quantize --model model.onnx
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```
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checkpoint
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model.pb
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model_11.cptk.data-00000-of-00001
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model_11.cptk.index
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model_11.cptk.meta
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This can take about 10 minutes depending on the size of your quantization data set and the size of your model.
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1. **Run test images on device**. You can use `test_snpe.py` to test your quantized model on a Qualcomm 888 dev board.
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You can find the dev board id using `adb devices`:
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```
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python test_snpe.py --device e6dc0375 --images ./data/test --model model.onnx --dlc ./snpe_models/model.quant.dlc
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```
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1. **Copy your onnx model**. In your experiment folder create a folder named `model` and copy your
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trained ONNX model into this folder. You should have something like:
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6. **Performance benchmark SNPE model**.
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```
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model.onnx
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python test_snpe.py --device e6dc0375 --benchmark --images ./data/test --model model.onnx --dlc ./snpe_models/model.quant.dlc
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```
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1. **Setup your snpe environment**. For onnx toolset use the following:
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```
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pushd ~/snpe/snpe-1.64.0.3605
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source bin/envsetup.sh -o ~/anaconda3/envs/snap/lib/python3.6/site-packages/onnx
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```
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For tensorflow use:
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```
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pushd ~/snpe/snpe-1.64.0.3605
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source bin/envsetup.sh -ot ~/anaconda3/envs/snap/lib/python3.6/site-packages/tensorflow
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```
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1. **Convert and quantize model**. Inside your experiment folder, run `bash convert_tf.sh` or `bash
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convert_onnx.sh modelname` which uses your SNPE SDK install to convert the tensorflow model in the
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model folder to a Qualcomm .dlc file, and then runs the SNPE quantization tool on that using the
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`quant` dataset to produce a quantized version of that model, so the output is
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`snpe_models/model.dlc` and `snpe_models/model.quant.dlc` and it is the quantized model we will run
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on the dev board using the DSP processor.
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1. **Run test images on device**. Inside your experiment folder, run `python run.py --help` and you
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will see the args you need to pass in order to upload the test images to the device, upload the
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model, then run the test on the DSP processor, then download the results. For example:
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```
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python run.py --images --model model.quant.dlc --run --download
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```
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6. **Profile SNPE model**.
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Update `benchmark/config.json` so it has the right paths and
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run `cd benchmark && bash run_benchmark.sh && cd ..`
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The above command line will generate a csv file with per layer profiling result.
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See [Performance Analysis Using Benchmarking Tools](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk/learning-resources/vision-based-ai-use-cases/performance-analysis-using-benchmarking-tools)
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@ -183,7 +183,7 @@ def convert_model(model, model_dir):
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basename, ext = os.path.splitext(filename)
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if ext != ".onnx":
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print(f"convert_model was not provided with a valid .onnx model")
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print("convert_model was not provided with a valid .onnx model")
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sys.exit(1)
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try:
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@ -205,13 +205,6 @@ def quantize_model(model, onnx_model, snpe_model_dir):
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from olive.model import SNPEModel
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from olive.passes import SNPEQuantization
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"""
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quant_set = os.path.join('data', 'quant', 'input_list.txt')
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if not os.path.isfile(quant_set):
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print(f"Quantize dataset {quant_set} is missing")
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sys.exit(1)
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"""
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snpe_model_dir = os.path.realpath(snpe_model_dir) # Olive requires the full path
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basename = os.path.splitext(os.path.basename(model))[0]
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output_model = os.path.join(snpe_model_dir, f"{basename}.quant.dlc")
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@ -538,11 +531,11 @@ def run_benchmark(onnx_model, dlc_model, images_dir, duration, workspace_dir):
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# This mirrors what the snpe_bench.py script is doing.
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options = SNPESessionOptions(
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android_target = get_device(),
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device = "dsp" if 'quant' in basename else "cpu",
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extra_args = "--perf_profile high_performance --profiling_level basic",
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workspace = workspace_dir,
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accumulate_outputs = True
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android_target=get_device(),
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device="dsp" if 'quant' in basename else "cpu",
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extra_args="--perf_profile high_performance --profiling_level basic",
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workspace=workspace_dir,
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accumulate_outputs=True
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)
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config = create_snpe_config(onnx_model, snpe_model_dir)
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@ -561,17 +554,17 @@ def run_benchmark(onnx_model, dlc_model, images_dir, duration, workspace_dir):
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output_folder = results['output_dir']
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latencies += [results['latencies']]
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return output_folder, latencies
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return (output_folder, latencies)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Run a model on the QUALCOMM DSP using adb and SNPE SDK to quantize ' +
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'the model')
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parser.add_argument('--device', '-d', help='The Android device id (as returned by adb devices)', default=None)
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parser.add_argument('--images', '-i', help='Location of local test image dataset (created with create_data.py)',
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default=TEST_IMAGES)
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parser.add_argument('--images', '-i', help='Location of local test image dataset (created with create_data.py)')
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parser.add_argument('--model', '-m', help='The path to the ONNX model to test')
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parser.add_argument('--dlc', help='The specific .dlc model to test, if not provided it will be converted from --model and stored in an snpe_models folder')
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parser.add_argument('--quantize', '-q', help='Quantize the given onnx or dlc model', action="store_true")
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parser.add_argument('--benchmark', '-b', help='Run a benchmark on the given model', action="store_true")
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parser.add_argument('--throughput', '-t', help='Run performance test of the given model', action="store_true")
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parser.add_argument('--duration', type=int, help='Duration of throughput and benchmark tests (default 10 seconds)', default=10)
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@ -579,10 +572,15 @@ if __name__ == '__main__':
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args = parser.parse_args()
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VERBOSE = args.verbose
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set_device(args.device)
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if args.device:
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set_device(args.device)
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model = args.model
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MODEL_DIR = "snpe_models"
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if not args.model:
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print("Please provide an onnx model as input")
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sys.exit(1)
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ndk = os.getenv("ANDROID_NDK_ROOT")
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if not ndk:
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print("you must have a ANDROID_NDK_ROOT installed, see the readme.md")
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@ -609,6 +607,12 @@ if __name__ == '__main__':
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config = create_snpe_config(args.model, '.')
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shape = config['io_config']['output_shapes'][0]
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if args.quantize:
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quantized_model, error = quantize_model(model, model, MODEL_DIR)
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if error is not None:
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print(error)
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sys.exit(1)
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if args.throughput:
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run_throughput(model, args.duration)
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sys.exit(0)
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@ -629,5 +633,5 @@ if __name__ == '__main__':
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print(f"batch completed in {end-start} seconds, results in {output_folder}")
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for m in latencies:
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print(f"total_inference_time={m['total_inference_time']}")
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img_shape = tuple(shape)[0:2] # e.g. (256,256)
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compute_results(img_shape, output_folder)
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input_shape = (1, shape[0], shape[1], 19)
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compute_results(input_shape, output_folder)
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