Comments (2)
The larq-compute-engine
python package contains the converter which converts your Keras models to .tflite
files. It is meant to be run on a x86 host machine. To run those .tflite
files on a platform like the Jetson Nano, you need the runtime which is separate from the python package.
The ARM tutorial that you linked to shows how to build the runtime. This is not a python package but an executable (in the example it is lce_benchmark_model
) which you can use to run the .tflite
files on the target device.
from compute-engine.
Oh ok thanks a lot for that explanation!
from compute-engine.
Related Issues (20)
- Upgrade TensorFlow dependency to 2.6 HOT 3
- Automatic release builds for benchmarking binaries are broken HOT 2
- Deployment on Cortex-M HOT 2
- Tensor transform triggers dequantization HOT 6
- Select indirect BGEMM kernels - Benchmarking grouped binary convolutions HOT 3
- LCEInterpreter and converter design HOT 1
- core dumped when number of threads is larger than 2 HOT 3
- Benchmarking custom model HOT 3
- Int8 quantization for microcontroller HOT 13
- Failed import 'org.tensorflow.lite.DataType' on Android project HOT 8
- `convert_keras_model()` does not work as expected for BinaryDenseNet37 Dilated and XNORNet HOT 1
- DoReFa quantizer with higher number of MACs/Ops, Grouped convs as custom ops on LCE 0.7.0 HOT 3
- Get Operator-wise Profiling Results HOT 1
- Error while performing benchmarking HOT 44
- Bool input tensor HOT 7
- extra model size induced by non-parameter layer HOT 1
- Fix Android benchmarker build
- Larq Compute Engine seems incompatible with tensorflow-lite-task-vision on Android (using the latest tensorflow lite demo code) HOT 2
- Dorefa model size and behavior with full precision model and ste_sign model HOT 13
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from compute-engine.