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name="TinyML" |
Preparing a custom version of MicroPython with TensorFlow
Introduction |
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Here are the steps to build this custom microPython version: |
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- The steps to build the firmware can be found in tflite-micro-micropython/.github/workflows/build_esp32.yml
In my case the Espressif development environment espidf has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.
Activate the virtual Python environment needed for espidf (if venvwrapper is installed: workon espidf)
Make sure that the modules Pillow and Wave have been installed on this virtual environment. If not, install them with pip:
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- The steps to build the firmware can be found in tflite-micro-micropython/.github/workflows/build_esp32.yml
In my case the Espressif development environment espidf has already been downloaded and set up earlier. I use espidf version 4.3.1, the latest stable version at time of writing.
Activate the virtual Python environment needed for espidf (if venvwrapper is installed: workon espidf)
Make sure that the modules Pillow and Wave have been installed on this virtual environment. If not, install them with pip:
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- pip3 install Pillow
- pip3 install Wave
- Setup the sub-modules needed for tflm:
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- cd ../micropython/mpy_cross
- make
- cd .. (the micropython folder of tflite-micro-micropython)
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- git apply ../boards/esp32/MICROLITE_SPIRAM_CAM/micropython.patch
!MicroPython allocates the full SPIRAM installed on the esp32-cam module for its heap. However, the esp32-camera driver needs some of the SPIRAM for its image buffer. The patch modifies main.c in micropython/ports/esp32 such that part of the SPIRAM is kept free for the camera driver.
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- git apply ../boards/esp32/MICROLITE_SPIRAM_CAM/micropython.patch
!MicroPython allocates the full SPIRAM installed on the esp32-cam module for its heap. However, the esp32-camera driver needs some SPIRAM for its image buffer. The patch modifies main.c in micropython/ports/esp32 such that part of the SPIRAM is kept free for the camera driver.
- cd boards/esp32/MICROLITE_SPIRAM_16M (when using the TTGO T7 Mini32 V1.5 ESP32-WROVER-B CPU board)
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- idf.py clean build (this builds the MicroPython firmware)
- flash the custom MicroPython interpreter
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< | Since all the necessary drivers and libraries are now available in MicroPython we can go ahead and try the TensorFlow Lite Micro examples. |
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> | Since all the necessary drivers and libraries are now ready in MicroPython we can go ahead and try the TensorFlow Lite Micro examples. |
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--  Uli Raich - 2022-01-31 |
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