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lib/libtf: Remove obsolete libtf.
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@ -1,32 +0,0 @@
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# This file is part of the OpenMV project.
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#
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# Copyright (c) 2013-2021 Ibrahim Abdelkader <iabdalkader@openmv.io>
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# Copyright (c) 2013-2021 Kwabena W. Agyeman <kwagyeman@openmv.io>
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#
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# This work is licensed under the MIT license, see the file LICENSE for details.
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#
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# LIBTF Makefile
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GENERATED := $(BUILD)/libtf_builtin_models.h $(BUILD)/libtf_builtin_models.c
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OBJS = $(BUILD)/libtf_builtin_models.o
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OBJ_DIRS = $(sort $(dir $(OBJS)))
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all: | $(OBJ_DIRS) $(OBJS)
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$(OBJ_DIRS):
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$(MKDIR) -p $@
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$(GENERATED): $(wildcard models/*)
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$(ECHO) "GEN $@"
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$(PYTHON) $(TOOLS)/$(TFLITE2C) --input models > $(BUILD)/libtf_builtin_models.c
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$(PYTHON) $(TOOLS)/$(TFLITE2C) --input models --header > $(BUILD)/libtf_builtin_models.h
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$(OBJS): $(GENERATED)
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$(BUILD)/%.o : %.c
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$(ECHO) "CC $<"
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$(CC) $(CFLAGS) -c -o $@ $<
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$(BUILD)/%.o : %.s
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$(ECHO) "AS $<"
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$(AS) $(AFLAGS) $< -o $@
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-include $(OBJS:%.o=%.d)
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@ -1,34 +0,0 @@
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# Tensorflow Support
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The OpenMV firmware supports loading quantized Tensorflow Lite models. The firmware supports loading external models that reside on the filesystem to memory (on boards with SDRAM), and internal models (embedded into the firmware) in place. To load an external Tensorflow model from the filesystem from Python use [`tf`](https://docs.openmv.io/library/omv.tf.html) Python module. For information on embedding Tensorflow models into the firmware, and loading them, please see the following sections.
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- [Training Tensorflow models](#training-tensorflow-models)
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- [Embedding Tensorflow models in the firmware](#embedding-tensorflow-models-in-the-firmware)
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- [Loading embedded Tensorflow models](#loading-embedded-tensorflow-models)
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## Training Tensorflow models
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You can use Tensorflow Lite with any high-level API, such as Keras or pytorch, to train and quantize models (for example, see [Training Mobilenet V2 for OpenMV with Keras](https://github.com/SingTown/openmv_tensorflow_training_scripts/blob/main/openmv_mobilenet_v2.ipynb)). However, the officially supported way to train models for OpenMV cameras is using EdgeImpulse. For more information on using EdgeImpulse with OpenMV cameras, please see the [EdgeImpulse tutorial for OpenMV](https://docs.edgeimpulse.com/docs/openmv-cam-h7-plus).
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## Embedding Tensorflow models in the firmware
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Tensorflow models can be embedded into the firmware for boards that don't have an SDRAM. The firmware ships with a default embedded model, that can be replaced by any other Tensorflow model. Additionally, more models can be embedded in the firmware, as many as the free flash size allows. Once you have a tflite model to replace the default model, or to add to the firmware, it can be embedded in the firmware by placing the model `.tflite` file and its `.txt` labels file in `src/libtf/models`. The model file and labels file must have the same basename, for example:
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```bash
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models/mask_detection.txt
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models/mask_detection.tflite
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```
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The labels `.txt` file contains model output labels arranged 1 label per line, for example:
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```
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>>cat mask_detection.txt
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mask
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no_mask
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```
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After replacing/adding a Tensorflow model, rebuild the firmware to use the new model. The build system will automatically detect the models and convert them to C structs that get embedded into the firmware image. For more information on how to build the OpenMV firmware from source, see [Building the Firmware From Source](https://github.com/openmv/openmv/blob/master/src/README.md)
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## Loading embedded Tensorflow models
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Built-in models can be loaded with `tf.load_builtin_model()` function, for example the following loads the built-in person detection model:
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```Python
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labels, net = tf.load_builtin_model('person_detection')
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```
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For more information on how to OpenMV Tensorflow Python module, see the [documentation](https://docs.openmv.io/library/omv.tf.html)
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/* This file is part of the OpenMV project.
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* Copyright (c) 2013-2024 Ibrahim Abdelkader <iabdalkader@openmv.io> & Kwabena W. Agyeman <kwagyeman@openmv.io>
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* This work is licensed under the MIT license, see the file LICENSE for details.
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*/
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#ifndef __LIBTF_H
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#define __LIBTF_H
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#define LIBTF_TENSOR_ARENA_ALIGNMENT 16
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#ifdef __cplusplus
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extern "C" {
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#endif
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typedef enum libtf_datatype {
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LIBTF_DATATYPE_UINT8,
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LIBTF_DATATYPE_INT8,
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LIBTF_DATATYPE_FLOAT
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} libtf_datatype_t;
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typedef struct libtf_parameters {
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size_t tensor_arena_size;
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size_t input_height, input_width, input_channels;
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libtf_datatype_t input_datatype;
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float input_scale;
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int input_zero_point;
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size_t output_height, output_width, output_channels;
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libtf_datatype_t output_datatype;
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float output_scale;
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int output_zero_point;
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} libtf_parameters_t;
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// Call this first to get the model parameters.
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// Returns 0 on success and 1 on failure.
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// Errors are printed to stdout.
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int libtf_get_parameters_default(const unsigned char *model_data, // TensorFlow Lite binary model (8-bit quant).
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unsigned char *tensor_arena, // As big as you can make it scratch buffer.
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size_t tensor_arena_size, // Size of the above scratch buffer.
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libtf_parameters_t *params); // Struct to hold model parameters.
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// Call this first to get the model parameters.
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// Returns 0 on success and 1 on failure.
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// Errors are printed to stdout.
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int libtf_get_parameters_fullops(const unsigned char *model_data, // TensorFlow Lite binary model (8-bit quant).
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unsigned char *tensor_arena, // As big as you can make it scratch buffer.
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size_t tensor_arena_size, // Size of the above scratch buffer.
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libtf_parameters_t *params); // Struct to hold model parameters.
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// Callback to populate the model input data byte array (laid out in [height][width][channel] order).
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typedef void (*libtf_input_data_callback_t)(void *callback_data,
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void *model_input,
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libtf_parameters_t *params);
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// Callback to use the model output data byte array (laid out in [height][width][channel] order).
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typedef void (*libtf_output_data_callback_t)(void *callback_data,
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void *model_output,
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libtf_parameters_t *params);
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// Returns 0 on success and 1 on failure.
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// Errors are printed to stdout.
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int libtf_invoke_default(const unsigned char *model_data, // TensorFlow Lite binary model (8-bit quant).
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unsigned char *tensor_arena, // As big as you can make it scratch buffer.
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libtf_parameters_t *params, // Struct with model parameters.
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libtf_input_data_callback_t input_callback, // Callback to populate the model input data byte array.
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void *input_callback_data, // User data structure passed to input callback.
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libtf_output_data_callback_t output_callback, // Callback to use the model output data byte array.
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void *output_callback_data); // User data structure passed to output callback.
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// Returns 0 on success and 1 on failure.
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// Errors are printed to stdout.
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int libtf_invoke_fullops(const unsigned char *model_data, // TensorFlow Lite binary model (8-bit quant).
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unsigned char *tensor_arena, // As big as you can make it scratch buffer.
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libtf_parameters_t *params, // Struct with model parameters.
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||||
libtf_input_data_callback_t input_callback, // Callback to populate the model input data byte array.
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void *input_callback_data, // User data structure passed to input callback.
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libtf_output_data_callback_t output_callback, // Callback to use the model output data byte array.
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void *output_callback_data); // User data structure passed to output callback.
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// Returns 0 on success and 1 on failure.
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// Errors are printed to stdout.
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int libtf_initialize_micro_features();
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// Returns 0 on success and 1 on failure.
|
||||
// Errors are printed to stdout.
|
||||
// Converts audio sample data into a more compact form
|
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// that's appropriate for feeding into a neural network.
|
||||
int libtf_generate_micro_features(const int16_t *input, // Audio samples
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||||
int input_size, // Audio sample size
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||||
int output_size, // Slice data size
|
||||
int8_t *output, // Slice data
|
||||
size_t *num_samples_read); // Number of samples used
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
#endif // __LIBTF_H
|
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||||
background
|
||||
face
|
||||
Loading…
Reference in New Issue
Block a user