petewarden

def StripTfliteFile(tflite_input, tflite_output):
  """Given 'tflite_input`, write stripped version, 'tflite_output'."""
  if not os.path.exists(tflite_input):
    raise RuntimeError("Invalid filename %r" % tflite_input)
  with open(tflite_input, "rb") as file_handle:
    file_data = bytearray(file_handle.read())
  model_obj = schema_fb.Model.GetRootAsModel(file_data, 0)
  model = schema_fb.ModelT.InitFromObj(model_obj)
  model.description = ""
  for subgraph in model.subgraphs:

petewarden

# To convert from .ogg files to .wavs:
# find . -iname "*.ogg" -print0 | xargs -0 basename -s .ogg | xargs -I {} ffmpeg -i {}.ogg -ar 16000 ../converted_early_wavs/{}.wav
# Then run the extract_loudest xcode project to get one-second clips.

import glob
import os
import re
import shutil

data_index = {}

petewarden

define call_test
%: %
	echo "foo"

foo/%: %
	echo "foo"
endef

$(call call_test)

petewarden

TensorFlow Lite for Microcontrollers

This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22KB.

Table of Contents

petewarden

TensorFlow Lite for Microcontrollers

This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22KB.

Table of Contents

petewarden

TensorFlow Lite for Microcontrollers

This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22KB.

Table of Contents

petewarden

/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,

petewarden

bazel-bin/tensorflow/examples/label_image/label_image \
 --input_mean=128 --input_std=128 --input_layer=Mul \
 --output_layer=softmax \
 --graph=/tmp/model/ranged_quantized_graph.pb \
 --labels=/tmp/model/imagenet_comp_graph_label_strings.txt

petewarden

bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
 --in_graph=/tmp/model/quantized_graph.pb \
 --out_graph=/tmp/model/ranged_quantized_graph.pb \
 --inputs=Mul \
 --outputs=softmax \
--transforms='freeze_requantization_ranges(min_max_log_file=/tmp/model/logged_ranges.txt)'

petewarden

bazel-bin/tensorflow/examples/label_image/label_image \
 --input_mean=128 --input_std=128 \
 --input_layer=Mul --output_layer=softmax \
 --graph=/tmp/model/logged_quantized_graph.pb \
 --labels=/tmp/model/imagenet_comp_graph_label_strings.txt 2> \
 /tmp/model/logged_ranges.txt
cat /tmp/model/logged_ranges.txt