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Merge branch 'tensorflow' into HackStrap

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This commit is contained in:
Gordon Williams 2019-09-11 14:21:24 +01:00
commit ec98c13e24
14 changed files with 1012 additions and 3 deletions
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13
Makefile
View File

@ -65,6 +65,7 @@ LIBS?=
DEFINES?=
CFLAGS?=-Wall -Wextra -Wconversion -Werror=implicit-function-declaration -fno-strict-aliasing -g
CFLAGS+=-Wno-expansion-to-defined # remove warnings created by Nordic's libs
CCFLAGS?= # specific flags when compiling cc files
LDFLAGS?=-Winline -g
OPTIMIZEFLAGS?=
#-fdiagnostics-show-option - shows which flags can be used with -Werror
@ -272,6 +273,7 @@ src/jsspi.c \
src/jshardware_common.c \
$(WRAPPERFILE)
CPPSOURCES =
CCSOURCES =
ifdef CFILE
WRAPPERSOURCES += $(CFILE)
@ -578,6 +580,10 @@ ifeq ($(USE_WIO_LTE),1)
SOURCES += targets/stm32/stm32_ws2812b_driver.c
endif
ifeq ($(USE_TENSORFLOW),1)
include make/misc/tensorflow.make
endif
endif # BOOTLOADER ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DON'T USE STUFF ABOVE IN BOOTLOADER
@ -593,6 +599,7 @@ include make/family/$(FAMILY).make
endif
# =========================================================================
ifdef USB
DEFINES += -DUSB
endif
@ -601,7 +608,7 @@ PININFOFILE=$(GENDIR)/jspininfo
SOURCES += $(PININFOFILE).c
SOURCES += $(WRAPPERSOURCES) $(TARGETSOURCES)
SOURCEOBJS = $(SOURCES:.c=.o) $(CPPSOURCES:.cpp=.o)
SOURCEOBJS = $(SOURCES:.c=.o) $(CPPSOURCES:.cpp=.o) $(CCSOURCES:.cc=.o)
OBJS = $(PRECOMPILED_OBJS) $(SOURCEOBJS)
@ -720,6 +727,10 @@ quiet_obj_to_bin= GEN $(PROJ_NAME).$2
@echo $($(quiet_)compile)
@$(call compile)
.cc.o: %.cc $(PLATFORM_CONFIG_FILE) $(PININFOFILE).h
@echo $($(quiet_)compile)
@$(CC) $(CCFLAGS) $(CFLAGS) $< -o $@
.cpp.o: $(PLATFORM_CONFIG_FILE) $(PININFOFILE).h
@echo $($(quiet_)compile)
@$(call compile)

1
boards/LINUX.py
View File

@ -22,6 +22,7 @@ info = {
'build' : {
'libraries' : [
'NET',
'TENSORFLOW',
'GRAPHICS',
'FILESYSTEM',
'CRYPTO','SHA256','SHA512',

203
libs/tensorflow/LICENSE Normal file
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@ -0,0 +1,203 @@
Copyright 2019 The TensorFlow Authors. All rights reserved.
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142
libs/tensorflow/README.md Normal file
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@ -0,0 +1,142 @@
Tensorflow for Espruino
=======================
Tensorflow code is Licensed under the Apache License. See `LICENSE` for more info.
Created with:
```Bash
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro
git clone https://github.com/tensorflow/tensorflow --depth=1
mv tensorflow git-tensorflow
cd git-tensorflow
make -f tensorflow/lite/experimental/micro/tools/make/Makefile
make -f tensorflow/lite/experimental/micro/tools/make/Makefile TARGET=make TAGS="disco_f746ng" generate_projects
cp -R tensorflow/lite/experimental/micro/tools/make/gen/make_x86_64/prj/hello_world/make/* ..
cd ..
mv tensorflow/tensorflow/lite/experimental/micro/examples/hello_world/* .
```
## Actually using it
Follow the steps here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb
Then right at the end when you have your model, do:
```Python
import base64;
print("var model=atob(\""+base64.b64encode(tflite_model)+"\");")
```
To output to base64 that you can easily stick in your code.
And to use it, do something like:
```JS
var model=atob("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");
var tf = require("tensorflow").create(2048, model);
tf.getInput()[0] = x;
tf.invoke();
print(tf.getOutput()[0]);
```
Example code (based off the tutorial linked above) to do the training is:
```Python
import tensorflow as tf
# Numpy is a math library
import numpy as np
# Matplotlib is a graphing library
import matplotlib.pyplot as plt
# math is Python's math library
import math
# We'll generate this many sample datapoints
SAMPLES = 1000
# Set a "seed" value, so we get the same random numbers each time we run this
# notebook
np.random.seed(1337)
# Generate a uniformly distributed set of random numbers in the range from
# 0 to 2pi, which covers a complete sine wave oscillation
x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)
# Shuffle the values to guarantee they're not in order
np.random.shuffle(x_values)
# Calculate the corresponding sine values
y_values = np.sin(x_values)
# Add a small random number to each y value
y_values += 0.1 * np.random.randn(*y_values.shape)
# We'll use 60% of our data for training and 20% for testing. The remaining 20%
# will be used for validation. Calculate the indices of each section.
TRAIN_SPLIT = int(0.6 * SAMPLES)
TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)
# Use np.split to chop our data into three parts.
# The second argument to np.split is an array of indices where the data will be
# split. We provide two indices, so the data will be divided into three chunks.
x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])
# Double check that our splits add up correctly
assert (x_train.size + x_validate.size + x_test.size) == SAMPLES
# We'll use Keras to create a simple model architecture
from tensorflow.keras import layers
model_2 = tf.keras.Sequential()
# First layer takes a scalar input and feeds it through 16 "neurons". The
# neurons decide whether to activate based on the 'relu' activation function.
model_2.add(layers.Dense(16, activation='relu', input_shape=(1,)))
# The new second layer may help the network learn more complex representations
model_2.add(layers.Dense(16, activation='relu'))
# Final layer is a single neuron, since we want to output a single value
model_2.add(layers.Dense(1))
# Compile the model using a standard optimizer and loss function for regression
model_2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
history_2 = model_2.fit(x_train, y_train, epochs=600, batch_size=16,
validation_data=(x_validate, y_validate))
# Calculate and print the loss on our test dataset
loss = model_2.evaluate(x_test, y_test)
# Make predictions based on our test dataset
predictions = model_2.predict(x_test)
# Graph the predictions against the actual values
plt.clf()
plt.title('Comparison of predictions and actual values')
plt.plot(x_test, y_test, 'b.', label='Actual')
plt.plot(x_test, predictions, 'r.', label='Predicted')
plt.legend()
plt.show()
# Convert the model to the TensorFlow Lite format without quantization
converter = tf.lite.TFLiteConverter.from_keras_model(model_2)
tflite_model = converter.convert()
# Save the model to disk
open("sine_model.tflite", "wb").write(tflite_model)
# Convert the model to the TensorFlow Lite format with quantization
converter = tf.lite.TFLiteConverter.from_keras_model(model_2)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = converter.convert()
# Save the model to disk
open("sine_model_quantized.tflite", "wb").write(tflite_model)
import base64;
print("var model=atob(\""+base64.b64encode(tflite_model)+"\");")
```

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libs/tensorflow/jswrap_tensorflow.c Normal file
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@ -0,0 +1,163 @@
/*
* This file is part of Espruino, a JavaScript interpreter for Microcontrollers
*
* Copyright (C) 2013 Gordon Williams <gw@pur3.co.uk>
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
* ----------------------------------------------------------------------------
* This file is designed to be parsed during the build process
*
* Contains JavaScript interface for Neopixel/WS281x/APA10x devices
* ----------------------------------------------------------------------------
*/
#include <jswrap_tensorflow.h>
#include "jsvariterator.h"
#include "jsparse.h"
#include "jsinteractive.h"
#include "jswrap_arraybuffer.h"
#include "tensorflow.h"
/*JSON{
"type" : "library",
"class" : "tensorflow"
}
*/
/*JSON{
"type" : "staticmethod",
"class" : "tensorflow",
"name" : "create",
"generate" : "jswrap_tensorflow_create",
"params" : [
["arenaSize","int","The TensorFlow Arena size"],
["model","JsVar","The model to use - this should be a flat array/string"]
],
"return" : ["JsVar","A tensorflow instance"],
"return_object" : "TFMicroInterpreter"
}
*/
JsVar *jswrap_tensorflow_create(int arena_size, JsVar *model) {
if (arena_size<512) {
jsExceptionHere(JSET_ERROR, "Invalid Arena Size");
return 0;
}
size_t modelSize = 0;
char *modelPtr = jsvGetDataPointer(model, &modelSize);
if (!modelPtr) {
jsExceptionHere(JSET_TYPEERROR, "Model is not a Flat String/ArrayBuffer");
return 0;
}
JsVar *tfmi = jspNewObject(NULL,"TFMicroInterpreter");
if (!tfmi) return 0;
size_t tfSize = tf_get_size((size_t)arena_size, modelSize);
JsVar *mi = jsvNewFlatStringOfLength(tfSize);
if (!mi) {
jsExceptionHere(JSET_ERROR, "Unable to allocate enough RAM for TensorFlow");
jsvUnLock(tfmi);
return 0;
}
char *tfPtr = jsvGetDataPointer(mi, &tfSize);
if (!tfPtr) {
assert(0);
return 0; // should never get here
}
if (!tf_create(tfPtr, (size_t)arena_size, modelPtr)) {
jsExceptionHere(JSET_ERROR, "MicroInterpreter creation failed");
jsvUnLock2(tfmi, mi);
}
jsvObjectSetChild(tfmi, "model", model); // so we keep a reference
jsvObjectSetChildAndUnLock(tfmi, "mi", mi); // so we keep a reference
return tfmi;
}
/*JSON{
"type" : "class",
"library" : "tensorflow",
"class" : "TFMicroInterpreter",
"ifdef" : "USE_TENSORFLOW"
}
Class containing an instance of TFMicroInterpreter
*/
void *jswrap_tfmicrointerpreter_getTFMI(JsVar *parent) {
JsVar *mi = jsvObjectGetChild(parent, "mi", 0);
size_t tfSize;
char *tfPtr = jsvGetDataPointer(mi, &tfSize);
jsvUnLock(mi);
if (!tfPtr)
jsExceptionHere(JSET_ERROR, "TFMicroInterpreter structure corrupted");
return tfPtr;
}
JsVar *jswrap_tfmicrointerpreter_tensorToArrayBuffer(JsVar *parent, TfLiteTensor *tensor) {
void *tfmi = jswrap_tfmicrointerpreter_getTFMI(parent);
JsVar *mi = jsvObjectGetChild(parent, "mi", 0);
if (!tensor && !mi) {
jsExceptionHere(JSET_ERROR, "Unable to get tensor");
return 0;
}
JsVarDataArrayBufferViewType abType = ARRAYBUFFERVIEW_UNDEFINED;
switch (tensor->type) {
case kTfLiteFloat32 :
abType = ARRAYBUFFERVIEW_FLOAT32; break;
default:
jsExceptionHere(JSET_TYPEERROR, "Unknown Tensor format");
return 0;
}
JsVar *ab = jsvNewArrayBufferFromString(mi,0);
JsVar *b = jswrap_typedarray_constructor(abType, ab, ((size_t)&tensor->data.f[0])-(size_t)tfmi, tensor->bytes / JSV_ARRAYBUFFER_GET_SIZE(abType));
jsvUnLock2(ab,mi);
return b;
}
/*JSON{
"type" : "method",
"class" : "TFMicroInterpreter",
"name" : "getInput",
"generate" : "jswrap_tfmicrointerpreter_getInput",
"return" : ["JsVar","An arraybuffer referencing the input data"],
"return_object" : "ArrayBufferView"
}
*/
JsVar *jswrap_tfmicrointerpreter_getInput(JsVar *parent) {
void *tfmi = jswrap_tfmicrointerpreter_getTFMI(parent);
if (!tfmi) return 0;
return jswrap_tfmicrointerpreter_tensorToArrayBuffer(parent, tf_get_input(tfmi, 0));
}
/*JSON{
"type" : "method",
"class" : "TFMicroInterpreter",
"name" : "getOutput",
"generate" : "jswrap_tfmicrointerpreter_getOutput",
"return" : ["JsVar","An arraybuffer referencing the output data"],
"return_object" : "ArrayBufferView"
}
*/
JsVar *jswrap_tfmicrointerpreter_getOutput(JsVar *parent) {
void *tfmi = jswrap_tfmicrointerpreter_getTFMI(parent);
if (!tfmi) return 0;
return jswrap_tfmicrointerpreter_tensorToArrayBuffer(parent, tf_get_output(tfmi, 0));
}
/*JSON{
"type" : "method",
"class" : "TFMicroInterpreter",
"name" : "invoke",
"generate" : "jswrap_tfmicrointerpreter_invoke"
}
*/
void jswrap_tfmicrointerpreter_invoke(JsVar *parent) {
void *tfmi = jswrap_tfmicrointerpreter_getTFMI(parent);
if (!tfmi) return;
if (!tf_invoke(tfmi)) {
jsExceptionHere(JSET_TYPEERROR, "TFMicroInterpreter invoke failed");
}
}
// FIXME: what about tf_destroy?

21
libs/tensorflow/jswrap_tensorflow.h Normal file
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@ -0,0 +1,21 @@
/*
* This file is part of Espruino, a JavaScript interpreter for Microcontrollers
*
* Copyright (C) 2013 Gordon Williams <gw@pur3.co.uk>
*
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
*
* ----------------------------------------------------------------------------
* Contains JavaScript interface for Tensorflow
* ----------------------------------------------------------------------------
*/
#include "jspin.h"
#include "jsvar.h"
JsVar *jswrap_tensorflow_create(int arena_size, JsVar *model);
JsVar *jswrap_tfmicrointerpreter_getInput(JsVar *parent);
JsVar *jswrap_tfmicrointerpreter_getOutput(JsVar *parent);
void jswrap_tfmicrointerpreter_invoke(JsVar *parent);

19
libs/tensorflow/patches/01-virtual-delete.diff Normal file
View File

@ -0,0 +1,19 @@
--- a/tensorflow/lite/experimental/micro/memory_planner/greedy_memory_planner.h 2019-09-11 09:14:45.509106591 +0100
+++ b/tensorflow/lite/experimental/micro/memory_planner/greedy_memory_planner.h 2019-09-11 09:13:54.204422772 +0100
@@ -17,6 +17,7 @@
#define TENSORFLOW_LITE_EXPERIMENTAL_MICRO_MEMORY_PLANNER_GREEDY_MEMORY_PLANNER_H_
#include "tensorflow/lite/experimental/micro/memory_planner/memory_planner.h"
+#include "tensorflow/lite/experimental/micro/compatibility.h"
namespace tflite {
@@ -125,6 +126,8 @@
// Whether buffers have been added since the last plan was calculated.
bool need_to_calculate_offsets_;
+
+ TF_LITE_REMOVE_VIRTUAL_DELETE
};
} // namespace tflite

25
libs/tensorflow/patches/02-public-delete.diff Normal file
View File

@ -0,0 +1,25 @@
--- a/tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h 2019-09-11 08:48:03.171950502 +0100
+++ b/tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h 2019-09-11 10:47:28.818528375 +0100
@@ -22,9 +22,8 @@
class AllOpsResolver : public MicroMutableOpResolver {
public:
AllOpsResolver();
-
- private:
TF_LITE_REMOVE_VIRTUAL_DELETE
+
};
} // namespace micro
--- a/tensorflow/lite/experimental/micro/micro_error_reporter.h 2019-09-11 08:48:03.155950292 +0100
+++ b/tensorflow/lite/experimental/micro/micro_error_reporter.h 2019-09-11 10:47:45.290747773 +0100
@@ -26,8 +26,6 @@
public:
~MicroErrorReporter() {}
int Report(const char* format, va_list args) override;
-
- private:
TF_LITE_REMOVE_VIRTUAL_DELETE
};

119
libs/tensorflow/tensorflow.cc Normal file
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@ -0,0 +1,119 @@
/* Copyright 2019 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,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/lite/experimental/micro/kernels/all_ops_resolver.h"
#include "tensorflow/lite/experimental/micro/micro_error_reporter.h"
#include "tensorflow/lite/experimental/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
extern "C" {
#include "jsinteractive.h"
#include "tensorflow.h"
void DebugLog(const char* s) { jsiConsolePrint(s); }
typedef struct {
// logging
tflite::MicroErrorReporter micro_error_reporter;
// This pulls in all the operation implementations we need
tflite::ops::micro::AllOpsResolver resolver;
// Build an interpreter to run the model with
tflite::MicroInterpreter interpreter;
// Create an area of memory to use for input, output, and intermediate arrays.
// Finding the minimum value for your model may require some trial and error.
uint8_t tensor_arena[0];
} TFData;
char tfDataPtr[sizeof(TFData)];
size_t tf_get_size(size_t arena_size, const char *model_data) {
return sizeof(TFData) + arena_size;
}
bool tf_create(void *dataPtr, size_t arena_size, const char *model_data) {
TFData *tf = (TFData*)dataPtr;
new (&tf->micro_error_reporter)tflite::MicroErrorReporter();
// Set up logging
tflite::ErrorReporter* error_reporter = &tf->micro_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
const tflite::Model* model = ::tflite::GetModel(model_data);
if (model->version() != TFLITE_SCHEMA_VERSION) {
error_reporter->Report(
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return false;
}
new (&tf->resolver)tflite::ops::micro::AllOpsResolver();
// Build an interpreter to run the model with
new (&tf->interpreter)tflite::MicroInterpreter(
model, tf->resolver, tf->tensor_arena,
arena_size, error_reporter);
// Allocate memory from the tensor_arena for the model's tensors
tf->interpreter.AllocateTensors();
/*
TfLiteTensor* input = tf->interpreter.input(0);
TfLiteTensor* output = tf->interpreter.output(0);
// Place our calculated x value in the model's input tensor
input->data.f[0] = x_val;
// Read the predicted y value from the model's output tensor
float y_val = output->data.f[0];*/
return true;
}
void tf_destroy(void *dataPtr) {
TFData *tf = (TFData*)dataPtr;
tf->interpreter.~MicroInterpreter();
}
bool tf_invoke(void *dataPtr) {
TFData *tf = (TFData*)dataPtr;
tflite::ErrorReporter* error_reporter = &tf->micro_error_reporter;
// Run inference, and report any error
//jsiConsolePrintf("in %f\n",tf->interpreter.input(0)->data.f[0]);
TfLiteStatus invoke_status = tf->interpreter.Invoke();
//jsiConsolePrintf("out %f\n",tf->interpreter.output(0)->data.f[0]);
if (invoke_status != kTfLiteOk) {
error_reporter->Report("Invoke failed");
return false;
}
return true;
}
TfLiteTensor *tf_get_input(void *dataPtr, int n) {
TFData *tf = (TFData*)dataPtr;
// Obtain pointers to the model's input and output tensors
return tf->interpreter.input(0);
}
TfLiteTensor *tf_get_output(void *dataPtr, int n) {
TFData *tf = (TFData*)dataPtr;
// Obtain pointers to the model's input and output tensors
return tf->interpreter.output(0);
}
} // extern "C"

8
libs/tensorflow/tensorflow.h Normal file
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@ -0,0 +1,8 @@
#include "tensorflow/lite/c/c_api_internal.h"
size_t tf_get_size(size_t arena_size, const char *model_data);
bool tf_create(void *dataPtr, size_t arena_size, const char *model_data);
void tf_destroy(void *dataPtr);
bool tf_invoke(void *dataPtr);
TfLiteTensor *tf_get_input(void *dataPtr, int n);
TfLiteTensor *tf_get_output(void *dataPtr, int n);

60
make/misc/tensorflow.make Normal file
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@ -0,0 +1,60 @@
TENSOR_ROOT := $(ROOT)/libs/tensorflow
CCSOURCES += \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/simple_memory_allocator.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/memory_helpers.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/micro_error_reporter.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/micro_mutable_op_resolver.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/debug_log_numbers.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/test_helpers.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/micro_interpreter.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/micro_allocator.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/pack.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/logical.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/reshape.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/depthwise_conv.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/conv.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/pooling.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/softmax.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/prelu.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/ceil.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/arg_min_max.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/unpack.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/add.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/split.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/neg.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/floor.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/comparisons.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/svdf.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/all_ops_resolver.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/fully_connected.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/maximum_minimum.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/elementwise.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/strided_slice.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/kernels/round.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/memory_planner/linear_memory_planner.cc \
$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/memory_planner/greedy_memory_planner.cc \
$(TENSOR_ROOT)/tensorflow/lite/core/api/error_reporter.cc \
$(TENSOR_ROOT)/tensorflow/lite/core/api/flatbuffer_conversions.cc \
$(TENSOR_ROOT)/tensorflow/lite/core/api/op_resolver.cc \
$(TENSOR_ROOT)/tensorflow/lite/core/api/tensor_utils.cc \
$(TENSOR_ROOT)/tensorflow/lite/kernels/kernel_util.cc \
$(TENSOR_ROOT)/tensorflow/lite/kernels/internal/quantization_util.cc \
$(TENSOR_ROOT)/tensorflow.cc
#$(TENSOR_ROOT)/tensorflow/lite/experimental/micro/debug_log.cc
SOURCES += \
$(TENSOR_ROOT)/tensorflow/lite/c/c_api_internal.c \
WRAPPERSOURCES += \
$(TENSOR_ROOT)/jswrap_tensorflow.c
INCLUDE += \
-I$(TENSOR_ROOT) \
-I$(TENSOR_ROOT)/tensorflow \
-I$(TENSOR_ROOT)/third_party/gemmlowp \
-I$(TENSOR_ROOT)/third_party/kissfft \
-I$(TENSOR_ROOT)/third_party/flatbuffers/include
CCFLAGS += -DNDEBUG -g -DTF_LITE_STATIC_MEMORY --std=c++11 -g -fno-rtti -fpermissive -Wno-sign-compare -Wno-conversion -Wno-sign-conversion -Wno-missing-field-initializers -Wno-type-limits -Wno-unused-parameter

2
src/jsvar.c
View File

@ -3592,7 +3592,7 @@ void _jsvTrace(JsVar *var, int indent, JsVar *baseVar, int level) {
else if (jsvIsFloat(var)) jsiConsolePrintf("Double %f", jsvGetFloat(var));
else if (jsvIsFunctionParameter(var)) jsiConsolePrintf("Param %q ", var);
else if (jsvIsArrayBufferName(var)) jsiConsolePrintf("ArrayBufferName[%d] ", jsvGetInteger(var));
else if (jsvIsArrayBuffer(var)) jsiConsolePrintf("%s ", jswGetBasicObjectName(var)?jswGetBasicObjectName(var):"unknown ArrayBuffer"); // way to get nice name
else if (jsvIsArrayBuffer(var)) jsiConsolePrintf("%s (offs %d, len %d)", jswGetBasicObjectName(var)?jswGetBasicObjectName(var):"unknown ArrayBuffer", var->varData.arraybuffer.byteOffset, var->varData.arraybuffer.length); // way to get nice name
else if (jsvIsString(var)) {
size_t blocks = 1;
if (jsvGetLastChild(var)) {

2
src/jsvar.h
View File

@ -760,7 +760,7 @@ JsVar *jsvNewDataViewWithData(JsVarInt length, unsigned char *data);
* allocate it. */
JsVar *jsvNewArrayBufferWithPtr(unsigned int length, char **ptr);
/** create an arraybuffer containing the given data */
/** create an arraybuffer containing the given data - this allocates new memory and copies 'data' */
JsVar *jsvNewArrayBufferWithData(JsVarInt length, unsigned char *data);
/** Allocate a flat area of memory inside Espruino's Variable storage space.

237
tests/test_tensorflow.js Normal file
View File

@ -0,0 +1,237 @@
// sine model from 'hello world' example
var sine_model_data =
E.toString([
0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,
0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,
0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00,
0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x0b, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x7c, 0x05, 0x00, 0x00,
0x24, 0x05, 0x00, 0x00, 0xd4, 0x04, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00,
0x74, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
0x14, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x54, 0xf6, 0xff, 0xff, 0x58, 0xf6, 0xff, 0xff, 0x5c, 0xf6, 0xff, 0xff,
0x60, 0xf6, 0xff, 0xff, 0xc2, 0xfa, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,
0x40, 0x00, 0x00, 0x00, 0x7c, 0x19, 0xa7, 0x3e, 0x99, 0x81, 0xb9, 0x3e,
0x56, 0x8b, 0x9f, 0x3e, 0x88, 0xd8, 0x12, 0xbf, 0x74, 0x10, 0x56, 0x3e,
0xfe, 0xc6, 0xdf, 0xbe, 0xf2, 0x10, 0x5a, 0xbe, 0xf0, 0xe2, 0x0a, 0xbe,
0x10, 0x5a, 0x98, 0xbe, 0xb9, 0x36, 0xce, 0x3d, 0x8f, 0x7f, 0x87, 0x3e,
0x2c, 0xb1, 0xfd, 0xbd, 0xe6, 0xa6, 0x8a, 0xbe, 0xa5, 0x3e, 0xda, 0x3e,
0x50, 0x34, 0xed, 0xbd, 0x90, 0x91, 0x69, 0xbe, 0x0e, 0xfb, 0xff, 0xff,
0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x67, 0x41, 0x48, 0xbf,
0x24, 0xcd, 0xa0, 0xbe, 0xb7, 0x92, 0x0c, 0xbf, 0x00, 0x00, 0x00, 0x00,
0x98, 0xfe, 0x3c, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4a, 0x17, 0x9a, 0xbe,
0x41, 0xcb, 0xb6, 0xbe, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x13, 0xd6, 0x1e, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x5a, 0xfb, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,
0x4b, 0x98, 0xdd, 0xbd, 0x40, 0x6b, 0xcb, 0xbe, 0x36, 0x0c, 0xd4, 0x3c,
0xbd, 0x44, 0xb5, 0x3e, 0x95, 0x70, 0xe3, 0x3e, 0xe7, 0xac, 0x86, 0x3e,
0x00, 0xc4, 0x4e, 0x3d, 0x7e, 0xa6, 0x1d, 0x3e, 0xbd, 0x87, 0xbb, 0x3e,
0xb4, 0xb8, 0x09, 0xbf, 0xa1, 0x1f, 0xf8, 0xbe, 0x8d, 0x90, 0xdd, 0x3e,
0xde, 0xfa, 0x6f, 0xbe, 0xb2, 0x75, 0xe4, 0x3d, 0x6e, 0xfe, 0x36, 0x3e,
0x20, 0x18, 0xc2, 0xbe, 0x39, 0xc7, 0xfb, 0xbe, 0xfe, 0xa4, 0x30, 0xbe,
0xf7, 0x91, 0xde, 0xbe, 0xde, 0xab, 0x24, 0x3e, 0xfb, 0xbb, 0xce, 0x3e,
0xeb, 0x23, 0x80, 0xbe, 0x7b, 0x58, 0x73, 0xbe, 0x9a, 0x2e, 0x03, 0x3e,
0x10, 0x42, 0xa9, 0xbc, 0x10, 0x12, 0x64, 0xbd, 0xe3, 0x8d, 0x0c, 0x3d,
0x9e, 0x48, 0x97, 0xbe, 0x34, 0x51, 0xd4, 0xbe, 0x02, 0x3b, 0x0d, 0x3e,
0x62, 0x67, 0x89, 0xbe, 0x74, 0xdf, 0xa2, 0x3d, 0xf3, 0x25, 0xb3, 0xbe,
0xef, 0x34, 0x7b, 0x3d, 0x61, 0x70, 0xe3, 0x3d, 0xba, 0x76, 0xc0, 0xbe,
0x7d, 0xe9, 0xa7, 0x3e, 0xc3, 0xab, 0xd0, 0xbe, 0xcf, 0x7c, 0xdb, 0xbe,
0x70, 0x27, 0x9a, 0xbe, 0x98, 0xf5, 0x3c, 0xbd, 0xff, 0x4b, 0x4b, 0x3e,
0x7e, 0xa0, 0xf8, 0xbd, 0xd4, 0x6e, 0x86, 0x3d, 0x00, 0x4a, 0x07, 0x3a,
0x4c, 0x24, 0x61, 0xbe, 0x54, 0x68, 0xf7, 0xbd, 0x02, 0x3f, 0x77, 0xbe,
0x23, 0x79, 0xb3, 0x3e, 0x1c, 0x83, 0xad, 0xbd, 0xc8, 0x92, 0x8d, 0x3e,
0xa8, 0xf3, 0x15, 0xbd, 0xe6, 0x4d, 0x6c, 0x3d, 0xac, 0xe7, 0x98, 0xbe,
0x81, 0xec, 0xbd, 0x3e, 0xe2, 0x55, 0x73, 0x3e, 0xc1, 0x77, 0xc7, 0x3e,
0x6e, 0x1b, 0x5e, 0x3d, 0x27, 0x78, 0x02, 0x3f, 0xd4, 0x21, 0x90, 0x3d,
0x52, 0xdc, 0x1f, 0x3e, 0xbf, 0xda, 0x88, 0x3e, 0x80, 0x79, 0xe3, 0xbd,
0x40, 0x6f, 0x10, 0xbe, 0x20, 0x43, 0x2e, 0xbd, 0xf0, 0x76, 0xc5, 0xbd,
0xcc, 0xa0, 0x04, 0xbe, 0xf0, 0x69, 0xd7, 0xbe, 0xb1, 0xfe, 0x64, 0xbe,
0x20, 0x41, 0x84, 0xbe, 0xb2, 0xc3, 0x26, 0xbe, 0xd8, 0xf4, 0x09, 0xbe,
0x64, 0x44, 0xd1, 0x3d, 0xd5, 0xe1, 0xc8, 0xbe, 0x35, 0xbc, 0x3f, 0xbe,
0xc0, 0x94, 0x82, 0x3d, 0xdc, 0x2b, 0xb1, 0xbd, 0x02, 0xdb, 0xbf, 0xbe,
0xa5, 0x7f, 0x8a, 0x3e, 0x21, 0xb4, 0xa2, 0x3e, 0xcd, 0x86, 0x56, 0xbf,
0x9c, 0x3b, 0x76, 0xbc, 0x85, 0x6d, 0x60, 0xbf, 0x86, 0x00, 0x3c, 0xbe,
0xc1, 0x23, 0x7e, 0x3e, 0x96, 0xcd, 0x3f, 0x3e, 0x86, 0x91, 0x2d, 0x3e,
0x55, 0xef, 0x87, 0x3e, 0x7e, 0x97, 0x03, 0xbe, 0x2a, 0xcd, 0x01, 0x3e,
0x32, 0xc9, 0x8e, 0xbe, 0x72, 0x77, 0x3b, 0xbe, 0xe0, 0xa1, 0xbc, 0xbe,
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0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00,
0x28, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
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0x08, 0x00, 0x00, 0x00, 0x49, 0x64, 0x65, 0x6e, 0x74, 0x69, 0x74, 0x79,
0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0a, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00,
0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x03, 0x00, 0x00, 0x00]);
var tf = require("tensorflow").create(2048, sine_model_data);
function t(x) {
tf.getInput()[0] = x;
tf.invoke();
var o = tf.getOutput()[0];
var r = Math.abs(o - Math.sin(x))<0.1;
print("Math.sin(",x,") => ",o," vs ",Math.sin(x), r?"ok":"fail");
return r;
}
result = t(0) && t(1) && t(2);