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Clean up the way logging is configured and improve error checking. Make failure conditions in micro_interpreter clearer. Reduce tensor arena size in micro_vision so that it builds for Sparkfun Edge.

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PiperOrigin-RevId: 258473403
This commit is contained in:
A. Unique TensorFlower 2019-07-16 17:28:22 -07:00 committed by TensorFlower Gardener
parent 41730b8b0d
commit 65db2281c3
7 changed files with 35 additions and 20 deletions
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13
tensorflow/lite/experimental/micro/apollo3evb/debug_log.cc
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@ -36,11 +36,16 @@ limitations under the License.
#include "tensorflow/lite/experimental/micro/debug_log.h"
#include <cstdio>
// These are headers from Ambiq's Apollo3 SDK.
#include "am_bsp.h" // NOLINT
#include "am_mcu_apollo.h" // NOLINT
#include "am_util.h" // NOLINT
extern "C" void DebugLog(const char* s) { am_util_stdio_printf("%s", s); }
extern "C" void DebugLog(const char* s) {
static bool is_initialized = false;
if (!is_initialized) {
am_bsp_itm_printf_enable();
is_initialized = true;
}
am_util_stdio_printf("%s", s);
}

6
tensorflow/lite/experimental/micro/examples/micro_vision/apollo3evb/image_provider.cc
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@ -120,9 +120,6 @@ void boost_mode_enable(tflite::ErrorReporter* error_reporter, bool bEnable) {
} // namespace
TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
// Enable the ITM print interface.
am_bsp_itm_printf_enable();
error_reporter->Report("Initializing HM01B0...\n");
am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0);
@ -187,8 +184,9 @@ TfLiteStatus GetImage(tflite::ErrorReporter* error_reporter, int frame_width,
hm01b0_blocking_read_oneframe_scaled(frame, frame_width, frame_height,
channels);
am_util_delay_ms(2000);
#ifdef DEMO_HM01B0_FRAMEBUFFER_DUMP_ENABLE
// Allow some time to see result of previous inference before dumping image.
am_util_delay_ms(2000);
hm01b0_framebuffer_dump(frame, frame_width * frame_height * channels);
#endif

3
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/Makefile.inc
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@ -1,10 +1,13 @@
ifeq ($(TARGET),$(filter $(TARGET),apollo3evb sparkfun_edge))
MICRO_VISION_SRCS += \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0.c \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_debug.c \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_optimized.c
MICRO_VISION_HDRS += \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0.h \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_debug.h \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_optimized.h \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_RAW8_QVGA_8bits_lsb_5fps.h \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/HM01B0_Walking1s_01.h \
tensorflow/lite/experimental/micro/examples/micro_vision/himax_driver/platform_Sparkfun_Edge.h

15
tensorflow/lite/experimental/micro/examples/micro_vision/main.cc
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@ -31,7 +31,7 @@ limitations under the License.
#ifdef ARDUINO
constexpr int tensor_arena_size = 10 * 1024;
#else // ARDUINO
constexpr int tensor_arena_size = 291 * 1024;
constexpr int tensor_arena_size = 270 * 1024;
#endif // ARDUINO
uint8_t tensor_arena[tensor_arena_size];
@ -46,7 +46,7 @@ int main(int argc, char* argv[]) {
if (model->version() != TFLITE_SCHEMA_VERSION) {
error_reporter->Report(
"Model provided is schema version %d not equal "
"to supported version %d.\n",
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
}
@ -63,13 +63,14 @@ int main(int argc, char* argv[]) {
while (true) {
// Get image from provider.
GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8);
if (kTfLiteOk != GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
error_reporter->Report("Image capture failed.");
}
// Run the model on this input and make sure it succeeds.
TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
error_reporter->Report("Invoke failed\n");
if (kTfLiteOk != interpreter.Invoke()) {
error_reporter->Report("Invoke failed.");
}
TfLiteTensor* output = interpreter.output(0);

3
tensorflow/lite/experimental/micro/examples/micro_vision/sparkfun_edge/image_provider.cc
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@ -120,9 +120,6 @@ void boost_mode_enable(tflite::ErrorReporter* error_reporter, bool bEnable) {
} // namespace
TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
// Enable the ITM print interface.
am_bsp_itm_printf_enable();
error_reporter->Report("Initializing HM01B0...\n");
am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0);

14
tensorflow/lite/experimental/micro/micro_interpreter.cc
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@ -69,7 +69,8 @@ MicroInterpreter::MicroInterpreter(const Model* model,
error_reporter_(error_reporter),
context_(),
allocator_(&context_, model_, tensor_arena, tensor_arena_size,
error_reporter_) {
error_reporter_),
tensors_allocated_(false) {
auto* subgraphs = model->subgraphs();
if (subgraphs->size() != 1) {
error_reporter->Report("Only 1 subgraph is currently supported.\n");
@ -92,7 +93,10 @@ TfLiteStatus MicroInterpreter::RegisterPreallocatedInput(uint8_t* buffer,
}
TfLiteStatus MicroInterpreter::AllocateTensors() {
return allocator_.AllocateTensors();
TfLiteStatus status = allocator_.AllocateTensors();
TF_LITE_ENSURE_OK(&context_, status);
tensors_allocated_ = true;
return kTfLiteOk;
}
TfLiteStatus MicroInterpreter::Invoke() {
@ -100,6 +104,12 @@ TfLiteStatus MicroInterpreter::Invoke() {
error_reporter_->Report("Invoke() called after initialization failed\n");
return kTfLiteError;
}
// Ensure tensors are allocated before the interpreter is invoked to avoid
// difficult to debug segfaults.
if (!tensors_allocated_) {
AllocateTensors();
}
TfLiteStatus status = kTfLiteOk;
auto opcodes = model_->operator_codes();
for (int i = 0; i < operators_->size(); ++i) {

1
tensorflow/lite/experimental/micro/micro_interpreter.h
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@ -76,6 +76,7 @@ class MicroInterpreter {
ErrorReporter* error_reporter_;
TfLiteContext context_;
MicroAllocator allocator_;
bool tensors_allocated_;
TfLiteStatus initialization_status_;
const flatbuffers::Vector<flatbuffers::Offset<Tensor>>* tensors_;