Improved Logging - Core Software
Logging macros:
Added ethosu_logging.h as a lib, containing logging macros controlled by
a parameter ETHOSU_LOG_SEVERITY set in core_software CMakeLists.txt
Updated inference_process:
Updated inference_process to include ethosu_logging and use the logging
macros rather than printf()
Updated message_process:
Updated message_process to include ethosu_logging and use the
logging macros rather than printf()
Updated ethosu_monitor:
Updated ethosu_monitor to include ethosu_logging and use the
logging macros rather than printf()
Updated layer_by_layer_profiler:
Updated layer_by_layer_profiler to include ethosu_logging and use the
logging macros rather than printf()
Updated mhu_v2:
Updated mhu_v2 to include ethosu_logging and use the
logging macros rather than printf()
Change-Id: I5d6fd80b7645b3e0af5b494eea6dbb7755f02122
diff --git a/applications/inference_process/src/inference_process.cpp b/applications/inference_process/src/inference_process.cpp
index 13cabb2..7058f9c 100644
--- a/applications/inference_process/src/inference_process.cpp
+++ b/applications/inference_process/src/inference_process.cpp
@@ -27,6 +27,7 @@
#ifdef ETHOSU
#include "layer_by_layer_profiler.hpp"
#endif
+#include "ethosu_log.h"
#include "inference_process.hpp"
@@ -39,30 +40,28 @@
namespace {
void tflu_debug_log(const char *s) {
- fprintf(stderr, "%s", s);
+ LOG_DEBUG("%s", s);
}
void print_output_data(TfLiteTensor *output, size_t bytesToPrint) {
const int numBytesToPrint = min(output->bytes, bytesToPrint);
-
- int dims_size = output->dims->size;
- printf("{\n");
- printf("\"dims\": [%d,", dims_size);
+ int dims_size = output->dims->size;
+ LOG("{\n");
+ LOG("\"dims\": [%d,", dims_size);
for (int i = 0; i < output->dims->size - 1; ++i) {
- printf("%d,", output->dims->data[i]);
+ LOG("%d,", output->dims->data[i]);
}
- printf("%d],\n", output->dims->data[dims_size - 1]);
-
- printf("\"data_address\": \"%08" PRIx32 "\",\n", (uint32_t)output->data.data);
- printf("\"data\":\"");
+ LOG("%d],\n", output->dims->data[dims_size - 1]);
+ LOG("\"data_address\": \"%08" PRIx32 "\",\n", (uint32_t)output->data.data);
+ LOG("\"data\":\"");
for (int i = 0; i < numBytesToPrint - 1; ++i) {
if (i % 16 == 0 && i != 0) {
- printf("\n");
+ LOG("\n");
}
- printf("0x%02x,", output->data.uint8[i]);
+ LOG("0x%02x,", output->data.uint8[i]);
}
- printf("0x%02x\"\n", output->data.uint8[numBytesToPrint - 1]);
- printf("}");
+ LOG("0x%02x\"\n", output->data.uint8[numBytesToPrint - 1]);
+ LOG("}");
}
bool copyOutput(const TfLiteTensor &src, InferenceProcess::DataPtr &dst) {
@@ -71,7 +70,7 @@
}
if (src.bytes > dst.size) {
- printf("Tensor size %d does not match output size %d.\n", src.bytes, dst.size);
+ LOG_ERR("Tensor size mismatch (bytes): actual=%d, expected%d.\n", src.bytes, dst.size);
return true;
}
@@ -181,7 +180,7 @@
}
bool InferenceProcess::runJob(InferenceJob &job) {
- printf("Running inference job: %s\n", job.name.c_str());
+ LOG_INFO("Running inference job: %s\n", job.name.c_str());
// Register debug log callback for profiling
RegisterDebugLogCallback(tflu_debug_log);
@@ -192,9 +191,9 @@
// Get model handle and verify that the version is correct
const tflite::Model *model = ::tflite::GetModel(job.networkModel.data);
if (model->version() != TFLITE_SCHEMA_VERSION) {
- printf("Model provided is schema version %" PRIu32 " not equal to supported version %d.\n",
- model->version(),
- TFLITE_SCHEMA_VERSION);
+ LOG_ERR("Model schema version unsupported: version=%" PRIu32 ", supported=%d.\n",
+ model->version(),
+ TFLITE_SCHEMA_VERSION);
return true;
}
@@ -211,7 +210,7 @@
// Allocate tensors
TfLiteStatus allocate_status = interpreter.AllocateTensors();
if (allocate_status != kTfLiteOk) {
- printf("AllocateTensors failed for inference job: %s\n", job.name.c_str());
+ LOG_ERR("Failed to allocate tensors for inference: job=%s\n", job.name.c_str());
return true;
}
@@ -225,9 +224,9 @@
}
}
if (job.input.size() != inputTensors.size()) {
- printf("Number of input buffers does not match number of non empty network tensors. input=%zu, network=%zu\n",
- job.input.size(),
- inputTensors.size());
+ LOG_ERR("Number of input buffers does not match number of non empty network tensors: input=%zu, network=%zu\n",
+ job.input.size(),
+ inputTensors.size());
return true;
}
@@ -237,11 +236,11 @@
const TfLiteTensor *tensor = inputTensors[i];
if (input.size != tensor->bytes) {
- printf("Input size does not match network size. job=%s, index=%zu, input=%zu, network=%u\n",
- job.name.c_str(),
- i,
- input.size,
- tensor->bytes);
+ LOG_ERR("Job input size does not match network input size: job=%s, index=%zu, input=%zu, network=%u\n",
+ job.name.c_str(),
+ i,
+ input.size,
+ tensor->bytes);
return true;
}
@@ -251,13 +250,13 @@
// Run the inference
TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
- printf("Invoke failed for inference job: %s\n", job.name.c_str());
+ LOG_ERR("Invoke failed for inference: job=%s\n", job.name.c_str());
return true;
}
- printf("%s : %zu\r\n", "arena_used_bytes", interpreter.arena_used_bytes());
+ LOG("arena_used_bytes : %zu\n", interpreter.arena_used_bytes());
- printf("Inference runtime: %u cycles\r\n", (unsigned int)profiler.GetTotalTicks());
+ LOG("Inference runtime: %u cycles\n", (unsigned int)profiler.GetTotalTicks());
if (job.pmuCycleCounterEnable != 0) {
job.pmuCycleCounterCount = profiler.GetTotalTicks();
@@ -266,7 +265,7 @@
// Copy output data
if (job.output.size() > 0) {
if (interpreter.outputs_size() != job.output.size()) {
- printf("Number of outputs mismatch. job=%zu, network=%u\n", job.output.size(), interpreter.outputs_size());
+ LOG_ERR("Output size mismatch: job=%zu, network=%u\n", job.output.size(), interpreter.outputs_size());
return true;
}
@@ -280,28 +279,26 @@
if (job.numBytesToPrint > 0) {
// Print all of the output data, or the first NUM_BYTES_TO_PRINT bytes,
// whichever comes first as well as the output shape.
- printf("num_of_outputs: %d\n", interpreter.outputs_size());
- printf("output_begin\n");
- printf("[\n");
-
+ LOG("num_of_outputs: %d\n", interpreter.outputs_size());
+ LOG("output_begin\n");
+ LOG("[\n");
for (unsigned int i = 0; i < interpreter.outputs_size(); i++) {
TfLiteTensor *output = interpreter.output(i);
print_output_data(output, job.numBytesToPrint);
if (i != interpreter.outputs_size() - 1) {
- printf(",\n");
+ LOG(",\n");
}
}
-
- printf("]\n");
- printf("output_end\n");
+ LOG("]\n");
+ LOG("output_end\n");
}
if (job.expectedOutput.size() > 0) {
if (job.expectedOutput.size() != interpreter.outputs_size()) {
- printf("Expected number of output tensors does not match network. job=%s, expected=%zu, network=%zu\n",
- job.name.c_str(),
- job.expectedOutput.size(),
- interpreter.outputs_size());
+ LOG_ERR("Expected number of output tensors mismatch: job=%s, expected=%zu, network=%zu\n",
+ job.name.c_str(),
+ job.expectedOutput.size(),
+ interpreter.outputs_size());
return true;
}
@@ -310,33 +307,33 @@
const TfLiteTensor *output = interpreter.output(i);
if (expected.size != output->bytes) {
- printf("Expected tensor size does not match output size. job=%s, index=%u, expected=%zu, network=%zu\n",
- job.name.c_str(),
- i,
- expected.size,
- output->bytes);
+ LOG_ERR("Expected output tensor size mismatch: job=%s, index=%u, expected=%zu, network=%zu\n",
+ job.name.c_str(),
+ i,
+ expected.size,
+ output->bytes);
return true;
}
for (unsigned int j = 0; j < output->bytes; ++j) {
if (output->data.uint8[j] != static_cast<uint8_t *>(expected.data)[j]) {
- printf("Expected data does not match output data. job=%s, index=%u, offset=%u, "
- "expected=%02x, network=%02x\n",
- job.name.c_str(),
- i,
- j,
- static_cast<uint8_t *>(expected.data)[j],
- output->data.uint8[j]);
+ LOG_ERR("Expected output tensor data mismatch: job=%s, index=%u, offset=%u, "
+ "expected=%02x, network=%02x\n",
+ job.name.c_str(),
+ i,
+ j,
+ static_cast<uint8_t *>(expected.data)[j],
+ output->data.uint8[j]);
return true;
}
}
}
}
- printf("Finished running job: %s\n", job.name.c_str());
+ LOG_INFO("Finished running job: %s\n", job.name.c_str());
return false;
-}
+} // namespace InferenceProcess
bool InferenceProcess::run(bool exitOnEmpty) {
bool anyJobFailed = false;
@@ -348,7 +345,7 @@
if (empty) {
if (exitOnEmpty) {
- printf("Exit from InferenceProcess::run() on empty job queue!\n");
+ LOG_INFO("Exit from InferenceProcess::run() due to empty job queue\n");
break;
}