Remove new line from log messages21.11-rc2

Remove new line from log messages to allow the log makros to format
the output.

Removing 'message process'. It has been replaced by the
'message handler' application in core platform.

Change-Id: Ie97063680c25a33844a8e52e7d39f042da0493e1

1 files changed, 12 insertions, 12 deletions

diff --git a/applications/inference_process/src/inference_process.cpp b/applications/inference_process/src/inference_process.cpp
index 3aa2550..7d6c7d7 100644
--- a/applications/inference_process/src/inference_process.cpp
+++ b/applications/inference_process/src/inference_process.cpp
@@ -75,7 +75,7 @@ bool copyOutput(const TfLiteTensor &src, InferenceProcess::DataPtr &dst) {
     }
 
     if (src.bytes > dst.size) {
-        LOG_ERR("Tensor size mismatch (bytes): actual=%d, expected%d.\n", src.bytes, dst.size);
+        LOG_ERR("Tensor size mismatch (bytes): actual=%d, expected%d.", src.bytes, dst.size);
         return true;
     }
 
@@ -185,7 +185,7 @@ bool InferenceProcess::push(const InferenceJob &job) {
 }
 
 bool InferenceProcess::runJob(InferenceJob &job) {
-    LOG_INFO("Running inference job: %s\n", job.name.c_str());
+    LOG_INFO("Running inference job: %s", job.name.c_str());
 
     // Register debug log callback for profiling
     RegisterDebugLogCallback(tflu_debug_log);
@@ -196,7 +196,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
     // 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) {
-        LOG_ERR("Model schema version unsupported: version=%" PRIu32 ", supported=%d.\n",
+        LOG_ERR("Model schema version unsupported: version=%" PRIu32 ", supported=%d.",
                 model->version(),
                 TFLITE_SCHEMA_VERSION);
         return true;
@@ -215,7 +215,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
     // Allocate tensors
     TfLiteStatus allocate_status = interpreter.AllocateTensors();
     if (allocate_status != kTfLiteOk) {
-        LOG_ERR("Failed to allocate tensors for inference: job=%s\n", job.name.c_str());
+        LOG_ERR("Failed to allocate tensors for inference: job=%s", job.name.c_str());
         return true;
     }
 
@@ -229,7 +229,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
         }
     }
     if (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",
+        LOG_ERR("Number of input buffers does not match number of non empty network tensors: input=%zu, network=%zu",
                 job.input.size(),
                 inputTensors.size());
         return true;
@@ -241,7 +241,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
         const TfLiteTensor *tensor = inputTensors[i];
 
         if (input.size != tensor->bytes) {
-            LOG_ERR("Job input size does not match network input size: job=%s, index=%zu, input=%zu, network=%u\n",
+            LOG_ERR("Job input size does not match network input size: job=%s, index=%zu, input=%zu, network=%u",
                     job.name.c_str(),
                     i,
                     input.size,
@@ -255,7 +255,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
     // Run the inference
     TfLiteStatus invoke_status = interpreter.Invoke();
     if (invoke_status != kTfLiteOk) {
-        LOG_ERR("Invoke failed for inference: job=%s\n", job.name.c_str());
+        LOG_ERR("Invoke failed for inference: job=%s", job.name.c_str());
         return true;
     }
 
@@ -270,7 +270,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
     // Copy output data
     if (job.output.size() > 0) {
         if (interpreter.outputs_size() != job.output.size()) {
-            LOG_ERR("Output size mismatch: job=%zu, network=%u\n", job.output.size(), interpreter.outputs_size());
+            LOG_ERR("Output size mismatch: job=%zu, network=%u", job.output.size(), interpreter.outputs_size());
             return true;
         }
 
@@ -300,7 +300,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
 
     if (job.expectedOutput.size() > 0) {
         if (job.expectedOutput.size() != interpreter.outputs_size()) {
-            LOG_ERR("Expected number of output tensors mismatch: job=%s, expected=%zu, network=%zu\n",
+            LOG_ERR("Expected number of output tensors mismatch: job=%s, expected=%zu, network=%zu",
                     job.name.c_str(),
                     job.expectedOutput.size(),
                     interpreter.outputs_size());
@@ -312,7 +312,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
             const TfLiteTensor *output = interpreter.output(i);
 
             if (expected.size != output->bytes) {
-                LOG_ERR("Expected output tensor size mismatch: job=%s, index=%u, expected=%zu, network=%zu\n",
+                LOG_ERR("Expected output tensor size mismatch: job=%s, index=%u, expected=%zu, network=%zu",
                         job.name.c_str(),
                         i,
                         expected.size,
@@ -335,7 +335,7 @@ bool InferenceProcess::runJob(InferenceJob &job) {
         }
     }
 
-    LOG_INFO("Finished running job: %s\n", job.name.c_str());
+    LOG_INFO("Finished running job: %s", job.name.c_str());
 
     return false;
 } // namespace InferenceProcess
@@ -350,7 +350,7 @@ bool InferenceProcess::run(bool exitOnEmpty) {
 
         if (empty) {
             if (exitOnEmpty) {
-                LOG_INFO("Exit from InferenceProcess::run() due to empty job queue\n");
+                LOG_INFO("Exit from InferenceProcess::run() due to empty job queue");
                 break;
             }