commit eb912395c25fbdeef4f322d7aea9226578228184
author Kristofer Jonsson <kristofer.jonsson@arm.com> Fri Nov 12 12:51:27 2021 +0100
committer Kristofer Jonsson <kristofer.jonsson@arm.com> Thu Nov 18 15:35:37 2021 +0100
tree b99aafbfc3aa6cfbd96200af8c5f84e9bd2d6474
parent d55ecdcec6350b89f785f6deb1dc34e6ffe9c8ee

Remove new line from log messages

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 It has been replaced by the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

applications/inference_process/src/inference_process.cpp

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 @@
     }
 
     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::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 @@
     // 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 @@
     // 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 @@
         }
     }
     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 @@
         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 @@
     // 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 @@
     // 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 @@
 
     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 @@
             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 @@
         }
     }
 
-    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 @@
 
         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;
             }