diff options
Diffstat (limited to 'applications/inference_process/src/inference_process.cpp')
-rw-r--r-- | applications/inference_process/src/inference_process.cpp | 24 |
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; } |