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samples: tflite-micro: Use FVP for test validation

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What is changed?
 - Replaced printk with printf in the sample

Why do we need this change?
 - Running the sample with fvp just prints the below output and not
   the complete logs that we expect:
    `sender 0: Sending inference`
   We get the expected output with CONFIG_LOG_IMMEDIATE=y but,
   its not ideal. printf works without the config and plus it
   looks a better fit in the current sample than printk.

This change is validate using `mps3/corstone300/fvp`.

Signed-off-by: Sudan Landge <sudan.landge@arm.com>
This commit is contained in:
Sudan Landge 2024-10-31 11:04:27 +00:00 committed by Benjamin Cabé
parent d38e40dcdd
commit 767e03f082
2 changed files with 22 additions and 22 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

20
samples/modules/tflite-micro/tflm_ethosu/src/inference_process.cpp
View file

@ -28,7 +28,7 @@ bool copyOutput(const TfLiteTensor &src, InferenceProcess::DataPtr &dst)
}
if (src.bytes > dst.size) {
printk("Tensor size mismatch (bytes): actual=%d, expected%d.\n", src.bytes,
printf("Tensor size mismatch (bytes): actual=%d, expected%d.\n", src.bytes,
dst.size);
return true;
}
@ -112,7 +112,7 @@ 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) {
printk("Model schema version unsupported: version=%" PRIu32 ", supported=%d.\n",
printf("Model schema version unsupported: version=%" PRIu32 ", supported=%d.\n",
model->version(), TFLITE_SCHEMA_VERSION);
return true;
}
@ -126,12 +126,12 @@ bool InferenceProcess::runJob(InferenceJob &job)
/* Allocate tensors */
TfLiteStatus allocate_status = interpreter.AllocateTensors();
if (allocate_status != kTfLiteOk) {
printk("Failed to allocate tensors for inference. job=%p\n", &job);
printf("Failed to allocate tensors for inference. job=%p\n", &job);
return true;
}
if (job.input.size() != interpreter.inputs_size()) {
printk("Number of job and network inputs do not match. input=%zu, network=%zu\n",
printf("Number of job and network inputs do not match. input=%zu, network=%zu\n",
job.input.size(), interpreter.inputs_size());
return true;
}
@ -142,7 +142,7 @@ bool InferenceProcess::runJob(InferenceJob &job)
const TfLiteTensor *tensor = interpreter.input(i);
if (input.size != tensor->bytes) {
printk("Input tensor size mismatch. index=%zu, input=%zu, network=%u\n", i,
printf("Input tensor size mismatch. index=%zu, input=%zu, network=%u\n", i,
input.size, tensor->bytes);
return true;
}
@ -154,14 +154,14 @@ bool InferenceProcess::runJob(InferenceJob &job)
/* Run the inference */
TfLiteStatus invoke_status = interpreter.Invoke();
if (invoke_status != kTfLiteOk) {
printk("Invoke failed for inference. job=%s\n", job.name.c_str());
printf("Invoke failed for inference. job=%s\n", job.name.c_str());
return true;
}
/* Copy output data */
if (job.output.size() > 0) {
if (interpreter.outputs_size() != job.output.size()) {
printk("Number of job and network outputs do not match. job=%zu, network=%u\n",
printf("Number of job and network outputs do not match. job=%zu, network=%u\n",
job.output.size(), interpreter.outputs_size());
return true;
}
@ -175,7 +175,7 @@ bool InferenceProcess::runJob(InferenceJob &job)
if (job.expectedOutput.size() > 0) {
if (job.expectedOutput.size() != interpreter.outputs_size()) {
printk("Number of job and network expected outputs do not match. job=%zu, network=%zu\n",
printf("Number of job and network expected outputs do not match. job=%zu, network=%zu\n",
job.expectedOutput.size(), interpreter.outputs_size());
return true;
}
@ -185,7 +185,7 @@ bool InferenceProcess::runJob(InferenceJob &job)
const TfLiteTensor *output = interpreter.output(i);
if (expected.size != output->bytes) {
printk("Expected output tensor size mismatch. index=%u, expected=%zu, network=%zu\n",
printf("Expected output tensor size mismatch. index=%u, expected=%zu, network=%zu\n",
i, expected.size, output->bytes);
return true;
}
@ -193,7 +193,7 @@ bool InferenceProcess::runJob(InferenceJob &job)
for (unsigned int j = 0; j < output->bytes; ++j) {
if (output->data.uint8[j] !=
static_cast<uint8_t *>(expected.data)[j]) {
printk("Expected output tensor data mismatch. index=%u, offset=%u, expected=%02x, network=%02x\n",
printf("Expected output tensor data mismatch. index=%u, offset=%u, expected=%02x, network=%02x\n",
i, j, static_cast<uint8_t *>(expected.data)[j],
output->data.uint8[j]);
return true;

24
samples/modules/tflite-micro/tflm_ethosu/src/main.cpp
View file

@ -108,7 +108,7 @@ void *allocateHeap(const size_t size)
uint8_t *buf = static_cast<uint8_t *>(k_malloc(size));
if ((buf == nullptr) || (heap == nullptr)) {
printk("Heap allocation failed. heap=%p, buf=%p, size=%zu\n", heap, buf, size);
printf("Heap allocation failed. heap=%p, buf=%p, size=%zu\n", heap, buf, size);
exit(1);
}
@ -133,17 +133,17 @@ void inferenceProcessTask(void *_name, void *heap, void *_params)
xInferenceJob *job =
static_cast<xInferenceJob *>(k_queue_get(params->queueHandle, Z_FOREVER));
printk("%s: Received inference job. job=%p\n", name->c_str(), job);
printf("%s: Received inference job. job=%p\n", name->c_str(), job);
/* Run inference */
job->status = inferenceProcess.runJob(*job);
printk("%s: Sending inference response. job=%p\n", name->c_str(), job);
printf("%s: Sending inference response. job=%p\n", name->c_str(), job);
/* Return inference message */
int ret = k_queue_alloc_append(job->responseQueue, job);
if (0 != ret) {
printk("%s: Failed to send message\n", name->c_str());
printf("%s: Failed to send message\n", name->c_str());
exit(1);
}
}
@ -177,13 +177,13 @@ void inferenceSenderTask(void *_name, void *heap, void *_queue)
{ DataPtr(expectedOutputData, sizeof(expectedOutputData)) },
&senderQueue);
printk("%s: Sending inference. job=%p, name=%s\n", name->c_str(), &job,
printf("%s: Sending inference. job=%p, name=%s\n", name->c_str(), &job,
job.name.c_str());
/* Queue job */
ret = k_queue_alloc_append(inferenceQueue, &job);
if (0 != ret) {
printk("%s: Failed to send message\n", name->c_str());
printf("%s: Failed to send message\n", name->c_str());
exit(1);
}
}
@ -193,7 +193,7 @@ void inferenceSenderTask(void *_name, void *heap, void *_queue)
xInferenceJob *job =
static_cast<xInferenceJob *>(k_queue_get(&senderQueue, Z_FOREVER));
printk("%s: Received job response. job=%p, status=%u\n", name->c_str(), job,
printf("%s: Received job response. job=%p, status=%u\n", name->c_str(), job,
job->status);
totalCompletedJobs++;
@ -229,7 +229,7 @@ int main()
const size_t stackSize = 2048;
k_thread_stack_t *stack = static_cast<k_thread_stack_t *>(k_malloc(stackSize));
if (stack == nullptr) {
printk("Failed to allocate stack to 'inferenceSenderTask%i'\n", n);
printf("Failed to allocate stack to 'inferenceSenderTask%i'\n", n);
exit(1);
}
@ -239,7 +239,7 @@ int main()
thread.id = k_thread_create(&thread.thread, stack, stackSize, inferenceSenderTask,
name, heapPtr, &inferenceQueue, 3, 0, K_FOREVER);
if (thread.id == 0) {
printk("Failed to create 'inferenceSenderTask%i'\n", n);
printf("Failed to create 'inferenceSenderTask%i'\n", n);
exit(1);
}
@ -252,7 +252,7 @@ int main()
const size_t stackSize = 8192;
k_thread_stack_t *stack = static_cast<k_thread_stack_t *>(k_malloc(stackSize));
if (stack == nullptr) {
printk("Failed to allocate stack to 'inferenceSenderTask%i'\n", n);
printf("Failed to allocate stack to 'inferenceSenderTask%i'\n", n);
exit(1);
}
@ -265,7 +265,7 @@ int main()
thread.id = k_thread_create(&thread.thread, stack, stackSize, inferenceProcessTask,
name, heapPtr, &taskParam, 2, 0, K_FOREVER);
if (thread.id == 0) {
printk("Failed to create 'inferenceProcessTask%i'\n", n);
printf("Failed to create 'inferenceProcessTask%i'\n", n);
exit(1);
}
@ -283,7 +283,7 @@ int main()
/* Safety belt */
k_thread_suspend(k_current_get());
printk("Zephyr application failed to initialise \n");
printf("Zephyr application failed to initialise \n");
return 1;
}