Rename cplusplus files for consistency

Change-Id: I908ea337ffb7229a33af3364b5374209c7e9df54

1 files changed, 0 insertions, 393 deletions

diff --git a/applications/inference_process/src/inference_process.cc b/applications/inference_process/src/inference_process.cc
deleted file mode 100644
index b5ed5c4..0000000
--- a/applications/inference_process/src/inference_process.cc
+++ /dev/null
@@ -1,393 +0,0 @@
-/*
- * Copyright (c) 2019-2020 Arm Limited. All rights reserved.
- *
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the License); you may
- * not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an AS IS BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "tensorflow/lite/micro/all_ops_resolver.h"
-#include "tensorflow/lite/micro/cortex_m_generic/debug_log_callback.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_interpreter.h"
-#include "tensorflow/lite/micro/micro_profiler.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/version.h"
-
-#include "inference_process.hpp"
-
-#include "cmsis_compiler.h"
-
-#include <inttypes.h>
-
-#ifndef TENSOR_ARENA_SIZE
-#define TENSOR_ARENA_SIZE (1024)
-#endif
-
-using namespace std;
-
-__attribute__((section(".bss.NoInit"), aligned(16))) uint8_t inferenceProcessTensorArena[TENSOR_ARENA_SIZE];
-
-namespace {
-
-void tflu_debug_log(const char *s) {
-    fprintf(stderr, "%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);
-    for (int i = 0; i < output->dims->size - 1; ++i) {
-        printf("%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\":\"");
-    for (int i = 0; i < numBytesToPrint - 1; ++i) {
-        if (i % 16 == 0 && i != 0) {
-            printf("\n");
-        }
-        printf("0x%02x,", output->data.uint8[i]);
-    }
-    printf("0x%02x\"\n", output->data.uint8[numBytesToPrint - 1]);
-    printf("}");
-}
-
-bool copyOutput(const TfLiteTensor &src, InferenceProcess::DataPtr &dst) {
-    if (dst.data == nullptr) {
-        return false;
-    }
-
-    if (src.bytes > dst.size) {
-        printf("Tensor size %d does not match output size %d.\n", src.bytes, dst.size);
-        return true;
-    }
-
-    copy(src.data.uint8, src.data.uint8 + src.bytes, static_cast<uint8_t *>(dst.data));
-    dst.size = src.bytes;
-
-    return false;
-}
-
-} // namespace
-
-namespace InferenceProcess {
-DataPtr::DataPtr(void *_data, size_t _size) : data(_data), size(_size) {}
-
-void DataPtr::invalidate() {
-#if defined(__DCACHE_PRESENT) && (__DCACHE_PRESENT == 1U)
-    SCB_InvalidateDCache_by_Addr(reinterpret_cast<uint32_t *>(data), size);
-#endif
-}
-
-void DataPtr::clean() {
-#if defined(__DCACHE_PRESENT) && (__DCACHE_PRESENT == 1U)
-    SCB_CleanDCache_by_Addr(reinterpret_cast<uint32_t *>(data), size);
-#endif
-}
-
-InferenceJob::InferenceJob() : numBytesToPrint(0) {}
-
-InferenceJob::InferenceJob(const string &_name,
-                           const DataPtr &_networkModel,
-                           const vector<DataPtr> &_input,
-                           const vector<DataPtr> &_output,
-                           const vector<DataPtr> &_expectedOutput,
-                           size_t _numBytesToPrint,
-                           const vector<uint8_t> &_pmuEventConfig,
-                           const uint32_t _pmuCycleCounterEnable) :
-    name(_name),
-    networkModel(_networkModel), input(_input), output(_output), expectedOutput(_expectedOutput),
-    numBytesToPrint(_numBytesToPrint), pmuEventConfig(_pmuEventConfig), pmuCycleCounterEnable(_pmuCycleCounterEnable),
-    pmuEventCount(), pmuCycleCounterCount(0) {
-#if defined(INFERENCE_PROC_TFLU_PROFILER) && defined(ETHOSU)
-    pmuEventCount = vector<uint32_t>(ETHOSU_PMU_NCOUNTERS, 0);
-#endif
-}
-
-void InferenceJob::invalidate() {
-    networkModel.invalidate();
-
-    for (auto &it : input) {
-        it.invalidate();
-    }
-
-    for (auto &it : output) {
-        it.invalidate();
-    }
-
-    for (auto &it : expectedOutput) {
-        it.invalidate();
-    }
-}
-
-void InferenceJob::clean() {
-    networkModel.clean();
-
-    for (auto &it : input) {
-        it.clean();
-    }
-
-    for (auto &it : output) {
-        it.clean();
-    }
-
-    for (auto &it : expectedOutput) {
-        it.clean();
-    }
-}
-
-InferenceProcess::InferenceProcess() : lock(0) {}
-
-// NOTE: Adding code for get_lock & free_lock with some corrections from
-// http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.dai0321a/BIHEJCHB.html
-// TODO: check correctness?
-void InferenceProcess::getLock() {
-    int status = 0;
-
-    do {
-        // Wait until lock_var is free
-        while (__LDREXW(&lock) != 0)
-            ;
-
-        // Try to set lock_var
-        status = __STREXW(1, &lock);
-    } while (status != 0);
-
-    // Do not start any other memory access until memory barrier is completed
-    __DMB();
-}
-
-// TODO: check correctness?
-void InferenceProcess::freeLock() {
-    // Ensure memory operations completed before releasing lock
-    __DMB();
-
-    lock = 0;
-}
-
-bool InferenceProcess::push(const InferenceJob &job) {
-    getLock();
-    inferenceJobQueue.push(job);
-    freeLock();
-
-    return true;
-}
-
-bool InferenceProcess::runJob(InferenceJob &job) {
-    printf("Running inference job: %s\n", job.name.c_str());
-
-    // Register debug log callback for profiling
-    RegisterDebugLogCallback(tflu_debug_log);
-
-    tflite::MicroErrorReporter microErrorReporter;
-    tflite::ErrorReporter *reporter = &microErrorReporter;
-
-    // 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);
-        return true;
-    }
-
-    // Create the TFL micro interpreter
-    tflite::AllOpsResolver resolver;
-    tflite::MicroProfiler profiler(reporter);
-
-#if defined(INFERENCE_PROC_TFLU_PROFILER) && defined(ETHOSU)
-    profiler.MonitorEthosuPMUEvents(ethosu_pmu_event_type(job.pmuEventConfig[0]),
-                                    ethosu_pmu_event_type(job.pmuEventConfig[1]),
-                                    ethosu_pmu_event_type(job.pmuEventConfig[2]),
-                                    ethosu_pmu_event_type(job.pmuEventConfig[3]));
-#endif
-
-    tflite::MicroInterpreter interpreter(
-        model, resolver, inferenceProcessTensorArena, TENSOR_ARENA_SIZE, reporter, &profiler);
-
-    // Allocate tensors
-    TfLiteStatus allocate_status = interpreter.AllocateTensors();
-    if (allocate_status != kTfLiteOk) {
-        printf("AllocateTensors failed for inference job: %s\n", job.name.c_str());
-        return true;
-    }
-
-    // Create a filtered list of non empty input tensors
-    vector<TfLiteTensor *> inputTensors;
-    for (size_t i = 0; i < interpreter.inputs_size(); ++i) {
-        TfLiteTensor *tensor = interpreter.input(i);
-
-        if (tensor->bytes > 0) {
-            inputTensors.push_back(tensor);
-        }
-    }
-
-    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());
-        return true;
-    }
-
-    // Copy input data
-    for (size_t i = 0; i < inputTensors.size(); ++i) {
-        const DataPtr &input       = job.input[i];
-        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);
-            return true;
-        }
-
-        copy(static_cast<char *>(input.data), static_cast<char *>(input.data) + input.size, tensor->data.uint8);
-    }
-
-    // Run the inference
-    TfLiteStatus invoke_status = interpreter.Invoke();
-    if (invoke_status != kTfLiteOk) {
-        printf("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());
-
-#ifdef INFERENCE_PROC_TFLU_PROFILER
-    printf("Inference runtime: %u cycles\r\n", (unsigned int)profiler.TotalInferenceTime());
-
-    if (job.pmuCycleCounterEnable != 0) {
-        job.pmuCycleCounterCount = profiler.TotalInferenceTime();
-    }
-
-#ifdef ETHOSU
-    for (uint32_t i = 0; i < ETHOSU_PMU_NCOUNTERS; i++) {
-        job.pmuEventCount[i] = profiler.GetEthosuPMUCounter(i);
-    }
-#endif
-#endif
-
-    // 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());
-            return true;
-        }
-
-        for (unsigned i = 0; i < interpreter.outputs_size(); ++i) {
-            if (copyOutput(*interpreter.output(i), job.output[i])) {
-                return true;
-            }
-        }
-    }
-
-    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");
-
-        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");
-            }
-        }
-
-        printf("]\n");
-        printf("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());
-            return true;
-        }
-
-        for (unsigned int i = 0; i < interpreter.outputs_size(); i++) {
-            const DataPtr &expected    = job.expectedOutput[i];
-            const TfLiteTensor *output = interpreter.output(i);
-
-            if (expected.size != output->bytes) {
-                printf(
-                    "Expected tensor size does not match network size. 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 tensor size does not match network size. 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]);
-                }
-            }
-        }
-    }
-
-    printf("Finished running job: %s\n", job.name.c_str());
-
-    return false;
-}
-
-bool InferenceProcess::run(bool exitOnEmpty) {
-    bool anyJobFailed = false;
-
-    while (true) {
-        getLock();
-        bool empty = inferenceJobQueue.empty();
-        freeLock();
-
-        if (empty) {
-            if (exitOnEmpty) {
-                printf("Exit from InferenceProcess::run() on empty job queue!\n");
-                break;
-            }
-
-            continue;
-        }
-
-        getLock();
-        InferenceJob job = inferenceJobQueue.front();
-        inferenceJobQueue.pop();
-        freeLock();
-
-        if (runJob(job)) {
-            anyJobFailed = true;
-            continue;
-        }
-    }
-
-    return anyJobFailed;
-}
-
-} // namespace InferenceProcess