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Diffstat (limited to 'applications/inference_process/src/inference_process.cc')
| -rw-r--r-- | applications/inference_process/src/inference_process.cc | 270 |
1 files changed, 270 insertions, 0 deletions
diff --git a/applications/inference_process/src/inference_process.cc b/applications/inference_process/src/inference_process.cc new file mode 100644 index 0000000..448e29b --- /dev/null +++ b/applications/inference_process/src/inference_process.cc @@ -0,0 +1,270 @@ +/* + * 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/micro_error_reporter.h" +#include "tensorflow/lite/micro/micro_interpreter.h" +#include "tensorflow/lite/schema/schema_generated.h" +#include "tensorflow/lite/version.h" + +#include "inference_process.hpp" + +#ifndef TENSOR_ARENA_SIZE +#define TENSOR_ARENA_SIZE (1024) +#endif + +__attribute__((section(".bss.NoInit"), aligned(16))) uint8_t inferenceProcessTensorArena[TENSOR_ARENA_SIZE]; + +namespace { +void print_output_data(TfLiteTensor *output, size_t bytesToPrint) { + const int numBytesToPrint = std::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\": \"%08x\",\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; + } + + std::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) {} + +InferenceJob::InferenceJob() : numBytesToPrint(0) {} + +InferenceJob::InferenceJob(const std::string &name, + const DataPtr &networkModel, + const DataPtr &input, + const DataPtr &output, + const DataPtr &expectedOutput, + size_t numBytesToPrint) : + name(name), + networkModel(networkModel), input(input), output(output), expectedOutput(expectedOutput), + numBytesToPrint(numBytesToPrint) {} + +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()); + + tflite::MicroErrorReporter microErrorReporter; + tflite::ErrorReporter *reporter = µErrorReporter; + + const tflite::Model *model = ::tflite::GetModel(job.networkModel.data); + if (model->version() != TFLITE_SCHEMA_VERSION) { + printf("Model provided is schema version %d not equal " + "to supported version %d.\n", + model->version(), + TFLITE_SCHEMA_VERSION); + return true; + } + + tflite::AllOpsResolver resolver; + + tflite::MicroInterpreter interpreter(model, resolver, inferenceProcessTensorArena, TENSOR_ARENA_SIZE, reporter); + + TfLiteStatus allocate_status = interpreter.AllocateTensors(); + if (allocate_status != kTfLiteOk) { + printf("AllocateTensors failed for inference job: %s\n", job.name.c_str()); + return true; + } + + bool inputSizeError = false; + // TODO: adapt for multiple inputs + // for (unsigned int i = 0; i < interpreter.inputs_size(); ++i) + for (unsigned int i = 0; i < 1; ++i) { + TfLiteTensor *input = interpreter.input(i); + if (input->bytes != job.input.size) { + // If input sizes don't match, then we could end up copying + // uninitialized or partial data. + inputSizeError = true; + printf("Allocated size: %d for input: %d doesn't match the " + "received input size: %d for job: %s\n", + input->bytes, + i, + job.input.size, + job.name.c_str()); + return true; + } + memcpy(input->data.uint8, job.input.data, input->bytes); + } + if (inputSizeError) { + return true; + } + + TfLiteStatus invoke_status = interpreter.Invoke(); + if (invoke_status != kTfLiteOk) { + printf("Invoke failed for inference job: %s\n", job.name.c_str()); + return true; + } + + copyOutput(*interpreter.output(0), job.output); + + 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.data != nullptr) { + bool outputSizeError = false; + // TODO: adapt for multiple outputs + // for (unsigned int i = 0; i < interpreter.outputs_size(); i++) + for (unsigned int i = 0; i < 1; i++) { + TfLiteTensor *output = interpreter.output(i); + if (job.expectedOutput.size != output->bytes) { + // If the expected output & the actual output size doesn't + // match, we could end up accessing out-of-bound data. + // Also there's no need to compare the data, as we know + // that sizes differ. + outputSizeError = true; + printf("Output size: %d for output: %d doesn't match with " + "the expected output size: %d for job: %s\n", + output->bytes, + i, + job.expectedOutput.size, + job.name.c_str()); + return true; + } + for (unsigned int j = 0; j < output->bytes; ++j) { + if (output->data.uint8[j] != (static_cast<uint8_t *>(job.expectedOutput.data))[j]) { + printf("Output data doesn't match expected output data at index: " + "%d, expected: %02X actual: %02X", + j, + (static_cast<uint8_t *>(job.expectedOutput.data))[j], + output->data.uint8[j]); + } + } + } + if (outputSizeError) { + return true; + } + } + 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 |