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Update ExecutorchModule to use data loader #485

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47 changes: 15 additions & 32 deletions ...demo_apps/ExecutorchMobileNet/ExecutorchMobileNet/ExecutorchMobileNet/ExecutorchModule.mm
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Original file line number Diff line number Diff line change
Expand Up @@ -15,8 +15,8 @@
#include <stdio.h>
#include <cstddef>
#include <string>
//
#include <executorch/extension/data_loader/file_data_loader.h>

#include <executorch/extension/data_loader/shared_ptr_data_loader.h>
#include <executorch/runtime/core/exec_aten/testing_util/tensor_factory.h>
#include <executorch/runtime/executor/method.h>
#include <executorch/runtime/executor/program.h>
Expand All @@ -33,7 +33,8 @@
#include <string>

using namespace torch::executor;
using torch::executor::util::FileDataLoader;

using torch::executor::util::SharedPtrDataLoader;
using torch::executor::testing::TensorFactory;

static constexpr size_t kRuntimeMemorySize = 64021120;
Expand Down Expand Up @@ -120,37 +121,21 @@ - (nullable instancetype)initWithFileAtPath:(NSString*)filePath {
- (char*)segmentImage:(void*)imageBuffer
withWidth:(int)width
withHeight:(int)height {
printf("running segmentImage...");
float* floatData = static_cast<float*>(imageBuffer);
for (int i = 0; i < 10; i++) {
printf("float Data first item: %f: ", floatData[i]);
}

printf("last element: %f: ", floatData[1 * 3 * 224 * 224 - 1]);

runtime_init();
Error status;

TensorFactory<ScalarType::Float> tensor_inputs;
// const std::vector<int32_t> sizes = {1};
// EValue evalue_inputs(tensor_inputs.make(sizes, /*data=*/{0.2}));

const std::vector<int32_t> sizes = {1, 3, 224, 224};
std::vector<float> floatVector(floatData, floatData + 1 * 3 * 224 * 224);
EValue evalue_inputs(tensor_inputs.make(sizes, /*data=*/floatVector));

// NSString *filePath = [[NSBundle mainBundle]
// pathForResource:@"lowered_sin" ofType:@"ff"]; executorch_module_name =
// filePath.UTF8String;
NSString* filePath = [[NSBundle mainBundle] pathForResource:@"mv2_softmax"
ofType:@"pte"];
// NSString *filePath = [[NSBundle mainBundle] pathForResource:@"mv3"
// ofType:@"ff"]; NSString *filePath = [[NSBundle mainBundle]
// pathForResource:@"lowered_sin" ofType:@"ff"];
NSLog(@" filePath: %@", filePath);
std::string log_message = "Start logging...\n";
ET_LOG(Info, "Hello world.");
NSString* objcString = @(log_message.c_str());

MemoryAllocator const_allocator{MemoryAllocator(0, nullptr)};
const_allocator.enable_profiling("const allocator");
Expand Down Expand Up @@ -191,40 +176,38 @@ - (char*)segmentImage:(void*)imageBuffer
// Allocate memory
std::shared_ptr<char> file_data = std::shared_ptr<char>(
new char[file_length + 1], std::default_delete<char[]>());

if (!file_data) {
ET_LOG(Error, "Unable to allocate memory to read file %s\n", file_name);
fclose(file);
}
ET_LOG(Info, "Allocate memory Finish.");
//
// // Read file contents into buffer

fread(file_data.get(), file_length, 1, file);
ET_LOG(Info, "Load file Finish.");
//
const void* program_data = file_data.get();

const auto program = torch::executor::Program(program_data);
// TODO(chenlai): use FileDataLoader or MmapDataLoader to load model
SharedPtrDataLoader data_loader(file_data, file_length);
Result<Program> program = Program::load(&data_loader);

if (!program.is_valid()) {
ET_LOG(Info, "Failed to parse model file %s", file_name);
if (!program.ok()) {
ET_LOG(Error, "Failed to parse model file %s", file_name);
return nil;
}

// Use the first method in the program.
const char* method_name = nullptr;
{
const auto method_name_result = program.get_method_name(0);
const auto method_name_result = program->get_method_name(0);
ET_CHECK_MSG(method_name_result.ok(), "Program has no methods");
method_name = *method_name_result;
}
ET_LOG(Info, "Loading method %s", method_name);
log_message = log_message + "Loading method " + method_name + "\n";
ET_LOG(Info, "Using method %s", method_name);

Result<Method> method = program.load_method(method_name, &memory_manager);
Result<Method> method = program->load_method(method_name, &memory_manager);

ET_CHECK(method.ok());
ET_LOG(Info, "Method loaded.");
method->set_input(evalue_inputs, 0);
// auto inputs = torch::executor::util::PrepareInputTensors(*method);

ET_LOG(Info, "Inputs prepared.");

Expand Down