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// Copyright 2015 Google Inc. All rights reserved.
//
// 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
//
// http://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.
#import "RunModelViewController.h"
#include <fstream>
#include <iostream>
#include <pthread.h>
#include <unistd.h>
#include <queue>
#include <sstream>
#include <string>
#include "tensorflow/contrib/lite/kernels/register.h"
#include "tensorflow/contrib/lite/model.h"
#include "tensorflow/contrib/lite/string_util.h"
#include "tensorflow/contrib/lite/tools/mutable_op_resolver.h"
#include "ios_image_load.h"
#define LOG(x) std::cerr
#define CHECK(x) if (!(x)) { LOG(ERROR) << #x << "failed"; exit(1); }
NSString* RunInferenceOnImage();
@interface RunModelViewController ()
@end
@implementation RunModelViewController {
}
- (IBAction)getUrl:(id)sender {
NSString* inference_result = RunInferenceOnImage();
self.urlContentTextView.text = inference_result;
}
@end
// Returns the top N confidence values over threshold in the provided vector,
// sorted by confidence in descending order.
static void GetTopN(
const float* prediction,
const int prediction_size,
const int num_results, const float threshold,
std::vector<std::pair<float, int> >* top_results) {
// Will contain top N results in ascending order.
std::priority_queue<std::pair<float, int>,
std::vector<std::pair<float, int> >,
std::greater<std::pair<float, int> > > top_result_pq;
const long count = prediction_size;
for (int i = 0; i < count; ++i) {
const float value = prediction[i];
// Only add it if it beats the threshold and has a chance at being in
// the top N.
if (value < threshold) {
continue;
}
top_result_pq.push(std::pair<float, int>(value, i));
// If at capacity, kick the smallest value out.
if (top_result_pq.size() > num_results) {
top_result_pq.pop();
}
}
// Copy to output vector and reverse into descending order.
while (!top_result_pq.empty()) {
top_results->push_back(top_result_pq.top());
top_result_pq.pop();
}
std::reverse(top_results->begin(), top_results->end());
}
NSString* FilePathForResourceName(NSString* name, NSString* extension) {
NSString* file_path = [[NSBundle mainBundle] pathForResource:name ofType:extension];
if (file_path == NULL) {
LOG(FATAL) << "Couldn't find '" << [name UTF8String] << "."
<< [extension UTF8String] << "' in bundle.";
}
return file_path;
}
NSString* RunInferenceOnImage() {
std::string graph;
const int num_threads = 1;
std::string input_layer_type = "float";
std::vector<int> sizes = {1, 224, 224, 3};
NSString* graph_path = FilePathForResourceName(@"mobilenet_v1_1.0_224", @"tflite");
std::unique_ptr<tflite::FlatBufferModel> model(tflite::FlatBufferModel::BuildFromFile([graph_path UTF8String]));
if (!model) {
LOG(FATAL) << "Failed to mmap model " << graph;
}
LOG(INFO) << "Loaded model " << graph;
model->error_reporter();
LOG(INFO) << "resolved reporter";
#ifdef TFLITE_CUSTOM_OPS_HEADER
tflite::MutableOpResolver resolver;
RegisterSelectedOps(&resolver);
#else
tflite::ops::builtin::BuiltinOpResolver resolver;
#endif
std::unique_ptr<tflite::Interpreter> interpreter;
tflite::InterpreterBuilder(*model, resolver)(&interpreter);
if (!interpreter) {
LOG(FATAL) << "Failed to construct interpreter";
}
if (num_threads != -1) {
interpreter->SetNumThreads(num_threads);
}
int input = interpreter->inputs()[0];
if (input_layer_type != "string") {
interpreter->ResizeInputTensor(input, sizes);
}
if (interpreter->AllocateTensors() != kTfLiteOk) {
LOG(FATAL) << "Failed to allocate tensors!";
}
// Read the label list
NSString* labels_path = FilePathForResourceName(@"labels", @"txt");
std::vector<std::string> label_strings;
std::ifstream t;
t.open([labels_path UTF8String]);
std::string line;
while(t){
std::getline(t, line);
label_strings.push_back(line);
}
t.close();
// Read the Grace Hopper image.
NSString* image_path = FilePathForResourceName(@"grace_hopper", @"jpg");
int image_width;
int image_height;
int image_channels;
std::vector<uint8_t> image_data = LoadImageFromFile([image_path UTF8String], &image_width, &image_height, &image_channels);
const int wanted_width = 224;
const int wanted_height = 224;
const int wanted_channels = 3;
const float input_mean = 127.5f;
const float input_std = 127.5f;
assert(image_channels >= wanted_channels);
uint8_t* in = image_data.data();
float* out = interpreter->typed_tensor<float>(input);
for (int y = 0; y < wanted_height; ++y) {
const int in_y = (y * image_height) / wanted_height;
uint8_t* in_row = in + (in_y * image_width * image_channels);
float* out_row = out + (y * wanted_width * wanted_channels);
for (int x = 0; x < wanted_width; ++x) {
const int in_x = (x * image_width) / wanted_width;
uint8_t* in_pixel = in_row + (in_x * image_channels);
float* out_pixel = out_row + (x * wanted_channels);
for (int c = 0; c < wanted_channels; ++c) {
out_pixel[c] = (in_pixel[c] - input_mean) / input_std;
}
}
}
if (interpreter->Invoke() != kTfLiteOk) {
LOG(FATAL) << "Failed to invoke!";
}
float* output = interpreter->typed_output_tensor<float>(0);
const int output_size = 1000;
const int kNumResults = 5;
const float kThreshold = 0.1f;
std::vector<std::pair<float, int> > top_results;
GetTopN(output, output_size, kNumResults, kThreshold, &top_results);
std::stringstream ss;
ss.precision(3);
for (const auto& result : top_results) {
const float confidence = result.first;
const int index = result.second;
ss << index << " " << confidence << " ";
// Write out the result as a string
if (index < label_strings.size()) {
// just for safety: theoretically, the output is under 1000 unless there
// is some numerical issues leading to a wrong prediction.
ss << label_strings[index];
} else {
ss << "Prediction: " << index;
}
ss << "\n";
}
LOG(INFO) << "Predictions: " << ss.str();
std::string predictions = ss.str();
NSString* result = @"";
result = [NSString stringWithFormat: @"%@ - %s", result,
predictions.c_str()];
return result;
}
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