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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 <pthread.h>
#include <unistd.h>
#include <queue>
#include <sstream>
#include <string>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/public/session.h"
#include "ios_image_load.h"
NSString* RunInferenceOnImage();
namespace {
class IfstreamInputStream : public ::google::protobuf::io::CopyingInputStream {
public:
explicit IfstreamInputStream(const std::string& file_name)
: ifs_(file_name.c_str(), std::ios::in | std::ios::binary) {}
~IfstreamInputStream() { ifs_.close(); }
int Read(void* buffer, int size) {
if (!ifs_) {
return -1;
}
ifs_.read(static_cast<char*>(buffer), size);
return (int)ifs_.gcount();
}
private:
std::ifstream ifs_;
};
} // namespace
@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 Eigen::TensorMap<Eigen::Tensor<float, 1, Eigen::RowMajor>,
Eigen::Aligned>& prediction,
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());
}
bool PortableReadFileToProto(const std::string& file_name,
::google::protobuf::MessageLite* proto) {
::google::protobuf::io::CopyingInputStreamAdaptor stream(
new IfstreamInputStream(file_name));
stream.SetOwnsCopyingStream(true);
// TODO(jiayq): the following coded stream is for debugging purposes to allow
// one to parse arbitrarily large messages for MessageLite. One most likely
// doesn't want to put protobufs larger than 64MB on Android, so we should
// eventually remove this and quit loud when a large protobuf is passed in.
::google::protobuf::io::CodedInputStream coded_stream(&stream);
// Total bytes hard limit / warning limit are set to 1GB and 512MB
// respectively.
coded_stream.SetTotalBytesLimit(1024LL << 20, 512LL << 20);
return proto->ParseFromCodedStream(&coded_stream);
}
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() {
tensorflow::SessionOptions options;
tensorflow::Session* session_pointer = nullptr;
tensorflow::Status session_status = tensorflow::NewSession(options, &session_pointer);
if (!session_status.ok()) {
std::string status_string = session_status.ToString();
return [NSString stringWithFormat: @"Session create failed - %s",
status_string.c_str()];
}
std::unique_ptr<tensorflow::Session> session(session_pointer);
LOG(INFO) << "Session created.";
tensorflow::GraphDef tensorflow_graph;
LOG(INFO) << "Graph created.";
NSString* network_path = FilePathForResourceName(@"tensorflow_inception_graph", @"pb");
PortableReadFileToProto([network_path UTF8String], &tensorflow_graph);
LOG(INFO) << "Creating session.";
tensorflow::Status s = session->Create(tensorflow_graph);
if (!s.ok()) {
LOG(ERROR) << "Could not create TensorFlow Graph: " << s;
return @"";
}
// Read the label list
NSString* labels_path = FilePathForResourceName(@"imagenet_comp_graph_label_strings", @"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<tensorflow::uint8> 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 = 117.0f;
const float input_std = 1.0f;
assert(image_channels >= wanted_channels);
tensorflow::Tensor image_tensor(
tensorflow::DT_FLOAT,
tensorflow::TensorShape({
1, wanted_height, wanted_width, wanted_channels}));
auto image_tensor_mapped = image_tensor.tensor<float, 4>();
tensorflow::uint8* in = image_data.data();
// tensorflow::uint8* in_end = (in + (image_height * image_width * image_channels));
float* out = image_tensor_mapped.data();
for (int y = 0; y < wanted_height; ++y) {
const int in_y = (y * image_height) / wanted_height;
tensorflow::uint8* 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;
tensorflow::uint8* 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;
}
}
}
NSString* result = [network_path stringByAppendingString: @" - loaded!"];
result = [NSString stringWithFormat: @"%@ - %lu, %s - %dx%d", result,
label_strings.size(), label_strings[0].c_str(), image_width, image_height];
std::string input_layer = "input";
std::string output_layer = "output";
std::vector<tensorflow::Tensor> outputs;
tensorflow::Status run_status = session->Run({{input_layer, image_tensor}},
{output_layer}, {}, &outputs);
if (!run_status.ok()) {
LOG(ERROR) << "Running model failed: " << run_status;
tensorflow::LogAllRegisteredKernels();
result = @"Error running model";
return result;
}
tensorflow::string status_string = run_status.ToString();
result = [NSString stringWithFormat: @"%@ - %s", result,
status_string.c_str()];
tensorflow::Tensor* output = &outputs[0];
const int kNumResults = 5;
const float kThreshold = 0.1f;
std::vector<std::pair<float, int> > top_results;
GetTopN(output->flat<float>(), 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();
tensorflow::string predictions = ss.str();
result = [NSString stringWithFormat: @"%@ - %s", result,
predictions.c_str()];
return result;
}