log serial number for all inference requests even No signal.

lib/image_inference_impl.cc

@@ -251,7 +251,7 @@ image_inference_impl::image_inference_impl(
       flip_(flip), max_rows_(max_rows), vlen_(vlen), rotate_secs_(rotate_secs),
       n_image_(n_image), n_inference_(n_inference), image_count_(0),
       inference_count_(0), samp_rate_(samp_rate), last_image_start_item_(0),
-      convert_alpha_(convert_alpha), norm_alpha_(norm_alpha),
+      serial_(0), convert_alpha_(convert_alpha), norm_alpha_(norm_alpha),
       norm_beta_(norm_beta), min_peak_points_(min_peak_points),
       confidence_(confidence), running_(true), last_rx_freq_(0),
       last_rx_time_(0) {
@@ -371,6 +371,7 @@ void image_inference_impl::create_image_(bool discard) {
         output_item.points_buffer = points_buffer_;
         output_item.points_mean = points_mean;
         output_item.image_buffer = NULL;
+        output_item.serial = ++serial_;
         if (!inference_q_.push(output_item)) {
           d_logger->error("inference request queue full, size {}",
                           MAX_INFERENCE);
@@ -527,6 +528,7 @@ void image_inference_impl::run_inference_() {
         std::to_string(output_item.start_item * vlen_);
     metadata_json["orig_rows"] = output_item.points_buffer->rows;
     metadata_json["sample_rate"] = std::to_string(samp_rate_);
+    metadata_json["serial"] = std::to_string(output_item.serial);
 
     const std::string secs_image_dir = secs_dir(image_dir_, rotate_secs_);
     ++image_count_;

lib/image_inference_impl.h

@@ -233,6 +233,7 @@ typedef struct output_item {
   double points_mean;
   double points_max;
   COUNT_T start_item;
+  COUNT_T serial;
 } output_item_type;
 
 class image_inference_impl : public image_inference, base_impl {
@@ -241,7 +242,7 @@ class image_inference_impl : public image_inference, base_impl {
   std::string image_dir_;
   int x_, y_, norm_type_, colormap_, interpolation_, flip_, max_rows_;
   COUNT_T vlen_, rotate_secs_, n_image_, n_inference_, image_count_,
-      inference_count_, samp_rate_, last_image_start_item_;
+      inference_count_, samp_rate_, last_image_start_item_, serial_;
   double convert_alpha_, norm_alpha_, norm_beta_, min_peak_points_, confidence_;
   bool running_;
   FREQ_T last_rx_freq_;

lib/iq_inference_impl.cc

@@ -240,10 +240,11 @@ iq_inference_impl::iq_inference_impl(
       batch_(vlen_ * n_vlen_), sample_buffer_(sample_buffer), sample_clock_(0),
       last_rx_freq_sample_clock_(0), n_inference_(n_inference),
       inference_count_(n_inference), samples_since_tag_(0), predictions_(0),
-      batch_inference_(batch), samp_rate_(samp_rate), last_full_time_(0),
-      min_peak_points_(min_peak_points), confidence_(confidence),
-      power_inference_(power_inference), background_(background),
-      running_(true), last_rx_time_(0), last_rx_freq_(0) {
+      batch_inference_(batch), serial_(0), samp_rate_(samp_rate),
+      last_full_time_(0), min_peak_points_(min_peak_points),
+      confidence_(confidence), power_inference_(power_inference),
+      background_(background), running_(true), last_rx_time_(0),
+      last_rx_freq_(0) {
   samples_lookback_.reset(new gr_complex[batch_ * sample_buffer]);
   unsigned int alignment = volk_get_alignment();
   samples_total_.reset((float *)volk_malloc(sizeof(float), alignment));
@@ -290,7 +291,7 @@ bool iq_inference_impl::stop() {
   running_ = false;
   io_service_->stop();
   threadpool_.join_all();
-  d_logger->info("published {} predictions", predictions_);
+  d_logger->info("iq_inference sent {} predictions", predictions_);
   return true;
 }
 
@@ -306,6 +307,7 @@ void iq_inference_impl::run_inference_(torchserve_client *client) {
     metadata_json["sample_rate"] = std::to_string(samp_rate_);
     metadata_json["rx_freq_sample_clock"] =
         std::to_string(output_item.rx_freq_sample_clock);
+    metadata_json["serial"] = std::to_string(output_item.serial);
     nlohmann::json output_json, results_json;
     COUNT_T signal_predictions = 0;
     std::string error;
@@ -360,15 +362,15 @@ void iq_inference_impl::run_inference_(torchserve_client *client) {
     }
 
     output_json["predictions"] = results_json;
-    // double new line to facilitate json parsing, since prediction may
-    // contain new lines.
-    if (signal_predictions) {
-      output_json["metadata"] = metadata_json;
-      const std::string output_json_str = output_json.dump();
-      json_result_type output_json;
+    output_json["metadata"] = metadata_json;
+    const std::string output_json_str = output_json.dump();
+    if (output_json_str.size() < MAX_JSON_SIZE) {
+      json_result_type output_json_chars;
       std::copy(output_json_str.begin(), output_json_str.end(),
-                output_json.data());
-      json_q_.push(output_json);
+                output_json_chars.data());
+      json_q_.push(output_json_chars);
+    } else {
+      d_logger->error("output json size too large");
     }
     delete_output_item_(output_item);
   }
@@ -396,6 +398,9 @@ void iq_inference_impl::process_items_(COUNT_T power_in_count,
     if (n_inference_ > 0 && --inference_count_) {
       continue;
     }
+    if (!last_rx_freq_) {
+      continue;
+    }
     inference_count_ = n_inference_;
     if (!model_names_.size()) {
       continue;
@@ -410,16 +415,13 @@ void iq_inference_impl::process_items_(COUNT_T power_in_count,
     output_item.rx_freq = last_rx_freq_;
     output_item.rx_freq_sample_clock = last_rx_freq_sample_clock_;
     output_item.sample_count = batch_;
+    output_item.serial = ++serial_;
     output_item.samples = new gr_complex[output_item.sample_count];
     output_item.power = new float[output_item.sample_count];
     memcpy(output_item.samples, (void *)&samples_lookback_[j * batch_],
            batch_ * sizeof(gr_complex));
     memcpy(output_item.power, (void *)power_in, batch_ * sizeof(float));
     if (background_) {
-      if (!last_rx_freq_) {
-
-        continue;
-      }
       if (!inference_q_.push(output_item)) {
         delete_output_item_(output_item);
         if (host_now_() - last_full_time_ > 5) {
@@ -477,6 +479,10 @@ int iq_inference_impl::general_work(int noutput_items,
                                     gr_vector_int &ninput_items,
                                     gr_vector_const_void_star &input_items,
                                     gr_vector_void_star &output_items) {
+  if (!background_ && !inference_q_.empty()) {
+    return 0;
+  }
+
   const gr_complex *samples_in =
       static_cast<const gr_complex *>(input_items[0]);
   const float *power_in = static_cast<const float *>(input_items[1]);

lib/iq_inference_impl.h

@@ -222,6 +222,7 @@ namespace gr {
 namespace iqtlabs {
 
 const COUNT_T MAX_INFERENCE = 5;
+const COUNT_T MAX_JSON_SIZE = 8192;
 
 typedef struct output_item {
   FREQ_T rx_freq;
@@ -232,16 +233,17 @@ typedef struct output_item {
   gr_complex *samples;
   float *power;
   COUNT_T rx_freq_sample_clock;
+  COUNT_T serial;
 } output_item_type;
 
-typedef std::array<char, 8192> json_result_type;
+typedef std::array<char, MAX_JSON_SIZE> json_result_type;
 
 class iq_inference_impl : public iq_inference, base_impl {
 private:
   pmt::pmt_t tag_;
   COUNT_T vlen_, n_vlen_, batch_, sample_buffer_, sample_clock_,
       last_rx_freq_sample_clock_, n_inference_, inference_count_,
-      samples_since_tag_, predictions_, batch_inference_;
+      samples_since_tag_, predictions_, batch_inference_, serial_;
   int samp_rate_;
   TIME_T last_full_time_;
   double min_peak_points_, confidence_;

lib/retuner_impl.h

@@ -235,6 +235,7 @@ class retuner_impl {
   bool need_retune_(COUNT_T n);
   void parse_tuning_ranges_(const std::string &tuning_ranges);
   void next_retune_(TIME_T host_now);
+
   std::string describe_ranges_();
   TIME_T apply_rx_time_slew_(TIME_T rx_time);
   COUNT_T samp_rate_, tune_jitter_hz_, freq_start_, freq_end_, tune_step_hz_,

python/iqtlabs/qa_image_inference.py

@@ -336,7 +336,7 @@ def test_instance(self):
                 self.assertTrue(stat.st_size)
                 self.assertEqual(imghdr.what(image_file), "png")
             self.assertTrue(json_raw_all)
-            for json_raw in json_raw_all:
+            for i, json_raw in enumerate(json_raw_all, start=1):
                 result = json.loads(json_raw)
                 print(result)
                 metadata_result = result["metadata"]
@@ -350,6 +350,7 @@ def test_instance(self):
                         "sample_rate",
                     )
                 ]
+                self.assertEqual(int(metadata_result["serial"]), i)
                 self.assertGreaterEqual(rssi_mean, rssi_min, metadata_result)
                 self.assertGreaterEqual(rssi_max, rssi_mean, metadata_result)
                 self.assertEqual(samp_rate, meta_samp_rate, metadata_result)

python/iqtlabs/qa_iq_inference.py

@@ -355,7 +355,7 @@ def test_good_predict(self):
             last_sc = 0
             last_ts = 0
             last_rx_freq = 0
-            for json_raw in json_raw_all:
+            for i, json_raw in enumerate(json_raw_all, start=1):
                 result = json.loads(json_raw)
                 print(result)
                 sample_value = float(
@@ -368,6 +368,7 @@ def test_good_predict(self):
                 rx_freq = float(result["metadata"]["rx_freq"])
                 ts = float(result["metadata"]["ts"])
                 sc = float(result["metadata"]["sample_clock"])
+                self.assertEqual(int(result["metadata"]["serial"]), i)
                 test_sample_clocks.add(sc)
                 count = float(result["metadata"]["sample_count"])
                 self.assertEqual(count, fft_size * n_vlen)