move image_inference output to simple log, since it generates labelle…

…d images itself.

docker/Dockerfile.base

@@ -87,7 +87,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
   libvulkan-dev \
   python3-numpy
 WORKDIR /root
-RUN git clone https://github.com/iqtlabs/gr-iqtlabs -b 1.0.46
+RUN git clone https://github.com/iqtlabs/gr-iqtlabs -b 1.0.48
 COPY --from=iqtlabs/gamutrf-vkfft:latest /root /root/gr-iqtlabs
 WORKDIR /root/gr-iqtlabs/build
 COPY --from=sigmf-builder /usr/local /usr/local

docs/README-airt.md

@@ -85,7 +85,7 @@ install gr-iqtlabs
 $ git clone https://github.com/google/flatbuffers -b v23.5.26
 $ git clone https://github.com/nlohmann/json -b v3.11.2
 $ git clone https://github.com/deepsig/libsigmf -b v1.0.2
-$ git clone https://github.com/iqtlabs/gr-iqtlabs -b 1.0.46
+$ git clone https://github.com/iqtlabs/gr-iqtlabs -b 1.0.48
 $ mkdir -p flatbuffers/build && cd flatbuffers/build && cmake -DCMAKE_INSTALL_PREFIX=~/.conda/envs/$CONDA_DEFAULT_ENV .. && make -j $(nproc) && make install && cd ../..
 $ mkdir -p json/build && cd json/build && cmake -DCMAKE_INSTALL_PREFIX=~/.conda/envs/$CONDA_DEFAULT_ENV .. && make -j $(nproc) && make install && cd ../..
 $ mkdir -p libsigmf/build && cd libsigmf/build && cmake -DUSE_SYSTEM_JSON=ON -DUSE_SYSTEM_FLATBUFFERS=ON -DCMAKE_INSTALL_PREFIX=~/.conda/envs/$CONDA_DEFAULT_ENV -DCMAKE_CXX_FLAGS="-I $HOME/.conda/envs/$CONDA_DEFAULT_ENV/include" .. && make -j $(nproc) && make install && cd ../..

gamutrf/grinference2mqtt.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import json
+import sys
+import numpy as np
+
+try:
+    from gnuradio import gr  # pytype: disable=import-error
+except ModuleNotFoundError as err:  # pragma: no cover
+    print(
+        "Run from outside a supported environment, please run via Docker (https://github.com/IQTLabs/gamutRF#readme): %s"
+        % err
+    )
+    sys.exit(1)
+
+
+DELIM = "\n\n"
+
+
+class inference2mqtt(gr.sync_block):
+    def __init__(
+        self,
+    ):
+        self.yaml_buffer = ""
+
+        gr.sync_block.__init__(
+            self,
+            name="inference2mqtt",
+            in_sig=[np.ubyte],
+            out_sig=None,
+        )
+
+    def work(self, input_items, output_items):
+        n = 0
+        for input_item in input_items:
+            raw_input_item = input_item.tobytes().decode("utf8")
+            n += len(raw_input_item)
+            self.yaml_buffer += raw_input_item
+        while True:
+            delim_pos = self.yaml_buffer.find(DELIM)
+            if delim_pos == -1:
+                break
+            raw_item = self.yaml_buffer[:delim_pos]
+            item = json.loads(raw_item)
+            self.yaml_buffer = self.yaml_buffer[delim_pos + len(DELIM) :]
+            self.process_item(item)
+        return n
+
+    def process_item(self, item):
+        print(item)
+        return

gamutrf/grscan.py

@@ -20,7 +20,7 @@
     sys.exit(1)
 
 from gamutrf.grsource import get_source
-from gamutrf.gryolo import yolo_bbox
+from gamutrf.grinference2mqtt import inference2mqtt
 from gamutrf.utils import endianstr
 
 
@@ -189,16 +189,14 @@ def __init__(
             image_vlen = np.prod(image_shape)
             prediction_shape = (1, 8, 8400)
             prediction_vlen = np.prod(prediction_shape)
-            image_dir = Path(inference_output_dir, "images")
             Path(inference_output_dir).mkdir(parents=True, exist_ok=True)
-            image_dir.mkdir(parents=True, exist_ok=True)
             self.inference_blocks = [
                 self.iqtlabs.image_inference(
                     tag="rx_freq",
                     vlen=nfft,
                     x=x,
                     y=y,
-                    image_dir=str(image_dir),
+                    image_dir=inference_output_dir,
                     convert_alpha=255,
                     norm_alpha=0,
                     norm_beta=1,
@@ -209,12 +207,9 @@ def __init__(
                     min_peak_points=inference_min_db,
                     model_server=inference_model_server,
                     model_name=inference_model_name,
+                    confidence=inference_min_confidence,
                 ),
-                yolo_bbox(
-                    str(Path(inference_output_dir, "predictions")),
-                    inference_min_confidence,
-                    inference_nms_threshold,
-                ),
+                inference2mqtt(),
             ]
 
         if pretune:

gamutrf/grsource.py

@@ -78,5 +78,5 @@ def get_source(
         )
         if sdr == "SoapyAIRT":
             workaround_start_hook = airt_workaround_start_hook
-
+    sources[0].set_thread_priority(99)
     return sources, cmd_port, workaround_start_hook

tests/test_grscan.py

@@ -43,6 +43,9 @@ def set_time_now(self, time_spec, _mb):
     def get_time_now(self):
         return self.time_spec
 
+    def set_thread_priority(self, _priority):
+        return
+
 
 class FakeUHD:
     def __init__(self):
@@ -63,6 +66,9 @@ def set_sample_rate(self, _channel, _sample_rate):
     def set_bandwidth(self, _channel, _bw):
         return
 
+    def set_thread_priority(self, _priority):
+        return
+
 
 class FakeSoapy:
     def __init__(self):

torchserve-cuda.yml

@@ -10,7 +10,7 @@ services:
     ports:
       - '8080:8080'
     volumes:
-      - '${VOL_PREFIX}:/model_store'
+      - '${VOL_PREFIX}/model_store:/model_store'
     deploy:
       resources:
         reservations:

torchserve.yml

@@ -10,6 +10,6 @@ services:
     ports:
       - '8080:8080'
     volumes:
-      - '${VOL_PREFIX}:/model_store'
+      - '${VOL_PREFIX}/model_store:/model_store'
     command:
       - --models mini2_snr=mini2_snr.mar