autoconvert tensor rt engine from onnx to specific CUDA gpu

Azaion.Annotator/Controls/CircleVisual.cs

@@ -80,7 +80,7 @@ namespace Azaion.Annotator.Controls
             FontWeights.Bold,
             FontStretches.Normal);
 
-        FormattedText _fText;
+        FormattedText _fText = null!;
 
         private Brush _background = Brushes.Blue;
 
@@ -187,7 +187,7 @@ namespace Azaion.Annotator.Controls
             IsChanged = true;
         }
 
-        string _text;
+        string _text = null!;
 
         public string Text
         {
@@ -205,7 +205,7 @@ namespace Azaion.Annotator.Controls
             }
         }
 
-        Visual _child;
+        Visual _child = null!;
 
         public virtual Visual Child
         {

Azaion.Common/Constants.cs

@@ -33,7 +33,7 @@ public class Constants
         AnnotationsDbFile = DEFAULT_ANNOTATIONS_DB_FILE
     };
 
-    public static readonly List<DetectionClass> DefaultAnnotationClasses =
+    private static readonly List<DetectionClass> DefaultAnnotationClasses =
     [
         new() { Id = 0,  Name = "ArmorVehicle",       ShortName = "Броня",       Color = "#FF0000".ToColor() },
         new() { Id = 1,  Name = "Truck",              ShortName = "Вантаж.",     Color = "#00FF00".ToColor() },

Azaion.CommonSecurity/DTO/ApiCredentials.cs

@@ -10,7 +10,4 @@ public class ApiCredentials(string email, string password) : EventArgs
 
     [Key(nameof(Password))]
     public string Password { get; set; } = password;
-
-    [Key(nameof(Folder))]
-    public string Folder { get; set; } = null!;
 }

Azaion.CommonSecurity/Services/AuthProvider.cs

@@ -70,7 +70,7 @@ public class AzaionApi(HttpClient client, ICache cache, ApiCredentials credentia
         if (response.StatusCode == HttpStatusCode.Conflict)
         {
             var result = JsonConvert.DeserializeObject<BusinessExceptionDto>(content);
-            throw new Exception($"Failed: {response.StatusCode}! Error Code: {result.ErrorCode}. Message: {result.Message}");
+            throw new Exception($"Failed: {response.StatusCode}! Error Code: {result?.ErrorCode}. Message: {result?.Message}");
         }
         throw new Exception($"Failed: {response.StatusCode}! Result: {content}");
     }

Azaion.Inference/annotation.pxd

@@ -14,5 +14,3 @@ cdef class Annotation:
 
     cdef format_time(self, ms)
     cdef bytes serialize(self)
-    cdef to_str(self, class_names)
-

Azaion.Inference/annotation.pyx

@@ -32,12 +32,12 @@ cdef class Annotation:
             d.annotation_name = self.name
         self.image = b''
 
-    cdef to_str(self, class_names):
+    def __str__(self):
         if not self.detections:
             return f"{self.name}: No detections"
 
         detections_str = ", ".join(
-            f"{class_names[d.cls]} {d.confidence * 100:.1f}% ({d.x:.2f}, {d.y:.2f})"
+            f"class: {d.cls} {d.confidence * 100:.1f}% ({d.x:.2f}, {d.y:.2f})"
             for d in self.detections
         )
         return f"{self.name}: {detections_str}"

Azaion.Inference/api_client.pyx

@@ -79,7 +79,7 @@ cdef class ApiClient:
         try:
             r = requests.post(url, headers=headers, files=files, allow_redirects=True)
             r.raise_for_status()
-            print(f"Upload success: {r.status_code}")
+            print(f"Uploaded {filename} to {constants.API_URL}/{folder} successfully: {r.status_code}.")
         except Exception as e:
             print(f"Upload fail: {e}")
 

Azaion.Inference/inference.pxd

@@ -10,7 +10,6 @@ cdef class Inference:
     cdef object on_annotation
     cdef Annotation _previous_annotation
     cdef AIRecognitionConfig ai_config
-    cdef object class_names
     cdef bint stop_signal
 
     cdef str model_input

Azaion.Inference/inference.pyx

@@ -24,7 +24,6 @@ cdef class Inference:
         self.model_width = 0
         self.model_height = 0
         self.engine = None
-        self.class_names = None
         self.is_building_engine = False
 
     cdef build_tensor_engine(self):
@@ -37,9 +36,11 @@ cdef class Inference:
         models_dir = constants.MODELS_FOLDER
         if not os.path.exists(os.path.join(<str> models_dir, f'{engine_filename}.big')):
             self.is_building_engine = True
+            time.sleep(5) # prevent simultaneously loading dll and models
             onnx_model = self.api_client.load_big_small_resource(constants.AI_ONNX_MODEL_FILE, models_dir, key)
             model_bytes = TensorRTEngine.convert_from_onnx(onnx_model)
             self.api_client.upload_big_small_resource(model_bytes, <str> engine_filename, models_dir, key)
+            print('uploaded ')
             self.is_building_engine = False
 
 
@@ -62,7 +63,6 @@ cdef class Inference:
             self.engine = OnnxEngine(model_bytes)
 
         self.model_height, self.model_width = self.engine.get_input_shape()
-        self.class_names = self.engine.get_class_names()
 
     cdef preprocess(self, frames):
         blobs = [cv2.dnn.blobFromImage(frame,
@@ -75,13 +75,11 @@ cdef class Inference:
         return np.vstack(blobs)
 
     cdef postprocess(self, output, ai_config):
-        print('enter postprocess')
         cdef list[Detection] detections = []
         cdef int ann_index
         cdef float x1, y1, x2, y2, conf, cx, cy, w, h
         cdef int class_id
         cdef list[list[Detection]] results = []
-        print('start try: code')
         try:
             for ann_index in range(len(output[0])):
                 detections.clear()
@@ -163,7 +161,7 @@ cdef class Inference:
                 images.append(m)
         # images first, it's faster
         if len(images) > 0:
-            for chunk in self.split_list_extend(images, self.engine.get_input_shape()):
+            for chunk in self.split_list_extend(images, self.engine.get_batch_size()):
                 print(f'run inference on {" ".join(chunk)}...')
                 self._process_images(cmd, ai_config, chunk)
         if len(videos) > 0:
@@ -189,7 +187,7 @@ cdef class Inference:
                 batch_frames.append(frame)
                 batch_timestamps.append(int(v_input.get(cv2.CAP_PROP_POS_MSEC)))
 
-            if len(batch_frames) == self.engine.get_input_shape():
+            if len(batch_frames) == self.engine.get_batch_size():
                 input_blob = self.preprocess(batch_frames)
 
                 outputs = self.engine.run(input_blob)
@@ -203,10 +201,9 @@ cdef class Inference:
                         annotation.image = image.tobytes()
                         self._previous_annotation = annotation
 
-                        print(annotation.to_str(self.class_names))
+                        print(annotation)
                         self.on_annotation(cmd, annotation)
 
-
                 batch_frames.clear()
                 batch_timestamps.clear()
         v_input.release()
@@ -231,7 +228,6 @@ cdef class Inference:
             annotation = Annotation(image_paths[i], timestamps[i], detections)
             _, image = cv2.imencode('.jpg', frames[i])
             annotation.image = image.tobytes()
-            print(annotation.to_str(self.class_names))
             self.on_annotation(cmd, annotation)
 
 

Azaion.Inference/inference_engine.pxd

@@ -6,7 +6,6 @@ cdef class InferenceEngine:
     cdef public int batch_size
     cdef tuple get_input_shape(self)
     cdef int get_batch_size(self)
-    cdef get_class_names(self)
     cpdef run(self, input_data)
 
 cdef class OnnxEngine(InferenceEngine):
@@ -14,7 +13,6 @@ cdef class OnnxEngine(InferenceEngine):
     cdef list model_inputs
     cdef str input_name
     cdef object input_shape
-    cdef object class_names
 
 cdef class TensorRTEngine(InferenceEngine):
     cdef object stream
@@ -26,7 +24,6 @@ cdef class TensorRTEngine(InferenceEngine):
     cdef object input_shape
     cdef object output_shape
     cdef object h_output
-    cdef object class_names
 
     @staticmethod
     cdef bytes convert_from_onnx(bytes onnx_model)

Azaion.Inference/inference_engine.pyx

@@ -22,9 +22,6 @@ cdef class InferenceEngine:
     cpdef run(self, input_data):
         raise NotImplementedError("Subclass must implement run")
 
-    cdef get_class_names(self):
-        raise NotImplementedError("Subclass must implement get_class_names")
-
 
 cdef class OnnxEngine(InferenceEngine):
     def __init__(self, model_bytes: bytes, batch_size: int = 1, **kwargs):
@@ -39,7 +36,6 @@ cdef class OnnxEngine(InferenceEngine):
         print(f'AI detection model input: {self.model_inputs} {self.input_shape}')
         model_meta = self.session.get_modelmeta()
         print("Metadata:", model_meta.custom_metadata_map)
-        self.class_names = eval(model_meta.custom_metadata_map["names"])
 
     cdef tuple get_input_shape(self):
         shape = self.input_shape
@@ -48,9 +44,6 @@ cdef class OnnxEngine(InferenceEngine):
     cdef int get_batch_size(self):
         return self.batch_size
 
-    cdef get_class_names(self):
-        return self.class_names
-
     cpdef run(self, input_data):
         return self.session.run(None, {self.input_name: input_data})
 
@@ -69,10 +62,14 @@ cdef class TensorRTEngine(InferenceEngine):
                 raise RuntimeError(f"Failed to load TensorRT engine from bytes")
 
             self.context = engine.create_execution_context()
+
             # input
             self.input_name = engine.get_tensor_name(0)
             engine_input_shape = engine.get_tensor_shape(self.input_name)
-            self.batch_size = self.input_shape[0] if self.input_shape[0] != -1 else batch_size
+            if engine_input_shape[0] != -1:
+                self.batch_size = engine_input_shape[0]
+            else:
+                self.batch_size = batch_size
 
             self.input_shape = [
                 self.batch_size,
@@ -154,7 +151,7 @@ cdef class TensorRTEngine(InferenceEngine):
             if plan is None:
                 print('Conversion failed.')
                 return None
-
+            print('conversion done!')
             return bytes(plan)
 
     cdef tuple get_input_shape(self):
@@ -163,9 +160,6 @@ cdef class TensorRTEngine(InferenceEngine):
     cdef int get_batch_size(self):
         return self.batch_size
 
-    cdef get_class_names(self):
-        return self.class_names
-
     cpdef run(self, input_data):
         try:
             cuda.memcpy_htod_async(self.d_input, input_data, self.stream)

Azaion.Inference/remote_command_handler.pyx

@@ -76,7 +76,7 @@ cdef class RemoteCommandHandler:
         with self._context.socket(zmq.DEALER) as socket:
             socket.connect("inproc://backend")
             socket.send_multipart([client_id, data])
-            constants.log(<str>f'Sent {len(data)} bytes.', client_id)
+            # constants.log(<str>f'Sent {len(data)} bytes.', client_id)
 
     cdef stop(self):
         self._shutdown_event.set()

Azaion.Suite/App.xaml.cs

@@ -94,7 +94,7 @@ public partial class App
         _resourceLoader = new ResourceLoader(_inferenceClient);
         var login = new Login();
 
-        login.CredentialsEntered += async (_, credentials) =>
+        login.CredentialsEntered += (_, credentials) =>
         {
             _inferenceClient.Send(RemoteCommand.Create(CommandType.Login, credentials));
             _azaionApi = new AzaionApi(new HttpClient { BaseAddress = new Uri(SecurityConstants.API_URL) }, _cache, credentials, _hardwareService);

Azaion.Suite/MainSuite.xaml.cs

@@ -50,9 +50,9 @@ public partial class MainSuite
         Loaded += OnLoaded;
         Closed += OnFormClosed;
 
-        SizeChanged += async (_, _) => await SaveUserSettings();
+        SizeChanged += (_, _) => SaveUserSettings();
-        LocationChanged += async (_, _) => await SaveUserSettings();
+        LocationChanged += (_, _) => SaveUserSettings();
-        StateChanged += async (_, _) => await SaveUserSettings();
+        StateChanged += (_, _) => SaveUserSettings();
         Left = (SystemParameters.WorkArea.Width - Width) / 2;
     }
 
@@ -130,7 +130,7 @@ public partial class MainSuite
     }
 
 
-    private async Task SaveUserSettings()
+    private void SaveUserSettings()
     {
         ThrottleExt.Throttle(() =>
         {