`ultralytics 8.0.200` move `on_val_start` callback for training (#5790)

Co-authored-by: Myyura <zz940521@gmail.com>

.github/workflows/ci.yaml

@@ -247,7 +247,7 @@ jobs:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
   Conda:
-    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true')
+    if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule_disabled' || github.event.inputs.conda == 'true')
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false

examples/YOLOv8-ONNXRuntime-CPP/inference.cpp

@@ -175,10 +175,18 @@ char *DCSP_CORE::TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std
             std::vector<int> class_ids;
             std::vector<float> confidences;
             std::vector<cv::Rect> boxes;
-            cv::Mat rowData(signalResultNum, strideNum, CV_32F, output);
-            rowData = rowData.t();
 
-            float *data = (float *) rowData.data;
+            cv::Mat rawData;
+            if (modelType == 1) {
+                // FP32
+                rawData = cv::Mat(signalResultNum, strideNum, CV_32F, output);
+            } else {
+                // FP16
+                rawData = cv::Mat(signalResultNum, strideNum, CV_16F, output);
+                rawData.convertTo(rawData, CV_32F);
+            }
+            rawData = rawData.t();
+            float *data = (float *) rawData.data;
 
             float x_factor = iImg.cols / 640.;
             float y_factor = iImg.rows / 640.;

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.199'
+__version__ = '8.0.200'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/validator.py

@@ -119,7 +119,6 @@ class BaseValidator:
             model.eval()
         else:
             callbacks.add_integration_callbacks(self)
-            self.run_callbacks('on_val_start')
             model = AutoBackend(model or self.args.model,
                                 device=select_device(self.args.device, self.args.batch),
                                 dnn=self.args.dnn,
@@ -152,6 +151,7 @@ class BaseValidator:
             model.eval()
             model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz))  # warmup
 
+        self.run_callbacks('on_val_start')
         dt = Profile(), Profile(), Profile(), Profile()
         bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
         self.init_metrics(de_parallel(model))