YoloV8 single channel train + Onnx trans

yolov8目前不支持单通道图片训练，需要修改后才能支持。本文将介绍如何修改yolov8代码，来训练单通道图的yolov8模型，以及使用onnx进行模型转换的简单实践。

1、修改代码


git diff ultralytics/utils/checks.py
diff --git a/ultralytics/utils/checks.py b/ultralytics/utils/checks.py
index 1ab031ba..c8fadcdc 100644
--- a/ultralytics/utils/checks.py
+++ b/ultralytics/utils/checks.py
@@ -648,7 +648,7 @@ def check_amp(model):try:from ultralytics import YOLO-        assert amp_allclose(YOLO("yolov8n.pt"), im)
+        # assert amp_allclose(YOLO("yolov8n.pt"), im)LOGGER.info(f"{prefix}checks passed ✅")except ConnectionError:LOGGER.warning(f"{prefix}checks skipped ⚠️, offline and unable to download YOLOv8n. {warning_msg}")diff --git a/ultralytics/nn/tasks.py b/ultralytics/nn/tasks.py
index 64ee7f50..c8f37677 100644
--- a/ultralytics/nn/tasks.py
+++ b/ultralytics/nn/tasks.py
@@ -263,6 +264,8 @@ class BaseModel(nn.Module):if not hasattr(self, "criterion"):self.criterion = self.init_criterion()+    # import ipdb;ipdb.set_trace()
+    batch['img'] = batch['img'][:,:1,:,:]preds = self.forward(batch["img"]) if preds is None else predsreturn self.criterion(preds, batch)diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 4d8c69c5..98469690 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -502,7 +502,7 @@ class AutoBackend(nn.Module):"""return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x-    def warmup(self, imgsz=(1, 3, 640, 640)):
+    def warmup(self, imgsz=(1, 1, 640, 640)):"""Warm up the model by running one forward pass with a dummy input.diff --git a/ultralytics/engine/validator.py b/ultralytics/engine/validator.py
index 41be54c1..82ed2f03 100644
--- a/ultralytics/engine/validator.py
+++ b/ultralytics/engine/validator.py
@@ -154,7 +154,8 @@ class BaseValidator:self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch)model.eval()
-        model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz))  # warmup
+        model.warmup(imgsz=(1 if pt else self.args.batch, 1, imgsz, imgsz))  # warmup gray image
+        # model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz))  # warmupself.run_callbacks("on_val_start")dt = (
@@ -175,6 +176,7 @@ class BaseValidator:# Inferencewith dt[1]:
+            batch["img"] = batch["img"][:,:1,:,:]preds = model(batch["img"], augment=augment)# Lossdiff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py
index e925902f..32745d6f 100644
--- a/ultralytics/engine/predictor.py
+++ b/ultralytics/engine/predictor.py
@@ -261,7 +261,8 @@ class BasePredictor:# Warmup modelif not self.done_warmup:
-        self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
+        self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 1, *self.imgsz))
+        # self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else the dataset.bs, 3, *self.imgsz))        self.done_warmup = Trueself.seen, self.windows, self.batch = 0, [], None