我想用我的 Mac 在自定义数据集上训练 Yolov8 模型，这是我第一次从事深度学习。不幸的是，我遇到了一个错误，
RuntimeError: "upsample_nearest2d_channels_last" not implemented for 'Half'当人们使用稳定扩散时，我在网上阅读了一些类似的问题和解决方案，我相信我需要将浮点从 16 更改为 32。我知道我可以在验证过程中通过输入参数 half=False 禁用半精度（FP16），但是训练过程中没有。如果有人可以提供帮助，我将非常感激。
我的代码：
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
results = model.train(data='MY_PATH/data.yaml', epochs=20, imgsz=640, device='mps')
错误：
New https://pypi.org/project/ultralytics/8.1.15 available 😃 Update with 'pip install -U ultralytics'
Ultralytics YOLOv8.1.11 🚀 Python-3.12.0 torch-2.2.0 MPS (Apple M2)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=/Users/victor/Desktop/object_recognition/shoe-detection-1/data.yaml, epochs=20, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=mps, workers=8, project=None, name=train23, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train23
Overriding model.yaml nc=80 with nc=4
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 752092 ultralytics.nn.modules.head.Detect [4, [64, 128, 256]]
Model summary: 225 layers, 3011628 parameters, 3011612 gradients
Transferred 319/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
train: Scanning /Users/victor/Desktop/object_recognition/shoe-detection-1/train/labels.cache... 29 images, 1 backgrounds, 0 corrupt: 100%|███
val: Scanning /Users/victor/Desktop/object_recognition/shoe-detection-1/valid/labels.cache... 8 images, 0 backgrounds, 0 corrupt: 100%|██████
Plotting labels to runs/detect/train23/labels.jpg...
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: AdamW(lr=0.00125, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs/detect/train23
Starting training for 20 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0%| | 0/2 [00:00<?, ?it/s]/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/functional.py:4001: UserWarning: MPS: passing scale factor to upsample ops is supported natively starting from macOS 13.0. Falling back on CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/UpSample.mm:246.)
return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/functional.py:905: UserWarning: MPS: _unique2 op is supported natively starting from macOS 13.0. Falling back on CPU. This may have performace implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/Unique.mm:323.)
output, inverse_indices, counts = torch._unique2(
/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/utils/loss.py:183: UserWarning: MPS: nonzero op is supported natively starting from macOS 13.0. Falling back on CPU. This may have performance implications. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/mps/operations/Indexing.mm:283.)
out[j, :n] = targets[matches, 1:]
1/20 0G 1.435 3.329 1.606 62 640: 100%|██████████| 2/2 [00:03<00:00, 1.73s/it]
Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 0/1 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/Users/victor/Desktop/object recognition/training.py", line 7, in <module>
results = model.train(data='/Users/victor/Desktop/object_recognition/shoe-detection-1/data.yaml', epochs=20, imgsz=640, device='mps')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/engine/model.py", line 601, in train
self.trainer.train()
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 208, in train
self._do_train(world_size)
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 424, in _do_train
self.metrics, self.fitness = self.validate()
^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/engine/trainer.py", line 543, in validate
metrics = self.validator(self)
^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/engine/validator.py", line 176, in __call__
preds = model(batch["img"], augment=augment)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 80, in forward
return self.predict(x, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 98, in predict
return self._predict_once(x, profile, visualize, embed)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/ultralytics/nn/tasks.py", line 119, in _predict_once
x = m(x) # run
^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/upsampling.py", line 157, in forward
return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/functional.py", line 4001, in interpolate
return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: "upsample_nearest2d_channels_last" not implemented for 'Half'
我的 MAC M1 也有同样的问题，只需将我的 mac 版本从 13 升级到当前最新版本（14.3.1）即可解决问题，尝试将您的 mac 版本升级到最新版本。