RuntimeError Traceback (most recent call last)
Cell In[19], line 2
1 # Train the model on the COCO8 example dataset for 100 epochs
----> 2 results = model.train(data="C:\\Users\\asus\\Downloads\\coco8.yaml", epochs=100, imgsz=640)
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\model.py:799, in Model.train(self, trainer, **kwargs)
796 self.model = self.trainer.model
798 self.trainer.hub_session = self.session # attach optional HUB session
--> 799 self.trainer.train()
800 # Update model and cfg after training
801 if RANK in {-1, 0}:
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\trainer.py:227, in BaseTrainer.train(self)
224 ddp_cleanup(self, str(file))
226 else:
--> 227 self._do_train(world_size)
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\trainer.py:348, in BaseTrainer._do_train(self, world_size)
346 if world_size > 1:
347 self._setup_ddp(world_size)
--> 348 self._setup_train(world_size)
350 nb = len(self.train_loader) # number of batches
351 nw = max(round(self.args.warmup_epochs * nb), 100) if self.args.warmup_epochs > 0 else -1 # warmup iterations
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\trainer.py:285, in BaseTrainer._setup_train(self, world_size)
283 if self.amp and RANK in {-1, 0}: # Single-GPU and DDP
284 callbacks_backup = callbacks.default_callbacks.copy() # backup callbacks as check_amp() resets them
--> 285 self.amp = torch.tensor(check_amp(self.model), device=self.device)
286 callbacks.default_callbacks = callbacks_backup # restore callbacks
287 if RANK > -1 and world_size > 1: # DDP
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\utils\checks.py:782, in check_amp(model)
779 try:
780 from ultralytics import YOLO
--> 782 assert amp_allclose(YOLO("yolo11n.pt"), im)
783 LOGGER.info(f"{prefix}checks passed ✅")
784 except ConnectionError:
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\utils\checks.py:770, in check_amp.<locals>.amp_allclose(m, im)
768 batch = [im] * 8
769 imgsz = max(256, int(model.stride.max() * 4)) # max stride P5-32 and P6-64
--> 770 a = m(batch, imgsz=imgsz, device=device, verbose=False)[0].boxes.data # FP32 inference
771 with autocast(enabled=True):
772 b = m(batch, imgsz=imgsz, device=device, verbose=False)[0].boxes.data # AMP inference
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\model.py:185, in Model.__call__(self, source, stream, **kwargs)
156 def __call__(
157 self,
158 source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
159 stream: bool = False,
160 **kwargs: Any,
161 ) -> list:
162 """
163 Alias for the predict method, enabling the model instance to be callable for predictions.
164
(...)
183 ... print(f"Detected {len(r)} objects in image")
184 """
--> 185 return self.predict(source, stream, **kwargs)
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\model.py:555, in Model.predict(self, source, stream, predictor, **kwargs)
553 if prompts and hasattr(self.predictor, "set_prompts"): # for SAM-type models
554 self.predictor.set_prompts(prompts)
--> 555 return self.predictor.predict_cli(source=source) if is_cli else self.predictor(source=source, stream=stream)
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\predictor.py:227, in BasePredictor.__call__(self, source, model, stream, *args, **kwargs)
225 return self.stream_inference(source, model, *args, **kwargs)
226 else:
--> 227 return list(self.stream_inference(source, model, *args, **kwargs))
File D:\anaconda\envs\pytorch_env\lib\site-packages\torch\autograd\grad_mode.py:43, in _DecoratorContextManager._wrap_generator.<locals>.generator_context(*args, **kwargs)
40 try:
41 # Issuing `None` to a generator fires it up
42 with self.clone():
---> 43 response = gen.send(None)
45 while True:
46 try:
47 # Forward the response to our caller and get its next request
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\predictor.py:326, in BasePredictor.stream_inference(self, source, model, *args, **kwargs)
324 # Preprocess
325 with profilers[0]:
--> 326 im = self.preprocess(im0s)
328 # Inference
329 with profilers[1]:
File D:\anaconda\envs\pytorch_env\lib\site-packages\ultralytics\engine\predictor.py:167, in BasePredictor.preprocess(self, im)
165 im = im.transpose((0, 3, 1, 2)) # BHWC to BCHW, (n, 3, h, w)
166 im = np.ascontiguousarray(im) # contiguous
--> 167 im = torch.from_numpy(im)
169 im = im.to(self.device)
170 im = im.half() if self.model.fp16 else im.float() # uint8 to fp16/32
RuntimeError: Numpy is not available
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