I am on Windows 10 trying to run YOLONAS prediction on Google Colab. I'm using the following tutorial:https://github.com/AarohiSingla/YOLO-NAS specifically the YOLONAS_Custom_dataset.ipynb file in Colab.
When I run prediction with the best model, I get the following error: RuntimeError: Input type (unsigned char) and bias type (c10::Half) should be the same
Entire error:
RuntimeError Traceback (most recent call last)
<ipython-input-14-3169f5cc4e6d> in <cell line: 11>()
9
10 test_image = 'test.JPG'
---> 11 best_model.predict(test_image).show()
16 frames
/usr/local/lib/python3.10/dist-packages/super_gradients/training/models/detection_models/customizable_detector.py in predict(self, images, iou, conf, fuse_model)
175 """
176 pipeline = self._get_pipeline(iou=iou, conf=conf, fuse_model=fuse_model)
--> 177 return pipeline(images) # type: ignore
178
179 def predict_webcam(self, iou: Optional[float] = None, conf: Optional[float] = None, fuse_model: bool = True):
/usr/local/lib/python3.10/dist-packages/super_gradients/training/pipelines/pipelines.py in __call__(self, inputs, batch_size)
94 return self.predict_video(inputs, batch_size)
95 elif check_image_typing(inputs):
---> 96 return self.predict_images(inputs, batch_size)
97 else:
98 raise ValueError(f"Input {inputs} not supported for prediction.")
/usr/local/lib/python3.10/dist-packages/super_gradients/training/pipelines/pipelines.py in predict_images(self, images, batch_size)
109 images = load_images(images)
110 result_generator = self._generate_prediction_result(images=images, batch_size=batch_size)
--> 111 return self._combine_image_prediction_to_images(result_generator, n_images=len(images))
112
113 def predict_video(self, video_path: str, batch_size: Optional[int] = 32) -> VideoPredictions:
/usr/local/lib/python3.10/dist-packages/super_gradients/training/pipelines/pipelines.py in _combine_image_prediction_to_images(self, images_predictions, n_images)
288 if n_images is not None and n_images == 1:
289 # Do not show tqdm progress bar if there is only one image
--> 290 images_predictions = [next(iter(images_predictions))]
291 else:
292 images_predictions = [image_predictions for image_predictions in tqdm(images_predictions, total=n_images, desc="Predicting Images")]
/usr/local/lib/python3.10/dist-packages/super_gradients/training/pipelines/pipelines.py in _generate_prediction_result(self, images, batch_size)
147 else:
148 for batch_images in generate_batch(images, batch_size):
--> 149 yield from self._generate_prediction_result_single_batch(batch_images)
150
151 def _generate_prediction_result_single_batch(self, images: Iterable[np.ndarray]) -> Iterable[ImagePrediction]:
/usr/local/lib/python3.10/dist-packages/super_gradients/training/pipelines/pipelines.py in _generate_prediction_result_single_batch(self, images)
174 if self.fuse_model:
175 self._fuse_model(torch_inputs)
--> 176 model_output = self.model(torch_inputs)
177 predictions = self._decode_model_output(model_output, model_input=torch_inputs)
178
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.10/dist-packages/super_gradients/training/models/detection_models/customizable_detector.py in forward(self, x)
85
86 def forward(self, x):
---> 87 x = self.backbone(x)
88 x = self.neck(x)
89 return self.heads(x)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.10/dist-packages/super_gradients/modules/detection_modules.py in forward(self, x)
78 all_layers = ["stem"] + [f"stage{i}" for i in range(1, self.num_stages + 1)] + ["context_module"]
79 for layer in all_layers:
---> 80 x = getattr(self, layer)(x)
81 if layer in self.out_layers:
82 outputs.append(x)
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.10/dist-packages/super_gradients/training/models/detection_models/yolo_nas/yolo_stages.py in forward(self, x)
136
137 def forward(self, x: Tensor) -> Tensor:
--> 138 return self.conv(x)
139
140
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.10/dist-packages/super_gradients/modules/qarepvgg_block.py in forward(self, inputs)
177 def forward(self, inputs):
178 if self.fully_fused:
--> 179 return self.se(self.nonlinearity(self.rbr_reparam(inputs)))
180
181 if self.partially_fused:
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py in forward(self, input)
461
462 def forward(self, input: Tensor) -> Tensor:
--> 463 return self._conv_forward(input, self.weight, self.bias)
464
465 class Conv3d(_ConvNd):
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
457 weight, bias, self.stride,
458 _pair(0), self.dilation, self.groups)
--> 459 return F.conv2d(input, weight, bias, self.stride,
460 self.padding, self.dilation, self.groups)
461
RuntimeError: Input type (unsigned char) and bias type (c10::Half) should be the same
I am on Windows 10 trying to run YOLONAS prediction on Google Colab. I'm using the following tutorial:https://github.com/AarohiSingla/YOLO-NAS specifically the YOLONAS_Custom_dataset.ipynb file in Colab.
When I run prediction with the best model, I get the following error:
RuntimeError: Input type (unsigned char) and bias type (c10::Half) should be the sameEntire error:
Any advice on how to solve this?