Args: tensor: an n-dimensional `torch.Tensor` gain: an optional scaling factor Examples: >>> w = torch.empty(3, 5) >>> nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain('relu')) """ fan_in, fan_out = nn.init._calculate_fan_in_and_fan_out(tensor[0, :, :]) std =...
Uncaught exception Traceback (most recent call last): File "/mnt/data/buduanban_detection/meteo.py", line 103, in <module> datamodule = ObjectDetectionData.from_coco( File "/data/miniconda3/envs/torch/lib/python3.9/site-packages/flash/image/detection/data.py", line 386, in ...
The error message suggests a possible connection to the graphics card itself or its drivers, which may explain the sudden disappearance of the Nvidia control center icon from the startup icons. I am encountering a TypeError with my augmentation functions and custom segmentation dataset class when ca...
The current torch.load() interprets data as native-endian if a given pickle file has no byteorder record. This behavior causes practical issues on big-endian machines when we load parameters for many DNN models saved on little-endian machines (e.g. x86_64). As a result, tensors would h...
# middle...mxnet_key += 'fc6_' mxnet_key += k[1] else: assert False, 'Unexpected...token' if debug: print(mxnet_key, '=> ', state_key, end=' ') mxnet_array...mxnet_aux[mxnet_key] if aux else mxnet_weights[mxnet_key] torch_tensor = torch.from_numpy(mxnet_arr...
type='RotatedRTMDetSepBNHead', use_hbbox_loss=False, with_objectness=False), data_preprocessor=dict( batch_augments=None, bgr_to_rgb=False, boxtype2tensor=False, mean=[ 103.53, 116.28, 123.675, ], std=[ 57.375, 57.12, 58.395,
device="cuda:0"iftorch.cuda.is_available()else"cpu"class_names=["cmn","deu","rus","fra","eng","jpn","spa","ita","kor","vie","nld","epo","por","tur","heb","hun","ell","ind","ara","arz","fin","bul","yue","swe","ukr","bel","que","ces","swh","nno","wuu...
(args)):iftorch.is_tensor(args[i]):graph_record.args[i].copy_(args[i])forkinkwargs:iftorch.is_tensor(kwargs[k]):graph_record.kwargs[k].copy_(kwargs[k])graph_record.graph.replay()returngraph_record.outputdefextract_batch_size(self,*args,**kwargs)->int:raiseNotImplementedErrordef_...
mask vit: your config + my pth:RuntimeError: Expected all tensors to be on the same device... My env: mmseg: 0.24.1 MMDeploy: 0.6.0 MMCV: 1.6.0 PyTorch: 1.12.0a0+2c916ef.nv22.3 TorchVision: 0.13.0 torch.cuda.is_available(): True ...
save_safetensors=False, save_steps=1000, save_strategy=steps, save_total_limit=None, seed=42, sharded_ddp=[], skip_memory_metrics=True, sortish_sampler=False, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, ...