火炬几何误差
FileNotFoundError: Could not find module '...\.conda\envs\urop\Lib\site-packages\torch_sparse\_convert_cuda.pyd' Try using the full path with constructor syntax.
版本:
torch_geometric==2.0.4
pytorch 1.11.0 py3.8_cpu_0 pytorch
pytorch-cluster 1
我正在建立PyTorch从源头上的数据自动化系统遵循。
uname -a: Linux ares 5.8.0-59-generic #66~20.04.1-Ubuntu SMP Thu Jun 17 11:14:10 UTC 2021 x86_64 x86_64 x86_64 GNU/Linux
cuda version: 11.1
当我运行python setup.py install时,会得到以下错误:
/home/angus/pytorch/torch/csrc/jit/ir/ir.cpp: In member function ‘bool torch::jit::Node::hasSi
自从昨天我试图在Google上使用GPU运行Pytorch时,我收到了下面提供的错误。以前它运作得很好。我试着安装不同版本的Pytorch,但是我有不同的错误。
# Use PyTorch to check versions, CUDA version and cuDNN
import torch
print("PyTorch version: ")
print(torch.__version__)
print("CUDA Version: ")
print(torch.version.cuda)
print("cuDNN version is: &
我有一个包含行的python文件:
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
它会生成错误:
File "C:\gdrive\python\a.py", line 5, in <module>
import torch.nn.
我是PyTorch新手,错误地安装了CUDA10.2版的PyTorch。实际上,我的系统中没有CUDA。因此,当我编译我的程序时,我得到以下错误:
RuntimeError: Detected that PyTorch and torch_cluster were compiled with different CUDA versions. PyTorch has CUDA version 10.2 and torch_cluster has CUDA version 0.0. Please reinstall the torch_cluster that matches your PyTor
我正在尝试使用以下命令在python 3.8.5 Windows 10计算机上安装Pytorch: pip install torch===1.7.0 -f https://download.pytorch.org/whl/torch_stable.html 和 pip install torch===1.6.0 torchvision===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html 但我总是得到这样的错误: ERROR: Could not find a version that satisfies the req
我正试图将一个TensorFlow模型转换成Pytorch,但却陷入了这个错误。有谁可以帮我?
#getting weights and biases from tensorflow model
weights, biases = model.layers[0].get_weights()
#[1] is the dropout layer
weights2, biases2 = model.layers[2].get_weights()
#initializing pytorch
class TwoLayerNet(torch.nn.Module):
def __init__(self,
我正在遵循为Pytorch创建一个C++扩展。我的C++代码显示以下错误:
test.cpp:3:10: fatal error: torch/torch.h: No such file or directory
#include <torch/torch.h>
如何获取torch.h头文件?有没有pytorch-dev版本?
我试图在Mac上使用NO_CUDA=1 python setup.py install编译py手电筒,但我得到了以下错误:
In file included from /Users/ezyang/Dev/pytorch-tmp/torch/lib/tmp_install/include/THPP/Tensor.hpp:3:
/Users/ezyang/Dev/pytorch-tmp/torch/lib/tmp_install/include/THPP/Storage.hpp:6:10: fatal error:
'cstdint' file not found
#
我正在试着在我的窗户上安装pytorch。 首先,我从here获取命令conda install pytorch torchvision cpuonly -c pytorch (PyTorch Build:Stable(1.3);Your OS:Windows;Package:Conda;Language:Python3.6;CUDA:None), there are some problems described as followings:
**(python36) C:\Users\li_dan0109>conda install pytorch torchvision cpuo
我转到PyTorch网站并选择以下选项
PyTorch构建:稳定(1.2)
您的操作系统: Windows
包装: pip
语言:Python3.7
库达:没有
(所有这些都是正确的)
而不是显示要运行的命令。
pip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
我已经试着把不同的选择混为一谈,但没有一种方案奏效。
错误:ERROR: Could not find a version that satisfies the requi
我想将我的pyTorch模型转换为ONNX。但是,我说错了
RuntimeError:提供的输入名称数(9)超过了输入数(7),但是,如果我从模型中取出两个Dropout层,我的代码运行得很完美。
为什么会这样呢?
这是我的代码:
# Define the model
model = torch.nn.Sequential(
torch.nn.Linear(D_in, H),
torch.nn.ReLU(),
torch.nn.Dropout(0.2), # problem with dropout layer
torch.nn.Linear(H, H
我已经使用HPC集群上的虚拟环境安装了Pytorch 1.8.1+cu102。
torch.cuda.is_available()
给我下面的输出
UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 10010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index
您好,我在尝试安装pytorch时遇到错误: PS C:\windows\system32> pip install torch===1.7.0+cu110 torchvision===0.8.1+cu110 torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
Looking in links: https://download.pytorch.org/whl/torch_stable.html
ERROR: Could not find a version that satisfies th
我一直在尝试使用预训练模型。使用collab模板中默认的所有内容,使用从huggingface/pytorch-transformers到bert-base-uncased的torch.hub.load()作为“模型”
代码示例
import torch
model = torch.hub.load('huggingface/pytorch-transformers', 'model', 'bert-base-uncased') # Download model and configuration from S3 and cache.
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