标签: torch

class loss(Function):
    @staticmethod
    def forward(ctx,x,INPUT):

        batch_size = x.shape[0]
        X = x.detach().numpy()
        input = INPUT.detach().numpy()
        Loss = 0
        for i in range(batch_size):
            t_R_r = input[i,0:4]
            R_r = t_R_r[np.newaxis,:]
            t_R_i = input[i,4:8]
            R_i = t_R_i[np.newaxis,:]
            t_H_r = input[i,8:12]
            H_r = t_H_r[np.newaxis,:]
            t_H_i = input[i,12:16]
            H_i = t_H_i[np.newaxis,:]

            t_T_r = input[i, 16:32]
            T_r = t_T_r.reshape(4,4)
            t_T_i = input[i, 32:48]
            T_i = t_T_i.reshape(4,4)

            R = np.concatenate((R_r, R_i), axis=1)
            H = np.concatenate((H_r, H_i), axis=1)


            temp_t1 = np.concatenate((T_r,T_i),axis=1)
            temp_t2 = np.concatenate((-T_i,T_r),axis=1)
            T = np.concatenate((temp_t1,temp_t2),axis=0)
            phi_r = …

我有两个火炬张量。一种有形[64, 4, 300]，一种有形[64, 300]。如何连接这两个张量以获得 shape 的合成张量[64, 5, 300]。我知道tensor.cat用于此的函数，但为了使用该函数，我需要重塑第二个张量以匹配张量的维数。我听说不应该对张量进行整形，因为它可能会弄乱张量中的数据。我该如何进行这种连接？

我试过重塑，但接下来的部分让我对这种重塑更加怀疑。

a = torch.rand(64,300)

a1 = a.reshape(64,1,300)

list(a1[0]) == list(a)
Out[32]: False

我正在尝试编写一个函数来创建 CNN 模型。每当我运行脚本时，我都会收到以下错误：

lua:15: '[' 附近有意外的符号

require('torch')

require('nn')

function CeateNvidiaModel()

    --The Nvidia model
    --Input dimensions
    local n_channels = 3
    local height = 66
    local width = 200
    local nvidia_model = nn.Sequential();
    --nvida_model:add(nn.Normalize()
    --Convolutional Layers
    nvidia_model:add(nn.SpatialConvolution(n_channels, 24, 5, 5, [2], [2]))
    nvidia_model:add(nn.ELU(true))
    nvidia_model:add(nn.SpatialConvolution(24, 36, 5, 5, [2], [2]))
    nvidia_model:add(nn.ELU(true))
    nvidia_model:add(nn.SpatialConvolution(36, 48, 5, 5, [2], [2]))
    nvidia_model:add(nn.ELU(true))
    nvidia_model:add(nn.SpatialConvolution(48, 64, 3, 3))
    nvidia_model:add(nn.ELU(true))
    nvidia_model:add(nn.SpatialConvolution(64, 64, 3, 3))
    nvidia_model:add(nn.ELU(true))
    -- Flatten Layer
    nvidia_model:add(nn.Reshape(1164))
    -- FC Layers
    nvida_model:add(nn.Linear(1164, 100))
    nvidia_model:add(nn.ELU(true))
    nvida_model:add(nn.Linear(100, 50))
    nvidia_model:add(nn.ELU(true))
    nvida_model:add(nn.Linear(50, 10)) …

我正在尝试从我制作的库中创建一个测试可执行文件。我们将它们命名为 lib1 和 lib2。lib1 的构建和测试都很好。lib2 的构建也没有任何问题。但是，每当我尝试将 lib2 与其测试可执行文件（即使用 lib2 的示例程序）链接时，我都会收到以下错误：

usr/bin/ld: CMakeFiles/Lib2_Test.dir/Lib2_Test.cpp.o: in function `main':
Lib2_Test.cpp:(.text+0xf3): undefined reference to `Lib2::Lib2(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, int, int)'
/usr/bin/ld: Lib2_Test.cpp:(.text+0x3f5): undefined reference to `Lib2::Evaluate(bool&, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >&, float&, cv::Mat&, cv::Mat&, bool)'
collect2: error: ld returned 1 exit status
make[2]: *** [CMakeFiles/Lib2_Test.dir/build.make:130: Lib2_Test] Error 1
make[1]: *** [CMakeFiles/Makefile2:76: CMakeFiles/Lib2_Test.dir/all] Error 2
make: *** [Makefile:130: all] Error 2

readelf -d我尝试使用命令查看标题ldd，这两个库似乎都有所有必要的引用。但是 lib1 没有任何问题，而 lib2 在链接到使用它的可执行文件时会生成未引用的相关错误。

下面是我为它们制作的 cmakeList，后来我还包含了readelf …

[编辑以包含原始源代码]

我尝试运行在这里找到的代码：https://colab.research.google.com/drive/1roZqqhsdpCXZr8kgV_Bx_ABVBPgea3lX ?usp=sharing（链接自：https: //www.youtube.com/watch ?v=-lz30by8 -sU )

!pip install transformers diffusers lpips accelerate
from huggingface_hub import notebook_login
notebook_login()

import torch
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, UNet2DConditionModel, LMSDiscreteScheduler
from tqdm.auto import tqdm
from torch import autocast
from PIL import Image
from matplotlib import pyplot as plt
import numpy
from torchvision import transforms as tfms

# For video display:
from IPython.display import HTML
from base64 import b64encode

# Set device
torch_device = "cuda" if torch.cuda.is_available() else "cpu" …

错误消息如下：

RuntimeError                              Traceback (most recent call last)
<ipython-input-24-06e96beb03a5> in <module>()
     11 
     12 x_test = np.array(test_features)
---> 13 x_test_cuda = torch.tensor(x_test, dtype=torch.float).cuda()
     14 test = torch.utils.data.TensorDataset(x_test_cuda)
     15 test_loader = torch.utils.data.DataLoader(test, batch_size=batch_size, shuffle=False)

/usr/local/lib/python3.6/dist-packages/torch/cuda/__init__.py in _lazy_init()
    160 class CudaError(RuntimeError):
    161     def __init__(self, code):
--> 162         msg = cudart().cudaGetErrorString(code).decode('utf-8')
    163         super(CudaError, self).__init__('{0} ({1})'.format(msg, code))
    164 

RuntimeError: cuda runtime error (38) : no CUDA-capable device is detected at /pytorch/aten/src/THC/THCGeneral.cpp:51