gt730 安装cuda pytorch

根据引用和引用的内容可以得出结论，GT 730的CUDA Capability为3.5，而当前版本的Pytorch只支持CUDA Capability 3.7以上的显卡。因此，GT 730无法在当前版本的Pytorch上发挥其GPU计算能力。如果你想在GT 730上安装CUDA和Pytorch，你需要找到一个与该显卡兼容的旧版本Pytorch。根据引用的内容，你可以尝试安装Pytorch 1.2.0和对应的torchvision 0.4.0，并使用cudatoolkit=10.0来安装CUDA，以满足GT 730的要求。

相关问题

Failed to load PyTorch C extensions

引用\[1\]:在安装torch，并且把"D:\py Anaconda\Lib\site-packages"下的torch相关文件复制到“D:\py Anaconda\envs\pychome\Lib\site-packages”后，运行pychram时错误“ImportError: Failed to load PyTorch C extensions:”。（注意，本人安装Anaconda的文件夹叫做“py Anaconda”。） 出现该错误并不代表是pytorch安装错误或者pytorch与CUDA版本不匹配，出现该错误的原因是没有选对python编辑器。解决办法： 第一步：File->Setting 第二步：点击Python Interpreter 第三步：点击右上角小齿轮，然后点击add 第四步：在打开的界面中依次点击如下步骤。 第五步：选择正确的python编辑器。在Anaconda安装文件夹下选择“python.exe”，然后点击OK就成功了。 2.解决错误RuntimeError:CUDA error:unknown error 。 问题: Failed to load PyTorch C extensions

回答: 出现"ImportError: Failed to load PyTorch C extensions"错误并不代表是pytorch安装错误或者pytorch与CUDA版本不匹配，而是因为没有选对python编辑器。解决办法是在PyCharm中进行以下步骤：首先点击File->Setting，然后点击Python Interpreter，接着点击右上角的小齿轮，再点击add。在打开的界面中依次点击如下步骤，选择正确的python编辑器，即在Anaconda安装文件夹下选择“python.exe”，最后点击OK即可解决该问题。\[1\]如果还出现"RuntimeError:CUDA error:unknown error"错误，可能是由于其他原因引起的。
#### 引用[.reference_title]
- *1* [解决pytorch错误ImportError: Failed to load PyTorch C extensions:和RuntimeError:CUDA error:unknown ...](https://blog.csdn.net/aixuexideou/article/details/125693484)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insert_down1,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]

请用pytorch搭建一个Faster R-CNN网络

好的，以下是一个简单的 Faster R-CNN 网络的搭建过程：

首先，需要导入 PyTorch 和必要的库：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np

接下来，定义 Faster R-CNN 网络的主体部分，即特征提取器和分类/回归头部。这里使用了 ResNet50 作为特征提取器，以及两个全连接层作为分类和回归头部：

class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()

        # Feature extractor
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
        )

        # Region proposal network
        self.rpn = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 18, kernel_size=1, stride=1)
        )

        # Region of interest pooling
        self.roi_pool = nn.AdaptiveMaxPool2d((7, 7))

        # Fully connected layers for classification
        self.fc_cls = nn.Sequential(
            nn.Linear(7 * 7 * 512, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 21)
        )

        # Fully connected layers for regression
        self.fc_reg = nn.Sequential(
            nn.Linear(7 * 7 * 512, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 84)
        )

其中，特征提取器部分使用了经典的 ResNet50 网络结构；RPN 部分使用了几个卷积层和一个输出通道数为 18 的卷积层，用于生成区域提议；ROI Pooling 部分用于将不同大小的区域池化为固定大小的特征图；分类和回归头部分别使用了两个全连接层。

接下来，定义 RPN 网络的损失函数，包括分类和回归损失：

class RPNLoss(nn.Module):
    def __init__(self, num_anchors):
        super(RPNLoss, self).__init__()
        self.num_anchors = num_anchors
        self.cls_loss = nn.CrossEntropyLoss(reduction='sum')
        self.reg_loss = nn.SmoothL1Loss(reduction='sum')

    def forward(self, cls_score, bbox_pred, labels, bbox_targets):
        batch_size, _, height, width = cls_score.size()

        # Reshape for cross-entropy loss
        cls_score = cls_score.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 2)
        labels = labels.view(batch_size, -1)

        # Compute classification loss
        cls_mask = labels >= 0
        cls_score = cls_score[cls_mask]
        labels = labels[cls_mask]
        rpn_cls_loss = self.cls_loss(cls_score, labels.long())

        # Compute regression loss
        bbox_pred = bbox_pred.permute(0, 2, 3, 1).contiguous().view(batch_size, -1, 4)
        bbox_targets = bbox_targets.view(batch_size, -1, 4)
        bbox_mask = labels > 0
        bbox_pred = bbox_pred[bbox_mask]
        bbox_targets = bbox_targets[bbox_mask]
        rpn_reg_loss = self.reg_loss(bbox_pred, bbox_targets)

        # Normalize by number of anchors
        num_anchors = float(cls_mask.sum())
        rpn_cls_loss /= num_anchors
        rpn_reg_loss /= num_anchors

        return rpn_cls_loss, rpn_reg_loss

最后，定义 Faster R-CNN 网络的前向传播函数，包括对输入图像进行特征提取、生成区域提议、对区域进行分类和回归等过程：

class FasterRCNN(nn.Module):
    def __init__(self):
        super(FasterRCNN, self).__init__()

        # Feature extractor
        self.features = nn.Sequential(
            # ...
        )

        # Region proposal network
        self.rpn = nn.Sequential(
            # ...
        )

        # Region of interest pooling
        self.roi_pool = nn.AdaptiveMaxPool2d((7, 7))

        # Fully connected layers for classification
        self.fc_cls = nn.Sequential(
            # ...
        )

        # Fully connected layers for regression
        self.fc_reg = nn.Sequential(
            # ...
        )

        # RPN loss
        self.rpn_loss = RPNLoss(num_anchors=9)

    def forward(self, x, scale=1.0):
        # Feature extraction
        features = self.features(x)

        # Region proposal network
        rpn_logits = self.rpn(features)
        rpn_probs = F.softmax(rpn_logits, dim=1)[:, 1]
        rpn_bbox = self.rpn_bbox_pred(features).exp()
        anchors = generate_anchors(features.size(2), features.size(3))
        proposals = apply_deltas(anchors, rpn_bbox)
        proposals = clip_boxes(proposals, x.size(2), x.size(3))
        keep = filter_boxes(proposals, min_size=16*scale)
        proposals = proposals[keep, :]
        rpn_probs = rpn_probs[keep]
        rpn_bbox = rpn_bbox[keep, :]

        # Region of interest pooling
        rois = torch.cat([torch.zeros(proposals.size(0), 1), proposals], dim=1)
        rois = Variable(rois.cuda())
        pooled_features = self.roi_pool(features, rois)
        pooled_features = pooled_features.view(pooled_features.size(0), -1)

        # Classification
        cls_score = self.fc_cls(pooled_features)
        cls_prob = F.softmax(cls_score, dim=1)

        # Regression
        bbox_pred = self.fc_reg(pooled_features)

        return cls_prob, bbox_pred, proposals, rpn_probs, rpn_bbox

    def loss(self, cls_score, bbox_pred, proposals, rpn_probs, rpn_bbox, gt_boxes):
        # RPN loss
        rpn_labels, rpn_bbox_targets = anchor_targets(gt_boxes, proposals)
        rpn_cls_loss, rpn_reg_loss = self.rpn_loss(rpn_probs, rpn_bbox, rpn_labels, rpn_bbox_targets)

        # Fast R-CNN loss
        rois, cls_labels, bbox_targets = roi_targets(proposals, gt_boxes)
        cls_mask = cls_labels >= 0
        cls_score = cls_score[cls_mask]
        cls_labels = cls_labels[cls_mask]
        cls_loss = F.cross_entropy(cls_score, cls_labels)

        bbox_pred = bbox_pred[cls_mask]
        bbox_targets = bbox_targets[cls_mask]
        reg_loss = F.smooth_l1_loss(bbox_pred, bbox_targets)

        return cls_loss, reg_loss, rpn_cls_loss, rpn_reg_loss

其中，前向传播函数中的 `generate_anchors`、`apply_deltas`、`clip_boxes`、`filter_boxes`、`anchor_targets`、`roi_targets` 等函数用于生成锚框、应用回归偏移量、裁剪边界框、过滤过小的边界框、计算 RPN 损失和 Fast R-CNN 损失等。这些函数的具体实现可以参考论文或开源代码。