model_urls = { 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', }啥意思
时间: 2024-03-29 20:41:07 浏览: 10
这是一个 Python 字典,其中包含了两个键值对。每个键表示一个预训练模型的名称,而对应的值则表示该模型的下载链接。具体来说,这个字典中包含了两个 ResNet 模型的下载链接,一个是 ResNet-50,一个是 ResNet-101。这些链接可以被用来下载对应的预训练模型参数,以便在 PyTorch 中使用。
相关问题
model_urls = { 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', }想将这个model下载到指定位置
你可以使用 Python 中的 `urllib` 或者 `requests` 库来下载文件。这里以 `requests` 为例:
```python
import requests
url = 'https://download.pytorch.org/models/resnet50-19c8e357.pth'
filename = 'resnet50.pth'
save_path = 'path/to/save' # 修改为你想要保存的路径
response = requests.get(url, stream=True)
with open(f'{save_path}/{filename}', 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
if chunk:
f.write(chunk)
```
其中,`url` 是下载链接,`filename` 是你想要保存的文件名,`save_path` 是你想要保存的目录,`stream=True` 表示以流的方式下载文件,可以避免一次性将整个文件下载到内存中导致内存不足。接下来,使用 `requests` 库请求数据流,然后逐个 chunk 写入到文件中。最终文件会保存在指定的目录下。
AttributeError: module 'torch.nn.modules' has no attribute 'resnet50'
这个错误通常是由于PyTorch版本问题引起的。在较旧的版本中,`resnet50`是在`torchvision.models`中定义的,而在较新的版本中,它被移动到了`torchvision.models.resnet`中。因此,如果您使用的是较旧的版本,请使用以下代码:
```python
import torchvision.models as models
resnet50 = models.resnet50(pretrained=True)
```
如果您使用的是较新的版本,请使用以下代码:
```python
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet50(pretrained=False, **kwargs):
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
state_dict = model_zoo.load_url(model_urls['resnet50'])
model.load_state_dict(state_dict)
return model
resnet50 = resnet50(pretrained=True)
```