if pre_trained_weights is not None:
时间: 2024-01-06 19:06:51 浏览: 27
这段代码是一个条件语句,用于判断是否有预训练的权重。如果pre_trained_weights不为None,那么就会执行if语句块中的代码,否则就会跳过if语句块中的代码,继续执行后面的代码。
在这里,if语句的目的是检查是否有预先训练的权重可用,如果有,那么就可以加载这些权重并将它们用作模型的初始权重。这对于在现有模型的基础上进行微调非常有用,因为它可以帮助模型更快地收敛并提高性能。
相关问题
尝试将 SCRIPT vgg16 作为函数执行:
好的,可以使用以下代码将 SCRIPT vgg16 作为函数执行:
```python
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
model_urls = {
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
}
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=True):
super(VGG, self).__init__()
self.features = features
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
if init_weights:
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def vgg16(pretrained=False, **kwargs):
"""VGG 16-layer model (configuration "D")
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
if pretrained:
kwargs['init_weights'] = False
model = VGG(make_layers(cfg['D']), **kwargs)
model.load_state_dict(model_zoo.load_url(model_urls['vgg16']))
else:
model = VGG(make_layers(cfg['D']), **kwargs)
return model
cfg = {
'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
```
现在,当你需要使用 VGG16 模型的时候,可以直接调用 `vgg16()` 函数,如下所示:
```python
model = vgg16(pretrained=True)
```
Segmentation Models: using `keras` framework.
Segmentation Models is a Python library for image segmentation using various deep learning frameworks including Keras. It provides implementations of various popular segmentation models such as UNet, SegNet, PSPNet, and FPN along with pre-trained weights and evaluation metrics. The library also allows users to easily train their own models on custom datasets.
To use Segmentation Models with Keras, you first need to install the library using pip:
```
pip install segmentation-models
```
Then, you can import the desired segmentation model from the `segmentation_models` module and use it like any other Keras model. For example, to create a UNet model with a ResNet34 backbone:
```python
from segmentation_models import Unet
from segmentation_models.backbones import get_preprocessing
# Load and preprocess input data
X_train, y_train = ...
X_train = get_preprocessing('resnet34')(X_train)
# Create UNet model
model = Unet('resnet34', input_shape=(None, None, 3), classes=1, activation='sigmoid')
# Compile and train model
model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(X_train, y_train, ...)
```
Note that the `get_preprocessing` function is used to apply the appropriate preprocessing for the chosen backbone model. In this example, we use the `resnet34` backbone, but other options such as `vgg16`, `efficientnetb3`, and `mobilenetv2` are also available.