resnet18如何加载本地权重

要加载本地权重，你需要使用PyTorch提供的torch.load()函数加载预训练权重文件。具体步骤如下：

1. 导入必要的库和模型

import torch
import torchvision.models as models

model = models.resnet18()

2. 加载本地权重文件

weight_path = '/path/to/weight/file.pth'
model.load_state_dict(torch.load(weight_path))

在上述代码中，'/path/to/weight/file.pth' 是你本地保存的权重文件的路径。请确保文件路径正确，并且权重文件与模型结构匹配。

3. 使用加载的权重进行预测

input_data = torch.randn(1, 3, 224, 224)
output = model(input_data)

现在，你已经成功加载了本地权重，并且可以使用模型进行预测。

相关问题

resnet50 pytorch 加载权重文件的pridict文件

以下是使用 PyTorch 加载 ResNet50 预训练权重文件并进行预测的示例代码：

import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# Load ResNet50 model
model = models.resnet50()
# Load pretrained weights
model.load_state_dict(torch.load('resnet50_weights.pth'))
# Set model to evaluation mode
model.eval()

# Load and preprocess image
image = Image.open('test_image.jpg')
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = transform(image)

# Make predictions
with torch.no_grad():
    output = model(image.unsqueeze(0))
    probabilities = torch.nn.functional.softmax(output, dim=1)[0]
    # Print top 5 predicted classes and their probabilities
    top5_prob, top5_classes = torch.topk(probabilities, k=5)
    for i in range(5):
        print(f"Class: {top5_classes[i]}, Probability: {top5_prob[i]}")

需要注意的是，这里的预训练权重文件 `resnet50_weights.pth` 需要先下载并保存到本地。可以从 PyTorch 官网下载地址中找到对应的权重文件，也可以从其他来源下载。

基于tensorflow的resnet18提取本地cifar10图像特征的python详细代码

以下是基于tensorflow的resnet18提取本地cifar10图像特征的Python详细代码：

import tensorflow as tf
from tensorflow.keras import layers, models, datasets

# 加载cifar10数据集
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# 数据预处理
train_images = train_images.astype('float32') / 255.0
test_images = test_images.astype('float32') / 255.0

# 定义ResNet18模型
def ResNet18():
    inputs = layers.Input(shape=(32, 32, 3))
    
    # 第一层卷积
    x = layers.Conv2D(64, (3, 3), padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    
    # 第一层残差块
    y = layers.Conv2D(64, (3, 3), padding='same')(x)
    y = layers.BatchNormalization()(y)
    y = layers.Activation('relu')(y)
    y = layers.Conv2D(64, (3, 3), padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.Add()([x, y])
    y = layers.Activation('relu')(y)
    
    # 第二层残差块
    x = layers.MaxPooling2D((2, 2))(y)
    x = layers.Conv2D(128, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    y = layers.Conv2D(128, (3, 3), padding='same')(x)
    y = layers.BatchNormalization()(y)
    y = layers.Activation('relu')(y)
    y = layers.Conv2D(128, (3, 3), padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.Add()([x, y])
    y = layers.Activation('relu')(y)
    
    # 第三层残差块
    x = layers.MaxPooling2D((2, 2))(y)
    x = layers.Conv2D(256, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    y = layers.Conv2D(256, (3, 3), padding='same')(x)
    y = layers.BatchNormalization()(y)
    y = layers.Activation('relu')(y)
    y = layers.Conv2D(256, (3, 3), padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.Add()([x, y])
    y = layers.Activation('relu')(y)
    
    # 第四层残差块
    x = layers.MaxPooling2D((2, 2))(y)
    x = layers.Conv2D(512, (3, 3), padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation('relu')(x)
    y = layers.Conv2D(512, (3, 3), padding='same')(x)
    y = layers.BatchNormalization()(y)
    y = layers.Activation('relu')(y)
    y = layers.Conv2D(512, (3, 3), padding='same')(y)
    y = layers.BatchNormalization()(y)
    y = layers.Add()([x, y])
    y = layers.Activation('relu')(y)
    
    # 全局池化层和最终分类层
    y = layers.GlobalAveragePooling2D()(y)
    outputs = layers.Dense(10, activation='softmax')(y)
    
    model = models.Model(inputs=inputs, outputs=outputs)
    return model

# 加载ResNet18模型
model = ResNet18()

# 加载已经训练好的权重
model.load_weights('resnet18_cifar10.h5')

# 提取特征
train_features = model.predict(train_images)
test_features = model.predict(test_images)

# 打印特征的形状
print('train_features shape:', train_features.shape)
print('test_features shape:', test_features.shape)

注：本代码中的`resnet18_cifar10.h5`是已经训练好的权重文件，可以在训练过程中保存下来，然后在测试或者提取特征时加载使用。如果没有权重文件，可以参考其他教程训练一个resnet18模型并保存权重。