详细解释以下代码device = torch.device("cuda:"+str(gpu_id) if torch.cuda.is_available() else "cpu")
时间: 2023-06-18 18:02:16 浏览: 76
这行代码的主要作用是为了指定模型的运行设备,其中包含以下几个步骤:
1. 判断当前计算机是否支持GPU,如果支持,则将设备指定为cuda,否则指定为cpu。
2. 如果当前计算机支持GPU,则通过str(gpu_id)的方式获取GPU的ID。其中,gpu_id是一个整数,表示第几块GPU。
3. 最后,将设备信息存储在torch.device对象中,以便后续使用。
举个例子,如果当前计算机支持GPU,且有两块GPU,那么可以通过以下代码将模型指定在第一块GPU上运行:
```
gpu_id = 0
device = torch.device("cuda:"+str(gpu_id) if torch.cuda.is_available() else "cpu")
```
如果当前计算机不支持GPU,则将设备信息指定为cpu:
```
device = torch.device("cpu")
```
这样可以保证模型在不同的设备上都能够正常运行。
相关问题
为以下每句代码做注释:import torch from model import resnet152 from PIL import Image from torchvision import transforms import matplotlib.pyplot as plt import json device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") data_transform = transforms.Compose( [transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) img = Image.open("./huanglongbing.JPG") plt.imshow(img) img = data_transform(img) img = torch.unsqueeze(img, dim=0) try: json_file = open('./class_indices.json', 'r') class_indict = json.load(json_file) except Exception as e: print(e) exit(-1) model = resnet152(num_classes=38) model_weight_path = "./resNet152.pth" model.load_state_dict(torch.load(model_weight_path, map_location=device)) model.eval() with torch.no_grad(): output = torch.squeeze(model(img)) predict = torch.softmax(output, dim=0) predict_cla = torch.argmax(predict).numpy() print(class_indict[str(predict_cla)], predict[predict_cla].numpy()) plt.show()
# 导入所需的库
import torch
from model import resnet152
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json
# 判断是否有GPU可用,若有则使用GPU,否则使用CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 定义数据预处理的步骤,包括图片的resize、中心裁剪、转换为张量、以及标准化
data_transform = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
# 打开图片并显示
img = Image.open("./huanglongbing.JPG")
plt.imshow(img)
# 对图片进行预处理,并增加一维作为batch_size
img = data_transform(img)
img = torch.unsqueeze(img, dim=0)
# 读取class_indices.json文件,获取类别标签
try:
json_file = open('./class_indices.json', 'r')
class_indict = json.load(json_file)
except Exception as e:
print(e)
exit(-1)
# 加载预训练好的模型,以及其对应的权重文件
model = resnet152(num_classes=38)
model_weight_path = "./resNet152.pth"
model.load_state_dict(torch.load(model_weight_path, map_location=device))
model.eval()
# 在不进行梯度计算的情况下,使用模型进行预测
with torch.no_grad():
output = torch.squeeze(model(img))
predict = torch.softmax(output, dim=0) # 对输出进行softmax处理
predict_cla = torch.argmax(predict).numpy() # 获取预测的类别
# 输出预测结果以及对应的概率,并显示图片
print(class_indict[str(predict_cla)], predict[predict_cla].numpy())
plt.show()
dataset = 'image/' sampler_loader = torch.utils.data.DataLoader( datasets.ImageFolder(dataset, transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]) ]))) unnormalize = NormalizeInverse(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = vgg11_bn(pretrained = True) model = model.to(device) fullgrad = FullGrad(model) simple_fullgrad = FullGradSimple(model) save_path = 'result' if os.path.isdir(save_path): os.mkdir(save_path) def compute_saliency_and_save(): for idx, (data, target) in enumerate(sampler_loader): data, target = data.to(device).requires_grad_(), target.to(device) # compute the saliency maps for the input data cam = fullgrad.fullgrad(data) cam_simple = simple_fullgrad.fullgrad(data) for i in range(data.size(0)): filename = save_path + str((idx + 1) * (i+1)) filename_simple = filename + "_simple" image = unnormalize(data[i,:,:,:].cpu()) save_saliency_map(image, cam[i,:,:,:], filename + ".jpg") save_saliency_map(image, cam_simple[i,:,:,:], filename_simple + ".jpg") if __name__ == "__main__": compute_saliency_and_save() 详细解释程序
这段程序是一个计算图像数据的显著性图并保存的过程。下面是对程序的详细解释:
1. 导入必要的库:
```python
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from models import vgg11_bn
from fullgrad import FullGrad, FullGradSimple
from utils import NormalizeInverse, save_saliency_map
import os
```
2. 定义数据集和数据加载器:
```python
dataset = 'image/'
sampler_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(dataset, transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
])))
```
这里使用了`datasets.ImageFolder`来加载数据集,`transform`参数指定了对图像进行的预处理操作,包括将图像大小调整为224x224,转换为张量和归一化。
3. 定义反归一化操作:
```python
unnormalize = NormalizeInverse(mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225])
```
`NormalizeInverse`是一个自定义的类,用于将经过归一化处理的张量反转回原始图像。
4. 检查设备是否支持CUDA并定义模型:
```python
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = vgg11_bn(pretrained = True)
model = model.to(device)
```
这里使用`torch.cuda.is_available()`来检查设备是否支持CUDA,并将模型移动到相应的设备(GPU或CPU)。
5. 定义FullGrad和Simple FullGrad对象:
```python
fullgrad = FullGrad(model)
simple_fullgrad = FullGradSimple(model)
```
`FullGrad`和`FullGradSimple`是自定义的类,用于计算图像数据的显著性图。
6. 定义结果保存路径:
```python
save_path = 'result'
if os.path.isdir(save_path):
os.mkdir(save_path)
```
如果保存路径不存在,则创建一个新的。
7. 定义计算显著性图并保存的函数:
```python
def compute_saliency_and_save():
for idx, (data, target) in enumerate(sampler_loader):
data, target = data.to(device).requires_grad_(), target.to(device)
cam = fullgrad.fullgrad(data)
cam_simple = simple_fullgrad.fullgrad(data)
for i in range(data.size(0)):
filename = save_path + str((idx + 1) * (i+1))
filename_simple = filename + "_simple"
image = unnormalize(data[i,:,:,:].cpu())
save_saliency_map(image, cam[i,:,:,:], filename + ".jpg")
save_saliency_map(image, cam_simple[i,:,:,:], filename_simple + ".jpg")
```
这个函数会遍历数据加载器中的每个数据,将其移动到设备上,并计算其对应的显著性图。然后,将图像、显著性图和保存路径传递给`save_saliency_map`函数以保存显著性图。
8. 主函数:
```python
if __name__ == "__main__":
compute_saliency_and_save()
```
当脚本被直接执行时,会调用`compute_saliency_and_save`函数来计算和保存显著性图。
总结:这段程序用于计算图像数据的显著性图并保存,它使用了预训练的VGG-11模型和FullGrad算法。程序首先加载数据集并进行预处理,然后将模型移动到合适的设备上。接下来,使用FullGrad和Simple FullGrad算法计算每个图像的显著性图,并将结果保存到指定路径下。