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《pytorch图像分类》p3VGG网络详解及感受野的计算
- 一、零碎知识点
- 1.nn.Sequential
- 2.**kwargs
- 二、VGG网络模型详解
- 1.感受野
- 2.模型手算
- 三、代码
- 1.module.py
- 2.train.py
- 3.predict.py
一、零碎知识点
论文连接：VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
代码链接：霹雳吧啦Wzdeep-learning-for-image-processing
1.nn.Sequential
nn.Sequential是PyTorch中的一个类，用于按顺序组织和堆叠神经网络的层或模块。它提供了一种便捷的方式来构建简单的前向传播网络。
import torch
import torch.nn as nnmodel = nn.Sequential(
in_channels,out_channels,kernel_sizenn.Conv2d(in_channels,out_channels,kernel_size)nn.ReLU(), # 添加激活函数nn.Linear(hidden_features, out_features) # 添加线性层
)
2.**kwargs
**kwargs是一个特殊的参数传递方式，它允许函数接受不定数量的关键字参数（Keyword Arguments）并将它们作为一个字典进行处理。
下面是一个简单的示例说明**kwargs的用法：
def example_func(**kwargs):for key, value in kwargs.items():print(key, value)example_func(name='Maverick', age=22, location='cheng du')
输出结果：
name Maverick
age 22
location cheng du
二、VGG网络模型详解
1.感受野
感受野（receptive field）是指在卷积神经网络（CNN）中的某一层输出特征图上的像素位置所对应的输入图像上的区域大小。
随着卷积核的增多（即网络的加深），感受野会越来越大。
当我们说一个神经网络层的感受野大小为N时，可以简单解释为：在该层输出特征图上的一个像素点，它所"看到"的输入图像区域大小是N×N。
随着网络的层数增加，感受野也会逐渐增大。最早的卷积层（例如卷积核为3x3）的感受野较小，但后续的层会通过池化或步幅更大的卷积来逐渐增加感受野的大小。
2.模型手算
VGG网络的常用配置是D，有16个层（包括13个卷积层和3个全连接层）
LRN是一种对神经网络中的特征图进行局部归一化的操作。其目的是增加网络的鲁棒性，防止某些特征具有过大的响应值而抑制其他特征的重要性。
具有鲁棒性的模型能够在输入数据中存在一定程度的扰动、噪声或异常情况下仍然保持良好的性能。
反复记忆：输出的特征矩阵的深度out_channels和卷积核的个数相同
因为彩色图形有rgb三个通道，所以最开始的特征矩阵深度为3
后面都是根据卷积核个数的不同产生不同的改变。
三、代码
1.module.py
import torch.nn as nn
import torch# official pretrain weights
model_urls = {'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth','vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth','vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth','vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth'
}class VGG(nn.Module):def __init__(self, features, num_classes=1000, init_weights=False):super(VGG, self).__init__()self.features = featuresself.classifier = nn.Sequential(nn.Linear(512*7*7, 4096),nn.ReLU(True),nn.Dropout(p=0.5),nn.Linear(4096, 4096),nn.ReLU(True),nn.Dropout(p=0.5),nn.Linear(4096, num_classes))if init_weights:self._initialize_weights()def forward(self, x):# N x 3 x 224 x 224x = self.features(x)# N x 512 x 7 x 7x = torch.flatten(x, start_dim=1)# N x 512*7*7x = self.classifier(x)return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')nn.init.xavier_uniform_(m.weight)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight)# nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)def make_features(cfg: list):layers = []in_channels = 3for 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)layers += [conv2d, nn.ReLU(True)]in_channels = vreturn nn.Sequential(*layers)cfgs = {'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}def vgg(model_name="vgg16", **kwargs):assert model_name in cfgs, "Warning: model number {} not in cfgs dict!".format(model_name)cfg = cfgs[model_name]model = VGG(make_features(cfg), **kwargs)return model
2.train.py
import os
import sys
import jsonimport torch
import torch.nn as nn
from torchvision import transforms, datasets
import torch.optim as optim
from tqdm import tqdmfrom model import vggdef main():device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")print("using {} device.".format(device))data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),"val": transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}data_root = os.path.abspath(os.path.join(os.getcwd(), "../..")) # get data root pathimage_path = os.path.join(data_root, "data_set", "flower_data") # flower data set pathassert os.path.exists(image_path), "{} path does not exist.".format(image_path)train_dataset = datasets.ImageFolder(root=os.path.join(image_path, "train"),transform=data_transform["train"])train_num = len(train_dataset)# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}flower_list = train_dataset.class_to_idxcla_dict = dict((val, key) for key, val in flower_list.items())# write dict into json filejson_str = json.dumps(cla_dict, indent=4)with open('class_indices.json', 'w') as json_file:json_file.write(json_str)batch_size = 2nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workersprint('Using {} dataloader workers every process'.format(nw))train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size, shuffle=True,num_workers=0)validate_dataset = datasets.ImageFolder(root=os.path.join(image_path, "val"),transform=data_transform["val"])val_num = len(validate_dataset)validate_loader = torch.utils.data.DataLoader(validate_dataset,batch_size=batch_size, shuffle=False,num_workers=0)print("using {} images for training, {} images for validation.".format(train_num,val_num))# test_data_iter = iter(validate_loader)# test_image, test_label = test_data_iter.next()model_name = "vgg16"net = vgg(model_name=model_name, num_classes=5, init_weights=True)net.to(device)loss_function = nn.CrossEntropyLoss()optimizer = optim.Adam(net.parameters(), lr=0.0001)epochs = 30best_acc = 0.0save_path = './{}Net.pth'.format(model_name)train_steps = len(train_loader)for epoch in range(epochs):# trainnet.train()running_loss = 0.0train_bar = tqdm(train_loader, file=sys.stdout)for step, data in enumerate(train_bar):images, labels = dataoptimizer.zero_grad()outputs = net(images.to(device))loss = loss_function(outputs, labels.to(device))loss.backward()optimizer.step()# print statisticsrunning_loss += loss.item()train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,epochs,loss)# validatenet.eval()acc = 0.0 # accumulate accurate number / epochwith torch.no_grad():val_bar = tqdm(validate_loader, file=sys.stdout)for val_data in val_bar:val_images, val_labels = val_dataoutputs = net(val_images.to(device))predict_y = torch.max(outputs, dim=1)[1]acc += torch.eq(predict_y, val_labels.to(device)).sum().item()val_accurate = acc / val_numprint('[epoch %d] train_loss: %.3f val_accuracy: %.3f' %(epoch + 1, running_loss / train_steps, val_accurate))if val_accurate > best_acc:best_acc = val_accuratetorch.save(net.state_dict(), save_path)print('Finished Training')if __name__ == '__main__':main()用的是老师的代码，我的gpu内存不够，我已经将批处理大小（batch size）减少到2了，还是运行不起来
CUDA out of memory. Tried to allocate 392.00 MiB (GPU 0; 2.00 GiB total capacity; 718.01 MiB already allocated; 341.00 MiB free; 740.00 MiB reserved in total by PyTorch)
3.predict.py
import os
import jsonimport torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as pltfrom model import vggdef main():device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")data_transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])# load imageimg_path = "../tulip.jpg"assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)img = Image.open(img_path)plt.imshow(img)# [N, C, H, W]img = data_transform(img)# expand batch dimensionimg = torch.unsqueeze(img, dim=0)# read class_indictjson_path = './class_indices.json'assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)with open(json_path, "r") as f:class_indict = json.load(f)# create modelmodel = vgg(model_name="vgg16", num_classes=5).to(device)# load model weightsweights_path = "./vgg16Net.pth"assert os.path.exists(weights_path), "file: '{}' dose not exist.".format(weights_path)model.load_state_dict(torch.load(weights_path, map_location=device))model.eval()with torch.no_grad():# predict classoutput = torch.squeeze(model(img.to(device))).cpu()predict = torch.softmax(output, dim=0)predict_cla = torch.argmax(predict).numpy()print_res = "class: {} prob: {:.3}".format(class_indict[str(predict_cla)],predict[predict_cla].numpy())plt.title(print_res)for i in range(len(predict)):print("class: {:10} prob: {:.3}".format(class_indict[str(i)],predict[i].numpy()))plt.show()if __name__ == '__main__':main()这篇关于霹雳吧啦Wz《pytorch图像分类》-p3VGG网络的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
