广西南宁公司网站制作,网站备案取消前置审批,这个域名的网站做违法的事,石家庄市网站制作RESNET的复现pytorch版本
使用的数据为Object_102_CaDataset#xff0c;可以在网上下载#xff0c;也可以在评论区问。
RESNET模型的亮点
1.提出了残差模块。
2.使用Batch Normalization加速训练
3.残差网络#xff1a;易于收敛#xff0c;很好的解决了退化问题#…RESNET的复现pytorch版本
使用的数据为Object_102_CaDataset可以在网上下载也可以在评论区问。
RESNET模型的亮点
1.提出了残差模块。
2.使用Batch Normalization加速训练
3.残差网络易于收敛很好的解决了退化问题模型可以很深准确率大大提高了。
残差结构如下所示 首先是模型构建部分
class ResBlock(nn.Module):def __init__(self, in_channels, out_channels, stride_11, stride_21, padding1, kernel_size(3, 3), short_cutNone):super(ResBlock, self).__init__()self.short_cut short_cutself.model Sequential(# 1.1Conv2d(in_channelsin_channels, out_channelsout_channels, kernel_sizekernel_size, stridestride_1,paddingpadding),BatchNorm2d(out_channels),ReLU(),Conv2d(in_channelsout_channels, out_channelsout_channels, kernel_sizekernel_size, stridestride_2,paddingpadding),BatchNorm2d(out_channels),ReLU(),)self.short_layer Sequential(Conv2d(in_channelsin_channels, out_channelsout_channels, kernel_size(1, 1), stride2, padding0),BatchNorm2d(out_channels),ReLU(),)self.R ReLU()def forward(self, x):f1 xif self.short_cut is not None:f1 self.short_layer(x)out self.model(x)out self.R(f1out)return out该部分为模型的残差块使用了3*3的卷积然后进行归一化。
对于整个模型的构建部分
class Resnet_easier(nn.Module):def __init__(self, num_classes):super(Resnet_easier, self).__init__()self.model0 Sequential(# 0# 输入3通道、输出64通道、卷积核大小、步长、补零、Conv2d(in_channels3, out_channels64, kernel_size(7, 7), stride2, padding3),BatchNorm2d(64),ReLU(),MaxPool2d(kernel_size(3, 3), stride2, padding1),)self.model1 ResBlock(64, 64)self.model2 ResBlock(64, 64)self.model3 ResBlock(64, 128, stride_12, stride_21, short_cutTrue)self.model4 ResBlock(128, 128)self.model5 ResBlock(128, 256, stride_12, stride_21, short_cutTrue)self.model6 ResBlock(256, 256)self.model7 ResBlock(256, 512, stride_12, stride_21, short_cutTrue)self.model8 ResBlock(512, 512)# AAP 自适应平均池化self.aap AdaptiveAvgPool2d((1, 1))# flatten 维度展平self.flatten Flatten(start_dim1)# FC 全连接层self.fc Linear(512, num_classes)def forward(self, x):x x.to(torch.float32)x self.model0(x)x self.model1(x)x self.model2(x)x self.model3(x)x self.model4(x)x self.model5(x)x self.model6(x)x self.model7(x)x self.model8(x)# 最后3个x self.aap(x)x self.flatten(x)x self.fc(x)return x接下来是读入数据模块
class Object_102_CaDataset(Dataset):def __init__(self, folder):mean [0.485, 0.456, 0.406]std [0.229, 0.224, 0.225]self.file_list []label_names [item for item in os.listdir(folder) if os.path.isdir(os.path.join(folder, item))] # 获取文件夹下的所有标签label_to_index dict((label, index) for index, label in enumerate(label_names)) # 将label转为数字self.all_picture_paths self.get_all_picture(folder) # 获取所有图片路径self.all_picture_labels [label_to_index[os.path.split(os.path.dirname(os.path.abspath(path)))[1]] for path inself.file_list]self.mean np.array(mean).reshape((1, 1, 3))self.std np.array(std).reshape((1, 1, 3))def __getitem__(self, index):img cv2.imread(self.all_picture_paths[index])if img is None:print(os.path.join(image, self.all_picture_paths[index]))img cv2.resize(img, (224, 224)) #统一图片的尺寸img img / 255img (img - self.mean) / self.stdimg np.transpose(img, [2, 0, 1])label self.all_picture_labels[index]img torch.tensor(img)label torch.tensor(label)return img, labeldef __len__(self):return len(self.all_picture_paths)def get_all_picture(self, folder):for filename in os.listdir(folder):file_path os.path.join(folder, filename)if os.path.isfile(file_path):self.file_list.append(file_path)elif os.path.isdir(file_path):self.file_list self.get_all_picture(file_path)return self.file_list使用上述dataloader可以方便的对数据进行读取操作。
接下来就是整个的训练模块
import torch
from torch import nn
from torch.utils.data import DataLoaderfrom ResNet.ResNet18 import Resnet18
from ResNet.ResNet18_easier import Resnet_easier
from ResNet.dataset import Object_102_CaDataset
from ResNet.res_net import ResNet, ResBlockfrom torchsummary import summary
data_dir E:\PostGraduate\Paper_review\computer_view_model/ResNet/data/101_ObjectCategories
Object_102 Object_102_CaDataset(data_dir)
train_size int(len(Object_102) * 0.7)
# print(train_size)
test_size len(Object_102) - train_size
train_dataset, test_dataset torch.utils.data.random_split(Object_102, [train_size, test_size])
mean [0.485, 0.456, 0.406]
std [0.229, 0.224, 0.225]
#显示数据此处的注释内容可以让我们看到读取的图片
# import random
# from matplotlib import pyplot as plt
# import matplotlib
# matplotlib.use(TkAgg)
# def denorm(img):
# for i in range(img.shape[0]):
# img[i] img[i] * std[i] mean[i]
# img torch.clamp(img, 0., 1.)
# return img
# plt.figure(figsize(8, 8))
# for i in range(9):
# img, label train_dataset[random.randint(0, len(train_dataset))]
# img denorm(img)
# img img.permute(1, 2, 0)
# ax plt.subplot(3, 3, i 1)
# ax.imshow(img.numpy()[:, :, ::-1])
# ax.set_title(label %d % label)
# ax.set_xticks([])
# ax.set_yticks([])
# plt.show()train_iter DataLoader(train_dataset, batch_size64, shuffleTrue)
test_iter DataLoader(train_dataset, batch_size64)
model Resnet_easier(102)
# print(summary(model, (3, 224, 224)))
epoch 50 # 训练轮次
optmizer torch.optim.SGD(model.parameters(), lr0.01, momentum0.9)
# optmizer torch.optim.Adam(model.parameters())
loss_fn nn.CrossEntropyLoss()#.cuda() # 定义交叉熵损失函数
log_interval 10train_losses []
train_counter []
test_losses []
test_counter [i * len(train_iter.dataset) for i in range(epoch 1)]# test_loop(model,cpu,test_iter)
def train_loop(n_epochs, optimizer, model, loss_fn, train_loader):for epoch in range(1, n_epochs 1):model.train()for i, data in enumerate(train_loader):correct 0(images, label) dataimages images#.cuda()label label#.cuda()# print(len(images))output model(images)loss loss_fn(output, label)optimizer.zero_grad()loss.backward()optimizer.step()pred output.data.max(1, keepdimTrue)[1]pred torch.tensor(pred, dtypetorch.float32)for index in range(0, len(pred)):if pred[index] label[index]:correct 1# correct torch.eq(pred, label).sum()# print(correct)if i % log_interval 0:print(Train Epoch: {} [{}/{} ({:.0f}%)]\t accuracy:[{}/{} ({:.0f}%)] \tLoss: {:.6f}.format(epoch, i * len(images), len(train_loader.dataset),100. * i / len(train_loader), correct, len(pred), 100. * correct / len(pred), loss.item()))train_losses.append(loss.item())train_counter.append((i * 64) ((epoch - 1) * len(train_loader.dataset)))torch.save(model.state_dict(), model_paramter/test/model.pth)torch.save(optimizer.state_dict(), model_paramter/test/optimizer.pth)# test_loop(model, cpu, test_iter)# PATH E:\\PostGraduate\\Paper_review\\computer_view_model\\ResNet/model_paramter/model.pth
# dictionary torch.load(PATH)
# model.load_state_dict(dictionary)
train_loop(epoch, optmizer, model, loss_fn, train_iter)# PATH E:\\PostGraduate\\Paper_review\\computer_view_model\\ResNet/model_paramter/model.pth
# dictionary torch.load(PATH)
# model.load_state_dict(dictionary)
# test_loop(model, cpu, test_iter)若要测试数据的准确度等内容可以参考之前的博文使用LSTm进行情感分析对test部分进行修改即可。
也可以参考下面的
PATH E:\\PostGraduate\\Paper_review\\computer_view_model\\ResNet/model_paramter/model.pth
dictionary torch.load(PATH)
model.load_state_dict(dictionary)
def test_loop(model, device, test_iter):model.eval()test_loss 0correct 0with torch.no_grad():for data, target in test_iter:data data.to(device)target target.to(device)output model(data)output output.data.max(1, keepdimTrue)[1]output torch.tensor(output, dtypetorch.float32)# loss_func loss_fn(output, target)# test_loss loss_funcpred outputfor index in range(0, len(pred)):if pred[index] target[index]:correct 1test_loss / len(test_iter.dataset)test_losses.append(test_loss)print(\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n.format(test_loss, correct, len(test_iter.dataset),100. * correct / len(test_iter.dataset)))test_loop(model,cpu,test_iter)loss / len(test_iter.dataset) test_losses.append(test_loss) print(‘\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n’.format( test_loss, correct, len(test_iter.dataset), 100. * correct / len(test_iter.dataset)))
test_loop(model,‘cpu’,test_iter)
