度学习pytorch实战六：ResNet50网络图像分类篇自建花数据集图像分类（5类）超详细代码

1.数据集简介、训练集与测试集划分
2.模型相关知识
3.model.py——定义ResNet50网络模型
4.train.py——加载数据集并训练，训练集计算损失值loss，测试集计算accuracy，保存训练好的网络参数
5.predict.py——利用训练好的网络参数后，用自己找的图像进行分类测试

一、数据集简介

1.自建数据文件夹

首先确定这次分类种类，采用爬虫、官网数据集和自己拍照的照片获取5类，新建个文件夹data，里面包含5个文件夹，文件夹名字取种类英文，每个文件夹照片数量最好一样多，五百多张以上。如我选了雏菊，蒲公英，玫瑰，向日葵，郁金香5类，如下图，每种类型有600~900张图像。如下图

花数据集下载链接https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
在这里插入图片描述
2.划分训练集与测试集

这是划分数据集代码，同一目录下运，复制改文件夹路径。

import os
from shutil import copy
import randomdef mkfile(file):if not os.path.exists(file):os.makedirs(file)# 获取 photos 文件夹下除 .txt 文件以外所有文件夹名（即3种分类的类名）
file_path = 'data/flower_photos'
flower_class = [cla for cla in os.listdir(file_path) if ".txt" not in cla]# 创建 训练集train 文件夹，并由3种类名在其目录下创建3个子目录
mkfile('flower_data/train')
for cla in flower_class:mkfile('flower_data/train/' + cla)# 创建 验证集val 文件夹，并由3种类名在其目录下创建3个子目录
mkfile('flower_data/val')
for cla in flower_class:mkfile('flower_data/val/' + cla)# 划分比例，训练集 : 验证集 = 9 : 1
split_rate = 0.1# 遍历3种花的全部图像并按比例分成训练集和验证集
for cla in flower_class:cla_path = file_path + '/' + cla + '/'  # 某一类别动作的子目录images = os.listdir(cla_path)  # iamges 列表存储了该目录下所有图像的名称num = len(images)eval_index = random.sample(images, k=int(num * split_rate))  # 从images列表中随机抽取 k 个图像名称for index, image in enumerate(images):# eval_index 中保存验证集val的图像名称if image in eval_index:image_path = cla_path + imagenew_path = 'flower_data/val/' + clacopy(image_path, new_path)  # 将选中的图像复制到新路径# 其余的图像保存在训练集train中else:image_path = cla_path + imagenew_path = 'flower_data/train/' + clacopy(image_path, new_path)print("\r[{}] processing [{}/{}]".format(cla, index + 1, num), end="")  # processing barprint()print("processing done!")

二、模型相关知识

之前有文章介绍模型，如果不清楚可以点下链接转过去学习。

深度学习卷积神经网络CNN之ResNet模型网络详解说明（超详细理论篇）

在这里插入图片描述

三、model.py——定义ResNet50网络模型

import torch.nn as nn
import torchclass BasicBlock(nn.Module):expansion = 1def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,kernel_size=3, stride=stride, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channel)self.relu = nn.ReLU()self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(out_channel)self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out += identityout = self.relu(out)return outclass Bottleneck(nn.Module):"""注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，这么做的好处是能够在top1上提升大概0.5%的准确率。可参考Resnet v1.5 https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch"""expansion = 4def __init__(self, in_channel, out_channel, stride=1, downsample=None,groups=1, width_per_group=64):super(Bottleneck, self).__init__()width = int(out_channel * (width_per_group / 64.)) * groupsself.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,kernel_size=1, stride=1, bias=False)  # squeeze channelsself.bn1 = nn.BatchNorm2d(width)# -----------------------------------------self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,kernel_size=3, stride=stride, bias=False, padding=1)self.bn2 = nn.BatchNorm2d(width)# -----------------------------------------self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,kernel_size=1, stride=1, bias=False)  # unsqueeze channelsself.bn3 = nn.BatchNorm2d(out_channel*self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)out += identityout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self,block,blocks_num,num_classes=1000,include_top=True,groups=1,width_per_group=64):super(ResNet, self).__init__()self.include_top = include_topself.in_channel = 64self.groups = groupsself.width_per_group = width_per_groupself.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,padding=3, bias=False)self.bn1 = nn.BatchNorm2d(self.in_channel)self.relu = nn.ReLU(inplace=True)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, blocks_num[0])self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)if self.include_top:self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (1, 1)self.fc = nn.Linear(512 * block.expansion, num_classes)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')def _make_layer(self, block, channel, block_num, stride=1):downsample = Noneif stride != 1 or self.in_channel != channel * block.expansion:downsample = nn.Sequential(nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(channel * block.expansion))layers = []layers.append(block(self.in_channel,channel,downsample=downsample,stride=stride,groups=self.groups,width_per_group=self.width_per_group))self.in_channel = channel * block.expansionfor _ in range(1, block_num):layers.append(block(self.in_channel,channel,groups=self.groups,width_per_group=self.width_per_group))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)if self.include_top:x = self.avgpool(x)x = torch.flatten(x, 1)x = self.fc(x)return xdef resnet34(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnet34-333f7ec4.pthreturn ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)def resnet50(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnet50-19c8e357.pthreturn ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)def resnet101(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnet101-5d3b4d8f.pthreturn ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)def resnext50_32x4d(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pthgroups = 32width_per_group = 4return ResNet(Bottleneck, [3, 4, 6, 3],num_classes=num_classes,include_top=include_top,groups=groups,width_per_group=width_per_group)def resnext101_32x8d(num_classes=1000, include_top=True):# https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pthgroups = 32width_per_group = 8return ResNet(Bottleneck, [3, 4, 23, 3],num_classes=num_classes,include_top=include_top,groups=groups,width_per_group=width_per_group)

四、model.py——定义ResNet34网络模型

batch_size = 16
epochs = 5

import os
import sys
import jsonimport torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, datasets
from tqdm import tqdm
from model import resnet50def 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([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),"val": transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}data_root = os.path.abspath(os.path.join(os.getcwd(), "../.."))  # get data root pathimage_path = os.path.join(data_root, "zjdata", "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 = 16nw = 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=nw)print("using {} images for training, {} images for validation.".format(train_num,val_num))net = resnet50(num_classes=5, include_top=True)net.to(device)# define loss functionloss_function = nn.CrossEntropyLoss()# construct an optimizerparams = [p for p in net.parameters() if p.requires_grad]optimizer = optim.Adam(params, lr=0.1)epochs = 5best_acc = 0.0save_path = './resNet50.pth'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()logits = net(images.to(device))loss = loss_function(logits, 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))# loss = loss_function(outputs, test_labels)predict_y = torch.max(outputs, dim=1)[1]acc += torch.eq(predict_y, val_labels.to(device)).sum().item()val_bar.desc = "valid epoch[{}/{}]".format(epoch + 1,epochs)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()

训练中截图
在这里插入图片描述

五、predict.py——利用训练好的网络参数后，用自己找的图像进行分类测试

import os
import jsonimport torch
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as pltfrom model import resnet34def main():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])])# load imageimg_path = "./1.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 = resnet34(num_classes=5).to(device)# load model weightsweights_path = "./resNet50.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))# predictionmodel.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()