机器学习 实验五 垃圾分类
机器学习 实验五 垃圾分类
一、实验环境
PC机，Python
二、代码
#%%
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.optim as optim
import torchvision.models as models
import PIL.Image as Image
import os
#%%
image_size = (256,256)
data_transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize(image_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
#%%
train_data=dset.ImageFolder(root="train",transform=data_transform)
# 数据集长度
totallen = len(train_data)
print('train data length:',totallen)
#%%
train_data
#%%
trainlen = int(totallen*0.9)
vallen = totallen - trainlen
train_db,val_db=torch.utils.data.random_split(train_data,[trainlen,vallen])
print('train:',len(train_db),'validation:',len(val_db))
#%%
# batch size
bs=8
# 训练集
train_loader=torch.utils.data.DataLoader(train_db,batch_size=bs, shuffle=True,num_workers=2)
# 验证集
val_loader=torch.utils.data.DataLoader(val_db,batch_size=bs, shuffle=True,num_workers=2)
#%%
train_data.classes
#%%
def get_num_correct(out, labels):
return out.argmax(dim=1).eq(labels).sum().item()
#%%
batch = next(iter(train_loader))
#%%
batch[1]
#%%
resnext101 = models.resnet.resnext101_32x8d(pretrained=True)
#%%
model = resnext101
n_classes = len(train_data.classes)
model.fc = nn.Linear(2048, n_classes)
#%%
import torch.nn.init as init
for name, module in model._modules.items():
if(name=='fc'):
# print(module.weight.shape)
init.kaiming_uniform_(module.weight, a=0, mode='fan_in')
#%%
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
#%%
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)
epoch_num = 10
model = model.to(device)
for epoch in range(epoch_num):
total_loss=0
total_correct=0
val_correct=0
for batch in train_loader:#GetBatch
images,labels=batch
images = images.to(device)
labels = labels.to(device)
outs=model(images)#PassBatch
loss=F.cross_entropy(outs,labels)#CalculateLoss
optimizer.zero_grad()
loss.backward()#CalculateGradients
optimizer.step()#UpdateWeights
total_loss+=loss.item()
total_correct+=get_num_correct(outs,labels)
for batch in val_loader:
images,labels=batch
images = images.to(device)
labels = labels.to(device)
outs=model(images)
val_correct+=get_num_correct(outs,labels)
print("loss:",total_loss,"train_correct:",total_correct/trainlen, "val_correct:",val_correct/vallen)
#%%
class Laji(torch.utils.data.Dataset):
def __init__(self, root, transform=None):
imgs = os.listdir(root)
self.imgs = [os.path.join(root, img) for img in imgs]
self.transform = transform
def __getitem__(self, index):
data = Image.open(self.imgs[index]).convert('RGB')
if self.transform:
data = self.transform(data)
return data
def __len__(self):
return len(self.imgs)
#%%
test_data=Laji(root="test",transform=data_transform)
#%%
len(test_data)
#%%
model.to('cpu')
pre=[]
for image in test:
output = model(image)
for i in range(bs):
print(output[i].shape)
prob = F.softmax(output[i], dim=0)
indexs = torch.argsort(-prob)
print("index:", indexs[0].item(), " prob: ", prob[indexs[0]])
pre.append(indexs[0].item())
#%%
import pandas as pd
import os
import csv
# 将id读取
path_test = 'test'
# 训练出来垃圾类别
dirs = os.listdir(path_test)
pa = []
count=0
for img in dirs:
pa.append(img+'\t'+train_data.classes[pre[count]])
count=count+1
# 输出submission文件
pa= pd.DataFrame(pa)
pa.to_csv('submission', quoting=csv.QUOTE_NONE, header=None, index=False)
二、实验结果与分析
1、猎豹平台提交结果：
2、承接上文实验三手写汉字识别，本文中所用到的就是利用pytorch的方法，pytorch中有许多预训练模型，可以方便地应用于图像识别的领域。在文中调参部分需要大家花长时间去做，图像的预处理部分对于预测结果的影响还是比较大的，模型选取也是需要考虑GPU资源的。在做本次实验时，我考虑使用efficientnet，但不知为何，其表现效果不如resnext101，有兴趣的读者可以尝试一下。
