pytorch實(shí)現(xiàn)圖像識別(實(shí)戰(zhàn))

更新時間：2022年02月18日 08:41:51 作者：AI?AX?AT

這篇文章主要介紹了pytorch實(shí)現(xiàn)圖像識別(實(shí)戰(zhàn)),文章主要分享實(shí)現(xiàn)代碼，但也具有一定的參考價值，需要的小伙伴可以才可以一下，希望對你有所幫助

1. 代碼講解

1.1 導(dǎo)庫

import os.path
from os import listdir
import numpy as np
import pandas as pd
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn import AdaptiveAvgPool2d
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from sklearn.model_selection import train_test_split

1.2 標(biāo)準(zhǔn)化、transform、設(shè)置GPU

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
normalize = transforms.Normalize(
? ?mean=[0.485, 0.456, 0.406],
? ?std=[0.229, 0.224, 0.225]
)
transform = transforms.Compose([transforms.ToTensor(), normalize]) ?# 轉(zhuǎn)換

1.3 預(yù)處理數(shù)據(jù)

class DogDataset(Dataset):
# 定義變量
? ? def __init__(self, img_paths, img_labels, size_of_images): ?
? ? ? ? self.img_paths = img_paths
? ? ? ? self.img_labels = img_labels
? ? ? ? self.size_of_images = size_of_images

# 多少長圖片
? ? def __len__(self):
? ? ? ? return len(self.img_paths)

# 打開每組圖片并處理每張圖片
? ? def __getitem__(self, index):
? ? ? ? PIL_IMAGE = Image.open(self.img_paths[index]).resize(self.size_of_images)
? ? ? ? TENSOR_IMAGE = transform(PIL_IMAGE)
? ? ? ? label = self.img_labels[index]
? ? ? ? return TENSOR_IMAGE, label


print(len(listdir(r'C:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\train')))
print(len(pd.read_csv(r'C:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\labels.csv')))
print(len(listdir(r'C:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\test')))

train_paths = []
test_paths = []
labels = []
# 訓(xùn)練集圖片路徑
train_paths_lir = r'C:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\train'
for path in listdir(train_paths_lir):
? ? train_paths.append(os.path.join(train_paths_lir, path)) ?
# 測試集圖片路徑
labels_data = pd.read_csv(r'C:\Users\AIAXIT\Desktop\DeepLearningProject\Deep_Learning_Data\dog-breed-identification\labels.csv')
labels_data = pd.DataFrame(labels_data) ?
# 把字符標(biāo)簽離散化，因?yàn)閿?shù)據(jù)有120種狗，不離散化后面把數(shù)據(jù)給模型時會報錯：字符標(biāo)簽過多。把字符標(biāo)簽從0-119編號
size_mapping = {}
value = 0
size_mapping = dict(labels_data['breed'].value_counts())
for kay in size_mapping:
? ? size_mapping[kay] = value
? ? value += 1
# print(size_mapping)
labels = labels_data['breed'].map(size_mapping)
labels = list(labels)
# print(labels)
print(len(labels))
# 劃分訓(xùn)練集和測試集
X_train, X_test, y_train, y_test = train_test_split(train_paths, labels, test_size=0.2)

train_set = DogDataset(X_train, y_train, (32, 32))
test_set = DogDataset(X_test, y_test, (32, 32))

train_loader = torch.utils.data.DataLoader(train_set, batch_size=64)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64)

1.4 建立模型

class LeNet(nn.Module):
? ? def __init__(self):
? ? ? ? super(LeNet, self).__init__()

? ? ? ? self.features = nn.Sequential(
? ? ? ? ? ? nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5), ?
? ? ? ? ? ? nn.ReLU(),
? ? ? ? ? ? nn.AvgPool2d(kernel_size=2, stride=2),
? ? ? ? ? ? nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5),
? ? ? ? ? ? nn.ReLU(),
? ? ? ? ? ? nn.AvgPool2d(kernel_size=2, stride=2)
? ? ? ? )
? ? ? ? self.classifier = nn.Sequential(
? ? ? ? ? ? nn.Linear(16 * 5 * 5, 120),
? ? ? ? ? ? nn.ReLU(),
? ? ? ? ? ? nn.Linear(120, 84),
? ? ? ? ? ? nn.ReLU(),
? ? ? ? ? ? nn.Linear(84, 120)
? ? ? ? )

? ? def forward(self, x):
? ? ? ? batch_size = x.shape[0]
? ? ? ? x = self.features(x)
? ? ? ? x = x.view(batch_size, -1)
? ? ? ? x = self.classifier(x)
? ? ? ? return x


model = LeNet().to(device)
criterion = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters())
TRAIN_LOSS = [] ?# 損失
TRAIN_ACCURACY = [] ?# 準(zhǔn)確率

1.5 訓(xùn)練模型

def train(epoch):
? ? model.train()
? ? epoch_loss = 0.0 # 損失
? ? correct = 0 ?# 精確率
? ? for batch_index, (Data, Label) in enumerate(train_loader):
? ? # 扔到GPU中
? ? ? ? Data = Data.to(device)
? ? ? ? Label = Label.to(device)
? ? ? ? output_train = model(Data)
? ? # 計算損失
? ? ? ? loss_train = criterion(output_train, Label)
? ? ? ? epoch_loss = epoch_loss + loss_train.item()
? ? # 計算精確率
? ? ? ? pred = torch.max(output_train, 1)[1]
? ? ? ? train_correct = (pred == Label).sum()
? ? ? ? correct = correct + train_correct.item()
? ? # 梯度歸零、反向傳播、更新參數(shù)
? ? ? ? optimizer.zero_grad()
? ? ? ? loss_train.backward()
? ? ? ? optimizer.step()
? ? print('Epoch: ', epoch, 'Train_loss: ', epoch_loss / len(train_set), 'Train correct: ', correct / len(train_set))

1.6 測試模型

和訓(xùn)練集差不多。

def test():
? ? model.eval()
? ? correct = 0.0
? ? test_loss = 0.0
? ? with torch.no_grad():
? ? ? ? for Data, Label in test_loader:
? ? ? ? ? ? Data = Data.to(device)
? ? ? ? ? ? Label = Label.to(device)
? ? ? ? ? ? test_output = model(Data)
? ? ? ? ? ? loss = criterion(test_output, Label)
? ? ? ? ? ? pred = torch.max(test_output, 1)[1]
? ? ? ? ? ? test_correct = (pred == Label).sum()
? ? ? ? ? ? correct = correct + test_correct.item()
? ? ? ? ? ? test_loss = test_loss + loss.item()
? ? print('Test_loss: ', test_loss / len(test_set), 'Test correct: ', correct / len(test_set))

1.7結(jié)果

epoch = 10
for n_epoch in range(epoch):
? ? train(n_epoch)
test()

到此這篇關(guān)于pytorch實(shí)現(xiàn)圖像識別(實(shí)戰(zhàn))的文章就介紹到這了,更多相關(guān)pytorch實(shí)現(xiàn)圖像識別內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

您可能感興趣的文章: