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

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

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(zhǎng)圖片
? ? def __len__(self):
? ? ? ? return len(self.img_paths)

# 打開(kāi)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(cè)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(shí)會(huì)報(bào)錯(cuò)：字符標(biāo)簽過(guò)多。把字符標(biāo)簽從0-119編號(hào)
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(cè)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)

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

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(jì)算損失
? ? ? ? loss_train = criterion(output_train, Label)
? ? ? ? epoch_loss = epoch_loss + loss_train.item()
? ? # 計(jì)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))

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))

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