import random
import torch
import torch.nn as nn
import numpy as np
import os
from PIL import Image #读取图片数据
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
from torchvision import transforms
import time
import matplotlib.pyplot as plt
from model_utils.model import initialize_model
from model_utils.myResNet2 import myResNet
from model_utils.attention import ChannelAttention, SpatialAttention
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
#################################################################
seed_everything(2)
###############################################
HW = 224
train_transform = transforms.Compose(
[
transforms.ToPILImage(), #224， 224， 3模型 ：3, 224, 224
transforms.RandomResizedCrop(224),
transforms.RandomRotation(50),
transforms.ToTensor()
]
)
val_transform = transforms.Compose(
[
transforms.ToPILImage(), #224， 224， 3模型 ：3, 224, 224
transforms.ToTensor()
]
)
class food_Dataset(Dataset):
def __init__(self, path, mode="train"):
self.mode = mode
if mode == "semi":
self.X = self.read_file(path)
else:
self.X, self.Y = self.read_file(path)
self.Y = torch.LongTensor(self.Y) #标签转为长整形\
if mode == "train":
self.transform = train_transform
else:
self.transform = val_transform
def read_file(self, path):
if self.mode == "semi":
file_list = os.listdir(path)
xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8)
# 列出文件夹下所有文件名字
for j, img_name in enumerate(file_list):
img_path = os.path.join(path, img_name)
img = Image.open(img_path)
img = img.resize((HW, HW))
xi[j, ...] = img
print("读到了%d个数据" % len(xi))
return xi
else:
for i in tqdm(range(11)):
file_dir = path + "/%02d" % i
file_list = os.listdir(file_dir)
xi = np.zeros((len(file_list), HW, HW, 3), dtype=np.uint8)
yi = np.zeros(len(file_list), dtype=np.uint8)
# 列出文件夹下所有文件名字
for j, img_name in enumerate(file_list):
img_path = os.path.join(file_dir, img_name)
img = Image.open(img_path)
img = img.resize((HW, HW))
xi[j, ...] = img
yi[j] = i
if i == 0:
X = xi
Y = yi
else:
X = np.concatenate((X, xi), axis=0)
Y = np.concatenate((Y, yi), axis=0)
print("读到了%d个数据" % len(Y))
return X, Y
def __getitem__(self, item):
if self.mode == "semi":
return self.transform(self.X[item]), self.X[item]
else:
return self.transform(self.X[item]), self.Y[item]
def __len__(self):
return len(self.X)
class semiDataset(Dataset):
def __init__(self, no_label_loder, model, device, thres=0.99):
x, y = self.get_label(no_label_loder, model, device, thres)
if x == []:
self.flag = False
else:
self.flag = True
self.X = np.array(x)
self.Y = torch.LongTensor(y)
self.transform = train_transform
def get_label(self, no_label_loader, model, device, thres):
model = model.to(device)
pred_prob = []
labels = []
x = []
y = []
soft = nn.Softmax()
with torch.no_grad():
for bat_x, _ in no_label_loader:
bat_x = bat_x.to(device)
pred = model(bat_x)
pred_soft = soft(pred)
pred_max, pred_value = pred_soft.max(1)
pred_prob.extend(pred_max.cpu().numpy().tolist())
labels.extend(pred_value.cpu().numpy().tolist())
for index, prob in enumerate(pred_prob):
if prob > thres:
x.append(no_label_loader.dataset[index][1]) #调用到原始的getitem
y.append(labels[index])
return x, y
def __getitem__(self, item):
return self.transform(self.X[item]), self.Y[item]
def __len__(self):
return len(self.X)
def get_semi_loader(no_label_loder, model, device, thres):
semiset = semiDataset(no_label_loder, model, device, thres)
if semiset.flag == False:
return None
else:
semi_loader = DataLoader(semiset, batch_size=16, shuffle=False)
return semi_loader
class myModel(nn.Module):
def __init__(self, num_class):
super(myModel, self).__init__()
#3 *224 *224 -> 512*7*7 -> 拉直 -》全连接分类
self.conv1 = nn.Conv2d(3, 64, 3, 1, 1) # 64*224*224
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.pool1 = nn.MaxPool2d(2) #64*112*112
self.layer1 = nn.Sequential(
nn.Conv2d(64, 128, 3, 1, 1), # 128*112*112
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2) #128*56*56
)
self.layer2 = nn.Sequential(
nn.Conv2d(128, 256, 3, 1, 1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.MaxPool2d(2) #256*28*28
)
self.layer3 = nn.Sequential(
nn.Conv2d(256, 512, 3, 1, 1),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2) #512*14*14
)
self.pool2 = nn.MaxPool2d(2) #512*7*7
self.fc1 = nn.Linear(25088, 1000) #25088->1000
self.relu2 = nn.ReLU()
self.fc2 = nn.Linear(1000, num_class) #1000-11
self.channelattention = ChannelAttention(256).to(device)
self.spatialattention = SpatialAttention().to(device)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.pool1(x)
x = self.layer1(x)
x = self.layer2(x) #batch*256*28*28
attention_weights = self.channelattention(x) #batch*256*1*1
x = x * attention_weights #PyTorch 支持广播机制，因此即使 attention_weights 的形状为 (batch_size, channels, 1, 1)，也可以直接与形状为 (batch_size, channels, height, width) 的 x 进行逐元素相乘。
spatial_weights = self.spatialattention(x) #16*1*28*28
x = x * spatial_weights
x = self.layer3(x)
x = self.pool2(x)
x = x.view(x.size()[0], -1)
x = self.fc1(x)
x = self.relu2(x)
x = self.fc2(x)
return x
def train_val(model, train_loader, val_loader, no_label_loader, device, epochs, optimizer, loss, thres, save_path): # scheduler):
model = model.to(device)
semi_loader = None
plt_train_loss = []
plt_val_loss = []
plt_train_acc = []
plt_val_acc = []
max_acc = 0.0
for epoch in range(epochs):
train_loss = 0.0
val_loss = 0.0
train_acc = 0.0
val_acc = 0.0
semi_loss = 0.0
semi_acc = 0.0
start_time = time.time()
model.train()
for batch_x, batch_y in tqdm(train_loader):
x, target = batch_x.to(device), batch_y.to(device)
pred = model(x)
train_bat_loss = loss(pred, target)
train_bat_loss.backward()
optimizer.step() # 更新参数 之后要梯度清零否则会累积梯度
optimizer.zero_grad()
train_loss += train_bat_loss.cpu().item()
train_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
plt_train_loss.append(train_loss / train_loader.__len__())
plt_train_acc.append(train_acc/train_loader.dataset.__len__()) #记录准确率，
if semi_loader!= None:
for batch_x, batch_y in tqdm(semi_loader):
x, target = batch_x.to(device), batch_y.to(device)
pred = model(x)
semi_bat_loss = loss(pred, target)
semi_bat_loss.backward()
optimizer.step() # 更新参数 之后要梯度清零否则会累积梯度
optimizer.zero_grad()
semi_loss += train_bat_loss.cpu().item()
semi_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
print("半监督数据集的训练准确率为", semi_acc/train_loader.dataset.__len__())
model.eval()
with torch.no_grad():
for batch_x, batch_y in tqdm(val_loader):
x, target = batch_x.to(device), batch_y.to(device)
pred = model(x)
val_bat_loss = loss(pred, target)
val_loss += val_bat_loss.cpu().item()
val_acc += np.sum(np.argmax(pred.detach().cpu().numpy(), axis=1) == target.cpu().numpy())
plt_val_loss.append(val_loss / val_loader.dataset.__len__())
plt_val_acc.append(val_acc / val_loader.dataset.__len__())
if epoch%2 == 0 and plt_val_acc[-1] > 0.7: #0.6
semi_loader = get_semi_loader(no_label_loader, model, device, thres)
if val_acc > max_acc:
torch.save(model, save_path)
max_acc = val_loss
print('[%03d/%03d] %2.2f sec(s) TrainLoss : %.6f | valLoss: %.6f Trainacc : %.6f | valacc: %.6f' % \
(epoch, epochs, time.time() - start_time, plt_train_loss[-1], plt_val_loss[-1], plt_train_acc[-1], plt_val_acc[-1])
) # 打印训练结果。 注意python语法， %2.2f 表示小数位为2的浮点数， 后面可以对应。
# # 更新学习率调度器
# scheduler.step()
plt.plot(plt_train_loss)
plt.plot(plt_val_loss)
plt.title("loss")
plt.legend(["train", "val"])
plt.show()
plt.plot(plt_train_acc)
plt.plot(plt_val_acc)
plt.title("acc")
plt.legend(["train", "val"])
plt.show()
# path = r"F:\pycharm\beike\classification\food_classification\food-11\training\labeled"
# train_path = r"F:\pycharm\beike\classification\food_classification\food-11\training\labeled"
# val_path = r"F:\pycharm\beike\classification\food_classification\food-11\validation"
train_path = r"C:\Users\Fengsj\Desktop\preparation\program\CNN\food-11\training\labeled"
val_path = r"C:\Users\Fengsj\Desktop\preparation\program\CNN\food-11\validation"
no_label_path = r"C:\Users\Fengsj\Desktop\preparation\program\CNN\food-11\training\unlabeled\00"
train_set = food_Dataset(train_path, "train")
val_set = food_Dataset(val_path, "val")
no_label_set = food_Dataset(no_label_path, "semi")
train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
val_loader = DataLoader(val_set, batch_size=32, shuffle=True)
no_label_loader = DataLoader(no_label_set, batch_size=32, shuffle=False)
# model = myModel(11)
# 迁移学习1
# model, _ = initialize_model("resnet18", 11, use_pretrained=True)
#迁移学习2
from torchvision.models import resnet18
model = resnet18(pretrained=True) #大佬的模型分成架构和参数，pretrained=True是参数也用大佬的
in_features = model.fc.in_features #把大佬分类头的全连接的输入特征维度提取出来（在resnet18里fc就是分类头）
model.fc = nn.Linear(in_features, 11) #把大佬模型的分类头的输出维度改为我的11个类
#
#我的模型
# model = myResNet(11)
lr = 0.002
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
device = "cuda" if torch.cuda.is_available() else "cpu"
save_path = "model_save/best_model.pth"
epochs = 100
thres = 0.99 #0.99
# 初始化学习率调度器
# scheduler_step_size = 5 # 每隔多少个 epoch 调整学习率
# scheduler_gamma = 0.5 # 学习率衰减因子,每隔 config["scheduler_step_size"] = 5 个 epoch，学习率会乘以 config["scheduler_gamma"] = 0.5。
# scheduler = torch.optim.lr_scheduler.StepLR(
# optimizer,
# step_size=scheduler_step_size,
# gamma=scheduler_gamma
# )
# #
train_val(model, train_loader, val_loader, no_label_loader, device, epochs, optimizer, loss, thres, save_path)# scheduler)