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train_ResMixer.py
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import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import time
import torch
import torchvision
from torch import nn, optim
from torch.utils import data
from torchvision import transforms
from dataloader import Mydataset
import argparse
from models.model_fuse import STNet, CheckinNet, ImgNet, Mixer, ResMixer
import torchvision.models as models
from CB_loss import CB_loss
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class FCViewer(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
def main(args):
# Create model
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
train_txt='data\\train_6_10.txt'
val_txt='data\\val_2_10.txt'
test_txt='data\\test_2_10.txt'
train_transform = torchvision.transforms.Compose([torchvision.transforms.Resize((256,256)),
# torchvision.transforms.RandomHorizontalFlip(p=0.3),
# torchvision.transforms.RandomVerticalFlip(p=0.3),
# torchvision.transforms.RandomCrop(size=256),
# torchvision.transforms.RandomRotation(180),
torchvision.transforms.ToTensor()
])
val_transform=torchvision.transforms.Compose([torchvision.transforms.Resize((256,256)),
torchvision.transforms.ToTensor()])
test_transform=torchvision.transforms.Compose([torchvision.transforms.Resize((256,256)),
torchvision.transforms.ToTensor()])
train_dataset=Mydataset(txt=train_txt,transform=train_transform)
val_dataset=Mydataset(txt=val_txt,transform=test_transform)
test_dataset=Mydataset(txt=test_txt,transform=test_transform)
# sampler_train = torch.utils.data.WeightedRandomSampler([634.0/len(train_dataset), 2013.0/len(train_dataset)], len(train_dataset))
# print(len(sampler_train))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, pin_memory=True)
val_loader=torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, pin_memory=True)
print("Train numbers:{:d}".format(len(train_dataset)))
print("Val numbers:{:d}".format(len(val_dataset)))
print("Test numbers:{:d}".format(len(test_dataset)))
model2 = ResMixer(2)
print('model2 parameters:', sum(p.numel() for p in model2.parameters() if p.requires_grad))
# print('model3 parameters:', sum(p.numel() for p in model3.parameters() if p.requires_grad))
# model1 = model1.to(device)
model2 = model2.to(device)
# model3 = model3.to(device)
# cost1 = nn.CrossEntropyLoss().to(device)
cost2 = nn.CrossEntropyLoss().to(device)
# cost2 = CB_loss(0.9999, 2.0).to(device)
# cost3 = nn.CrossEntropyLoss().to(device)
# Optimization
# optimizer1 = optim.Adam(model1.parameters(), lr=args.lr, weight_decay=1e-6)
optimizer2 = optim.Adam(model2.parameters(), lr=args.lr, weight_decay=1e-6)
# optimizer2 = torch.optim.SGD(model2.parameters(), lr=args.lr, momentum=0.9)
# scheduler2 = torch.optim.lr_scheduler.OneCycleLR(optimizer2,max_lr=0.9,total_steps=100, verbose=False)
# scheduler2 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer2, mode='min', factor=0.9, patience=4, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=0.000001, eps=1e-08)
# criterion=nn.CrossEntropyLoss()
# optimizer3 = optim.Adam(model3.parameters(), lr=args.lr, weight_decay=1e-6)
# best_acc_1 = 0.
best_acc_2 = 0.
best_epoch = 0
# best_acc_3 = 0.
for epoch in range(1, args.epochs + 1):
# model1.train()
model2.train()
# model3.train()
# start time
start = time.time()
index = 0
for images, msi, checkin, labels in train_loader:
images = images.to(device)
# print(images.shape)
labels = labels.to(device)
msi = msi.to(device)
checkin = checkin.to(device)
checkin = checkin.clone().detach().float()
# Forward pass
# outputs1 = model1(images)
img_feature, outputs2 = model2(images, msi, checkin)
# outputs3 = model3(images)
# loss1 = cost1(outputs1, labels)
loss2 = cost2(outputs2, labels)
# loss3 = cost3(outputs3, labels)
# if index % 10 == 0:
# print (loss)
# Backward and optimize
# optimizer1.zero_grad()
optimizer2.zero_grad()
# optimizer3.zero_grad()
# loss1.backward()
loss2.backward()
# loss3.backward()
# optimizer1.step()
optimizer2.step()
# scheduler2.step(loss2)
# optimizer3.step()
index += 1
if epoch % 1 == 0:
end = time.time()
# print("Epoch [%d/%d], Loss: %.8f, Time: %.1fsec!" % (epoch, args.epochs, loss1.item(), (end-start) * 2))
print("Epoch [%d/%d], Loss: %.8f, Time: %.1fsec!" % (epoch, args.epochs, loss2.item(), (end-start) * 2))
# print("Epoch [%d/%d], Loss: %.8f, Time: %.1fsec!" % (epoch, args.epochs, loss3.item(), (end-start) * 2))
# model1.eval()
model2.eval()
# model3.eval()
# classes = ('bareland', 'cropland', 'forest', 'impervious', 'shrub', 'water')
classes = ('其他', '非正规居住区') # {'住宅区': 0, '公共服务区域': 1, '商业区': 2, '工业区': 3}
# classes = ('Airport', 'BareLand', 'BaseballField', 'Beach', 'Bridge', 'Center', 'Church', 'Commercial', 'DenseResidential', 'Desert', 'Farmland', 'Forest', 'Industrial', 'Meadow', 'MediumResidential', 'Mountain', 'Park', 'Parking', 'Playground', 'Pond', 'Port', 'RailwayStation', 'Resort', 'River', 'School', 'SparseResidential', 'Square', 'Stadium', 'StorageTanks', 'Viaduct')
# classes = ('1 industrial land', '10 shrub land', '11 natural grassland', '12 artificial grassland', '13 river', '14 lake', '15 pond', '2 urban residential', '3 rural residential', '4 traffic land', '5 paddy field', '6 irrigated land', '7 dry cropland', '8 garden plot', '9 arbor woodland')
class_correct1 = list(0. for i in range(args.num_class))
class_total1 = list(0. for i in range(args.num_class))
class_correct2 = list(0. for i in range(args.num_class))
class_total2 = list(0. for i in range(args.num_class))
class_correct3 = list(0. for i in range(args.num_class))
class_total3 = list(0. for i in range(args.num_class))
class_correct_all = list(0. for i in range(args.num_class))
class_total_all = list(0. for i in range(args.num_class))
correct_prediction_1 = 0.
total_1 = 0
correct_prediction_2 = 0.
total_2 = 0
correct_prediction_3 = 0.
total_3 = 0
correct_prediction_all = 0.
total_all = 0
with torch.no_grad():
for images, msi, checkin, labels in val_loader:
images = images.to(device)
# print(images.shape)
labels = labels.to(device)
msi = msi.to(device)
checkin = checkin.to(device)
checkin = checkin.clone().detach().float()
img_feature, outputs2 = model2(images, msi, checkin)
_2, predicted2 = torch.max(outputs2, 1)
c2 = (predicted2 == labels).squeeze()
# print(len(labels))
for label_idx in range(len(labels)):
# print(label_idx)
label = labels[label_idx]
# print(label)
class_correct2[label] += c2[label_idx].item()
class_total2[label] += 1
total_2 += labels.size(0)
# add correct
correct_prediction_2 += (predicted2 == labels).sum().item()
for i in range(args.num_class):
print('Model ResNeXt - Accuracy of %5s : %2d %%: Correct Num: %d in Total Num: %d' % (
classes[i], 100 * class_correct2[i] / class_total2[i], class_correct2[i], class_total2[i]))
acc_2 = correct_prediction_2 / total_2
print("Total Acc Model: %.4f" % (correct_prediction_2 / total_2))
print('----------------------------------------------------')
if acc_2 > best_acc_2:
print('save new best acc_2', acc_2)
torch.save(model2, os.path.join(args.model_path, 'ResMixer-6-2-2.pth'))
best_acc_2 = acc_2
best_epoch = epoch
# if acc_3 > best_acc_3:
# print('save new best acc_3', acc_3)
# torch.save(model3, os.path.join(args.model_path, 'AID-30-teacher-densenet121-%s.pth' % (args.model_name)))
# best_acc_3 = acc_3
# print("Model save to %s."%(os.path.join(args.model_path, 'UFZ-teacher-model-%s.pth' % (args.model_name))))
# print('save new best acc_1', best_acc_1)
print('save new best acc_2', best_acc_2, best_epoch)
# print('save new best acc_3', best_acc_3)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='train hyper-parameter')
parser.add_argument("--num_class", default=2, type=int)
parser.add_argument("--epochs", default=200, type=int)
# parser.add_argument("--net", default='ResNet50', type=str)
# parser.add_argument("--depth", default=50, type=int)
parser.add_argument("--lr", default=1e-3, type=float)
parser.add_argument("--batch_size", default=32, type=int)
# parser.add_argument("--num_workers", default=2, type=int)
parser.add_argument("--model_name", default='', type=str)
parser.add_argument("--model_path", default='./model-UIS', type=str)
parser.add_argument("--pretrained", default=False, type=bool)
parser.add_argument("--pretrained_model", default='', type=str)
args = parser.parse_args()
main(args)