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train_SAM_CD.py
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train_SAM_CD.py
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import time
import os
import torch.autograd
from skimage import io
from torch import optim
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
import torch.nn.functional as F
working_path = os.path.abspath('.')
from utils.loss import LatentSimilarity
from utils.utils import binary_accuracy as accuracy
from utils.utils import AverageMeter
###################### Data and Model ########################
from models.SAM_CD import SAM_CD as Net
NET_NAME = 'SAM_CD'
from datasets import Levir_CD as RS
DATA_NAME = 'Levir_CD'
#from datasets import WHU_CD_list as RS
#DATA_NAME = 'WHU_CD_0.05'
###################### Data and Model ########################
########################## Parameters ########################
args = {
'train_batch_size': 4,
'val_batch_size': 4,
'lr': 0.1,
'epochs': 50,
'gpu': True,
'dev_id': 0,
'multi_gpu': None, #"0,1,2,3",
'weight_decay': 5e-4,
'momentum': 0.9,
'print_freq': 50,
'predict_step': 5,
'crop_size': 512,
'pred_dir': os.path.join(working_path, 'results', DATA_NAME),
'chkpt_dir': os.path.join(working_path, 'checkpoints', DATA_NAME),
'log_dir': os.path.join(working_path, 'logs', DATA_NAME, NET_NAME),
'load_path': os.path.join(working_path, 'checkpoints', DATA_NAME, 'xxx.pth')}
########################## Parameters ########################
if not os.path.exists(args['log_dir']): os.makedirs(args['log_dir'])
if not os.path.exists(args['chkpt_dir']): os.makedirs(args['chkpt_dir'])
if not os.path.exists(args['pred_dir']): os.makedirs(args['pred_dir'])
writer = SummaryWriter(args['log_dir'])
def main():
net = Net()
#net.load_state_dict(torch.load(args['load_path']), strict=False)
if args['multi_gpu']:
net = torch.nn.DataParallel(net, [int(id) for id in args['multi_gpu'].split(',')])
net.to(device=torch.device('cuda', int(args['dev_id'])))
train_set = RS.RS('train', random_crop=True, crop_nums=10, crop_size=args['crop_size'], random_flip=True) #'5_train_supervised',
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=4, shuffle=True)
val_set = RS.RS('val', sliding_crop=False, crop_size=args['crop_size'], random_flip=False)
val_loader = DataLoader(val_set, batch_size=args['val_batch_size'], num_workers=4, shuffle=False)
optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), args['lr'],
weight_decay=args['weight_decay'], momentum=args['momentum'], nesterov=True)
train(train_loader, net, optimizer, val_loader)
writer.close()
print('Training finished.')
def train(train_loader, net, optimizer, val_loader):
bestF = 0.0
bestacc = 0.0
bestIoU = 0.0
bestloss = 1.0
bestaccT = 0.0
curr_epoch = 0
begin_time = time.time()
all_iters = float(len(train_loader) * args['epochs'])
criterion_sem = LatentSimilarity(T=3.0).to(torch.device('cuda', int(args['dev_id'])))
while True:
torch.cuda.empty_cache()
net.train()
start = time.time()
acc_meter = AverageMeter()
train_loss = AverageMeter()
curr_iter = curr_epoch * len(train_loader)
for i, data in enumerate(train_loader):
running_iter = curr_iter + i + 1
adjust_lr(optimizer, running_iter, all_iters, args)
imgs_A, imgs_B, labels = data
if args['gpu']:
imgs_A = imgs_A.to(torch.device('cuda', int(args['dev_id']))).float()
imgs_B = imgs_B.to(torch.device('cuda', int(args['dev_id']))).float()
labels = labels.to(torch.device('cuda', int(args['dev_id']))).float().unsqueeze(1)
optimizer.zero_grad()
outputs, outA, outB = net(imgs_A, imgs_B)
assert outputs.shape[1] == 1
loss_bn = F.binary_cross_entropy_with_logits(outputs, labels)
loss_t = criterion_sem(outA, outB, labels)
loss = loss_bn + loss_t
loss.backward()
optimizer.step()
labels = labels.cpu().detach().numpy()
outputs = outputs.cpu().detach()
preds = F.sigmoid(outputs).numpy()
acc_curr_meter = AverageMeter()
for (pred, label) in zip(preds, labels):
acc, precision, recall, F1, IoU = accuracy(pred, label)
acc_curr_meter.update(acc)
acc_meter.update(acc_curr_meter.avg)
train_loss.update(loss.cpu().detach().numpy())
curr_time = time.time() - start
if (i + 1) % args['print_freq'] == 0:
print('[epoch %d] [iter %d / %d %.1fs] [lr %f] [train loss %.4f acc %.2f]' % (
curr_epoch, i + 1, len(train_loader), curr_time, optimizer.param_groups[0]['lr'],
train_loss.val, acc_meter.val * 100))
writer.add_scalar('train loss', train_loss.val, running_iter)
loss_rec = train_loss.val
writer.add_scalar('train accuracy', acc_meter.val, running_iter)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], running_iter)
val_F, val_acc, val_IoU, val_loss = validate(val_loader, net, curr_epoch)
if val_F > bestF:
bestF = val_F
bestacc = val_acc
bestIoU = val_IoU
torch.save(net.state_dict(), os.path.join(args['chkpt_dir'], NET_NAME + '_e%d_OA%.2f_F%.2f_IoU%.2f.pth' % (
curr_epoch, val_acc * 100, val_F * 100, val_IoU * 100)))
if acc_meter.avg > bestaccT: bestaccT = acc_meter.avg
print('[epoch %d/%d %.1fs] Best rec: Train %.2f, Val %.2f, F1 score: %.2f IoU %.2f' \
% (curr_epoch, args['epochs'], time.time() - begin_time, bestaccT * 100, bestacc * 100, bestF * 100,
bestIoU * 100))
curr_epoch += 1
if curr_epoch >= args['epochs']:
return
def validate(val_loader, net, curr_epoch):
# the following code is written assuming that batch size is 1
net.eval()
torch.cuda.empty_cache()
start = time.time()
val_loss = AverageMeter()
F1_meter = AverageMeter()
IoU_meter = AverageMeter()
Acc_meter = AverageMeter()
for vi, data in enumerate(val_loader):
imgs_A, imgs_B, labels = data
if args['gpu']:
imgs_A = imgs_A.to(torch.device('cuda', int(args['dev_id']))).float()
imgs_B = imgs_B.to(torch.device('cuda', int(args['dev_id']))).float()
labels = labels.to(torch.device('cuda', int(args['dev_id']))).float().unsqueeze(1)
with torch.no_grad():
outputs, outA, outB = net(imgs_A, imgs_B)
loss = F.binary_cross_entropy_with_logits(outputs, labels)
val_loss.update(loss.cpu().detach().numpy())
outputs = outputs.cpu().detach()
labels = labels.cpu().detach().numpy()
preds = F.sigmoid(outputs).numpy()
for (pred, label) in zip(preds, labels):
acc, precision, recall, F1, IoU = accuracy(pred, label)
F1_meter.update(F1)
Acc_meter.update(acc)
IoU_meter.update(IoU)
if curr_epoch % args['predict_step'] == 0 and vi == 0:
pred_color = RS.Index2Color(preds[0].squeeze())
io.imsave(os.path.join(args['pred_dir'], NET_NAME + '.png'), pred_color)
print('Prediction saved!')
curr_time = time.time() - start
print('%.1fs Val loss %.2f Acc %.2f F %.2f' % (
curr_time, val_loss.average(), Acc_meter.average() * 100, F1_meter.average() * 100))
writer.add_scalar('val_loss', val_loss.average(), curr_epoch)
writer.add_scalar('val_Accuracy', Acc_meter.average(), curr_epoch)
return F1_meter.avg, Acc_meter.avg, IoU_meter.avg, val_loss.avg
def adjust_lr(optimizer, curr_iter, all_iter, args):
scale_running_lr = ((1. - float(curr_iter) / all_iter) ** 3.0)
running_lr = args['lr'] * scale_running_lr
for param_group in optimizer.param_groups:
param_group['lr'] = running_lr
if __name__ == '__main__':
main()