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train.py
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train.py
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import os
import time
import random
import numpy as np
import torch.autograd
from skimage import io
from torch import optim
import torch.nn.functional as F
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
working_path = os.path.dirname(os.path.abspath(__file__))
###############################################
from datasets import RS_XT as RS
#from models.MS_FCN import MS_FCN as Net
from models.MPResNet import MPResNet as Net
NET_NAME = 'MPResNet'
DATA_NAME = 'XT'
###############################################
from utils.loss import CrossEntropyLoss2d, FocalLoss2d
from utils.utils import accuracy, FWIoU, intersectionAndUnion, AverageMeter
args = {
'lr': 0.1,
'gpu': True,
'epochs': 200,
'momentum': 0.9,
'print_freq': 10,
'predict_step': 5,
'val_batch_size': 8,
'train_batch_size': 8,
'weight_decay': 5e-4,
'lr_decay_power': 1.5,
'train_crop_size': False,
'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)
}
writer = SummaryWriter(args['log_dir'])
if not os.path.exists(args['log_dir']): os.makedirs(args['log_dir'])
if not os.path.exists(args['pred_dir']): os.makedirs(args['pred_dir'])
if not os.path.exists(args['chkpt_dir']): os.makedirs(args['chkpt_dir'])
def main():
net = Net(4, num_classes=RS.num_classes+1) #, pretrained=True
if args['gpu']: net = net.cuda()
train_set = RS.PolSAR(mode='train', random_flip=True, crop_size=args['train_crop_size'])
val_set = RS.PolSAR(mode='val', random_flip=False, crop_size=False)
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=4, shuffle=True)
val_loader = DataLoader(val_set, batch_size=args['val_batch_size'], num_workers=4, shuffle=False)
criterion = CrossEntropyLoss2d(ignore_index=0)
#criterion = FocalLoss2d(gamma=2.0, ignore_index=0)
if args['gpu']: criterion = criterion.cuda()
optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=args['lr'], weight_decay=args['weight_decay'], momentum=args['momentum'], nesterov=True)
scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.95, last_epoch=-1)
train(train_loader, net, criterion, optimizer, scheduler, args, val_loader)
writer.close()
print('Training finished.')
def train(train_loader, net, criterion, optimizer, scheduler, args, val_loader):
bestaccT=0
bestfwiou=0.5
bestaccV=0.0
bestloss=1
begin_time = time.time()
all_iters = float(len(train_loader)*args['epochs'])
curr_epoch=0
while True:
if args['gpu']: torch.cuda.empty_cache()
net.train()
start = time.time()
acc_meter = AverageMeter()
train_main_loss = AverageMeter()
train_aux_loss = AverageMeter()
curr_iter = curr_epoch*len(train_loader)
for i, (imgs, labels) in enumerate(train_loader):
running_iter = curr_iter+i+1
adjust_lr(optimizer, running_iter, all_iters)
#imgs = torch.squeeze(imgs)
imgs = imgs.float()
labels = labels.long()
#imgs, labels = data
if args['gpu']:
imgs = imgs.cuda().float()
labels = labels.cuda().long()
optimizer.zero_grad()
outputs, aux = net(imgs) #
assert outputs.shape[1] == RS.num_classes+1
loss_main = criterion(outputs, labels)
loss_aux = criterion(aux, labels)
loss = loss_main*0.7 + loss_aux*0.3
loss.backward()
optimizer.step()
labels = labels.cpu().detach().numpy()
outputs = outputs.cpu().detach()
_, preds = torch.max(outputs, dim=1)
preds = preds.numpy()
# batch_valid_sum = 0
acc_curr_meter = AverageMeter()
for (pred, label) in zip(preds, labels):
acc, _ = accuracy(pred, label)
acc_curr_meter.update(acc)
acc_meter.update(acc_curr_meter.avg)
train_main_loss.update(loss.cpu().detach().numpy())
train_aux_loss.update(loss_aux.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_main_loss.val, acc_meter.val*100))
writer.add_scalar('train_main_loss', train_main_loss.val, running_iter)
writer.add_scalar('train_accuracy', acc_meter.val, running_iter)
writer.add_scalar('train_aux_loss', train_aux_loss.avg, running_iter)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], running_iter)
acc_v, fwiou_v, loss_v = validate(val_loader, net, criterion, curr_epoch)
if acc_meter.avg > bestaccT: bestaccT=acc_meter.avg
if fwiou_v>bestfwiou:
bestfwiou=fwiou_v
bestloss=loss_v
bestaccV=acc_v
torch.save(net.state_dict(), os.path.join(args['chkpt_dir'], NET_NAME+'_%de_OA%.2f_fwIoU%.2f.pth'%(curr_epoch, acc_v*100, fwiou_v*100)) )
print('Total time: %.1fs Best rec: Train acc %.2f, Val acc %.2f fwiou %.2f, Val_loss %.4f' %(time.time()-begin_time, bestaccT*100, bestaccV*100, bestfwiou*100, bestloss))
curr_epoch += 1
#scheduler.step()
if curr_epoch >= args['epochs']:
return
def validate(val_loader, net, criterion, curr_epoch):
# the following code is written assuming that batch size is 1
net.eval()
if args['gpu']:
torch.cuda.empty_cache()
start = time.time()
val_loss = AverageMeter()
acc_meter = AverageMeter()
fwIoU_meter = AverageMeter()
for vi, (imgs, labels) in enumerate(val_loader):
imgs = imgs.float()
labels = labels.long()
if args['gpu']:
imgs = imgs.cuda().float()
labels = labels.cuda().long()
with torch.no_grad():
outputs, aux = net(imgs)
loss = criterion(outputs, labels)
val_loss.update(loss.cpu().detach().numpy())
outputs = outputs.cpu().detach()
labels = labels.cpu().detach().numpy()
_, preds = torch.max(outputs, dim=1)
preds = preds.numpy()
for (pred, label) in zip(preds, labels):
acc, valid_sum = accuracy(pred, label)
fwiou = FWIoU(pred.squeeze(), label.squeeze(), ignore_zero=True)
acc_meter.update(acc)
fwIoU_meter.update(fwiou)
if curr_epoch%args['predict_step']==0 and vi==0:
pred_color = RS.Index2Color(preds[0])
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, Accuracy: %.2f, fwIoU: %.2f'%(curr_time, val_loss.average(), acc_meter.average()*100, fwIoU_meter.average()*100))
writer.add_scalar('val_loss', val_loss.average(), curr_epoch)
writer.add_scalar('val_Accuracy', acc_meter.average(), curr_epoch)
writer.add_scalar('val_fwIoU', fwIoU_meter.average(), curr_epoch)
return acc_meter.avg, fwIoU_meter.avg, val_loss.avg
def adjust_lr(optimizer, curr_iter, all_iter, init_lr=args['lr']):
scale_running_lr = ((1. - float(curr_iter) / all_iter) ** args['lr_decay_power'])
running_lr = init_lr * scale_running_lr
for param_group in optimizer.param_groups:
param_group['lr'] = running_lr
if __name__ == '__main__':
main()