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Train_WiCo_PD.py
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Train_WiCo_PD.py
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import os
import time
import torch.autograd
from skimage import io
from torch import optim
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
from utils.loss import CrossEntropyLoss2d
from utils.utils import accuracy, AverageMeter
working_path = os.path.dirname(os.path.abspath(__file__))
# Choose model and data
##################################################
from datasets import PD_WiCo as PD
from models.WiCoNet import WiCoNet as Net
NET_NAME = 'WiCoNet_3hw4L'
DATA_NAME = 'PD'
##################################################
args = {
'train_batch_size': 32,
'val_batch_size': 32,
'lr': 0.1,
'epochs': 50,
'gpu': True,
'size_local': 256,
'size_context': 256*3,
'momentum': 0.9,
'crop_nums': 1000,
'print_freq': 100,
'weight_decay': 5e-4,
'lr_decay_power': 1.5,
'save_pred': True,
'num_workers': 4,
'data_dir': 'YOUR_DATA_DIR',
'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, 'saved_model.pth')
}
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'])
if not os.path.exists(args['log_dir']): os.makedirs(args['log_dir'])
writer = SummaryWriter(args['log_dir'])
def main():
net = Net(5, num_classes=PD.num_classes+1, size_context=args['size_context'], size_local=args['size_local']).cuda()
net.load_state_dict(torch.load(args['load_path']))
train_set = PD.Loader(args['data_dir'], 'train', random_crop=True, crop_nums=args['crop_nums'], random_flip=True,
size_context=args['size_context'], size_local=args['size_local'])
train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=args['num_workers'], shuffle=True)
val_set = PD.Loader(args['data_dir'], 'val', sliding_crop=True, size_context=args['size_context'], size_local=args['size_local'])
val_loader = DataLoader(val_set, batch_size=args['val_batch_size'], num_workers=args['num_workers'], shuffle=False)
criterion = CrossEntropyLoss2d(ignore_index=0).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)
train(train_loader, net, criterion, optimizer, val_loader)
writer.close()
print('Training finished.')
def train(train_loader, net, criterion, optimizer, val_loader):
bestaccT=0
bestaccV=0.5
bestloss=1
curr_epoch = 0
begin_time = time.time()
all_iters = float(len(train_loader)*args['epochs'])
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_learning_rate(optimizer, running_iter, all_iters)
imgs_s, labels_s, imgs, labels = data
if args['gpu']:
imgs = imgs.cuda().float()
labels = labels.cuda().long()
imgs_s = imgs_s.cuda().float()
labels_s = labels_s.cuda().long()
optimizer.zero_grad()
outputs, aux = net(imgs_s, imgs)
alpha = calc_alpha(running_iter, all_iters)
main_loss = criterion(outputs, labels)
aux_loss = criterion(aux, labels_s)
loss = main_loss + alpha*aux_loss
loss.backward()
optimizer.step()
labels = labels.cpu().detach().numpy()
outputs = outputs.cpu().detach()
preds = torch.argmax(outputs, dim=1)
preds = preds.numpy()
# batch_valid_sum = 0
acc_curr_meter = AverageMeter()
for (pred, label) in zip(preds, labels):
acc, valid_sum = 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)
writer.add_scalar('train accuracy', acc_meter.val, running_iter)
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], running_iter)
acc_v, loss_v = validate(val_loader, net, criterion, curr_epoch)
if acc_meter.avg>bestaccT: bestaccT=acc_meter.avg
if acc_v>bestaccV:
bestaccV=acc_v
bestloss=loss_v
save_path = os.path.join(args['chkpt_dir'], NET_NAME+'_%de_OA%.2f.pth'%(curr_epoch, acc_v*100))
torch.save(net.state_dict(), save_path)
print('Total time: %.1fs Best rec: Train %.2f, Val %.2f, Val_loss %.4f'\
%(time.time()-begin_time, bestaccT*100, bestaccV*100, bestloss))
curr_epoch += 1
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()
torch.cuda.empty_cache()
start = time.time()
val_loss = AverageMeter()
acc_meter = AverageMeter()
for vi, data in enumerate(val_loader):
imgs_s, labels_s, imgs, labels = data
if args['gpu']:
imgs = imgs.cuda().float()
labels = labels.cuda().long()
imgs_s = imgs_s.cuda().float()
with torch.no_grad():
outputs, _ = net(imgs_s, imgs) #
loss = criterion(outputs, labels)
val_loss.update(loss.cpu().detach().numpy())
outputs = outputs.cpu().detach()
labels = labels.cpu().detach().numpy()
preds = torch.argmax(outputs, dim=1)
preds = preds.numpy()
for (pred, label) in zip(preds, labels):
acc, valid_sum = accuracy(pred, label)
acc_meter.update(acc)
if args['save_pred'] and vi==0:
pred_color = PD.Index2Color(preds[0])
pred_path = os.path.join(args['pred_dir'], NET_NAME+'.png')
io.imsave(pred_path, pred_color)
print('Prediction saved!')
curr_time = time.time() - start
print('%.1fs Val loss: %.2f Accuracy: %.2f'%(curr_time, val_loss.average(), acc_meter.average()*100))
writer.add_scalar('val_loss', val_loss.average(), curr_epoch)
writer.add_scalar('val_Accuracy', acc_meter.average(), curr_epoch)
return acc_meter.avg, val_loss.avg
def calc_alpha(curr_iter, all_iters, weight=1.0):
r = (1.0-float(curr_iter)/all_iters)** 2.0
return weight*r
def adjust_learning_rate(optimizer, curr_iter, all_iter):
scale_running_lr = ((1. - float(curr_iter) / all_iter) ** args['lr_decay_power'])
running_lr = args['lr'] * scale_running_lr
for param_group in optimizer.param_groups:
param_group['lr'] = running_lr
if __name__ == '__main__':
main()