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Train_WiCo_GID.py
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Train_WiCo_GID.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
working_path = os.path.dirname(os.path.abspath(__file__))
from utils.loss import CrossEntropyLoss2d
from utils.utils import accuracy, AverageMeter
# Choose model and data
##################################################
from datasets import GID_WiCo as GID
from models.WiCoNet import WiCoNet as Net
NET_NAME = 'WiCoNet_3hw4L'
DATA_NAME = 'GID'
##################################################
# Change training parameters here
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': 100,
'weight_decay': 5e-4,
'lr_decay_power': 1.5,
'print_freq': 100,
'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)
}
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(4, num_classes=GID.num_classes + 1, size_context=args['size_context'],
size_local=args['size_local']).cuda()
train_set = GID.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 = GID.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 = GID.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()