def validate(epoch):
model.eval()
top1 = AverageMeter()
top5 = AverageMeter()
global best_pred, acclist_train, acclist_val
is_best = False
for batch_idx, (data, target) in enumerate(val_loader):
data, target = data.cuda(args.gpu), target.cuda(args.gpu)
with torch.no_grad():
output = model(data)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
top1.update(acc1[0], data.size(0))
top5.update(acc5[0], data.size(0))
# sum all
sum1, cnt1, sum5, cnt5 = torch_dist_sum(args.gpu, top1.sum, top1.count,
top5.sum, top5.count)
if args.eval:
if args.gpu == 0:
top1_acc = sum(sum1) / sum(cnt1)
top5_acc = sum(sum5) / sum(cnt5)
print('Validation: Top1: %.3f | Top5: %.3f' %
(top1_acc, top5_acc))
return
if args.gpu == 0:
top1_acc = sum(sum1) / sum(cnt1)
top5_acc = sum(sum5) / sum(cnt5)
print('Validation: Top1: %.3f | Top5: %.3f' % (top1_acc, top5_acc))
# save checkpoint
acclist_val += [top1_acc]
if top1_acc > best_pred:
best_pred = top1_acc
is_best = True
state_dict = {
'epoch': epoch,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
'acclist_train': acclist_train,
'acclist_val': acclist_val,
}
if args.amp:
state_dict['amp'] = amp.state_dict()
encoding.utils.save_checkpoint(state_dict,
args=args,
is_best=is_best)
def validation(epoch):
# Fast test during the training using single-crop only
global best_pred
is_best = False
model.eval()
metric.reset()
for i, (image, target) in enumerate(valloader):
with torch.no_grad():
pred = model(image)[0]
target = target.cuda(args.gpu)
metric.update(target, pred)
if i % 100 == 0:
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU: %.3f' % (pixAcc, mIoU))
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU: %.3f' % (pixAcc, mIoU))
if args.eval: return
new_pred = (pixAcc + mIoU)/2
if new_pred > best_pred:
is_best = True
best_pred = new_pred
utils.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
}, args, is_best)
def validation(epoch):
# Fast test during the training using single-crop only
global best_pred
is_best = False
model.eval()
metric.reset()
for i, (image, target) in enumerate(valloader):
with torch.no_grad():
pred = model(image)[0]
target = target.cuda(args.gpu)
metric.update(target, pred)
if i % 100 == 0:
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU1: %.3f' % (pixAcc, mIoU))
all_metircs = metric.get_all()
all_metircs = utils.torch_dist_sum(args.gpu, *all_metircs)
pixAcc, mIoU = utils.get_pixacc_miou(*all_metircs)
if args.gpu == 0:
print('pixAcc: %.3f, mIoU2: %.3f' % (pixAcc, mIoU))
p_m.append((pixAcc, mIoU))
plt.plot(p_m)
plt.xlabel('10 Epoch')
plt.ylabel('pixAcc, mIoU')
plt.title('pixAcc, mIoU')
plt.grid()
plt.legend(('pixAcc', 'mIoU'))
plt.savefig('./loss_fig/pixAcc_mIoU.pdf')
plt.savefig('./loss_fig/pixAcc_mIoU.svg')
plt.close()
if args.eval: return
new_pred = (pixAcc + mIoU) / 2
new_preds.append(new_pred)
plt.plot(new_preds)
plt.xlabel('10 Epoch')
plt.ylabel('new_predication')
plt.title('new_predication')
plt.grid()
plt.savefig('./loss_fig/new_predication.pdf')
plt.savefig('./loss_fig/new_predication.svg')
plt.close()
if new_pred > best_pred:
is_best = True
best_pred = new_pred
utils.save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
'best_pred': best_pred,
},
args,
is_best,
filename='checkpoint_train_{}.pth.tar'.format(epoch + 1))