def valid(net,
optimizer,
valid_loader,
use_lovasz=False,
save_imgs=False,
fold_num=0):
net.eval()
# keep track of losses
val_ious = []
val_iter_loss = 0.
# no gradients during validation
with torch.no_grad():
for i, data in enumerate(valid_loader):
valid_imgs = data['img'].to(device)
valid_msks = data['msk'].to(device)
valid_msk_bool = data['has_msk'].float().to(device)
# get predictions
msk_vpreds, bool_vpreds = net(valid_imgs)
msk_blend_vpreds = msk_vpreds * bool_vpreds.view(
valid_imgs.size(0), 1, 1, 1)
if save_imgs:
img_grid = vsn.utils.make_grid(valid_imgs, normalize=True)
msk_grid = vsn.utils.make_grid(msk_vpreds)
vsn.utils.save_image(
img_grid,
'../imgs/valid_imgs_fold-{}.png'.format(fold_num))
vsn.utils.save_image(
msk_grid,
'../imgs/valid_msks_fold-{}.png'.format(fold_num))
# calculate loss
if use_lovasz:
vloss = L.lovasz_hinge(msk_vpreds, valid_msks)
#vloss += focal_loss(msk_vpreds, valid_msks)
#vloss -= dice(msk_vpreds.sigmoid(), valid_msks)
else:
vloss = focal_loss(msk_vpreds, valid_msks)
vloss += L.lovasz_hinge(msk_vpreds, valid_msks)
#vloss -= dice(msk_vpreds.sigmoid(), valid_msks)
vloss += args.lambda_bool * bce(bool_vpreds,
valid_msk_bool.view(-1, 1))
vloss += args.lambda_bool * focal_loss(msk_blend_vpreds,
valid_msks)
#vloss += args.lambda_dice * dice(msk_vpreds.sigmoid(), valid_msks)
# get validation stats
val_iter_loss += vloss.item()
val_ious.append(
get_iou_vector(
valid_msks.cpu().numpy()[:, :, 13:114, 13:114],
msk_vpreds.sigmoid().cpu().numpy()[:, :, 13:114, 13:114]))
epoch_vloss = val_iter_loss / (len(valid_loader.dataset) / args.batch_size)
print('Avg Eval Loss: {:.4}, Avg IOU: {:.4}'.format(
epoch_vloss, np.mean(val_ious)))
return epoch_vloss, np.mean(val_ious)
def valid(net, optimizer, valid_loader, mtype='student', use_lovasz=False):
net.eval()
# keep track of losses
val_ious = []
val_iter_loss = 0.
# no gradients during validation
with torch.no_grad():
for i, data in enumerate(valid_loader):
valid_imgs = data['img'].cuda()
valid_msks = data['msk'].cuda()
valid_msk_bool = data['has_msk'].float().cuda()
# get predictions
msk_vpreds, bool_v = net(valid_imgs)
# calculate loss
if use_lovasz:
vloss = L.lovasz_hinge(msk_vpreds, valid_msks)
else:
vloss = focal_loss(msk_vpreds, valid_msks)
vloss += L.lovasz_hinge(msk_vpreds, valid_msks)
vloss += bce(bool_v, valid_msk_bool.view(-1, 1))
# get validation stats
val_iter_loss += vloss.item()
val_ious.append(
get_iou_vector(
valid_msks.cpu().numpy()[:, :, 13:114, 13:114],
msk_vpreds.sigmoid().cpu().numpy()[:, :, 13:114, 13:114]))
epoch_vloss = val_iter_loss / (len(valid_loader.dataset) / args.batch_size)
print('{} Avg Eval Loss: {:.4}, Avg IOU: {:.4}'.format(
mtype, epoch_vloss, np.mean(val_ious)))
return epoch_vloss, np.mean(val_ious)
def valid(net, optimizer, valid_loader, save_imgs=False, fold_num=0):
net.eval()
# keep track of losses
val_ious = []
val_iter_loss = 0.
# no gradients during validation
with torch.no_grad():
for i, data in enumerate(valid_loader):
valid_imgs = data['img'].to(device)
valid_msks = data['msk']
#valid_msks_half = data['msk_half'].to(device)
#valid_msks_qrtr = data['msk_qrtr'].to(device)
valid_msk_bool = data['has_msk'].to(device)
# get predictions
msk_vpreds = net(valid_imgs)
# calculate loss
# main mask loss
vloss = focal_loss(msk_vpreds[0], valid_msks[0].to(device))
vloss += L.lovasz_hinge(msk_vpreds[0], valid_msks[0].to(device))
# aux mask losses
for j in range(len(valid_msks)):
#print(msk_preds[i+1].size(), msks[i].size())
vloss += 0.1 * focal_loss(
msk_vpreds[j + 1][valid_msk_bool],
valid_msks[j][valid_msk_bool].to(device))
vloss += 0.1 * L.lovasz_hinge(
msk_vpreds[j + 1][valid_msk_bool],
valid_msks[j][valid_msk_bool].to(device))
# binary loss
vloss += 0.05 * bce(msk_vpreds[-1],
valid_msk_bool.float().view(-1, 1))
# get validation stats
val_iter_loss += vloss.item()
val_ious.append(
get_iou_vector(
valid_msks[0].cpu().numpy()[:, :, 13:114, 13:114],
msk_vpreds[0].sigmoid().cpu().numpy()[:, :, 13:114,
13:114]))
epoch_vloss = val_iter_loss / (len(valid_loader.dataset) / args.batch_size)
print('Avg Eval Loss: {:.4}, Avg IOU: {:.4}'.format(
epoch_vloss, np.mean(val_ious)))
return epoch_vloss, np.mean(val_ious)