def validate(args, val_dataloader, model, auxiliarynet, epoch):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
error = AverageMeter('error', ':6.2f')
progress = ProgressMeter(len(val_dataloader),
batch_time,
data_time,
losses,
error,
prefix="Val Epoch: [{}]".format(epoch))
model.eval()
# auxiliarynet.eval()
end = time.time()
with torch.no_grad():
end = time.time()
for i, (patch, gaze_norm_g, head_norm,
rot_vec_norm) in enumerate(val_dataloader):
# measure data loading time
data_time.update(time.time() - end)
patch = patch.to(args.device)
gaze_norm_g = gaze_norm_g.to(args.device)
head_norm = head_norm.to(args.device)
rot_vec_norm = rot_vec_norm.to(args.device)
# model = model.to(args.device)
gaze_pred, _ = model(patch)
# hp_pred = auxiliarynet(features)
head_norm = 10 * head_norm
gaze_norm_g = 10 * gaze_norm_g
# loss = criterion(gaze_norm_g, head_norm, gaze_pred[:,0:2], gaze_pred[:,2:4])
angle_error = mean_angle_error(
gaze_pred.cpu().detach().numpy() / 10,
gaze_norm_g.cpu().detach().numpy() / 10,
rot_vec_norm.cpu().detach().numpy())
# losses.update(loss.item())
error.update(angle_error)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
progress.print(i + 1)
# img = patch.cpu().detach().numpy()[0].deepcopy()
# to_visualize = draw_gaze(img[0], (0.25 * img.shape[1], 0.25 * img.shape[1]), gaze_pred,
# gaze_norm_g, length=80.0, thickness=1)
# cv2.imshow('vis', to_visualize)
# cv2.waitKey(1)
return losses.get_avg(), error.get_avg()
def train(args, train_loader, model, auxiliarynet, criterion, optimizer,
epoch):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
error = AverageMeter('error', ':6.2f')
progress = ProgressMeter(len(train_loader), batch_time, data_time, losses, error,
prefix="Train Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
auxiliarynet.train()
end = time.time()
for batch_idx, (eyes, face, gaze_norm_g, head_norm, rot_vec_norm) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
eyes.requires_grad = False
eyes = eyes.to(args.device)
face.requires_grad = False
face = face.to(args.device)
gaze_norm_g.requires_grad = False
gaze_norm_g = gaze_norm_g.to(args.device)
head_norm.requires_grad = False
head_norm = head_norm.to(args.device)
rot_vec_norm.requires_grad = False
rot_vec_norm = rot_vec_norm.to(args.device)
face_feature = auxiliarynet(face)
# gaze_pred, head_pred = model(eyes, face_feature)
gaze_pred = model(eyes, face_feature)
# print(features.size())
head_norm = 100 * head_norm
gaze_norm_g = 100 * gaze_norm_g
loss = criterion(gaze_norm_g, head_norm, gaze_pred[:, 0:2], gaze_pred[:, 2:4])
# loss = criterion(gaze_norm_g, head_norm, gaze_pred, None)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
angle_error = mean_angle_error(gaze_pred[:,0:2].cpu().detach().numpy()/100,
gaze_norm_g.cpu().detach().numpy()/100,
rot_vec_norm.cpu().detach().numpy())
losses.update(loss.item(), eyes.size(0))
error.update(angle_error, eyes.size(0))
if(batch_idx + 1) % args.print_freq == 0:
progress.print(batch_idx+1)
return losses.get_avg(), error.get_avg()
def train(device, train_loader, model, criterion, optimizer, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
error = AverageMeter('error', ':6.2f')
progress = ProgressMeter(len(train_loader),
batch_time,
data_time,
losses,
error,
prefix="Train Epoch: [{}]".format(epoch))
# switch to train mode
model.train()
end = time.time()
for batch_idx, (patch, gaze_norm_g, head_norm,
rot_vec_norm) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
patch.requires_grad = False
patch = patch.to(device)
gaze_norm_g.requires_grad = False
gaze_norm_g = gaze_norm_g.to(device)
head_norm.requires_grad = False
head_norm = head_norm.to(device)
rot_vec_norm.requires_grad = False
rot_vec_norm = rot_vec_norm.to(device)
model = model.to(device)
gaze_norm_gt = torch.clamp((100 * (gaze_norm_g + 0.75)).long(),
-74,
75,
out=None)
gaze_p, gaze_y = model(patch)
loss = criterion(gaze_p, gaze_norm_gt[:, 0]) + criterion(
gaze_y, gaze_norm_gt[:, 1])
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
_, preds_p = torch.max(gaze_p, 1)
_, preds_y = torch.max(gaze_y, 1)
gaze_pred = torch.cat(
[preds_p.view(patch.size(0), 1),
preds_y.view(patch.size(0), 1)], 1)
angle_error = mean_angle_error(
gaze_pred.cpu().detach().numpy().astype(float) / 100 - 0.75,
gaze_norm_g.cpu().detach().numpy(),
rot_vec_norm.cpu().detach().numpy())
losses.update(loss.item(), patch.size(0))
error.update(angle_error, patch.size(0))
if (batch_idx + 1) % args.print_freq == 0:
progress.print(batch_idx + 1)
return losses.get_avg(), error.get_avg()
def validate(device, val_dataloader, model, criterion, epoch, args):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
error = AverageMeter('error', ':6.2f')
progress = ProgressMeter(len(val_dataloader),
batch_time,
data_time,
losses,
error,
prefix="Val Epoch: [{}]".format(epoch))
model.eval()
with torch.no_grad():
end = time.time()
for i, (patch, gaze_norm_g, head_norm,
rot_vec_norm) in enumerate(val_dataloader):
# measure data loading time
data_time.update(time.time() - end)
patch = patch.to(device)
gaze_norm_g = gaze_norm_g.to(device)
head_norm = head_norm.to(device)
rot_vec_norm = rot_vec_norm.to(device)
model = model.to(device)
gaze_norm_gt = torch.clamp((100 * (gaze_norm_g + 0.75)).long(),
-74,
75,
out=None)
gaze_p, gaze_y = model(patch)
loss = criterion(gaze_p, gaze_norm_gt[:, 0]) + criterion(
gaze_y, gaze_norm_gt[:, 1])
_, preds_p = torch.max(gaze_p, 1)
_, preds_y = torch.max(gaze_y, 1)
gaze_pred = torch.cat([
preds_p.view(patch.size(0), 1),
preds_y.view(patch.size(0), 1)
], 1)
angle_error = mean_angle_error(
gaze_pred.cpu().detach().numpy().astype(float) / 100 - 0.75,
gaze_norm_g.cpu().detach().numpy(),
rot_vec_norm.cpu().detach().numpy())
losses.update(loss.item(), patch.size(0))
error.update(angle_error, patch.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % args.print_freq == 0:
progress.print(i + 1)
return losses.get_avg(), error.get_avg()