def validate(train_loader, visae, attae, epoch):
visae.eval()
attae.eval()
train_losses = AverageMeter()
train_acces = AverageMeter()
for i, (emb, target) in enumerate(train_loader):
emb = emb.to(device)
with torch.no_grad():
seen_class_embeddings = attae(
seen_s2v_labels.to(device).float())[2]
vis_emb = torch.log(1 + emb)
vis_emb.retain_grad()
vis_emb.requires_grad_(True)
vis_hidden, vis_out, vis_trans_out = visae(vis_emb)
vis_recons_loss = contractive_loss(vis_out,
vis_emb,
vis_hidden,
criterion2,
device,
gamma=0.0001)
att_emb = get_text_data(target, s2v_labels.float()).to(device)
att_hidden, att_out, att_trans_out = attae(att_emb)
att_recons_loss = criterion2(att_out, att_emb)
# mmd loss
loss_mmd = MMDLoss(vis_hidden, att_hidden).to(device)
# supervised binary prediction loss
pred_loss = multi_class_hinge_loss(vis_trans_out, att_trans_out,
target, seen_class_embeddings)
loss = pred_loss + vis_recons_loss + att_recons_loss + loss_mmd
acc, _ = accuracy(seen_class_embeddings, vis_trans_out.detach(),
target.to(device), seen_inds)
train_losses.update(loss.item(), emb.size(0))
train_acces.update(acc, emb.size(0))
if i % 20 == 0:
print('Epoch-{:<3d} {:3d} batches\t'
'loss {loss.val:.4f} ({loss.avg:.4f})\t'
'accu {acc.val:.3f} ({acc.avg:.3f})\t'.format(
epoch,
len(train_loader),
loss=train_losses,
acc=train_acces))
print('Vis Reconstruction Loss {:.4f}\t'
'Att Reconstruction Loss {:.4f}\t'
'MMD Loss {:.4f}\t'
'Supervised Binary Prediction Loss {:.4f}'.format(
vis_recons_loss.item(), att_recons_loss.item(),
loss_mmd.item(), pred_loss.item()))
return train_losses.avg, train_acces.avg
def zsl_validate(val_loader, visae, attae, epoch):
with torch.no_grad():
losses = AverageMeter()
acces = AverageMeter()
ce_loss_vals = []
ce_acc_vals = []
visae.eval()
attae.eval()
class_embeddings = attae(unseen_s2v_labels.to(device).float())[2]
for i, (emb, target) in enumerate(val_loader):
emb = emb.to(device)
vis_emb = torch.log(1 + emb)
vis_hidden, vis_out, vis_trans_out = visae(vis_emb)
vis_recons_loss = criterion2(vis_out, vis_emb)
att_emb = get_text_data(target, s2v_labels.float()).to(device)
att_hidden, att_out, att_trans_out = attae(att_emb)
att_recons_loss = criterion2(att_out, att_emb)
# mmd loss
loss_mmd = MMDLoss(vis_hidden, att_hidden).to(device)
# supervised binary prediction loss
pred_loss = multi_class_hinge_loss(vis_trans_out, att_trans_out,
target, class_embeddings)
loss = pred_loss + vis_recons_loss + att_recons_loss + loss_mmd
acc, _ = accuracy(class_embeddings, vis_trans_out,
target.to(device), unseen_inds)
losses.update(loss.item(), emb.size(0))
acces.update(acc, emb.size(0))
ce_loss_vals.append(loss.cpu().detach().numpy())
ce_acc_vals.append(acc)
if i % 20 == 0:
print('ZSL Validation Epoch-{:<3d} {:3d}/{:3d} batches \t'
'loss {loss.val:.4f} ({loss.avg:.4f})\t'
'accu {acc.val:.3f} ({acc.avg:.3f})\t'.format(
epoch, i, len(val_loader), loss=losses, acc=acces))
print('Vis Reconstruction Loss {:.4f}\t'
'Att Reconstruction Loss {:.4f}\t'
'MMD Loss {:.4f}\t'
'Supervised Binary Prediction Loss {:.4f}'.format(
vis_recons_loss.item(), att_recons_loss.item(),
loss_mmd.item(), pred_loss.item()))
return losses.avg, acces.avg