Hi, I have a problem with my MaskRCNN training process. The loss in training mode is decreasing well, but the validation metrics are always the same (since first to last epoch) and look very bad (mostly only one class is predicted). I wanted to take a look how prediction looks during training but I don’t know how to get access to them, because `output = model(x, y)`

returns only losses. Do you know how to take it?

I have also another question - do you know why validation doesn’t work? Why predictions are always the same during single training process?

I should also add that I added another `head`

to the `backbone`

and that’s actually what I wish to train (it’s age of people on the picture).

training function:

def train(train_loader, model, optimizer, epoch, device):

`model.train() loss_monitor = AverageMeter() lr_scheduler = None if epoch == 0: warmup_factor = 1. / 1000 warmup_iters = min(1000, len(train_loader) - 1) lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor) with tqdm(train_loader) as _tqdm: for x, y in _tqdm: x = x.to(device) for key, value in y.items(): y[key] = torch.tensor(value).to(device) y_list = [] for i in range(0, len(x)): y_list.append(y) outputs = model(x, y_list) print(outputs) # calc loss cur_loss = outputs["loss_age"] # measure accuracy and record loss sample_num = x.size(0) loss_monitor.update(cur_loss, sample_num) # compute gradient and do step optimizer.zero_grad() (outputs["loss_age"]).backward() optimizer.step() if lr_scheduler is not None: lr_scheduler.step() _tqdm.set_postfix( OrderedDict(stage="train", epoch=epoch, loss=loss_monitor.avg), ) return loss_monitor.avg`

validation function:

def validate(val_loader, model, epoch, device):

`model.eval() preds = [] gt = [] print("Validating function running...") with torch.no_grad(): with tqdm(val_loader) as _tqdm: for x, y in _tqdm: x = x.to(device) for key, value in y.items(): y[key] = torch.tensor(value).to(device) gt.append(y["age"].cpu().numpy()) outputs = model(x) print(outputs) for output in outputs: # I just change format of predictions over here pred = F.softmax(output["age"], dim=-1).cpu().numpy() pred = (pred * np.arange(0, pred.size)).sum(axis=-1) preds.append(np.array([pred])) _tqdm.set_postfix(OrderedDict(stage="val", epoch=epoch),) mae = calculate_mae(gt, preds) # my own functions - works well f1 = calculate_f1(gt, preds) return mae, f1`

main loop:>

`model = PornRCNN.create_resnet_50() model = model.to(device) model.set_age_loss_fn(loss_age) params = [p for p in model.parameters() if p.requires_grad] optimizer = torch.optim.SGD(params, lr=0.0003, momentum=0.9, weight_decay=0.0005) lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=1, T_mult=2) num_epoch = 100 checkpoint_dir = Path("checkpoints") for epoch in range(start_epoch, num_epoch): train_loss = train(train_loader, model, optimizer, epoch, device) mae, f1 = validate(val_loader, model, epoch, device)`

Anyone could help me?