| """Training and validation loop for the AV segmentation model.""" |
| import numpy |
| import torch |
| from torch.utils.data import DataLoader |
| from tqdm import tqdm |
|
|
|
|
| class Trainer: |
| """Wraps train/valid steps with optional loss, metrics, and logging.""" |
|
|
| def __init__(self, hyp_param, loss, tensorboard, metrics): |
| self.param = hyp_param |
| self.loss = loss |
| self.tensorboard = tensorboard |
| self.metrics = metrics |
| from loss.training.contrastive_learning import ContrastLoss |
| self.cl = ContrastLoss(self.param) |
|
|
| @torch.no_grad() |
| def valid(self, epoch, dataloader, model, process=''): |
| """Evaluate foreground IoU / F-score. `process` selects SAM multimask decoding (see branch below).""" |
| if not isinstance(dataloader, DataLoader): |
| raise TypeError( |
| "valid() expects a torch.utils.data.DataLoader (do not pass iter(dataloader) first)." |
| ) |
| self.metrics['foreground_iou'].reset() |
| self.metrics['foreground_f-score'].reset() |
| dataloader_length = len(dataloader) |
| tbar = range(dataloader_length) |
| tbar = tqdm(tbar, ncols=135) if self.param.local_rank <= 0 else tbar |
| iou_pool = [None] * self.param.gpus |
| fscore_pool = [None] * self.param.gpus |
|
|
| data_iter = iter(dataloader) |
| for batch_index in tbar: |
| items = next(data_iter) |
| frame, spect, label, prompt_dicts = items['frame'], items['spectrogram'], items['label'], items['prompts'] |
|
|
| frame = torch.flatten(frame, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
| spect = torch.flatten(spect, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
| label = torch.flatten(label, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
|
|
| with torch.autocast("cuda", dtype=torch.bfloat16): |
| outputs, _ = model.module(frame, spect, prompt_dicts, sam_process=True) |
| logits = torch.cat([torch.cat(i['multistep_pred_multimasks_high_res']) for i in outputs]) |
| ious_scores = torch.cat([torch.cat(i['multistep_pred_ious']) for i in outputs]) |
| occ_scores = torch.cat([torch.cat(i['multistep_object_score_logits']) for i in outputs]) |
| |
| if process == 'iou_select': |
| ious_scores = torch.argmax(ious_scores, dim=1) |
| logits = logits[torch.arange(0, frame.shape[0]), ious_scores, ...] |
| elif process == 'iou_occ_select': |
| ious_scores = torch.argmax(ious_scores, dim=1) |
| logits = logits[torch.arange(0, frame.shape[0]), ious_scores, ...] |
| logits[occ_scores.squeeze() < 0, ...] = 0. |
| else: |
| logits = logits[:, 0, ...] |
|
|
| masks = logits > 0. |
| foreground_iou_rank = self.metrics['foreground_iou'].calculate_iou(masks.squeeze().long(), |
| label.squeeze().long(), |
| get_entire_list=True) |
|
|
| foreground_f_score_rank = self.metrics['foreground_f-score'].calculate_f_score(logits.squeeze(), |
| label.squeeze(), |
| get_entire_list=True) |
| torch.distributed.all_gather_object(iou_pool, foreground_iou_rank) |
| torch.distributed.all_gather_object(fscore_pool, foreground_f_score_rank) |
| foreground_iou = sum([i['foreground_iou'][0].cpu() for i in iou_pool]) / sum( |
| [i['foreground_iou'][1] for i in iou_pool]) |
| foreground_f_score = sum([i['foreground_f-score'][0] for i in fscore_pool]) / sum( |
| [i['foreground_f-score'][1] for i in fscore_pool]) |
|
|
| if self.param.local_rank <= 0: |
| tbar.set_description('epoch {} | valid.f_iou {}, valid.f_f-score {}'.format(epoch, |
| numpy.round( |
| foreground_iou.cpu().numpy(), |
| 5), |
| numpy.round( |
| foreground_f_score, |
| 5))) |
| torch.cuda.empty_cache() |
|
|
| final_iou = foreground_iou |
| final_fscore = foreground_f_score |
| if self.param.local_rank <= 0 and self.tensorboard is not None: |
| self.tensorboard.upload_wandb_info({"valid.f_iou/{}".format(process): final_iou, |
| "valid.f_f-score/{}".format(process): final_fscore}) |
|
|
| def _to_float(x): |
| if isinstance(x, torch.Tensor): |
| return float(x.detach().cpu().item()) |
| return float(x) |
|
|
| return numpy.round(_to_float(final_iou), 5), numpy.round(_to_float(final_fscore), 5) |
|
|
| def train(self, epoch, dataloader, model, optimiser): |
| """One epoch: SAM frozen, AuralFuser + heads trained with composite loss + contrastive term.""" |
| if not isinstance(dataloader, DataLoader): |
| raise TypeError( |
| "train() expects a torch.utils.data.DataLoader (do not pass iter(dataloader) first)." |
| ) |
| self.metrics['foreground_iou'].reset() |
| self.metrics['foreground_f-score'].reset() |
|
|
| dataloader_length = len(dataloader) |
| tbar = range(dataloader_length) |
| tbar = tqdm(tbar, ncols=135) if self.param.local_rank <= 0 else tbar |
|
|
| data_iter = iter(dataloader) |
| for batch_index in tbar: |
| current_index = dataloader_length * epoch + batch_index |
| items = next(data_iter) |
|
|
| frame, spect, label, prompt_dicts = items['frame'], items['spectrogram'], items['label'], items['prompts'] |
| frame = torch.flatten(frame, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
| spect = torch.flatten(spect, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
| label = torch.flatten(label, start_dim=0, end_dim=1).cuda(self.param.local_rank, non_blocking=True) |
| with torch.autocast("cuda", dtype=torch.bfloat16): |
| outputs, proj_feats = model(frame, spect, prompt_dicts, sam_process=False) |
|
|
| |
| _ids = items['id'] |
| _id_list = _ids if isinstance(_ids, (list, tuple)) else [_ids] |
| if any("/v1s/" in str(x) for x in _id_list): |
| outputs = outputs[0:1] |
| label = label[0:1, ...] |
| vision_feats, audio_feats = proj_feats |
| proj_feats = ([t[0:1] for t in vision_feats], [t[0:1] for t in audio_feats]) |
|
|
| loss_dict = self.loss(outputs, label.unsqueeze(1)) |
| cl_loss = self.cl(proj_feats, outputs, label) |
|
|
| optimiser.zero_grad() |
| (loss_dict['core_loss'] + cl_loss).backward() |
| optimiser.step() |
|
|
| current_lr = self.param.lr * (1 - current_index / (dataloader_length * self.param.epochs)) ** 0.9 |
| for params_lr in optimiser.param_groups: |
| names = params_lr.get("name", []) |
| if names and any("vgg" in n for n in names): |
| params_lr['lr'] = current_lr * 0.1 |
| else: |
| params_lr['lr'] = current_lr |
|
|
| if self.param.local_rank <= 0: |
| logits = torch.cat([i['multistep_pred_multimasks_high_res'][0] for i in outputs]) |
| foreground_iou = self.metrics['foreground_iou'].calculate_iou((logits > 0)[:, 0, ...].long(), |
| label.long()) |
|
|
| self.tensorboard.upload_wandb_info({"loss": loss_dict['core_loss'].item(), "f_iou": foreground_iou.item(), |
| "lr": optimiser.param_groups[0]['lr'], |
| "loss_dice": loss_dict['loss_dice'], |
| "loss_focal": loss_dict['loss_mask'], |
| "loss_contras": cl_loss.item()}) |
| tbar.set_description('epoch {} | loss {}, f_iou {}'.format(epoch, loss_dict['core_loss'].item(), |
| foreground_iou.item())) |
| ''' |
| if batch_index % 200 == 0: |
| pred_mask = (logits > 0)[:, 0, ...].long() |
| n_vis = min(4, frame.shape[0], pred_mask.shape[0], label.shape[0]) |
| self.tensorboard.upload_wandb_image( |
| frame[:n_vis], pred_mask[:n_vis], label[:n_vis].long() |
| ) |
| ''' |
| return |
|
|
