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c6dfc69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | """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])
# process: '' = first multimask; iou_select = argmax IoU head; iou_occ_select = + objectness gate
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)
# v1s: only first frame is supervised (artifacts). Any sample in the batch may be v1s (shuffle order).
_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
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