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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 | """Training and validation for Ref-AVS (text + audio + SAM2 multimask decoding)."""
import numpy
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
_DECODE_MODES = frozenset({'', 'iou_select', 'iou_occ_select'})
def _decode_mode_and_wandb_tag(process):
"""Match tmp.code: `process` is decode mode for known strings; else Ref split tag + default decode."""
if process in _DECODE_MODES:
return process, process
return 'iou_select', process
class Trainer:
"""Train / valid / null-valid steps with composite loss, contrastive term, and metrics."""
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_null(self, epoch, dataloader, model, process='test_n'):
if not isinstance(dataloader, DataLoader):
raise TypeError("valid_null() expects a torch.utils.data.DataLoader (do not pass iter(dataloader) first).")
decode_mode, wandb_tag = _decode_mode_and_wandb_tag(process)
self.metrics['foreground_s'].reset()
dataloader_length = len(dataloader)
tbar = range(dataloader_length)
tbar = tqdm(tbar, ncols=135) if self.param.local_rank <= 0 else tbar
p_pool = [None] * self.param.gpus
n_pool = [None] * self.param.gpus
data_iter = iter(dataloader)
for _ in tbar:
items = next(data_iter)
frame, spect, prompt_dicts = items['frame'], items['spectrogram'], items['text']
logits = []
for frame_, spect_, prompt_dicts_ in zip(frame, spect, prompt_dicts):
frame_ = frame_.cuda(self.param.local_rank, non_blocking=True)
spect_ = spect_.cuda(self.param.local_rank, non_blocking=True)
prompt_dicts_ = [prompt_dicts_]
with torch.autocast("cuda", dtype=torch.bfloat16):
outputs, _ = model.module(frame_, spect_, prompt_dicts_, sam_process=False)
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 decode_mode == 'iou_select':
ious_scores = torch.argmax(ious_scores, dim=1)
logits_ = logits_[torch.arange(0, frame_.shape[0]), ious_scores, ...]
elif decode_mode == '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, ...]
logits.append(logits_)
logits = torch.cat(logits).reshape(frame.shape[0], -1, self.param.image_size, self.param.image_size)
if len(logits.shape) == 3:
logits = logits.unsqueeze(1)
foreground_s = self.metrics['foreground_s'].metric_s_for_null(logits, get_entire_list=True)
torch.distributed.all_gather_object(p_pool, foreground_s['foreground_p'])
torch.distributed.all_gather_object(n_pool, foreground_s['foreground_n'])
foreground_s = sum([i[0].cpu() for i in p_pool]) / sum([i[0] for i in n_pool])
if self.param.local_rank <= 0:
tbar.set_description(
'epoch {} | valid.null_s {}'.format(epoch, numpy.round(foreground_s, 5)),
)
torch.cuda.empty_cache()
final_s = foreground_s
if self.param.local_rank <= 0 and self.tensorboard is not None:
self.tensorboard.upload_wandb_info({"valid.f_s/{}".format(wandb_tag): final_s})
return numpy.round(final_s, 5)
@torch.no_grad()
def valid(self, epoch, dataloader, model, process='iou_select'):
"""Evaluate IoU / F-score; `process` is decode mode (tmp) or split tag (test_s / test_u). Wandb keys like tmp."""
if not isinstance(dataloader, DataLoader):
raise TypeError("valid() expects a torch.utils.data.DataLoader (do not pass iter(dataloader) first).")
decode_mode, wandb_tag = _decode_mode_and_wandb_tag(process)
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 _ in tbar:
items = next(data_iter)
frame, spect, label, prompt_dicts = (
items['frame'], items['spectrogram'], items['label'], items['text']
)
logits = []
labels = []
for frame_, spect_, label_, prompt_dicts_ in zip(frame, spect, label, prompt_dicts):
frame_ = frame_.cuda(self.param.local_rank, non_blocking=True)
spect_ = spect_.cuda(self.param.local_rank, non_blocking=True)
label_ = label_.cuda(self.param.local_rank, non_blocking=True)
prompt_dicts_ = [prompt_dicts_]
with torch.autocast("cuda", dtype=torch.bfloat16):
outputs, _ = model.module(frame_, spect_, prompt_dicts_, sam_process=False)
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 decode_mode == 'iou_select':
ious_scores = torch.argmax(ious_scores, dim=1)
logits_ = logits_[torch.arange(0, frame_.shape[0]), ious_scores, ...]
elif decode_mode == '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, ...]
logits.append(logits_)
labels.append(label_)
logits = torch.cat(logits)
labels = torch.cat(labels)
foreground_iou_rank = self.metrics['foreground_iou'].calculate_iou(
(logits > 0.).squeeze().long(), labels.squeeze().long(), get_entire_list=True,
)
foreground_f_score_rank = self.metrics['foreground_f-score'].calculate_f_score(
logits.squeeze(), labels.squeeze().long(), 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(wandb_tag): final_iou,
"valid.f_f-score/{}".format(wandb_tag): 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):
if not isinstance(dataloader, DataLoader):
raise TypeError("train() expects a torch.utils.data.DataLoader (do not pass iter(dataloader) first).")
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['text'],
)
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)
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 and self.tensorboard is not None:
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(),
),
)
return
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