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from torch.utils.data.distributed import DistributedSampler
import torch.nn as nn
# datasets related
from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet, Imagenet1k,VastTrack
from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
from lib.train.dataset import VisEvent, LasHeR, DepthTrack
from lib.train.dataset import Otb99_lang, Tnl2k, RefCOCOSeq,OTB_Lang
from lib.train.data import sampler, opencv_loader, processing, LTRLoader
import lib.train.data.transforms as tfm
from lib.utils.misc import is_main_process
from lib.train.dataset.refcoco_seq_JointNLT import RefCOCOSeq as RefCOCOSeq_jointnlt
def update_settings(settings, cfg):
settings.print_interval = cfg.TRAIN.PRINT_INTERVAL
settings.search_area_factor = {'template': getattr(cfg.DATA.TEMPLATE, "FACTOR", None),
'search': getattr(cfg.DATA.SEARCH, "FACTOR", None)}
settings.output_sz = {'template': getattr(cfg.DATA.TEMPLATE, "SIZE", 128),
'search': getattr(cfg.DATA.SEARCH, "SIZE", 256)}
settings.center_jitter_factor = {'template': getattr(cfg.DATA.TEMPLATE, "CENTER_JITTER", None),
'search':getattr(cfg.DATA.SEARCH, "CENTER_JITTER", None)}
settings.scale_jitter_factor = {'template': getattr(cfg.DATA.TEMPLATE, "SCALE_JITTER", None),
'search': getattr(cfg.DATA.SEARCH, "SCALE_JITTER", None)}
settings.grad_clip_norm = cfg.TRAIN.GRAD_CLIP_NORM
settings.print_stats = None
settings.batchsize = cfg.TRAIN.BATCH_SIZE
settings.scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
settings.multi_modal_vision = getattr(cfg.DATA, "MULTI_MODAL_VISION", False)
settings.multi_modal_language = getattr(cfg.DATA, "MULTI_MODAL_LANGUAGE", False)
train_type = getattr(cfg.TRAIN, "TYPE", None)
if train_type == "peft":
settings.fix_norm = True
else:
settings.fix_norm = False
def names2datasets(name_list: list, settings, image_loader):
assert isinstance(name_list, list)
datasets = []
for name in name_list:
assert name in ["LASOT", "GOT10K_vottrain", "GOT10K_votval", "GOT10K_train_full",
"COCO17", "VID", "TRACKINGNET", "IMAGENET1K",
"DepthTrack_train", "DepthTrack_val", "LasHeR_all", "LasHeR_train","LasHeR_val", "VisEvent",
"REFCOCOG", "TNL2K_train", "OTB99_train","OTB_Lang",
"VastTrack",'RefCOCO14']
if name == "LASOT":
if settings.use_lmdb:
print("Building lasot dataset from lmdb")
datasets.append(Lasot_lmdb(settings.env.lasot_lmdb_dir, split='train', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language))
else:
datasets.append(Lasot(settings.env.lasot_dir, split='train', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language))
if name == "GOT10K_vottrain":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='vottrain', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='vottrain', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "GOT10K_train_full":
if settings.use_lmdb:
print("Building got10k_train_full from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='train_full', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='train_full', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "GOT10K_votval":
if settings.use_lmdb:
print("Building got10k from lmdb")
datasets.append(Got10k_lmdb(settings.env.got10k_lmdb_dir, split='votval', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
else:
datasets.append(Got10k(settings.env.got10k_dir, split='votval', image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "COCO17":
if settings.use_lmdb:
print("Building COCO2017 from lmdb")
datasets.append(MSCOCOSeq_lmdb(settings.env.coco_lmdb_dir, version="2017", image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
else:
datasets.append(MSCOCOSeq(settings.env.coco_dir, version="2017", image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "VID":
if settings.use_lmdb:
print("Building VID from lmdb")
datasets.append(ImagenetVID_lmdb(settings.env.imagenet_lmdb_dir, image_loader=image_loader))
else:
datasets.append(ImagenetVID(settings.env.imagenet_dir, image_loader=image_loader))
if name == "TRACKINGNET":
if settings.use_lmdb:
print("Building TrackingNet from lmdb")
datasets.append(TrackingNet_lmdb(settings.env.trackingnet_lmdb_dir, image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
else:
# raise ValueError("NOW WE CAN ONLY USE TRACKINGNET FROM LMDB")
datasets.append(TrackingNet(settings.env.trackingnet_dir, image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "IMAGENET1K":
datasets.append(Imagenet1k(settings.env.imagenet1k_dir, image_loader=image_loader))
if name == "DepthTrack_train":
datasets.append(DepthTrack(settings.env.depthtrack_dir, dtype='rgbcolormap', split='train',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "DepthTrack_val":
datasets.append(DepthTrack(settings.env.depthtrack_dir, dtype='rgbcolormap', split='val',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "LasHeR_all":
datasets.append(LasHeR(settings.env.lasher_dir, dtype='rgbrgb', split='all',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "LasHeR_train":
datasets.append(LasHeR(settings.env.lasher_dir, dtype='rgbrgb', split='train',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "LasHeR_val":
datasets.append(LasHeR(settings.env.lasher_dir, dtype='rgbrgb', split='val',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "VisEvent":
datasets.append(VisEvent(settings.env.visevent_dir, dtype='rgbrgb', split='train',
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "REFCOCOG":
datasets.append(RefCOCOSeq(settings.env.refcoco_dir, split="train", image_loader=image_loader,
name="refcocog", splitBy="google",
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
if name == "RefCOCO14":
# datasets.append(RefCOCOSeq(settings.env.ref_coco_dir, refcoco_type="refcoco-unc", version="2014", image_loader=image_loader))
datasets.append(RefCOCOSeq_jointnlt(settings.env.ref_coco_dir, split="train", image_loader=image_loader,
name="refcocog", splitBy='google'))
if name == "TNL2K_train":
datasets.append(Tnl2k(settings.env.tnl2k_dir, split=None, image_loader=image_loader,
multi_modal_vision=settings.multi_modal_vision,
multi_modal_language=settings.multi_modal_language
))
elif name == "OTB99_train":
# datasets.append(Otb99_lang(settings.env.otb99_dir, split='train', image_loader=image_loader,
# multi_modal_vision=settings.multi_modal_vision,
# multi_modal_language=settings.multi_modal_language
# ))
datasets.append(OTB_Lang(settings.env.otb99_dir, split='train', image_loader=image_loader))
if name == "VastTrack":
datasets.append(VastTrack(settings.env.vasttrack_dir, split='train', image_loader=image_loader))
return datasets
def build_dataloaders(cfg, settings):
settings.num_template = getattr(cfg.DATA.TEMPLATE, "NUMBER", 1)
settings.num_search = getattr(cfg.DATA.SEARCH, "NUMBER", 1)
# Data transform
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
tfm.RandomHorizontalFlip_Norm(probability=0.5),
tfm.Normalize(mean=cfg.DATA.MEAN, std=cfg.DATA.STD))
# The tracking pairs processing module
output_sz = settings.output_sz
search_area_factor = settings.search_area_factor
data_processing_train = processing.SeqTrackProcessing(search_area_factor=search_area_factor,
output_sz=output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_train,
joint_transform=transform_joint,
multi_modal_language=settings.multi_modal_language,
settings=settings)
# Train sampler and loader
sampler_mode = getattr(cfg.DATA, "SAMPLER_MODE", "causal")
dataset_train = sampler.TrackingSampler(datasets=names2datasets(cfg.DATA.TRAIN.DATASETS_NAME, settings, opencv_loader),
p_datasets=cfg.DATA.TRAIN.DATASETS_RATIO,
samples_per_epoch=cfg.DATA.TRAIN.SAMPLE_PER_EPOCH,
max_gap=cfg.DATA.MAX_SAMPLE_INTERVAL, num_search_frames=settings.num_search,
num_template_frames=settings.num_template, processing=data_processing_train,
frame_sample_mode=sampler_mode
)
train_sampler = DistributedSampler(dataset_train) if settings.local_rank != -1 else None
shuffle = False if settings.local_rank != -1 else True
loader_train = LTRLoader('train', dataset_train, training=True, batch_size=cfg.TRAIN.BATCH_SIZE, shuffle=shuffle,
num_workers=cfg.TRAIN.NUM_WORKER, drop_last=True, stack_dim=1, sampler=train_sampler)
return loader_train
def get_optimizer_scheduler(net, cfg):
train_type = getattr(cfg.TRAIN, "TYPE", None)
if train_type == "target_state":
trainable_keywords = getattr(cfg.TRAIN, "TARGET_STATE_TRAINABLE", [
"target_state_encoder.projector",
"target_state_encoder.film_ln",
"target_state_encoder.film",
"target_state_encoder.film_gate",
"lora_",
"box_head",
"confidence_pred",
])
# Keywords for params that start from scratch and need higher LR
qwen_lr_keywords = [
"target_state_encoder.projector",
"target_state_encoder.film_ln",
"target_state_encoder.film",
"target_state_encoder.film_gate",
"lora_",
]
for n, p in net.named_parameters():
p.requires_grad = any(key in n for key in trainable_keywords)
target_encoder = getattr(net, "target_state_encoder", None)
if target_encoder is not None:
target_encoder.configure_token_embedding_training(
getattr(cfg.MODEL.TARGET_STATE, "TRAIN_TOKEN_EMBEDDING", False)
)
qwen_embedding_param = None
if target_encoder is not None:
try:
qwen_embedding_param = target_encoder.qwen.get_input_embeddings().weight
except AttributeError:
qwen_embedding_param = None
qwen_lr_mult = getattr(cfg.TRAIN, "QWEN_LR_MULTIPLIER", 1.0)
base_lr = cfg.TRAIN.LR
tracker_params = [] # box_head, confidence_pred → baseline LR
qwen_params = [] # projector, film, lora → higher LR
embedding_params = [] # TARGET_STATE embedding row → baseline LR, wd=0
for n, p in net.named_parameters():
if not p.requires_grad:
continue
if qwen_embedding_param is not None and p is qwen_embedding_param:
embedding_params.append(p)
elif any(key in n for key in qwen_lr_keywords):
qwen_params.append(p)
else:
tracker_params.append(p)
param_dicts = []
if tracker_params:
param_dicts.append({"params": tracker_params, "lr": base_lr})
if qwen_params:
param_dicts.append({"params": qwen_params, "lr": base_lr * qwen_lr_mult})
if embedding_params:
param_dicts.append({"params": embedding_params, "lr": base_lr, "weight_decay": 0.0})
if is_main_process():
print("Learnable parameters are shown below.")
print(f" (base_lr={base_lr}, qwen_lr={base_lr * qwen_lr_mult})")
for n, p in net.named_parameters():
if p.requires_grad:
print(f" {n}")
elif train_type == "peft":
param_dicts = [
{"params": [p for n, p in net.named_parameters() if "prompt" in n or "interface" in n and p.requires_grad]},
]
for n, p in net.named_parameters():
if ("prompt" not in n) and ("interface" not in n):
p.requires_grad = False
if is_main_process():
print("Learnable parameters are shown below.")
for n, p in net.named_parameters():
if p.requires_grad:
print(n)
else:
for n, p in net.named_parameters():
if 'text_encoder' in n and p.requires_grad:
p.requires_grad = False
print("Freeze: ", n)
# for n, p in net.named_parameters():
# if 'backbone' in n and p.requires_grad:
# p.requires_grad = False
# print("Freeze: ", n)
param_dicts = [
{"params": [p for n, p in net.named_parameters() if "backbone" not in n and p.requires_grad]},
{
"params": [p for n, p in net.named_parameters() if "backbone" in n and p.requires_grad],
"lr": cfg.TRAIN.LR * cfg.TRAIN.ENCODER_MULTIPLIER,
},
]
train_n_list = []
if is_main_process():
print("Learnable parameters are shown below.")
for n, p in net.named_parameters():
if p.requires_grad:
train_n_list.append(n)
print(n)
if cfg.TRAIN.OPTIMIZER == "ADAMW":
optimizer = torch.optim.AdamW(param_dicts, lr=cfg.TRAIN.LR,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
else:
raise ValueError("Unsupported Optimizer")
# ---- LR scheduler ----
scheduler_type = cfg.TRAIN.SCHEDULER.TYPE
if scheduler_type == 'step':
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, cfg.TRAIN.LR_DROP_EPOCH
)
elif scheduler_type == "Mstep":
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=cfg.TRAIN.SCHEDULER.MILESTONES,
gamma=cfg.TRAIN.SCHEDULER.GAMMA,
)
elif scheduler_type == "cosine":
# Cosine annealing with optional linear warmup.
import math as _math
warmup_epochs = getattr(cfg.TRAIN, "WARMUP_EPOCHS", 0)
total_epochs = cfg.TRAIN.EPOCH
T_max = max(1, total_epochs - warmup_epochs)
if warmup_epochs > 0:
def _lr_lambda(epoch):
if epoch < warmup_epochs:
return float(epoch + 1) / float(max(1, warmup_epochs))
progress = float(epoch - warmup_epochs) / float(T_max)
return 0.5 * (1.0 + _math.cos(_math.pi * progress))
else:
def _lr_lambda(epoch):
progress = float(epoch) / float(max(1, total_epochs))
return 0.5 * (1.0 + _math.cos(_math.pi * progress))
lr_scheduler = torch.optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=_lr_lambda
)
else:
raise ValueError(f"Unsupported scheduler type: {scheduler_type}")
return optimizer, lr_scheduler
|