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tomp101.py
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tomp101.py
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import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import tompnet
import ltr.models.loss as ltr_losses
import ltr.actors.tracking as actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
from ltr import MultiGPU
from ltr.models.loss.bbr_loss import GIoULoss
def run(settings):
settings.description = 'ToMP101'
settings.batch_size = 24
settings.num_workers = 12
settings.multi_gpu = True
settings.print_interval = 1
settings.normalize_mean = [0.485, 0.456, 0.406]
settings.normalize_std = [0.229, 0.224, 0.225]
settings.search_area_factor = 5.0
settings.output_sigma_factor = 1/4
settings.target_filter_sz = 1
settings.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 0., 'test': 4.5}
settings.scale_jitter_factor = {'train': 0., 'test': 0.5}
settings.hinge_threshold = 0.05
settings.num_train_frames = 2
settings.num_test_frames = 1
settings.num_encoder_layers = 6
settings.num_decoder_layers = 6
settings.frozen_backbone_layers = ['conv1', 'bn1', 'layer1', 'layer2']
settings.freeze_backbone_bn_layers = True
settings.crop_type = 'inside_major'
settings.max_scale_change = 1.5
settings.max_gap = 200
settings.train_samples_per_epoch = 40000
settings.val_samples_per_epoch = 10000
settings.val_epoch_interval = 5
settings.num_epochs = 300
settings.weight_giou = 1.0
settings.weight_clf = 100.0
settings.normalized_bbreg_coords = True
settings.center_sampling_radius = 1.0
settings.use_test_frame_encoding = False # Set to True to use the same as in the paper but is less stable to train.
# Train datasets
lasot_train = Lasot(settings.env.lasot_dir, split='train')
got10k_train = Got10k(settings.env.got10k_dir, split='vottrain')
trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4)))
coco_train = MSCOCOSeq(settings.env.coco_dir)
# Validation datasets
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# 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(probability=0.5),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
transform_val = tfm.Transform(tfm.ToTensor(),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
# The tracking pairs processing module
output_sigma = settings.output_sigma_factor / settings.search_area_factor
label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz}
data_processing_train = processing.LTRBDenseRegressionProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
mode='sequence',
label_function_params=label_params,
transform=transform_train,
joint_transform=transform_joint,
use_normalized_coords=settings.normalized_bbreg_coords,
center_sampling_radius=settings.center_sampling_radius)
data_processing_val = processing.LTRBDenseRegressionProcessing(search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
mode='sequence',
label_function_params=label_params,
transform=transform_val,
joint_transform=transform_joint,
use_normalized_coords=settings.normalized_bbreg_coords,
center_sampling_radius=settings.center_sampling_radius)
# Train sampler and loader
dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [1, 1, 1, 1],
samples_per_epoch=settings.train_samples_per_epoch, max_gap=settings.max_gap,
num_test_frames=settings.num_test_frames, num_train_frames=settings.num_train_frames,
processing=data_processing_train)
loader_train = LTRLoader('train', dataset_train, training=True, batch_size=settings.batch_size, num_workers=settings.num_workers,
shuffle=True, drop_last=True, stack_dim=1)
# Validation samplers and loaders
dataset_val = sampler.DiMPSampler([got10k_val], [1], samples_per_epoch=settings.val_samples_per_epoch,
max_gap=settings.max_gap, num_test_frames=settings.num_test_frames,
num_train_frames=settings.num_train_frames, processing=data_processing_val)
loader_val = LTRLoader('val', dataset_val, training=False, batch_size=settings.batch_size, num_workers=settings.num_workers,
shuffle=False, drop_last=True, epoch_interval=settings.val_epoch_interval, stack_dim=1)
# Create network and actor
net = tompnet.tompnet101(filter_size=settings.target_filter_sz, backbone_pretrained=True, head_feat_blocks=0,
head_feat_norm=True, final_conv=True, out_feature_dim=256, feature_sz=settings.feature_sz,
frozen_backbone_layers=settings.frozen_backbone_layers,
num_encoder_layers=settings.num_encoder_layers,
num_decoder_layers=settings.num_decoder_layers,
use_test_frame_encoding=settings.use_test_frame_encoding)
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {'giou': GIoULoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)}
loss_weight = {'giou': settings.weight_giou, 'test_clf': settings.weight_clf}
actor = actors.ToMPActor(net=net, objective=objective, loss_weight=loss_weight)
# Optimizer
optimizer = optim.AdamW([
{'params': actor.net.head.parameters(), 'lr': 1e-4},
{'params': actor.net.feature_extractor.layer3.parameters(), 'lr': 2e-5}
], lr=2e-4, weight_decay=0.0001)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[150, 250], gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler,
freeze_backbone_bn_layers=settings.freeze_backbone_bn_layers)
trainer.train(settings.num_epochs, load_latest=True, fail_safe=True)