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dimp50.py
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dimp50.py
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import torch.nn as nn
import torch.optim as optim
import torchvision.transforms
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as dltransforms
from ltr import MultiGPU
def run(settings):
settings.description = 'Default train settings for DiMP with ResNet50 as backbone.'
settings.batch_size = 10
settings.num_workers = 8
settings.multi_gpu = False
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 = 4
settings.feature_sz = 18
settings.output_sz = settings.feature_sz * 16
settings.center_jitter_factor = {'train': 3, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.hinge_threshold = 0.05
# settings.print_stats = ['Loss/total', 'Loss/iou', 'ClfTrain/init_loss', 'ClfTrain/test_loss']
# 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 = dltransforms.ToGrayscale(probability=0.05)
transform_train = torchvision.transforms.Compose([dltransforms.ToTensorAndJitter(0.2),
torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
transform_val = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=settings.normalize_mean, std=settings.normalize_std)])
# The tracking pairs processing module
output_sigma = settings.output_sigma_factor / settings.search_area_factor
proposal_params = {'min_iou': 0.1, 'boxes_per_frame': 8, 'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]}
label_params = {'feature_sz': settings.feature_sz, 'sigma_factor': output_sigma, 'kernel_sz': settings.target_filter_sz}
data_processing_train = processing.DiMPProcessing(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,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_train,
joint_transform=transform_joint)
data_processing_val = processing.DiMPProcessing(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,
mode='sequence',
proposal_params=proposal_params,
label_function_params=label_params,
transform=transform_val,
joint_transform=transform_joint)
# Train sampler and loader
dataset_train = sampler.DiMPSampler([lasot_train, got10k_train, trackingnet_train, coco_train], [0.25,1,1,1],
samples_per_epoch=26000, max_gap=30, num_test_frames=3, num_train_frames=3,
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=5000, max_gap=30,
num_test_frames=3, num_train_frames=3,
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=5, stack_dim=1)
# Create network and actor
net = dimpnet.dimpnet50(filter_size=settings.target_filter_sz, backbone_pretrained=True, optim_iter=5,
clf_feat_norm=True, clf_feat_blocks=0, final_conv=True, out_feature_dim=512,
optim_init_step=0.9, optim_init_reg=0.1,
init_gauss_sigma=output_sigma * settings.feature_sz, num_dist_bins=100,
bin_displacement=0.1, mask_init_factor=3.0, target_mask_act='sigmoid', score_act='relu')
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {'iou': nn.MSELoss(), 'test_clf': ltr_losses.LBHinge(threshold=settings.hinge_threshold)}
loss_weight = {'iou': 1, 'test_clf': 100, 'test_init_clf': 100, 'test_iter_clf': 400}
actor = actors.DiMPActor(net=net, objective=objective, loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam([{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 5e-5},
{'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 5e-4},
{'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 5e-5},
{'params': actor.net.bb_regressor.parameters()},
{'params': actor.net.feature_extractor.parameters(), 'lr': 2e-5}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=15, gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
trainer.train(50, load_latest=True, fail_safe=True)