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rts50.py
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rts50.py
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import torch.optim as optim
from ltr.dataset import YouTubeVOS, Davis, Got10k, Got10kVOS, LasotVOS
from ltr.data import processing, sampler, LTRLoader
import ltr.models.rts.rts_net as rts_networks
import ltr.actors.segmentation as segm_actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
from ltr import MultiGPU
import ltr.models.loss as ltr_losses
from ltr.admin.loading import load_pretrained
import os
def run(settings):
settings.description = 'Default train settings for training full network'
settings.batch_size = 15
settings.num_workers = 45
settings.multi_gpu = False # RAN WITH 1 A100 GPU
settings.print_interval = 50
settings.normalize_mean = [102.9801, 115.9465, 122.7717]
settings.normalize_std = [1.0, 1.0, 1.0]
settings.feature_sz = (52, 30)
settings.output_sz = (settings.feature_sz[0] * 16, settings.feature_sz[1] * 16)
settings.search_area_factor = 5.0
settings.crop_type = 'inside_major'
settings.max_scale_change = None
settings.center_jitter_factor = {'train': 3, 'test': (5.5, 4.5)}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.clf_encoder_add = True
# The tracking pairs processing module
settings.clf_feature_sz = (52//2, 30//2)
settings.clf_target_filter_sz = 4
settings.clf_hinge_threshold = 0.05
settings.clf_output_sigma_factor = 1/4
clf_output_sigma = settings.clf_output_sigma_factor / settings.search_area_factor
label_params = {'feature_sz': settings.clf_feature_sz,
'sigma_factor': clf_output_sigma,
'kernel_sz': settings.clf_target_filter_sz}
# Datasets
ytvos_train = YouTubeVOS(version="2019", multiobj=False, split='jjtrain')
davis_train = Davis(version='2017', multiobj=False, split='train')
anno_path = os.path.join(settings.env.pregenerated_masks, "got10k_masks")
got10k_train = Got10kVOS(anno_path=anno_path, split='vottrain')
lasot_anno_path = os.path.join(settings.env.pregenerated_masks, "lasot_masks")
lasot_train = LasotVOS(anno_path=lasot_anno_path, split='train')
ytvos_val = YouTubeVOS(version="2019", multiobj=False, split='jjvalid')
got10k_val = Got10k(settings.env.got10k_dir, split='votval')
# Data transform
transform_joint = tfm.Transform(
tfm.ToBGR(),
tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(
tfm.RandomAffine(p_flip=0.0, max_rotation=15.0,
max_shear=0.0, max_ar_factor=0.0,
max_scale=0.2, pad_amount=0),
tfm.ToTensorAndJitter(0.2, normalize=False),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
transform_val = tfm.Transform(
tfm.ToTensorAndJitter(0.0, normalize=False),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
data_processing_train = processing.RTSProcessing(
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',
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
transform=transform_train,
joint_transform=transform_joint,
label_function_params=label_params,
new_roll=True)
data_processing_val = processing.RTSProcessing(
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',
crop_type=settings.crop_type,
max_scale_change=settings.max_scale_change,
transform=transform_val,
joint_transform=transform_joint,
label_function_params=label_params,
new_roll=True)
# Train sampler and loader
dataset_train = sampler.LWLSampler(
[ytvos_train, davis_train, got10k_train, lasot_train], [6, 1, 6, 6],
samples_per_epoch=settings.batch_size * 1000, max_gap=100,
num_test_frames=3,
num_train_frames=1,
processing=data_processing_train)
dataset_val = sampler.LWLSampler(
[ytvos_val], [1],
samples_per_epoch=settings.batch_size * 100, max_gap=100,
num_test_frames=3,
num_train_frames=1,
processing=data_processing_val)
loader_train = LTRLoader(
'train', dataset_train, training=True, num_workers=settings.num_workers,
stack_dim=1, batch_size=settings.batch_size)
loader_val = LTRLoader(
'val', dataset_val, training=False, num_workers=settings.num_workers,
epoch_interval=5, stack_dim=1, batch_size=settings.batch_size)
# Network
frozen_backbone_layers = ['conv1', 'bn1', 'layer1']
# frozen_backbone_layers = 'all'
net = rts_networks.steepest_descent_resnet50_with_clf_encoder(
filter_size=3, num_filters=16, optim_iter=5,
backbone_pretrained=True,
out_feature_dim=512,
frozen_backbone_layers=frozen_backbone_layers,
label_encoder_dims=(16, 32, 64),
use_bn_in_label_enc=False,
clf_feat_blocks=0,
final_conv=True,
backbone_type='mrcnn',
clf_filter_size=settings.clf_target_filter_sz,
clf_score_act='relu',
clf_hinge_threshold=settings.clf_hinge_threshold,
clf_activation_leak=0.1,
clf_with_extractor=True,
clf_enc_input='sc')
# Load pretrained
net_pre, _ = load_pretrained(
'lwl', 'lwl_ytvos', backbone_pretrained=True, frozen_backbone_layers=frozen_backbone_layers,
checkpoint=os.path.join(settings.env.pretrained_networks, "lwl_stage2.pth"))
net.target_model = net_pre.target_model
net.label_encoder = net_pre.label_encoder
# net.clf_encoder = net_pre.clf_encoder
# net.fusion_module = net_pre.fusion_module
# net.classifier = net_pre.classifier
net.feature_extractor = net_pre.feature_extractor
net.decoder = net_pre.decoder
for p in net.parameters():
p.requires_grad_(True)
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
# Loss function
objective = {
'segm': ltr_losses.LovaszSegLoss(per_image=False),
'test_clf': ltr_losses.LBHinge(threshold=settings.clf_hinge_threshold),
}
loss_weight = {
'segm': 10.0,
'test_clf': 100.0,
'test_init_clf': 100.0,
'test_iter_clf': 400.0,
}
actor = segm_actors.RTSActor(net=net, objective=objective, loss_weight=loss_weight,
num_refinement_iter=2, disable_all_bn=True)
# Optimizer
optimizer = optim.Adam([
{'params': actor.net.feature_extractor.layer2.parameters(), 'lr': 4e-5},
{'params': actor.net.feature_extractor.layer3.parameters(), 'lr': 4e-5},
{'params': actor.net.feature_extractor.layer4.parameters(), 'lr': 4e-5},
{'params': actor.net.target_model.filter_initializer.parameters(), 'lr': 8e-5},
{'params': actor.net.target_model.filter_optimizer.parameters(), 'lr': 8e-5},
{'params': actor.net.target_model.feature_extractor.parameters(), 'lr': 8e-5},
{'params': actor.net.label_encoder.parameters(), 'lr': 8e-5},
{'params': actor.net.decoder.parameters(), 'lr': 8e-5},
{'params': actor.net.clf_encoder.parameters(), 'lr': 2e-4},
{'params': actor.net.fusion_module.parameters(), 'lr': 2e-4},
{'params': actor.net.classifier.filter_initializer.parameters(), 'lr': 2e-4},
{'params': actor.net.classifier.filter_optimizer.parameters(), 'lr': 2e-4},
{'params': actor.net.classifier.feature_extractor.parameters(), 'lr': 2e-4},
], lr=4e-5)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [25, 115, 160], gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
trainer.train(200, load_latest=True, fail_safe=True)