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lwl_boxinit.py
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lwl_boxinit.py
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import torch.optim as optim
from ltr.dataset import YouTubeVOS, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
import ltr.models.lwl.lwl_box_net as lwt_box_networks
import ltr.actors.segmentation as lwtl_actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
from ltr import MultiGPU
from ltr.models.loss.segmentation import LovaszSegLoss
import ltr.admin.loading as network_loading
def run(settings):
settings.description = 'Default train settings for training VOS with box initialization.'
settings.batch_size = 8
settings.num_workers = 4
settings.multi_gpu = False
settings.print_interval = 1
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.device = "cuda:0"
settings.center_jitter_factor = {'train': 3, 'test': (5.5, 4.5)}
settings.scale_jitter_factor = {'train': 0.25, 'test': 0.5}
settings.min_target_area = 500
ytvos_train = YouTubeVOS(version="2019", multiobj=False, split='jjtrain')
ytvos_valid = YouTubeVOS(version="2019", multiobj=False, split='jjvalid')
coco_train = MSCOCOSeq()
# Data transform
transform_joint = tfm.Transform(tfm.ToBGR(),
tfm.ToGrayscale(probability=0.05),
tfm.RandomHorizontalFlip(probability=0.5))
transform_train = tfm.Transform(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.LWLProcessing(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,
new_roll=True)
data_processing_val = processing.LWLProcessing(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,
new_roll=True)
# Train sampler and loader
dataset_train = sampler.LWLSampler([ytvos_train, coco_train], [1, 1],
samples_per_epoch=settings.batch_size * 1000, max_gap=100,
num_test_frames=1,
num_train_frames=1,
processing=data_processing_train)
dataset_val = sampler.LWLSampler([ytvos_valid], [1],
samples_per_epoch=settings.batch_size * 100, max_gap=100,
num_test_frames=1,
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)
net = lwt_box_networks.steepest_descent_resnet50(filter_size=3, num_filters=16, optim_iter=5,
backbone_pretrained=True,
out_feature_dim=512,
frozen_backbone_layers=['conv1', 'bn1', 'layer1'],
label_encoder_dims=(16, 32, 64),
use_bn_in_label_enc=False,
clf_feat_blocks=0,
final_conv=True,
backbone_type='mrcnn',
box_label_encoder_dims=(64, 64,),
final_bn=False)
base_net_weights = network_loading.load_trained_network(settings.env.workspace_dir,
'ltr/lwl/lwl_stage2/LWTLNet_ep0080.pth.tar')
# Copy weights
net.feature_extractor.load_state_dict(base_net_weights.feature_extractor.state_dict())
net.target_model.load_state_dict(base_net_weights.target_model.state_dict())
net.decoder.load_state_dict(base_net_weights.decoder.state_dict())
net.label_encoder.load_state_dict(base_net_weights.label_encoder.state_dict())
# Wrap the network for multi GPU training
if settings.multi_gpu:
net = MultiGPU(net, dim=1)
objective = {
'segm': LovaszSegLoss(per_image=False),
}
loss_weight = {
'segm': 100.0,
'segm_box': 10.0,
'segm_train': 10,
}
actor = lwtl_actors.LWLBoxActor(net=net, objective=objective, loss_weight=loss_weight)
# Optimizer
optimizer = optim.Adam([{'params': actor.net.box_label_encoder.parameters(), 'lr': 1e-3}],
lr=2e-4)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.2)
trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler)
trainer.train(50, load_latest=True, fail_safe=True)