def run(settings):
# Most common settings are assigned in the settings struct
settings.device = 'cuda'
settings.description = 'TransT with default settings.'
settings.batch_size = 16 # 38
settings.num_workers = min(settings.batch_size, 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 = 4.0
settings.template_area_factor = 2.0
settings.search_feature_sz = 32
settings.template_feature_sz = 16
settings.search_sz = settings.search_feature_sz * 8
settings.temp_sz = settings.template_feature_sz * 8
settings.center_jitter_factor = {'search': 3, 'template': 0}
settings.scale_jitter_factor = {'search': 0.25, 'template': 0}
settings.sequence_length = 16
settings.search_gap = 8
settings.init_ckpt = "pytracking/networks/transt.pth"
# Transformer
settings.position_embedding = 'sine'
settings.hidden_dim = 256
settings.dropout = 0.1
settings.nheads = 8
settings.dim_feedforward = 2048
settings.featurefusion_layers = 4
settings.sigma = 1 / 4 / 5.
settings.kernel = 4
settings.feature = 32 # 18
settings.output_sz = 256 # settings.feature * 16
settings.end_pad_if_even = False
settings.label_function_params = True
# 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)
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = tfm.Transform(
tfm.ToTensorAndJitter(0.2),
tfm.Normalize(mean=settings.normalize_mean,
std=settings.normalize_std))
# Data processing to do on the training pairs
data_processing_train = processing.TransTProcessing(
search_area_factor=settings.search_area_factor,
template_area_factor=settings.template_area_factor,
search_sz=settings.search_sz,
temp_sz=settings.temp_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
transform=transform_train,
label_function_params=settings.label_function_params,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.TransTSampler(
[got10k_train], [1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=100,
processing=data_processing_train,
num_search_frames=settings.sequence_length)
# dataset_train = sampler.TransTSampler([lasot_train, got10k_train, coco_train, trackingnet_train], [1,1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=100, processing=data_processing_train)
# The loader for training
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=0)
# Create network and actor
model = transt_models.transt_resnet50(settings)
# Wrap the network for multi GPU training
if settings.multi_gpu:
model = MultiGPU(model, dim=0)
objective = transt_models.transt_loss(settings)
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
actor = actors.CircuitTranstActor(net=model, objective=objective)
# Optimizer
param_dicts = [
{
"params": [
p for n, p in model.named_parameters()
if "backbone" not in n and p.requires_grad
]
},
{
"params": [
p for n, p in model.named_parameters()
if "backbone" in n and p.requires_grad
],
"lr":
1e-5,
},
]
optimizer = torch.optim.AdamW(param_dicts, lr=1e-4, weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 500)
# Create trainer
trainer = LTRTrainer(actor, [loader_train], optimizer, settings,
lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(1000, load_latest=True, fail_safe=True)
def run(settings):
# Most common settings are assigned in the settings struct
debug = False
if debug:
settings.batch_size = 4 # 8 # 4 # 120 # 70 # 38
settings.num_workers = 0 # 24 # 30 # 10 # 35 # 30 min(settings.batch_size, 16)
settings.multi_gpu = False # True # True # True # True # True # True # True
else:
settings.batch_size = 38 # 8 # 4 # 120 # 70 # 38
settings.num_workers = 20 # 24 # 30 # 10 # 35 # 30 min(settings.batch_size, 16)
settings.multi_gpu = True # True # True # True # True # True # True # True
settings.device = 'cuda'
settings.description = 'TransT with default settings.'
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 = 6. # 4.0
settings.template_area_factor = 2.
settings.search_feature_sz = 32
settings.template_feature_sz = 16
settings.search_sz = settings.search_feature_sz * 8
settings.temp_sz = settings.template_feature_sz * 8
settings.center_jitter_factor = {'search': 2.0, 'template': 0} # 3
settings.scale_jitter_factor = {'search': 0.05, 'template': 0} # 0.25
settings.sequence_length = 34 # 30 # 64 NEXT # Same as PT
settings.rand = False
# settings.search_gap = 1 # Depreciated
settings.init_ckpt = "pytracking/networks/transt.pth"
# Transformer
settings.position_embedding = 'sine'
settings.hidden_dim = 256
settings.dropout = 0.1
settings.nheads = 8
settings.dim_feedforward = 2048
settings.featurefusion_layers = 4
# settings.sigma = 1 / 4 / 5.
# settings.kernel = 4
# settings.feature = 32 # 18
# settings.output_sz = 256 # settings.feature * 16
# settings.end_pad_if_even = False
# settings.label_function_params = True
settings.move_data_to_gpu = True
# Train datasets
lasot_train = Lasot(settings.env.lasot_dir, split='train')
got10k_train = Got10k(settings.env.got10k_dir, split='vottrain') # votval
trackingnet_train = TrackingNet(settings.env.trackingnet_dir, set_ids=list(range(4)))
# coco_train = MSCOCOSeq(settings.env.coco_dir)
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = tfm.Transform(tfm.ToGrayscale(probability=0.05))
# The augmentation transform applied to the training set (individually to each image in the pair)
# transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
# tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
transform_train = tfm.Transform(tfm.ToTensorAndJitter(0.2),
# tfm.RandomHorizontalFlip(),
# tfm.RandomAffine(p_flip=0.5, max_scale=1.5),
# tfm.RandomBlur(1),
tfm.Normalize(mean=settings.normalize_mean, std=settings.normalize_std))
# Data processing to do on the training pairs
data_processing_train = processing.TransTProcessing(search_area_factor=settings.search_area_factor,
template_area_factor = settings.template_area_factor,
search_sz=settings.search_sz,
temp_sz=settings.temp_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
joint=False, # Whether or not to apply same transform to every image
transform=transform_train,
rand=settings.rand,
label_function_params=None, # settings.label_function_params,
joint_transform=transform_joint)
# The sampler for training
# dataset_train = sampler.TransTSampler([got10k_train], [1], samples_per_epoch=1000*settings.batch_size, max_gap=100, processing=data_processing_train, num_search_frames=settings.sequence_length, frame_sample_mode="rnn_causal")
# dataset_train = sampler.TransTSampler([got10k_train, trackingnet_train], [1, 1], samples_per_epoch=1000*settings.batch_size, max_gap=100, processing=data_processing_train, num_search_frames=settings.sequence_length, frame_sample_mode="interval")
dataset_train = sampler.TransTSampler([lasot_train, got10k_train, trackingnet_train], [1,1,1], samples_per_epoch=1000*settings.batch_size, max_gap=100, processing=data_processing_train)
# The loader for training
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=0, pin_memory=settings.move_data_to_gpu == False)
# Create network and actor
model = transt_models.transt_resnet50(settings)
# Wrap the network for multi GPU training
if settings.multi_gpu:
model = MultiGPU(model, dim=0)
objective = transt_models.transt_loss(settings)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params:', n_parameters)
actor = actors.TranstActor(net=model, objective=objective)
# Optimizer
# Change learning rate forthe Q we have changed and the RNN and the readout
# q = self.mix_q(torch.cat([q, self.mix_norm(exc)], -1))
# self.class_embed_new = MLP(hidden_dim * 2, hidden_dim, num_classes + 1, 3)
# self.bbox_embed_new = MLP(hidden_dim * 2, hidden_dim, 4, 3)
param_dicts = [
{"params": [p for n, p in model.named_parameters() if "backbone" not in n and p.requires_grad]},
{
"params": [p for n, p in model.named_parameters() if "backbone" in n and p.requires_grad],
"lr": 1e-5,
},
]
optimizer = torch.optim.AdamW(param_dicts, lr=1e-4,
weight_decay=1e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 500)
# Create trainer
trainer = LTRTrainer(actor, [loader_train], optimizer, settings, lr_scheduler)
# Run training (set fail_safe=False if you are debugging)
trainer.train(1000, load_latest=True, fail_safe=True)
