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adapt_cosmix_ssda.py
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adapt_cosmix_ssda.py
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import os
import time
import argparse
import numpy as np
import torch
from torch.utils.data import DataLoader
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger
import MinkowskiEngine as ME
import utils.models as models
from utils.datasets.initialization import get_dataset, get_concat_dataset
from configs import get_config
from utils.collation import CollateFN, CollateMerged
from utils.pipelines.masked_ssda_pipeline import SimMaskedSemiAdaptation
from utils.sampling.ssda import SupervisedSampler
from utils.common.momentum import MomentumUpdater
parser = argparse.ArgumentParser()
parser.add_argument("--config_file",
default="configs/source/synlidar_semantickitti.yaml",
type=str,
help="Path to config file")
def load_model(checkpoint_path, model):
# reloads model
def clean_state_dict(state):
# clean state dict from names of PL
for k in list(ckpt.keys()):
if "model" in k:
ckpt[k.replace("model.", "")] = ckpt[k]
del ckpt[k]
return state
ckpt = torch.load(checkpoint_path, map_location=torch.device('cpu'))["state_dict"]
ckpt = clean_state_dict(ckpt)
model.load_state_dict(ckpt, strict=True)
return model
def adapt(config):
def get_dataloader(dataset, batch_size, shuffle=False, pin_memory=True, collation=None):
if collation is None:
collation = CollateFN()
return DataLoader(dataset,
batch_size=batch_size,
collate_fn=collation,
shuffle=shuffle,
num_workers=config.pipeline.dataloader.num_workers,
pin_memory=pin_memory)
try:
source_mapping_path = config.source_dataset.mapping_path
except AttributeError('--> Setting default class mapping path for source!'):
source_mapping_path = None
try:
target_mapping_path = config.target_dataset.mapping_path
except AttributeError('--> Setting default class mapping path for target!'):
target_mapping_path = None
source_training_dataset, source_validation_dataset, _ = get_dataset(dataset_name=config.source_dataset.name,
dataset_path=config.source_dataset.dataset_path,
voxel_size=config.source_dataset.voxel_size,
augment_data=config.source_dataset.augment_data,
version=config.source_dataset.version,
sub_num=config.source_dataset.num_pts,
num_classes=config.model.out_classes,
ignore_label=config.source_dataset.ignore_label,
mapping_path=source_mapping_path,
target_name=config.target_dataset.name,
weights_path=config.source_dataset.weights_path)
target_training_dataset, target_validation_dataset, _ = get_dataset(dataset_name=config.target_dataset.name,
dataset_path=config.target_dataset.dataset_path,
voxel_size=config.target_dataset.voxel_size,
augment_data=config.target_dataset.augment_data,
version=config.target_dataset.version,
sub_num=config.target_dataset.num_pts,
num_classes=config.model.out_classes,
ignore_label=config.target_dataset.ignore_label,
mapping_path=target_mapping_path)
training_dataset = get_concat_dataset(source_dataset=source_training_dataset,
target_dataset=target_training_dataset,
augment_data=config.masked_dataset.augment_data,
augment_mask_data=config.masked_dataset.augment_mask_data,
remove_overlap=config.masked_dataset.remove_overlap)
ssda_sampler = SupervisedSampler(dataset=target_training_dataset,
method=config.adaptation.ssda_sampler.method,
num_frames=config.adaptation.ssda_sampler.num_frames)
training_collation = CollateMerged()
training_dataloader = get_dataloader(training_dataset,
batch_size=config.pipeline.dataloader.train_batch_size,
shuffle=True,
collation=training_collation)
source_validation_dataloader = get_dataloader(source_validation_dataset,
batch_size=config.pipeline.dataloader.train_batch_size,
shuffle=False,
collation=CollateFN())
validation_dataloaders = [source_validation_dataloader]
Model = getattr(models, config.model.name)
student_model = Model(config.model.in_feat_size, config.model.out_classes)
student_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(student_model)
if config.adaptation.student_checkpoint is not None:
student_model = load_model(config.adaptation.student_checkpoint, student_model)
print(f'--> Loaded student checkpoint {config.adaptation.student_checkpoint}')
else:
print(f'--> Using pristine student model!')
teacher_model = Model(config.model.in_feat_size, config.model.out_classes)
teacher_model = ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm(teacher_model)
if config.adaptation.teacher_checkpoint is not None:
teacher_model = load_model(config.adaptation.teacher_checkpoint, teacher_model)
print(f'--> Loaded teacher checkpoint {config.adaptation.teacher_checkpoint}')
else:
print(f'--> Using pristine teacher model!')
momentum_updater = MomentumUpdater(base_tau=0.999, final_tau=0.999)
if config.adaptation.self_paced:
target_confidence_th = np.linspace(config.adaptation.target_confidence_th, 0.6, config.pipeline.epochs)
else:
target_confidence_th = config.adaptation.target_confidence_th
pl_module = SimMaskedSemiAdaptation(training_dataset=training_dataset,
source_validation_dataset=source_validation_dataset,
target_validation_dataset=target_validation_dataset,
student_model=student_model,
teacher_model=teacher_model,
momentum_updater=momentum_updater,
source_criterion=config.adaptation.losses.source_criterion,
target_criterion=config.adaptation.losses.target_criterion,
other_criterion=config.adaptation.losses.other_criterion,
source_weight=config.adaptation.losses.source_weight,
target_weight=config.adaptation.losses.target_weight,
other_weight=config.adaptation.losses.other_weight,
filtering=config.adaptation.filtering,
adaptive_weight=config.adaptation.adaptive_weight,
optimizer_name=config.pipeline.optimizer.name,
train_batch_size=config.pipeline.dataloader.train_batch_size,
val_batch_size=config.pipeline.dataloader.val_batch_size,
lr=config.pipeline.optimizer.lr,
num_classes=config.model.out_classes,
num_workers=config.pipeline.dataloader.num_workers,
clear_cache_int=config.pipeline.lightning.clear_cache_int,
scheduler_name=config.pipeline.scheduler.name,
update_every=config.adaptation.momentum.update_every,
shuffle_batches=config.adaptation.shuffle_batches,
source_filtering=config.adaptation.source_filtering,
weighted_sampling=config.adaptation.weighted_sampling,
target_confidence_th=target_confidence_th,
self_paced=config.adaptation.self_paced,
selection_perc=config.adaptation.selection_perc,
supervised_sampler=ssda_sampler)
run_time = time.strftime("%Y_%m_%d_%H:%M", time.gmtime())
if config.pipeline.wandb.run_name is not None:
run_name = run_time + '_' + config.pipeline.wandb.run_name
else:
run_name = run_time
save_dir = os.path.join(config.pipeline.save_dir, run_name)
wandb_logger = WandbLogger(project=config.pipeline.wandb.project_name,
entity=config.pipeline.wandb.entity_name,
name=run_name,
offline=config.pipeline.wandb.offline)
loggers = [wandb_logger]
checkpoint_callback = ModelCheckpoint(dirpath=os.path.join(save_dir, 'checkpoints'), save_top_k=-1)
callbacks = [checkpoint_callback]
if config.pipeline.gpus is not None:
strategy = "ddp" if len(config.pipeline.gpus) > 1 else None
else:
strategy = None
trainer = Trainer(max_epochs=config.pipeline.epochs,
gpus=config.pipeline.gpus,
accelerator=strategy,
default_root_dir=config.pipeline.save_dir,
weights_save_path=save_dir,
precision=config.pipeline.precision,
logger=loggers,
check_val_every_n_epoch=config.pipeline.lightning.check_val_every_n_epoch,
val_check_interval=1.0,
num_sanity_val_steps=config.pipeline.lightning.num_sanity_val_steps,
resume_from_checkpoint=config.pipeline.lightning.resume_checkpoint,
callbacks=callbacks)
trainer.fit(pl_module,
train_dataloaders=training_dataloader,
val_dataloaders=validation_dataloaders)
if __name__ == '__main__':
args = parser.parse_args()
config = get_config(args.config_file)
# fix random seed
os.environ['PYTHONHASHSEED'] = str(config.pipeline.seed)
np.random.seed(config.pipeline.seed)
torch.manual_seed(config.pipeline.seed)
torch.cuda.manual_seed(config.pipeline.seed)
torch.backends.cudnn.benchmark = True
adapt(config)