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main.py
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import os
import sys
import yaml
import torch
import pprint
from munch import munchify
from models import VisModelingModel
from pytorch_lightning.plugins import DDPPlugin
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks import ModelCheckpoint
def load_config(filepath):
with open(filepath, 'r') as stream:
try:
trainer_params = yaml.safe_load(stream)
return trainer_params
except yaml.YAMLError as exc:
print(exc)
def seed(cfg):
torch.manual_seed(cfg.seed)
if cfg.if_cuda:
torch.cuda.manual_seed(cfg.seed)
def main():
config_filepath = str(sys.argv[1])
cfg = load_config(filepath=config_filepath)
pprint.pprint(cfg)
cfg = munchify(cfg)
seed(cfg)
seed_everything(cfg.seed)
log_dir = '_'.join([cfg.log_dir,
cfg.model_name,
cfg.tag,
str(cfg.seed)])
model = VisModelingModel(lr=cfg.lr,
seed=cfg.seed,
dof=cfg.dof,
if_cuda=cfg.if_cuda,
if_test=False,
gamma=cfg.gamma,
log_dir=log_dir,
train_batch=cfg.train_batch,
val_batch=cfg.val_batch,
test_batch=cfg.test_batch,
num_workers=cfg.num_workers,
model_name=cfg.model_name,
data_filepath=cfg.data_filepath,
loss_type = cfg.loss_type,
coord_system=cfg.coord_system,
lr_schedule=cfg.lr_schedule)
# define trainer
trainer = Trainer(gpus=cfg.num_gpus,
max_epochs=cfg.epochs,
deterministic=True,
plugins=DDPPlugin(find_unused_parameters=False),
amp_backend='native',
default_root_dir=log_dir)
trainer.fit(model)
def main_kinematic():
config_filepath = str(sys.argv[1])
cfg = load_config(filepath=config_filepath)
pprint.pprint(cfg)
cfg = munchify(cfg)
seed(cfg)
seed_everything(cfg.seed)
log_dir = '_'.join([cfg.log_dir,
cfg.model_name,
cfg.tag,
str(cfg.seed)])
model = VisModelingModel(lr=cfg.lr,
seed=cfg.seed,
dof=cfg.dof,
if_cuda=cfg.if_cuda,
if_test=False,
gamma=cfg.gamma,
log_dir=log_dir,
train_batch=cfg.train_batch,
val_batch=cfg.val_batch,
test_batch=cfg.test_batch,
num_workers=cfg.num_workers,
model_name=cfg.model_name,
data_filepath=cfg.data_filepath,
loss_type = cfg.loss_type,
coord_system=cfg.coord_system,
lr_schedule=cfg.lr_schedule)
# define callback for selecting checkpoints during training
checkpoint_callback = ModelCheckpoint(
filename=log_dir + "{epoch}_{val_loss}",
verbose=True,
monitor='val_loss',
mode='min',
prefix='')
# define trainer
trainer = Trainer(gpus=cfg.num_gpus,
max_epochs=cfg.epochs,
deterministic=True,
plugins=DDPPlugin(find_unused_parameters=False),
amp_backend='native',
default_root_dir=log_dir,
val_check_interval=1.0,
checkpoint_callback=checkpoint_callback)
model.extract_kinematic_encoder_model(sys.argv[3])
trainer.fit(model)
def main_kinematic_scratch():
config_filepath = str(sys.argv[1])
cfg = load_config(filepath=config_filepath)
pprint.pprint(cfg)
cfg = munchify(cfg)
seed(cfg)
seed_everything(cfg.seed)
log_dir = '_'.join([cfg.log_dir,
cfg.model_name,
cfg.tag,
str(cfg.seed)])
model = VisModelingModel(lr=cfg.lr,
seed=cfg.seed,
dof=cfg.dof,
if_cuda=cfg.if_cuda,
if_test=False,
gamma=cfg.gamma,
log_dir=log_dir,
train_batch=cfg.train_batch,
val_batch=cfg.val_batch,
test_batch=cfg.test_batch,
num_workers=cfg.num_workers,
model_name=cfg.model_name,
data_filepath=cfg.data_filepath,
loss_type = cfg.loss_type,
coord_system=cfg.coord_system,
lr_schedule=cfg.lr_schedule)
# define callback for selecting checkpoints during training
checkpoint_callback = ModelCheckpoint(
filename=log_dir + "{epoch}_{val_loss}",
verbose=True,
monitor='val_loss',
mode='min',
prefix='')
# define trainer
trainer = Trainer(gpus=cfg.num_gpus,
max_epochs=cfg.epochs,
deterministic=True,
plugins=DDPPlugin(find_unused_parameters=False),
amp_backend='native',
default_root_dir=log_dir,
val_check_interval=1.0,
checkpoint_callback=checkpoint_callback)
trainer.fit(model)
if __name__ == '__main__':
if sys.argv[2] == 'kinematic':
main_kinematic()
elif sys.argv[2] == 'kinematic-scratch':
main_kinematic_scratch()
else:
main()