def main_train(validation_dataset):
logger.critical('Building the model.')
model = desc.make_model(args)
if args.use_gpu:
model.cuda()
# Use the customized data parallel if applicable.
if args.gpu_parallel:
from jactorch.parallel import JacDataParallel
# from jactorch.parallel import UserScatteredJacDataParallel as JacDataParallel
model = JacDataParallel(model, device_ids=args.gpus).cuda()
# Disable the cudnn benchmark.
cudnn.benchmark = False
trainer = TrainerEnv(model, None)
if args.load:
if trainer.load_weights(args.load):
logger.critical(
'Loaded weights from pretrained model: "{}".'.format(
args.load))
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
logger.critical('Building the data loader.')
validation_dataloader = validation_dataset.make_dataloader(
args.batch_size,
shuffle=False,
drop_last=False,
nr_workers=args.data_workers)
meters.reset()
model.eval()
if not os.path.isdir(args.output_attr_path):
os.makedirs(args.output_attr_path)
validate_attribute(model, validation_dataloader, meters, args.setname,
logger, args.output_attr_path)
logger.critical(
meters.format_simple(args.setname,
{k: v
for k, v in meters.avg.items() if v != 0},
compressed=False))
return meters
def make_model():
model = desc.make_model(args, vocab)
model.cuda()
# Use the customized data parallel if applicable.
cudnn.benchmark = False
from jactorch.optim import AdamW
trainable_parameters = filter(lambda x: x.requires_grad,
model.parameters())
optimizer = AdamW(trainable_parameters,
0.1,
weight_decay=configs.train.weight_decay)
trainer = TrainerEnv(model, optimizer)
_ = trainer.load_checkpoint(args.resume)
model = trainer.model
model.eval()
return model
def main_train(train_dataset, validation_dataset, extra_dataset=None):
logger.critical('Building the model.')
model = desc.make_model(args, train_dataset.unwrapped.vocab)
if args.use_gpu:
model.cuda()
# Use the customized data parallel if applicable.
if args.gpu_parallel:
from jactorch.parallel import JacDataParallel
# from jactorch.parallel import UserScatteredJacDataParallel as JacDataParallel
model = JacDataParallel(model, device_ids=args.gpus).cuda()
# Disable the cudnn benchmark.
cudnn.benchmark = False
if hasattr(desc, 'make_optimizer'):
logger.critical('Building customized optimizer.')
optimizer = desc.make_optimizer(model, args.lr)
else:
from jactorch.optim import AdamW
from torch.optim import RMSProp
trainable_parameters_monet = [x for name, x in model.named_parameters() if x.requires_grad and 'monet' in name
x.requires_grad and 'monet' in name]
trainable_parameters_nscl = [x for x in model.parameters if x.requires_grad and x not in trainable_parameters_monet]
optimizer_monet = RMSProp(trainable_parameters_monet)
optimizer_nscl = AdamW(trainable_parameters, args.lr, weight_decay=configs.train.weight_decay)
if args.acc_grad > 1:
from jactorch.optim import AccumGrad
optimizer = AccumGrad(optimizer, args.acc_grad)
logger.warning('Use accumulated grad={:d}, effective iterations per epoch={:d}.'.format(args.acc_grad, int(args.iters_per_epoch / args.acc_grad)))
trainer = TrainerEnv(model, optimizer)
if args.resume:
extra = trainer.load_checkpoint(args.resume)
if extra:
args.start_epoch = extra['epoch']
logger.critical('Resume from epoch {}.'.format(args.start_epoch))
elif args.load:
if trainer.load_weights(args.load):
logger.critical('Loaded weights from pretrained model: "{}".'.format(args.load))
if args.use_tb and not args.debug:
from jactorch.train.tb import TBLogger, TBGroupMeters
tb_logger = TBLogger(args.tb_dir)
meters = TBGroupMeters(tb_logger)
logger.critical('Writing tensorboard logs to: "{}".'.format(args.tb_dir))
else:
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
if not args.debug:
logger.critical('Writing meter logs to file: "{}".'.format(args.meter_file))
if args.clip_grad:
logger.info('Registering the clip_grad hook: {}.'.format(args.clip_grad))
def clip_grad(self, loss):
from torch.nn.utils import clip_grad_norm_
clip_grad_norm_(self.model.parameters(), max_norm=args.clip_grad)
trainer.register_event('backward:after', clip_grad)
if hasattr(desc, 'customize_trainer'):
desc.customize_trainer(trainer)
if args.embed:
from IPython import embed; embed()
logger.critical('Building the data loader.')
validation_dataloader = validation_dataset.make_dataloader(args.batch_size, shuffle=False, drop_last=False, nr_workers=args.data_workers)
if extra_dataset is not None:
extra_dataloader = extra_dataset.make_dataloader(args.batch_size, shuffle=False, drop_last=False, nr_workers=args.data_workers)
if args.evaluate:
meters.reset()
model.eval()
validate_epoch(0, trainer, validation_dataloader, meters)
if extra_dataset is not None:
validate_epoch(0, trainer, extra_dataloader, meters, meter_prefix='validation_extra')
logger.critical(meters.format_simple('Validation', {k: v for k, v in meters.avg.items() if v != 0}, compressed=False))
return meters
# assert args.curriculum == 'off', 'Unimplemented feature: curriculum mode {}.'.format(args.curriculum)
curriculum_strategy = [
(0, 3, 4),
(5, 3, 6),
(10, 3, 8),
(15, 4, 8),
(25, 4, 12),
(35, 5, 12),
(45, 6, 12),
(55, 7, 16),
(65, 8, 20),
(75, 9, 22),
(90, 10, 25),
(1e9, None, None)
]
# trainer.register_event('backward:after', backward_check_nan)
for epoch in range(args.start_epoch + 1, args.epochs + 1):
meters.reset()
model.train()
this_train_dataset = train_dataset
if args.curriculum != 'off':
for si, s in enumerate(curriculum_strategy):
if curriculum_strategy[si][0] < epoch <= curriculum_strategy[si + 1][0]:
max_scene_size, max_program_size = s[1:]
if args.curriculum in ('scene', 'all'):
this_train_dataset = this_train_dataset.filter_scene_size(max_scene_size)
if args.curriculum in ('program', 'all'):
this_train_dataset = this_train_dataset.filter_program_size_raw(max_program_size)
logger.critical('Building the data loader. Curriculum = {}/{}, length = {}.'.format(*s[1:], len(this_train_dataset)))
break
train_dataloader = this_train_dataset.make_dataloader(args.batch_size, shuffle=True, drop_last=True, nr_workers=args.data_workers)
for enum_id in range(args.enums_per_epoch):
train_epoch(epoch, trainer, train_dataloader, meters)
if epoch % args.validation_interval == 0:
model.eval()
validate_epoch(epoch, trainer, validation_dataloader, meters)
if not args.debug:
meters.dump(args.meter_file)
logger.critical(meters.format_simple(
'Epoch = {}'.format(epoch),
{k: v for k, v in meters.avg.items() if epoch % args.validation_interval == 0 or not k.startswith('validation')},
compressed=False
))
if epoch % args.save_interval == 0 and not args.debug:
fname = osp.join(args.ckpt_dir, 'epoch_{}.pth'.format(epoch))
trainer.save_checkpoint(fname, dict(epoch=epoch, meta_file=args.meta_file))
if epoch > int(args.epochs * 0.6):
trainer.set_learning_rate(args.lr * 0.1)
def main():
# directories
if not args.debug:
args.dump_dir = ensure_path(osp.join('dumps', args.series_name, args.desc_name, args.run_name))
args.ckpt_dir = ensure_path(osp.join(args.dump_dir, 'checkpoints'))
args.vis_dir = ensure_path(osp.join(args.dump_dir, 'visualizations'))
args.meta_file = osp.join(args.dump_dir, 'metainfo.json')
args.log_file = osp.join(args.dump_dir, 'log.log')
args.meter_file = osp.join(args.dump_dir, 'meter.json')
# Initialize the tensorboard.
if args.use_tb:
args.tb_dir = ensure_path(osp.join(args.dump_dir, 'tensorboard'))
else:
args.tb_dir = None
if not args.debug:
logger.critical('Writing logs to file: "{}".'.format(args.log_file))
set_output_file(args.log_file)
logger.critical('Writing metainfo to file: "{}".'.format(args.meta_file))
with open(args.meta_file, 'w') as f:
f.write(dump_metainfo(args=args.__dict__, configs=configs))
if args.debug and args.use_tb:
logger.warning('Disabling the tensorboard in the debug mode.')
args.use_tb = False
if args.evaluate and args.use_tb:
logger.warning('Disabling the tensorboard in the evaluation mode.')
args.use_tb = False
# TODO(Jiayuan Mao @ 04/23): load the dataset.
logger.critical('Loading the dataset.')
train_dataset = None
validation_dataset = None
# configs.validate_dataset_compatibility(train_dataset)
# TODO(Jiayuan Mao @ 04/23): build the model.
logger.critical('Building the model.')
model = desc.make_model(args)
if args.use_gpu:
model.cuda()
# Use the customized data parallel if applicable.
if args.gpu_parallel:
from jactorch.parallel import JacDataParallel
# Set user_scattered because we will add a multi GPU wrapper to the dataloader. See below.
model = JacDataParallel(model, device_ids=args.gpus, user_scattered=True).cuda()
# TODO(Jiayuan Mao @ 04/23): disable the cudnn benchmark.
# Disable the cudnn benchmark.
cudnn.benchmark = False
if hasattr(desc, 'make_optimizer'):
logger.critical('Building customized optimizer.')
optimizer = desc.make_optimizer(model, args.lr)
else:
from jactorch.optim import AdamW
# TODO(Jiayuan Mao @ 04/23): set the default optimizer.
trainable_parameters = filter(lambda x: x.requires_grad, model.parameters())
optimizer = AdamW(trainable_parameters, args.lr, weight_decay=configs.train.weight_decay)
if args.acc_grad > 1:
from jactorch.optim import AccumGrad
optimizer = AccumGrad(optimizer, args.acc_grad)
logger.warning('Use accumulated grad={:d}, effective iterations per epoch={:d}.'.format(args.acc_grad, int(args.iters_per_epoch / args.acc_grad)))
trainer = TrainerEnv(model, optimizer)
if args.resume:
extra = trainer.load_checkpoint(args.resume)
if extra:
args.start_epoch = extra['epoch']
logger.critical('Resume from epoch {}.'.format(args.start_epoch))
elif args.load:
if trainer.load_weights(args.load):
logger.critical('Loaded weights from pretrained model: "{}".'.format(args.load))
if args.use_tb:
from jactorch.train.tb import TBLogger, TBGroupMeters
tb_logger = TBLogger(args.tb_dir)
meters = TBGroupMeters(tb_logger)
logger.critical('Writing tensorboard logs to: "{}".'.format(args.tb_dir))
else:
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
if not args.debug:
logger.critical('Writing metainfo to file: "{}".'.format(args.meta_file))
with open(args.meta_file, 'w') as f:
f.write(dump_metainfo(args=args.__dict__, configs=configs))
logger.critical('Writing meter logs to file: "{}".'.format(args.meter_file))
logger.critical('Initializing MLDash.')
mldash.init(
desc_name=args.series_name + '/' + args.desc_name,
expr_name=args.expr,
run_name=args.run_name,
args=args,
highlight_args=parser,
configs=configs,
)
mldash.update(metainfo_file=args.meta_file, log_file=args.log_file, meter_file=args.meter_file, tb_dir=args.tb_dir)
if args.embed:
from IPython import embed; embed()
if hasattr(desc, 'customize_trainer'):
desc.customize_trainer(trainer)
# TODO(Jiayuan Mao @ 04/23): make the data loader.
logger.critical('Building the data loader.')
train_dataloader = train_dataset.make_dataloader(args.batch_size, shuffle=True, drop_last=True, nr_workers=args.data_workers)
validation_dataloader = validation_dataset.make_dataloader(args.batch_size, shuffle=False, drop_last=False, nr_workers=args.data_workers)
if args.use_gpu and args.gpu_parallel:
from jactorch.data.dataloader import JacDataLoaderMultiGPUWrapper
train_dataloader = JacDataLoaderMultiGPUWrapper(train_dataloader, args.gpus)
validation_dataloader = JacDataLoaderMultiGPUWrapper(validation_dataloader, args.gpus)
if args.evaluate:
epoch = 0
model.eval()
validate_epoch(epoch, trainer, validation_dataloader, meters)
if not args.debug:
meters.dump(args.meter_file)
logger.critical(meters.format_simple('Epoch = {}'.format(epoch), compressed=False))
return
for epoch in range(args.start_epoch + 1, args.epochs + 1):
meters.reset()
model.train()
train_epoch(epoch, trainer, train_dataloader, meters)
if args.validation_interval > 0 and epoch % args.validation_interval == 0:
model.eval()
with torch.no_grad():
validate_epoch(epoch, trainer, validation_dataloader, meters)
if not args.debug:
meters.dump(args.meter_file)
# TODO(Jiayuan Mao @ 02/15): config the MLDash.
if not args.debug:
mldash.log_metric('epoch', epoch, desc=False, expr=False)
for key, value in meters.items():
if key.startswith('loss') or key.startswith('validation/loss'):
mldash.log_metric_min(key, value.avg)
for key, value in meters.items():
if key.startswith('acc') or key.startswith('validation/acc'):
mldash.log_metric_max(key, value.avg)
logger.critical(meters.format_simple('Epoch = {}'.format(epoch), compressed=False))
if not args.debug:
if epoch % args.save_interval == 0:
fname = osp.join(args.ckpt_dir, 'epoch_{}.pth'.format(epoch))
trainer.save_checkpoint(fname, dict(epoch=epoch, meta_file=args.meta_file))
def main_train(
train_dataset,
validation_dataset,
test_dataset=None,
prototype_dataset=None,
one_shot_dataset=None,
):
logger.critical("Building the model.")
model = desc.make_model(args, train_dataset.unwrapped.vocab)
if args.use_gpu:
model.cuda()
# Use the customized data parallel if applicable.
if args.gpu_parallel:
from jactorch.parallel import JacDataParallel
# from jactorch.parallel import UserScatteredJacDataParallel as JacDataParallel
model = JacDataParallel(model, device_ids=args.gpus).cuda()
# Disable the cudnn benchmark.
cudnn.benchmark = False
if hasattr(desc, "make_optimizer"):
logger.critical("Building customized optimizer.")
optimizer = desc.make_optimizer(model, args.lr)
else:
from jactorch.optim import AdamW
trainable_parameters = filter(lambda x: x.requires_grad,
model.parameters())
optimizer = AdamW(trainable_parameters,
args.lr,
weight_decay=configs.train.weight_decay)
if args.acc_grad > 1:
from jactorch.optim import AccumGrad
optimizer = AccumGrad(optimizer, args.acc_grad)
logger.warning(
"Use accumulated grad={:d}, effective iterations per epoch={:d}.".
format(args.acc_grad, int(args.iters_per_epoch / args.acc_grad)))
trainer = TrainerEnv(model, optimizer)
if args.resume:
extra = trainer.load_checkpoint(args.resume)
if extra:
args.start_epoch = extra["epoch"]
logger.critical("Resume from epoch {}.".format(args.start_epoch))
elif args.load:
if trainer.load_weights(args.load):
logger.critical(
'Loaded weights from pretrained model: "{}".'.format(
args.load))
if args.use_tb and not args.debug:
from jactorch.train.tb import TBLogger, TBGroupMeters
tb_logger = TBLogger(args.tb_dir)
meters = TBGroupMeters(tb_logger)
logger.critical('Writing tensorboard logs to: "{}".'.format(
args.tb_dir))
else:
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
if not args.debug:
logger.critical('Writing meter logs to file: "{}".'.format(
args.meter_file))
if args.clip_grad:
logger.info("Registering the clip_grad hook: {}.".format(
args.clip_grad))
def clip_grad(self, loss):
from torch.nn.utils import clip_grad_norm_
clip_grad_norm_(self.model.parameters(), max_norm=args.clip_grad)
trainer.register_event("backward:after", clip_grad)
if hasattr(desc, "customize_trainer"):
desc.customize_trainer(trainer)
if args.embed:
from IPython import embed
embed()
logger.critical("Building the data loader.")
validation_dataloader = validation_dataset.make_dataloader(
args.batch_size,
shuffle=False,
drop_last=False,
nr_workers=args.data_workers)
if test_dataset is not None:
# test_dataloader = {
# dataset: test_dataset[dataset].make_dataloader(
# args.batch_size,
# shuffle=False,
# drop_last=False,
# nr_workers=args.data_workers,
# )
# for dataset in test_dataset
# }
# if args.evaluate:
# meters.reset()
# model.eval()
# validate_epoch(args.start_epoch, trainer, validation_dataloader, meters)
# if test_dataset is not None:
# for dataloader in test_dataloader:
# validate_epoch(
# args.start_epoch,
# trainer,
# test_dataloader[dataloader],
# meters,
# meter_prefix=dataloader,
# )
# logger.critical(
# meters.format_simple(
# "Validation",
# {k: v for k, v in meters.avg.items() if v != 0},
# compressed=False,
# )
# )
main_one_shot(
prototype_dataset,
one_shot_dataset,
model,
args.start_epoch,
trainer,
meters,
args.batch_size,
)
if not args.debug:
meters.dump(args.meter_file)
return meters
# assert args.curriculum == 'off', 'Unimplemented feature: curriculum mode {}.'.format(args.curriculum)
curriculum_strategy = [
(0, 3, 4),
(5, 3, 6),
(10, 3, 8),
(15, 4, 8),
(25, 4, 12),
(35, 5, 12),
(45, 6, 12),
(55, 7, 16),
(65, 8, 20),
(75, 9, 22),
(90, 10, 25),
(1e9, None, None),
]
# trainer.register_event('backward:after', backward_check_nan)
# args.curriculum = "off"
for epoch in range(args.start_epoch + 1, args.epochs + 1):
meters.reset()
model.train()
this_train_dataset = train_dataset
if args.curriculum != "off":
for si, s in enumerate(curriculum_strategy):
if curriculum_strategy[si][0] < epoch <= curriculum_strategy[
si + 1][0]:
max_scene_size, max_program_size = s[1:]
if args.curriculum in ("scene", "all"):
this_train_dataset = this_train_dataset.filter_scene_size(
max_scene_size)
if args.curriculum in ("program", "all"):
this_train_dataset = this_train_dataset.filter_program_size_raw(
max_program_size)
logger.critical(
"Building the data loader. Curriculum = {}/{}, length = {}."
.format(*s[1:], len(this_train_dataset)))
break
train_dataloader = this_train_dataset.make_dataloader(
args.batch_size,
shuffle=True,
drop_last=True,
nr_workers=args.data_workers)
for enum_id in range(args.enums_per_epoch):
train_epoch(epoch, trainer, train_dataloader, meters)
if epoch % args.validation_interval == 0:
model.eval()
validate_epoch(epoch, trainer, validation_dataloader, meters)
if not args.debug:
meters.dump(args.meter_file)
logger.critical(
meters.format_simple(
"Epoch = {}".format(epoch),
{
k: v
for k, v in meters.avg.items()
if epoch % args.validation_interval == 0
or not (k.startswith("validation") or k.startswith("test"))
},
compressed=False,
))
if epoch % args.save_interval == 0 and not args.debug:
fname = osp.join(args.ckpt_dir, "epoch_{}.pth".format(epoch))
trainer.save_checkpoint(
fname, dict(epoch=epoch, meta_file=args.meta_file))
if epoch > int(args.epochs * 0.6):
trainer.set_learning_rate(args.lr * 0.1)
if test_dataset is not None:
model.eval()
for dataloader in test_dataloader:
validate_epoch(
epoch,
trainer,
test_dataloader[dataloader],
meters,
meter_prefix=dataloader,
)
if not args.debug:
meters.dump(args.meter_file)
main_one_shot(
prototype_dataset,
one_shot_dataset,
model,
epoch,
trainer,
meters,
args.batch_size,
)
def main_train(train_dataset, validation_dataset, extra_dataset=None):
logger.critical('Building the model.')
model = desc.make_model(args)
if args.version=='v3':
desc_pred = load_source(args.pred_model_path)
model.build_temporal_prediction_model(args, desc_pred)
elif args.version=='v4':
desc_pred = load_source(args.pred_model_path)
desc_spatial_pred = load_source(args.pred_spatial_model_path)
model.build_temporal_prediction_model(args, desc_pred, desc_spatial_pred)
elif args.version=='v2_1':
model.make_relation_embedding_for_unseen_events(args)
if args.use_gpu:
model.cuda()
# Disable the cudnn benchmark.
cudnn.benchmark = False
if hasattr(desc, 'make_optimizer'):
logger.critical('Building customized optimizer.')
optimizer = desc.make_optimizer(model, args.lr)
else:
from jactorch.optim import AdamW
if args.freeze_learner_flag==1:
if args.reconstruct_flag:
parameters = list(model._model_pred.parameters())+list(model._decoder.parameters())
trainable_parameters = filter(lambda x: x.requires_grad, parameters)
elif args.version=='v4':
trainable_parameters = filter(lambda x: x.requires_grad, model._model_pred.parameters())
else:
trainable_parameters = filter(lambda x: x.requires_grad, model.parameters())
optimizer = AdamW(trainable_parameters, args.lr, weight_decay=configs.train.weight_decay)
if args.acc_grad > 1:
from jactorch.optim import AccumGrad
optimizer = AccumGrad(optimizer, args.acc_grad)
logger.warning('Use accumulated grad={:d}, effective iterations per epoch={:d}.'.format(args.acc_grad, int(args.iters_per_epoch / args.acc_grad)))
trainer = TrainerEnv(model, optimizer)
if args.resume:
extra = trainer.load_checkpoint(args.resume)
if extra:
args.start_epoch = extra['epoch']
logger.critical('Resume from epoch {}.'.format(args.start_epoch))
elif args.load:
if trainer.load_weights(args.load):
logger.critical('Loaded weights from pretrained model: "{}".'.format(args.load))
if args.version=='v3':
if args.pretrain_pred_model_path:
model._model_pred.load_state_dict(torch.load(args.pretrain_pred_model_path))
logger.critical('Loaded weights from pretrained temporal model: "{}".'.format(args.pretrain_pred_model_path))
elif args.version=='v4':
if args.pretrain_pred_spatial_model_path:
model._model_spatial_pred.load_state_dict(torch.load(args.pretrain_pred_spatial_model_path))
logger.critical('Loaded spatial models from pretrained temporal model: "{}".'.format(args.pretrain_pred_spatial_model_path))
if args.pretrain_pred_feature_model_path:
model._model_pred.load_state_dict(torch.load(args.pretrain_pred_feature_model_path))
logger.critical('Loaded feature models from pretrained temporal model: "{}".'.format(args.pretrain_pred_feature_model_path))
#pdb.set_trace()
if args.pretrain_pred_model_path:
model._model_pred.load_state_dict(torch.load(args.pretrain_pred_model_path))
logger.critical('Loaded weights from pretrained temporal model: "{}".'.format(args.pretrain_pred_model_path))
elif args.version =='v2_1':
model.reasoning.embedding_relation_future.load_state_dict(model.reasoning.embedding_relation.state_dict())
model.reasoning.embedding_relation_counterfact.load_state_dict(model.reasoning.embedding_relation.state_dict())
logger.critical('Copy original relation weights into counterfact and future relation.')
if args.use_tb and not args.debug:
from jactorch.train.tb import TBLogger, TBGroupMeters
tb_logger = TBLogger(args.tb_dir)
meters = TBGroupMeters(tb_logger)
logger.critical('Writing tensorboard logs to: "{}".'.format(args.tb_dir))
else:
from jacinle.utils.meter import GroupMeters
meters = GroupMeters()
if not args.debug:
logger.critical('Writing meter logs to file: "{}".'.format(args.meter_file))
if args.clip_grad:
logger.info('Registering the clip_grad hook: {}.'.format(args.clip_grad))
def clip_grad(self, loss):
from torch.nn.utils import clip_grad_norm_
clip_grad_norm_(self.model.parameters(), max_norm=args.clip_grad)
trainer.register_event('backward:after', clip_grad)
if hasattr(desc, 'customize_trainer'):
desc.customize_trainer(trainer)
if args.embed:
from IPython import embed; embed()
if args.debug:
shuffle_flag=False
else:
shuffle_flag=True
logger.critical('Building the data loader.')
validation_dataloader = validation_dataset.make_dataloader(args.batch_size, shuffle=False, drop_last=False, nr_workers=args.data_workers)
if extra_dataset is not None:
extra_dataloader = extra_dataset.make_dataloader(args.batch_size, shuffle=False, drop_last=False, nr_workers=args.data_workers)
if args.evaluate:
meters.reset()
model.eval()
validate_epoch(0, trainer, validation_dataloader, meters)
if extra_dataset is not None:
validate_epoch(0, trainer, extra_dataloader, meters, meter_prefix='validation_extra')
logger.critical(meters.format_simple('Validation', {k: v for k, v in meters.avg.items() if v != 0}, compressed=False))
return meters
for epoch in range(args.start_epoch + 1, args.epochs + 1):
meters.reset()
model.train()
this_train_dataset = train_dataset
train_dataloader = this_train_dataset.make_dataloader(args.batch_size, shuffle=shuffle_flag, drop_last=True, nr_workers=args.data_workers)
for enum_id in range(args.enums_per_epoch):
train_epoch(epoch, trainer, train_dataloader, meters)
if epoch % args.validation_interval == 0:
model.eval()
validate_epoch(epoch, trainer, validation_dataloader, meters)
if not args.debug:
meters.dump(args.meter_file)
logger.critical(meters.format_simple(
'Epoch = {}'.format(epoch),
{k: v for k, v in meters.avg.items() if epoch % args.validation_interval == 0 or not k.startswith('validation')},
compressed=False
))
if epoch % args.save_interval == 0 and not args.debug:
fname = osp.join(args.ckpt_dir, 'epoch_{}.pth'.format(epoch))
trainer.save_checkpoint(fname, dict(epoch=epoch, meta_file=args.meta_file))
if epoch > int(args.epochs * 0.6):
trainer.set_learning_rate(args.lr * 0.1)
def main():
logger.critical('Writing metainfo to file: {}.'.format(args.meta_file))
with open(args.meta_file, 'w') as f:
f.write(dump_metainfo(args=args.__dict__))
logger.critical('Loading the word embedding.')
vocab, word_embeddings = load_word_embedding(args.vse)
logger.critical('Building up the model.')
model = CompletionModel(word_embeddings)
if args.use_gpu:
model.cuda()
assert not args.gpu_parallel
# Disable the cudnn benchmark.
cudnn.benchmark = False
logger.critical('Loading the dataset.')
train_dataset = CompletionDataset(vocab, pjoin(args.data_dir, args.train_img), pjoin(args.data_dir, args.train_cap))
logger.critical('Building up the data loader.')
train_loader = make_dataloader(train_dataset, num_workers=args.data_workers, batch_size=args.batch_size, shuffle=True, drop_last=True, pin_memory=True)
# Default optimizer.
optimizer = AdamW(filter(lambda x: x.requires_grad, model.parameters()), args.lr, weight_decay=args.weight_decay)
if args.acc_grad > 1:
optimizer = AccumGrad(optimizer, args.acc_grad)
logger.warning('Use accumulated grad={:d}, effective iterations per epoch={:d}.'.format(args.acc_grad, int(args.iters_per_epoch / args.acc_grad)))
trainer = TrainerEnv(model, optimizer)
if args.resume:
extra = trainer.load_checkpoint(args.resume)
if extra:
args.start_epoch = extra['epoch']
logger.critical('Resume from epoch {}.'.format(args.start_epoch))
elif args.load:
if trainer.load_weights(args.load):
logger.critical('Loaded weights from pretrained model: {}.'.format(args.load))
if args.use_tb:
logger.critical('Writing tensorboard logs to: {}.'.format(args.tb_dir))
tb_logger = TBLogger(args.tb_dir)
meters = TBGroupMeters(tb_logger)
else:
meters = GroupMeters()
# Switch to train mode.
model.train()
if args.embed:
from IPython import embed; embed()
return
for epoch in range(args.start_epoch + 1, args.epochs + 1):
train_epoch(epoch, train_loader, trainer, meters)
logger.critical(format_meters('Epoch = {}'.format(epoch), meters.avg, '\t{} = {:.4f}', '\n'))
if epoch % args.save_interval == 0:
fname = osp.join(args.ckpt_dir, 'epoch_{}.pth'.format(epoch))
trainer.save_checkpoint(fname, dict(epoch=epoch, meta_file=args.meta_file))