def main(args):
log_file_path = args.log
if is_main_process() and log_file_path is not None:
setup_log_file(os.path.expanduser(log_file_path))
distributed, device_ids = init_distributed_mode(args.world_size,
args.dist_url)
logger.info(args)
cudnn.benchmark = True
set_seed(args.seed)
config = yaml_util.load_yaml_file(os.path.expanduser(args.config))
device = torch.device(args.device)
dataset_dict = util.get_all_datasets(config['datasets'])
models_config = config['models']
teacher_model_config = models_config.get('teacher_model', None)
teacher_model = load_model(
teacher_model_config,
device) if teacher_model_config is not None else None
student_model_config =\
models_config['student_model'] if 'student_model' in models_config else models_config['model']
ckpt_file_path = student_model_config['ckpt']
student_model = load_model(student_model_config, device)
if args.log_config:
logger.info(config)
if not args.test_only:
train(teacher_model, student_model, dataset_dict, ckpt_file_path,
device, device_ids, distributed, config, args)
student_model_without_ddp =\
student_model.module if module_util.check_if_wrapped(student_model) else student_model
load_ckpt(student_model_config['ckpt'],
model=student_model_without_ddp,
strict=True)
test_config = config['test']
test_data_loader_config = test_config['test_data_loader']
test_data_loader = util.build_data_loader(
dataset_dict[test_data_loader_config['dataset_id']],
test_data_loader_config, distributed)
log_freq = test_config.get('log_freq', 1000)
iou_types = args.iou_types
if not args.student_only and teacher_model is not None:
evaluate(teacher_model,
test_data_loader,
iou_types,
device,
device_ids,
distributed,
log_freq=log_freq,
title='[Teacher: {}]'.format(teacher_model_config['name']))
evaluate(student_model,
test_data_loader,
iou_types,
device,
device_ids,
distributed,
log_freq=log_freq,
title='[Student: {}]'.format(student_model_config['name']))
def train_one_epoch(training_box, device, epoch, log_freq):
metric_logger = MetricLogger(delimiter=' ')
metric_logger.add_meter('lr', SmoothedValue(window_size=1, fmt='{value}'))
metric_logger.add_meter('img/s', SmoothedValue(window_size=10, fmt='{value}'))
header = 'Epoch: [{}]'.format(epoch)
for sample_batch, targets, supp_dict in \
metric_logger.log_every(training_box.train_data_loader, log_freq, header):
start_time = time.time()
sample_batch, targets = sample_batch.to(device), targets.to(device)
loss = training_box(sample_batch, targets, supp_dict)
training_box.update_params(loss)
batch_size = sample_batch.shape[0]
metric_logger.update(loss=loss.item(), lr=training_box.optimizer.param_groups[0]['lr'])
metric_logger.meters['img/s'].update(batch_size / (time.time() - start_time))
if (torch.isnan(loss) or torch.isinf(loss)) and is_main_process():
raise ValueError('The training loop was broken due to loss = {}'.format(loss))
def train(teacher_model, student_model, dataset_dict, ckpt_file_path, device,
device_ids, distributed, config, args):
logger.info('Start training')
train_config = config['train']
lr_factor = args.world_size if distributed and args.adjust_lr else 1
training_box = get_training_box(student_model, dataset_dict, train_config,
device, device_ids, distributed, lr_factor) if teacher_model is None \
else get_distillation_box(teacher_model, student_model, dataset_dict, train_config,
device, device_ids, distributed, lr_factor)
best_val_map = 0.0
optimizer, lr_scheduler = training_box.optimizer, training_box.lr_scheduler
if file_util.check_if_exists(ckpt_file_path):
best_val_map, _, _ = load_ckpt(ckpt_file_path,
optimizer=optimizer,
lr_scheduler=lr_scheduler)
log_freq = train_config['log_freq']
iou_types = args.iou_types
val_iou_type = iou_types[0] if isinstance(
iou_types, (list, tuple)) and len(iou_types) > 0 else 'bbox'
student_model_without_ddp = student_model.module if module_util.check_if_wrapped(
student_model) else student_model
start_time = time.time()
for epoch in range(args.start_epoch, training_box.num_epochs):
training_box.pre_process(epoch=epoch)
train_one_epoch(training_box, device, epoch, log_freq)
val_coco_evaluator =\
evaluate(student_model, training_box.val_data_loader, iou_types, device, device_ids, distributed,
log_freq=log_freq, header='Validation:')
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ]
val_map = val_coco_evaluator.coco_eval[val_iou_type].stats[0]
if val_map > best_val_map and is_main_process():
logger.info('Best mAP ({}): {:.4f} -> {:.4f}'.format(
val_iou_type, best_val_map, val_map))
logger.info('Updating ckpt at {}'.format(ckpt_file_path))
best_val_map = val_map
save_ckpt(student_model_without_ddp, optimizer, lr_scheduler,
best_val_map, config, args, ckpt_file_path)
training_box.post_process()
if distributed:
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
training_box.clean_modules()
def main(args):
log_file_path = args.log
if is_main_process() and log_file_path is not None:
setup_log_file(os.path.expanduser(log_file_path))
distributed, device_ids = init_distributed_mode(args.world_size,
args.dist_url)
logger.info(args)
cudnn.benchmark = True
config = yaml_util.load_yaml_file(os.path.expanduser(args.config))
device = torch.device(args.device)
dataset_dict = util.get_all_dataset(config['datasets'])
models_config = config['models']
teacher_model_config = models_config['teacher_model']
teacher_model = get_model(teacher_model_config, device)
student_model_config = models_config['student_model']
student_model = get_model(student_model_config, device)
if not args.test_only:
distill(teacher_model, student_model, dataset_dict, device, device_ids,
distributed, config, args)
student_model_without_ddp =\
student_model.module if module_util.check_if_wrapped(student_model) else student_model
load_ckpt(student_model_config['ckpt'],
model=student_model_without_ddp,
strict=True)
test_config = config['test']
test_data_loader_config = test_config['test_data_loader']
test_data_loader = util.build_data_loader(
dataset_dict[test_data_loader_config['dataset_id']],
test_data_loader_config, distributed)
num_classes = args.num_classes
if not args.student_only:
evaluate(teacher_model,
test_data_loader,
device,
device_ids,
distributed,
num_classes=num_classes,
title='[Teacher: {}]'.format(teacher_model_config['name']))
evaluate(student_model,
test_data_loader,
device,
device_ids,
distributed,
num_classes=num_classes,
title='[Student: {}]'.format(student_model_config['name']))
def distill(teacher_model, student_model, dataset_dict, device, device_ids,
distributed, config, args):
logger.info('Start distillation')
train_config = config['train']
lr_factor = args.world_size if distributed and args.adjust_lr else 1
distillation_box =\
get_distillation_box(teacher_model, student_model, dataset_dict,
train_config, device, device_ids, distributed, lr_factor)
ckpt_file_path = config['models']['student_model']['ckpt']
best_val_top1_accuracy = 0.0
optimizer, lr_scheduler = distillation_box.optimizer, distillation_box.lr_scheduler
if file_util.check_if_exists(ckpt_file_path):
best_val_top1_accuracy, _, _ = load_ckpt(ckpt_file_path,
optimizer=optimizer,
lr_scheduler=lr_scheduler)
log_freq = train_config['log_freq']
student_model_without_ddp = student_model.module if module_util.check_if_wrapped(
student_model) else student_model
start_time = time.time()
for epoch in range(args.start_epoch, distillation_box.num_epochs):
distillation_box.pre_process(epoch=epoch)
distill_one_epoch(distillation_box, device, epoch, log_freq)
val_top1_accuracy = evaluate(student_model,
distillation_box.val_data_loader,
device,
device_ids,
distributed,
log_freq=log_freq,
header='Validation:')
if val_top1_accuracy > best_val_top1_accuracy and is_main_process():
logger.info('Updating ckpt (Best top1 accuracy: '
'{:.4f} -> {:.4f})'.format(best_val_top1_accuracy,
val_top1_accuracy))
best_val_top1_accuracy = val_top1_accuracy
save_ckpt(student_model_without_ddp, optimizer, lr_scheduler,
best_val_top1_accuracy, config, args, ckpt_file_path)
distillation_box.post_process()
if distributed:
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
distillation_box.clean_modules()
def train(teacher_model, student_model, dataset_dict, ckpt_file_path, device,
device_ids, distributed, config, args):
logger.info('Start training')
train_config = config['train']
lr_factor = args.world_size if distributed and args.adjust_lr else 1
training_box = get_training_box(student_model, dataset_dict, train_config,
device, device_ids, distributed, lr_factor) if teacher_model is None \
else get_distillation_box(teacher_model, student_model, dataset_dict, train_config,
device, device_ids, distributed, lr_factor)
best_val_miou = 0.0
optimizer, lr_scheduler = training_box.optimizer, training_box.lr_scheduler
if file_util.check_if_exists(ckpt_file_path):
best_val_miou, _, _ = load_ckpt(ckpt_file_path,
optimizer=optimizer,
lr_scheduler=lr_scheduler)
log_freq = train_config['log_freq']
student_model_without_ddp = student_model.module if module_util.check_if_wrapped(
student_model) else student_model
start_time = time.time()
for epoch in range(args.start_epoch, training_box.num_epochs):
training_box.pre_process(epoch=epoch)
train_one_epoch(training_box, device, epoch, log_freq)
val_seg_evaluator =\
evaluate(student_model, training_box.val_data_loader, device, device_ids, distributed,
num_classes=args.num_classes, log_freq=log_freq, header='Validation:')
val_acc_global, val_acc, val_iou = val_seg_evaluator.compute()
val_miou = val_iou.mean().item()
if val_miou > best_val_miou and is_main_process():
logger.info('Updating ckpt (Best mIoU: {:.4f} -> {:.4f})'.format(
best_val_miou, val_miou))
best_val_miou = val_miou
save_ckpt(student_model_without_ddp, optimizer, lr_scheduler,
best_val_miou, config, args, ckpt_file_path)
training_box.post_process()
if distributed:
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
training_box.clean_modules()
def save_module_ckpt(module, ckpt_file_path):
if is_main_process():
make_parent_dirs(ckpt_file_path)
state_dict = module.module.state_dict() if check_if_wrapped(module) else module.state_dict()
save_on_master(state_dict, ckpt_file_path)
def main(args):
log_file_path = args.log
if is_main_process() and log_file_path is not None:
setup_log_file(os.path.expanduser(log_file_path))
world_size = args.world_size
logger.info(args)
cudnn.benchmark = True
set_seed(args.seed)
config = yaml_util.load_yaml_file(os.path.expanduser(args.config))
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
accelerator = Accelerator()
distributed = accelerator.state.distributed_type == DistributedType.MULTI_GPU
device_ids = [accelerator.device.index]
if distributed:
setup_for_distributed(is_main_process())
logger.info(accelerator.state)
device = accelerator.device
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(
logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# Load pretrained model and tokenizer
task_name = args.task_name
models_config = config['models']
teacher_model_config = models_config.get('teacher_model', None)
teacher_tokenizer, teacher_model = (None, None) if teacher_model_config is None \
else load_tokenizer_and_model(teacher_model_config, task_name, True)
student_model_config =\
models_config['student_model'] if 'student_model' in models_config else models_config['model']
student_tokenizer, student_model = load_tokenizer_and_model(
student_model_config, task_name)
ckpt_dir_path = student_model_config['ckpt']
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
dataset_dict, label_names_dict, is_regression = \
get_all_datasets(config['datasets'], task_name, student_tokenizer, student_model)
# Update config with dataset size len(data_loader)
customize_lr_config(config, dataset_dict, world_size)
# register collate function
register_collate_func(
DataCollatorWithPadding(
student_tokenizer,
pad_to_multiple_of=(8 if accelerator.use_fp16 else None)))
# Get the metric function
metric = get_metrics(task_name)
if not args.test_only:
train(teacher_model, student_model, dataset_dict, is_regression,
ckpt_dir_path, metric, device, device_ids, distributed, config,
args, accelerator)
student_tokenizer.save_pretrained(ckpt_dir_path)
test_config = config['test']
test_data_loader_config = test_config['test_data_loader']
test_data_loader = util.build_data_loader(
dataset_dict[test_data_loader_config['dataset_id']],
test_data_loader_config, distributed)
test_data_loader = accelerator.prepare(test_data_loader)
if not args.student_only and teacher_model is not None:
teacher_model = teacher_model.to(accelerator.device)
evaluate(teacher_model,
test_data_loader,
metric,
is_regression,
accelerator,
title='[Teacher: {}]'.format(teacher_model_config['name']))
# Reload best checkpoint based on validation result
student_tokenizer, student_model = load_tokenizer_and_model(
student_model_config, task_name, True)
student_model = accelerator.prepare(student_model)
evaluate(student_model,
test_data_loader,
metric,
is_regression,
accelerator,
title='[Student: {}]'.format(student_model_config['name']))
# Output prediction for private dataset(s) if both the config and output dir path are given
private_configs = config.get('private', None)
private_output_dir_path = args.private_output
if private_configs is not None and private_output_dir_path is not None and is_main_process(
):
predict_private(student_model, dataset_dict, label_names_dict,
is_regression, accelerator, private_configs,
private_output_dir_path)
def log_info(*args, **kwargs):
force = kwargs.pop('force', False)
if is_main_process() or force:
logger.info(*args, **kwargs)
