def train(teacher_model, student_model, dataset_dict, is_regression,
ckpt_dir_path, metric, device, device_ids, distributed, config, args,
accelerator):
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, accelerator) if teacher_model is None \
else get_distillation_box(teacher_model, student_model, dataset_dict, train_config,
device, device_ids, distributed, lr_factor, accelerator)
# Only show the progress bar once on each machine.
log_freq = train_config['log_freq']
best_val_number = 0.0
for epoch in range(training_box.num_epochs):
train_one_epoch(training_box, epoch, log_freq)
val_dict = evaluate(student_model,
training_box.val_data_loader,
metric,
is_regression,
accelerator,
header='Validation: ')
val_value = sum(val_dict.values())
if val_value > best_val_number:
logger.info('Updating ckpt at {}'.format(ckpt_dir_path))
best_val_number = val_value
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(student_model)
unwrapped_model.save_pretrained(ckpt_dir_path,
save_function=accelerator.save)
training_box.post_process()
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 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_top1_accuracy = 0.0
optimizer, lr_scheduler = training_box.optimizer, training_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, training_box.num_epochs):
training_box.pre_process(epoch=epoch)
train_one_epoch(training_box, device, epoch, log_freq)
val_top1_accuracy = evaluate(student_model,
training_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)
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()