def main(args):
config = yaml_util.load_yaml_file(args.config)
if args.json is not None:
main_util.overwrite_config(config, args.json)
distributed, device_ids = main_util.init_distributed_mode(
args.world_size, args.dist_url)
device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
teacher_model = get_model(config['teacher_model'], device)
module_util.freeze_module_params(teacher_model)
student_model_config = config['student_model']
student_model = get_model(student_model_config, device)
freeze_modules(student_model, student_model_config)
print('Updatable parameters: {}'.format(
module_util.get_updatable_param_names(student_model)))
distill_backbone_only = student_model_config['distill_backbone_only']
train_config = config['train']
train_sampler, train_data_loader, val_data_loader, test_data_loader = \
data_util.get_coco_data_loaders(config['dataset'], train_config['batch_size'], distributed)
if distributed:
teacher_model = DataParallel(teacher_model, device_ids=device_ids)
student_model = DistributedDataParallel(student_model,
device_ids=device_ids)
if args.distill:
distill(teacher_model, student_model, train_sampler, train_data_loader,
val_data_loader, device, distributed, distill_backbone_only,
config, args)
load_ckpt(
config['student_model']['ckpt'],
model=student_model.module if isinstance(
student_model, DistributedDataParallel) else student_model)
evaluate(teacher_model, student_model, test_data_loader, device,
args.skip_teacher_eval, args.transform_bottleneck)
def train(model, train_sampler, train_data_loader, val_data_loader, device, distributed, config, args, ckpt_file_path):
train_config = config['train']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(model, optim_config['type'], optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'], scheduler_config['params'])
best_val_map = 0.0
if file_util.check_if_exists(ckpt_file_path):
best_val_map, _, _ = load_ckpt(ckpt_file_path, optimizer=optimizer, lr_scheduler=lr_scheduler)
num_epochs = train_config['num_epochs']
log_freq = train_config['log_freq']
start_time = time.time()
for epoch in range(num_epochs):
if distributed:
train_sampler.set_epoch(epoch)
train_model(model, optimizer, train_data_loader, device, epoch, log_freq)
lr_scheduler.step()
# evaluate after every epoch
coco_evaluator = main_util.evaluate(model, val_data_loader, device=device)
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ]
val_map = coco_evaluator.coco_eval['bbox'].stats[0]
if val_map > best_val_map:
print('Updating ckpt (Best BBox mAP: {:.4f} -> {:.4f})'.format(best_val_map, val_map))
best_val_map = val_map
save_ckpt(model, optimizer, lr_scheduler, best_val_map, config, args, ckpt_file_path)
lr_scheduler.step()
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def distill(teacher_model, student_model, train_sampler, train_data_loader,
val_data_loader, device, distributed, distill_backbone_only,
config, args):
train_config = config['train']
distillation_box = DistillationBox(teacher_model, student_model,
train_config['criterion'])
ckpt_file_path = config['student_model']['ckpt']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(student_model, optim_config['type'],
optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'],
scheduler_config['params'])
use_bottleneck_transformer = args.transform_bottleneck
best_val_map = 0.0
if file_util.check_if_exists(ckpt_file_path):
best_val_map, _, _ = load_ckpt(ckpt_file_path,
optimizer=optimizer,
lr_scheduler=lr_scheduler)
num_epochs = train_config['num_epochs']
log_freq = train_config['log_freq']
teacher_model_without_dp = teacher_model.module if isinstance(
teacher_model, DataParallel) else teacher_model
student_model_without_ddp =\
student_model.module if isinstance(student_model, DistributedDataParallel) else student_model
start_time = time.time()
for epoch in range(num_epochs):
if distributed:
train_sampler.set_epoch(epoch)
teacher_model.eval()
student_model.train()
teacher_model_without_dp.distill_backbone_only = distill_backbone_only
student_model_without_ddp.distill_backbone_only = distill_backbone_only
student_model_without_ddp.backbone.body.layer1.use_bottleneck_transformer = False
distill_model(distillation_box, train_data_loader, optimizer, log_freq,
device, epoch)
student_model_without_ddp.distill_backbone_only = False
student_model_without_ddp.backbone.body.layer1.use_bottleneck_transformer = use_bottleneck_transformer
coco_evaluator = main_util.evaluate(student_model,
val_data_loader,
device=device)
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ]
val_map = coco_evaluator.coco_eval['bbox'].stats[0]
if val_map > best_val_map and misc_util.is_main_process():
print('Updating ckpt (Best BBox mAP: {:.4f} -> {:.4f})'.format(
best_val_map, val_map))
best_val_map = val_map
save_ckpt(student_model_without_ddp, optimizer, lr_scheduler,
best_val_map, config, args, ckpt_file_path)
lr_scheduler.step()
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
distributed, device_ids = main_util.init_distributed_mode(
args.world_size, args.dist_url)
config = yaml_util.load_yaml_file(args.config)
if args.json is not None:
main_util.overwrite_config(config, args.json)
device = torch.device(args.device)
print(args)
print('Loading data')
train_config = config['train']
train_sampler, train_data_loader, val_data_loader, test_data_loader =\
data_util.get_coco_data_loaders(config['dataset'], train_config['batch_size'], distributed)
print('Creating model')
model_config = config['model']
model = get_model(model_config, device, strict=False)
module_util.freeze_module_params(model)
ext_classifier = model.get_ext_classifier()
module_util.unfreeze_module_params(ext_classifier)
print('Updatable parameters: {}'.format(
module_util.get_updatable_param_names(model)))
model.train_ext()
if distributed:
model = nn.parallel.DistributedDataParallel(model,
device_ids=device_ids)
if args.train:
print('Start training')
start_time = time.time()
ckpt_file_path = model_config['backbone']['ext_config']['ckpt']
train(model, ext_classifier, train_sampler, train_data_loader,
val_data_loader, device, distributed, config, args,
ckpt_file_path)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
load_ckpt(ckpt_file_path, model=ext_classifier)
evaluate(model,
test_data_loader,
device=device,
min_recall=args.min_recall,
split_name='Test')
def distill(teacher_model, student_model, train_sampler, train_data_loader, val_data_loader,
device, distributed, distill_backbone_only, config, args):
train_config = config['train']
student_config = config['student_model']
distillation_box = DistillationBox(teacher_model, student_model,
train_config['criterion'], student_config)
ckpt_file_path = config['student_model']['ckpt']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(student_model, optim_config['type'], optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'], scheduler_config['params'])
if file_util.check_if_exists(ckpt_file_path):
best_val_map, _, _ = load_ckpt(ckpt_file_path, optimizer=optimizer, lr_scheduler=lr_scheduler)
save_ckpt(student_model, optimizer, lr_scheduler, best_val_map, config, args, ckpt_file_path)
def train(model, ext_classifier, train_sampler, train_data_loader,
val_data_loader, device, distributed, config, args, ckpt_file_path):
train_config = config['train']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(ext_classifier, optim_config['type'],
optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'],
scheduler_config['params'])
best_val_roc_auc = 0.0
if file_util.check_if_exists(ckpt_file_path):
best_val_roc_auc, _, _ =\
load_ckpt(ckpt_file_path, model=ext_classifier, optimizer=optimizer, lr_scheduler=lr_scheduler)
num_epochs = train_config['num_epochs']
log_freq = train_config['log_freq']
start_time = time.time()
for epoch in range(num_epochs):
if distributed:
train_sampler.set_epoch(epoch)
train_model(model, optimizer, train_data_loader, device, epoch,
log_freq)
lr_scheduler.step()
# evaluate after every epoch
val_roc_auc = evaluate(model,
val_data_loader,
device,
min_recall=args.min_recall,
split_name='Validation')
if val_roc_auc > best_val_roc_auc:
print('Updating ckpt (Best ROC-AUC: {:.4f} -> {:.4f})'.format(
best_val_roc_auc, val_roc_auc))
best_val_roc_auc = val_roc_auc
save_ckpt(ext_classifier, optimizer, lr_scheduler,
best_val_roc_auc, config, args, ckpt_file_path)
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def main(args):
config = yaml_util.load_yaml_file(args.config)
if args.json is not None:
main_util.overwrite_config(config, args.json)
distributed, device_ids = main_util.init_distributed_mode(args.world_size, args.dist_url)
device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
teacher_model = get_model(config['teacher_model'], device)
module_util.freeze_module_params(teacher_model)
student_model_config = config['student_model']
student_model = get_model(student_model_config, device)
freeze_modules(student_model, student_model_config)
ckpt_file_path = config['student_model']['ckpt']
train_config = config['train']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(student_model, optim_config['type'], optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'], scheduler_config['params'])
if file_util.check_if_exists(ckpt_file_path):
best_val_map, _, _ = load_ckpt(ckpt_file_path, optimizer=optimizer, lr_scheduler=lr_scheduler)
save_ckpt(student_model, optimizer, lr_scheduler, best_val_map, config, args, ckpt_file_path)
def distill(teacher_model, student_model, train_sampler, train_data_loader,
val_data_loader, device, distributed, distill_backbone_only,
config, args):
train_config = config['train']
student_config = config['student_model']
distillation_box = DistillationBox(teacher_model, student_model,
train_config['criterion'],
student_config)
ckpt_file_path = config['student_model']['ckpt']
optim_config = train_config['optimizer']
optimizer = func_util.get_optimizer(student_model, optim_config['type'],
optim_config['params'])
scheduler_config = train_config['scheduler']
lr_scheduler = func_util.get_scheduler(optimizer, scheduler_config['type'],
scheduler_config['params'])
use_bottleneck_transformer = args.transform_bottleneck
best_val_map = 0.0
if file_util.check_if_exists(ckpt_file_path):
if args.ignore_optimizer:
best_val_map, _, _ = load_ckpt(ckpt_file_path,
optimizer=None,
lr_scheduler=None)
else:
best_val_map, _, _ = load_ckpt(ckpt_file_path,
optimizer=optimizer,
lr_scheduler=lr_scheduler)
num_epochs = train_config['num_epochs']
log_freq = train_config['log_freq']
teacher_model_without_dp = teacher_model.module if isinstance(
teacher_model, DataParallel) else teacher_model
student_model_without_ddp = \
student_model.module if isinstance(student_model, DistributedDataParallel) else student_model
start_time = time.time()
post_bn = False
if 'post_batch_norm' in config['train']:
post_bn = config['train']['post_batch_norm']
for epoch in range(lr_scheduler.last_epoch, num_epochs):
if distributed:
train_sampler.set_epoch(epoch)
teacher_model.eval()
student_model.train()
teacher_model_without_dp.distill_backbone_only = distill_backbone_only
student_model_without_ddp.distill_backbone_only = distill_backbone_only
set_bottleneck_transformer(student_model_without_ddp, False)
distill_model(distillation_box, train_data_loader, optimizer, log_freq,
device, epoch)
student_model_without_ddp.distill_backbone_only = False
set_bottleneck_transformer(student_model_without_ddp,
use_bottleneck_transformer)
val_map = 0
width_list = [1.0]
if 'slimmable' in student_config['backbone']['params']:
width_list = [0.25, 0.5, 0.75, 1.0]
width_list = [
w for w in width_list
if w in student_config['backbone']['params']['width_mult_list']
]
for width in width_list:
set_width(student_model, width)
if post_bn:
ComputeBN(student_model, train_data_loader)
print('\n[Student model@width={}]'.format(width))
coco_evaluator = main_util.evaluate(student_model,
val_data_loader,
device=device)
val_map += coco_evaluator.coco_eval['bbox'].stats[0]
val_map = val_map / len(width_list)
print('BBox mAP: {:.4f})'.format(val_map))
if val_map > best_val_map and misc_util.is_main_process():
print('Updating ckpt (Best BBox mAP: {:.4f} -> {:.4f})'.format(
best_val_map, val_map))
best_val_map = val_map
save_ckpt(student_model_without_ddp, optimizer, lr_scheduler,
best_val_map, config, args, ckpt_file_path)
lr_scheduler.step()
if distributed:
dist.barrier()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))