def main(params):
params['anchor_list'] = ANCHOR_LIST
logger = logging.getLogger(params['alias'])
set_device(logger, params['gpu_id'])
saver = ModelSaver(params, os.path.abspath('./third_party/densevid_eval'))
model = construct_model(params, saver, logger)
model = model.cuda()
training_set = ANetDataSample(params['train_data'], params['feature_path'],
params['translator_path'], params['video_sample_rate'], logger)
val_set = ANetDataFull(params['val_data'], params['feature_path'],
params['translator_path'], params['video_sample_rate'], logger)
train_loader = DataLoader(training_set, batch_size=params['batch_size'], shuffle=True,
num_workers=params['num_workers'], collate_fn=collate_fn, drop_last=True)
val_loader = DataLoader(val_set, batch_size=params['batch_size']/3, shuffle=True,
num_workers=params['num_workers'], collate_fn=collate_fn, drop_last=True)
optimizer = torch.optim.SGD(model.get_parameter_group(params),
lr=params['lr'], weight_decay=params['weight_decay'], momentum=params['momentum'])
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=params['lr_step'], gamma=params["lr_decay_rate"])
eval(model, val_loader, params, logger, 0, saver)
for step in range(params['training_epoch']):
lr_scheduler.step()
train(model, train_loader, params, logger, step, optimizer)
# validation and saving
if step % params['test_interval'] == 0:
eval(model, val_loader, params, logger, step, saver)
if step % params['save_model_interval'] == 0 and step != 0:
saver.save_model(model,step, {'step': step})
def main(params):
params['anchor_list'] = ANCHOR_LIST
logger = logging.getLogger(params['alias'])
set_device(logger, params['gpu_id'])
saver = ModelSaver(params, os.path.abspath('./third_party/densevid_eval'))
model = construct_model(params, saver, logger)
model = model.cuda()
val_set = ANetDataFull(params['val_data'], params['feature_path'],
params['translator_path'],
params['video_sample_rate'], logger)
val_loader = DataLoader(val_set,
batch_size=params['batch_size'] / 3,
shuffle=True,
num_workers=params['num_workers'],
collate_fn=collate_fn,
drop_last=True)
eval(model, val_loader, params, logger, 0, saver)
def main(params):
logger = logging.getLogger(params['alias'])
set_device(logger, params['gpu_id'])
saver = ModelSaver(params, os.path.abspath('./third_party/densevid_eval'))
torch.manual_seed(params['rand_seed'])
model_sl, model_cg = construct_model(params, saver, logger)
model_sl, model_cg = model_sl.cuda(), model_cg.cuda()
val_set = ANetDataFull(params['val_data'], params['feature_path'],
params['translator_path'],
params['video_sample_rate'], logger)
val_loader = DataLoader(val_set,
batch_size=params['batch_size'],
shuffle=True,
num_workers=params['num_workers'],
collate_fn=collate_fn,
drop_last=True)
print(params)
eval(model_sl, model_cg, val_loader, logger, saver, params)
def main(params):
logger = logging.getLogger(params['alias'])
gpu_id = set_device(logger, params['gpu_id'])
logger = logging.getLogger(params['alias'] + '(%d)' % gpu_id)
set_device(logger, params['gpu_id'])
saver = ModelSaver(params, os.path.abspath('./third_party/densevid_eval'))
model_sl, model_cg = construct_model(params, saver, logger)
model_sl, model_cg = model_sl.cuda(), model_cg.cuda()
training_set = ANetDataSample(params['train_data'], params['feature_path'],
params['translator_path'],
params['video_sample_rate'], logger)
val_set = ANetDataFull(params['val_data'], params['feature_path'],
params['translator_path'],
params['video_sample_rate'], logger)
train_loader_cg = DataLoader(training_set,
batch_size=params['batch_size'],
shuffle=True,
num_workers=params['num_workers'],
collate_fn=collate_fn,
drop_last=True)
train_loader_sl = DataLoader(training_set,
batch_size=6,
shuffle=True,
num_workers=params['num_workers'],
collate_fn=collate_fn,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=8,
shuffle=True,
num_workers=params['num_workers'],
collate_fn=collate_fn,
drop_last=True)
optimizer_sl = torch.optim.SGD(model_sl.get_parameter_group(params),
lr=params['lr'],
weight_decay=params['weight_decay'],
momentum=params['momentum'])
optimizer_cg = torch.optim.SGD(list(
chain(model_sl.get_parameter_group_c(params),
model_cg.get_parameter_group(params))),
lr=params['lr'],
weight_decay=params['weight_decay'],
momentum=params['momentum'])
optimizer_cg_n = torch.optim.SGD(model_cg.get_parameter_group(params),
lr=params['lr'],
weight_decay=params['weight_decay'],
momentum=params['momentum'])
lr_scheduler_sl = torch.optim.lr_scheduler.MultiStepLR(
optimizer_sl,
milestones=params['lr_step'],
gamma=params["lr_decay_rate"])
lr_scheduler_cg = torch.optim.lr_scheduler.MultiStepLR(
optimizer_cg,
milestones=params['lr_step'],
gamma=params["lr_decay_rate"])
evaluator = CaptionEvaluator(training_set)
saver.save_model(
model_sl, 0, {
'step': 0,
'model_sl': model_sl.state_dict(),
'model_cg': model_cg.state_dict()
})
# eval(model_sl, model_cg, val_loader, logger, saver, params, -1)
for step in range(params['training_epoch']):
lr_scheduler_cg.step()
lr_scheduler_sl.step()
if step < params['pretrain_epoch']:
pretrain_cg(model_cg, train_loader_cg, params, logger, step,
optimizer_cg_n)
elif step % params['alter_step'] != 0:
train_cg(model_cg, model_sl, train_loader_cg, params, logger, step,
optimizer_cg)
else:
train_sl(model_cg, model_sl, train_loader_sl, evaluator, params,
logger, step, optimizer_sl)
# validation and saving
# if step % params['test_interval'] == 0:
# eval(model_sl, model_cg, val_loader, logger, saver, params, step)
if step % params['save_model_interval'] == 0 and step != 0:
saver.save_model(
model_sl, step, {
'step': step,
'model_sl': model_sl.state_dict(),
'model_cg': model_cg.state_dict()
})