def BSN_Train_PEM(opt):
writer = SummaryWriter()
model = PEM(opt)
model = torch.nn.DataParallel(model, device_ids=GPU_IDs).cuda()
optimizer = optim.Adam(model.parameters(),lr=opt["pem_training_lr"],weight_decay = opt["pem_weight_decay"])
def collate_fn(batch):
batch_data = torch.cat([x[0] for x in batch])
batch_iou = torch.cat([x[1][0] for x in batch])
batch_is_whole_lenght = torch.cat([x[1][1] for x in batch])
return batch_data,batch_iou, batch_is_whole_lenght
train_loader = torch.utils.data.DataLoader(ProposalDataSet(opt,subset="train"),
batch_size=model.module.batch_size, shuffle=True,
num_workers=8, pin_memory=True,drop_last=True,collate_fn=collate_fn)
test_loader = torch.utils.data.DataLoader(ProposalDataSet(opt,subset="validation"),
batch_size=model.module.batch_size, shuffle=True,
num_workers=8, pin_memory=True,drop_last=True,collate_fn=collate_fn)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,step_size = opt["pem_step_size"], gamma = opt["pem_step_gamma"])
for epoch in range(opt["pem_epoch"]):
scheduler.step()
train_PEM(train_loader,model,optimizer,epoch,writer,opt)
test_PEM(test_loader,model,epoch,writer,opt)
writer.close()
def BSN_Train_PEM(opt):
model = PEM(opt)
model = torch.nn.DataParallel(model).cuda()
optimizer = optim.Adam(model.parameters(),
lr=opt["pem_training_lr"],
weight_decay=opt["pem_weight_decay"])
print('Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
def collate_fn(batch):
batch_data = torch.cat([x[0] for x in batch])
batch_iou = torch.cat([x[1] for x in batch])
return batch_data, batch_iou
train_dataset = ProposalDataSet(opt, subset="train")
train_sampler = ProposalSampler(train_dataset.proposals,
train_dataset.indices,
max_zero_weight=opt['pem_max_zero_weight'])
global_step = 0
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=model.module.batch_size,
shuffle=False,
sampler=train_sampler,
num_workers=opt['data_workers'],
pin_memory=True,
drop_last=False,
collate_fn=collate_fn if not opt['pem_do_index'] else None)
subset = "validation" if opt['dataset'] == 'activitynet' else "test"
test_loader = torch.utils.data.DataLoader(
ProposalDataSet(opt, subset=subset),
batch_size=model.module.batch_size,
shuffle=True,
num_workers=opt['data_workers'],
pin_memory=True,
drop_last=False,
collate_fn=collate_fn if not opt['pem_do_index'] else None)
milestones = [int(k) for k in opt['pem_lr_milestones'].split(',')]
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=milestones, gamma=opt['pem_step_gamma'])
if opt['log_to_comet']:
comet_exp = CometExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="bsnpem",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False)
elif opt['local_comet_dir']:
comet_exp = OfflineExperiment(api_key="hIXq6lDzWzz24zgKv7RYz6blo",
project_name="bsnpem",
workspace="cinjon",
auto_metric_logging=True,
auto_output_logging=None,
auto_param_logging=False,
offline_directory=opt['local_comet_dir'])
else:
comet_exp = None
if comet_exp:
comet_exp.log_parameters(opt)
comet_exp.set_name(opt['name'])
test_PEM(test_loader, model, -1, -1, comet_exp, opt)
for epoch in range(opt["pem_epoch"]):
global_step = train_PEM(train_loader, model, optimizer, epoch,
global_step, comet_exp, opt)
test_PEM(test_loader, model, epoch, global_step, comet_exp, opt)
scheduler.step()
