def main_worker(args):
train, validate, modifier = get_trainer(args)
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model and optimizer
model = get_model(args)
model = set_gpu(args, model)
wandb.watch(model)
if args.pretrained:
pretrained(args, model)
optimizer = get_optimizer(args, model)
data = get_dataset(args)
lr_policy = get_policy(args.lr_policy)(optimizer, args)
if args.label_smoothing is None:
criterion = nn.CrossEntropyLoss().cuda()
else:
criterion = LabelSmoothing(smoothing=args.label_smoothing)
# optionally resume from a checkpoint
best_acc1 = 0.0
best_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if args.resume:
best_acc1 = resume(args, model, optimizer)
# Data loading code
if args.evaluate:
acc1, acc5 = validate(data.val_loader,
model,
criterion,
args,
writer=None,
epoch=args.start_epoch)
return
# Set up directories
run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args)
args.ckpt_base_dir = ckpt_base_dir
writer = SummaryWriter(log_dir=log_base_dir)
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
progress_overall = ProgressMeter(1,
[epoch_time, validation_time, train_time],
prefix="Overall Timing")
end_epoch = time.time()
args.start_epoch = args.start_epoch or 0
acc1 = None
# Save the initial state
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1 if acc1 else "Not evaluated",
},
False,
filename=ckpt_base_dir / f"initial.state",
save=False,
)
# Start training
for epoch in range(args.start_epoch, args.epochs):
lr_policy(epoch, iteration=None)
modifier(args, epoch, model)
cur_lr = get_lr(optimizer)
# train for one epoch
start_train = time.time()
train_acc1, train_acc5 = train(data.train_loader,
model,
criterion,
optimizer,
epoch,
args,
writer=writer)
train_time.update((time.time() - start_train) / 60)
# evaluate on validation set
start_validation = time.time()
acc1, acc5 = validate(data.val_loader, model, criterion, args, writer,
epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
best_acc5 = max(acc5, best_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = ((epoch % args.save_every) == 0) and args.save_every > 0
if is_best or save or epoch == args.epochs - 1:
if is_best:
print(
f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}"
)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1,
"curr_acc5": acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_{epoch}.state",
save=save,
)
wandb.log({
"curr_acc1": acc1,
"curr_acc5": acc5,
})
epoch_time.update((time.time() - end_epoch) / 60)
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(writer,
prefix="diagnostics",
global_step=epoch)
if args.conv_type == "SampleSubnetConv":
count = 0
sum_pr = 0.0
for n, m in model.named_modules():
if isinstance(m, SampleSubnetConv):
# avg pr across 10 samples
pr = 0.0
for _ in range(10):
pr += ((torch.rand_like(m.clamped_scores) >=
m.clamped_scores).float().mean().item())
pr /= 10.0
writer.add_scalar("pr/{}".format(n), pr, epoch)
sum_pr += pr
count += 1
args.prune_rate = sum_pr / count
writer.add_scalar("pr/average", args.prune_rate, epoch)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
write_result_to_csv(
best_acc1=best_acc1,
best_acc5=best_acc5,
best_train_acc1=best_train_acc1,
best_train_acc5=best_train_acc5,
prune_rate=args.prune_rate,
curr_acc1=acc1,
curr_acc5=acc5,
base_config=args.config,
name=args.name,
)
def trn(cfg, model):
cfg.logger.info(cfg)
if cfg.seed is not None:
random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
torch.cuda.manual_seed(cfg.seed)
torch.cuda.manual_seed_all(cfg.seed)
train, validate = get_trainer(cfg)
if cfg.gpu is not None:
cfg.logger.info("Use GPU: {} for training".format(cfg.gpu))
linear_classifier_layer = model.module[1]
optimizer = get_optimizer(cfg, linear_classifier_layer)
cfg.logger.info(f"=> Getting {cfg.set} dataset")
dataset = getattr(data, cfg.set)(cfg)
lr_policy = get_policy(cfg.lr_policy)(optimizer, cfg)
softmax_criterion = nn.CrossEntropyLoss().cuda()
criterion = lambda output, target: softmax_criterion(output, target)
# optionally resume from a checkpoint
best_val_acc1 = 0.0
best_val_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if cfg.resume:
best_val_acc1 = resume(cfg, model, optimizer)
run_base_dir, ckpt_base_dir, log_base_dir = path_utils.get_directories(
cfg, cfg.gpu)
cfg.ckpt_base_dir = ckpt_base_dir
writer = SummaryWriter(log_dir=log_base_dir)
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
progress_overall = ProgressMeter(1,
[epoch_time, validation_time, train_time],
cfg,
prefix="Overall Timing")
end_epoch = time.time()
cfg.start_epoch = cfg.start_epoch or 0
last_val_acc1 = None
start_time = time.time()
gpu_info = gpu_utils.GPU_Utils(gpu_index=cfg.gpu)
# Start training
for epoch in range(cfg.start_epoch, cfg.epochs):
cfg.logger.info('Model conv 1 {} at epoch {}'.format(
torch.sum(model.module[0].conv1.weight),
epoch)) ## make sure backbone is not updated
if cfg.world_size > 1:
dataset.sampler.set_epoch(epoch)
lr_policy(epoch, iteration=None)
cur_lr = net_utils.get_lr(optimizer)
start_train = time.time()
train_acc1, train_acc5 = train(dataset.trn_loader,
model,
criterion,
optimizer,
epoch,
cfg,
writer=writer)
train_time.update((time.time() - start_train) / 60)
if (epoch + 1) % cfg.test_interval == 0:
if cfg.gpu == cfg.base_gpu:
# evaluate on validation set
start_validation = time.time()
last_val_acc1, last_val_acc5 = validate(
dataset.val_loader, model.module, criterion, cfg, writer,
epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = last_val_acc1 > best_val_acc1
best_val_acc1 = max(last_val_acc1, best_val_acc1)
best_val_acc5 = max(last_val_acc5, best_val_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = (((epoch + 1) % cfg.save_every)
== 0) and cfg.save_every > 0
if save or epoch == cfg.epochs - 1:
if is_best:
cfg.logger.info(
f"==> best {last_val_acc1:.02f} saving at {ckpt_base_dir / 'model_best.pth'}"
)
net_utils.save_checkpoint(
{
"epoch": epoch + 1,
"arch": cfg.arch,
"state_dict": model.state_dict(),
"best_acc1": best_val_acc1,
"best_acc5": best_val_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": last_val_acc1,
"curr_acc5": last_val_acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_{epoch}.state",
save=save or epoch == cfg.epochs - 1,
)
elapsed_time = time.time() - start_time
seconds_todo = (cfg.epochs - epoch) * (elapsed_time /
cfg.test_interval)
estimated_time_complete = timedelta(seconds=int(seconds_todo))
start_time = time.time()
cfg.logger.info(
f"==> ETA: {estimated_time_complete}\tGPU-M: {gpu_info.gpu_mem_usage()}\tGPU-U: {gpu_info.gpu_utilization()}"
)
epoch_time.update((time.time() - end_epoch) / 60)
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(writer,
prefix="diagnostics",
global_step=epoch)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
if cfg.world_size > 1:
dist.barrier()
def trn(cfg,model):
cfg.logger.info(cfg)
if cfg.seed is not None:
random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
torch.cuda.manual_seed(cfg.seed)
torch.cuda.manual_seed_all(cfg.seed)
train, validate_knn = get_trainer(cfg)
if cfg.gpu is not None:
cfg.logger.info("Use GPU: {} for training".format(cfg.gpu))
# if cfg.pretrained:
# net_utils.load_pretrained(cfg.pretrained,cfg.multigpu[0], model)
optimizer = get_optimizer(cfg, model)
cfg.logger.info(f"=> Getting {cfg.set} dataset")
dataset = getattr(data, cfg.set)(cfg)
lr_policy = get_policy(cfg.lr_policy)(optimizer, cfg)
if cfg.arch == 'SimSiam':
# L = D(p1, z2) / 2 + D(p2, z1) / 2
base_criterion = lambda bb1_z1_p1_emb, bb2_z2_p2_emb: simsiam.SimSaimLoss(bb1_z1_p1_emb[2], bb2_z2_p2_emb[1]) / 2 +\
simsiam.SimSaimLoss(bb2_z2_p2_emb[2], bb1_z1_p1_emb[1]) / 2
elif cfg.arch == 'SimCLR':
base_criterion = lambda z1,z2 : simclr.NT_XentLoss(z1, z2)
else:
raise NotImplemented
run_base_dir, ckpt_base_dir, log_base_dir = path_utils.get_directories(cfg,cfg.gpu)
_, zero_gpu_ckpt_base_dir, _ = path_utils.get_directories(cfg, 0)
# if cfg.resume:
saved_epochs = sorted(glob.glob(str(zero_gpu_ckpt_base_dir) + '/epoch_*.state'), key=os.path.getmtime)
# assert len(epochs) < 2, 'Should be only one saved epoch -- the last one'
if len(saved_epochs) > 0:
cfg.resume = saved_epochs[-1]
resume(cfg, model, optimizer)
cfg.ckpt_base_dir = ckpt_base_dir
writer = SummaryWriter(log_dir=log_base_dir)
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
progress_overall = ProgressMeter(
1, [epoch_time, validation_time, train_time], cfg, prefix="Overall Timing"
)
end_epoch = time.time()
cfg.start_epoch = cfg.start_epoch or 0
start_time = time.time()
gpu_info = gpu_utils.GPU_Utils(gpu_index=cfg.gpu)
cfg.logger.info('Start Training: Model conv 1 initialization {}'.format(torch.sum(model.module.backbone.conv1.weight)))
# Start training
for n,m in model.module.named_modules():
if hasattr(m, "weight") and m.weight is not None:
cfg.logger.info('{} ({}): {}'.format(n,type(m).__name__,m.weight.shape))
criterion = base_criterion
cfg.logger.info('Using Vanilla Criterion')
for epoch in range(cfg.start_epoch, cfg.epochs):
if cfg.world_size > 1:
dataset.sampler.set_epoch(epoch)
lr_policy(epoch, iteration=None)
cur_lr = net_utils.get_lr(optimizer)
start_train = time.time()
train(dataset.trn_loader, model,criterion, optimizer, epoch, cfg, writer=writer)
train_time.update((time.time() - start_train) / 60)
if (epoch + 1) % cfg.test_interval == 0:
if cfg.gpu == cfg.base_gpu:
# evaluate on validation set
start_validation = time.time()
acc = validate_knn(dataset.trn_loader, dataset.val_loader, model.module, cfg, writer, epoch)
validation_time.update((time.time() - start_validation) / 60)
csv_utils.write_generic_result_to_csv(path=cfg.exp_dir,name=os.path.basename(cfg.exp_dir[:-1]),
epoch=epoch,
knn_acc=acc)
save = (((epoch+1) % cfg.save_every) == 0) and cfg.save_every > 0
if save or epoch == cfg.epochs - 1:
# if is_best:
# print(f"==> best {last_val_acc1:.02f} saving at {ckpt_base_dir / 'model_best.pth'}")
net_utils.save_checkpoint(
{
"epoch": epoch + 1,
"arch": cfg.arch,
"state_dict": model.state_dict(),
"ACC": acc,
"optimizer": optimizer.state_dict(),
},
is_best=False,
filename=ckpt_base_dir / f"epoch_{epoch:04d}.state",
save=save or epoch == cfg.epochs - 1,
)
elapsed_time = time.time() - start_time
seconds_todo = (cfg.epochs - epoch) * (elapsed_time / cfg.test_interval)
estimated_time_complete = timedelta(seconds=int(seconds_todo))
start_time = time.time()
cfg.logger.info(
f"==> ETA: {estimated_time_complete}\tGPU-M: {gpu_info.gpu_mem_usage()}\tGPU-U: {gpu_info.gpu_utilization()}")
epoch_time.update((time.time() - end_epoch) / 60)
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(
writer, prefix="diagnostics", global_step=epoch
)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
if cfg.world_size > 1:
# cfg.logger.info('GPU {} going into the barrier'.format(cfg.gpu))
dist.barrier()
def main_worker(args):
# NEW: equivalent to MPI init.
print("world size ", os.environ['OMPI_COMM_WORLD_SIZE'])
print("rank ", os.environ['OMPI_COMM_WORLD_RANK'])
torch.distributed.init_process_group(
backend="nccl",
init_method="env://",
world_size=int(os.environ['OMPI_COMM_WORLD_SIZE']),
rank=int(os.environ['OMPI_COMM_WORLD_RANK']))
# NEW: lookup number of ranks in the job, and our rank
args.world_size = torch.distributed.get_world_size()
print("world size ", args.world_size)
args.rank = torch.distributed.get_rank()
print("rank ", args.rank)
ngpus_per_node = torch.cuda.device_count()
print("ngpus_per_node ", ngpus_per_node)
local_rank = args.rank % ngpus_per_node
print("local_rank ", local_rank)
# NEW: Globalize variables
global best_acc1
global best_acc5
global best_train_acc1
global best_train_acc5
#args.gpu = None
# NEW: Specify gpu
args.gpu = local_rank
train, validate, modifier = get_trainer(args)
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model and optimizer
model = get_model(args)
# NEW: Distributed data
#if args.distributed:
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
#model = set_gpu(args, model)
# NEW: Modified function for loading gpus on multinode setups
model = lassen_set_gpu(args, model)
if args.pretrained:
pretrained(args, model)
optimizer = get_optimizer(args, model)
data = get_dataset(args)
lr_policy = get_policy(args.lr_policy)(optimizer, args)
if args.label_smoothing is None:
#criterion = nn.CrossEntropyLoss().cuda()
# NEW: Specify gpu
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
else:
criterion = LabelSmoothing(smoothing=args.label_smoothing)
# optionally resume from a checkpoint
best_acc1 = 0.0
best_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if args.resume:
best_acc1 = resume(args, model, optimizer)
# Data loading code
if args.evaluate:
acc1, acc5 = validate(data.val_loader,
model,
criterion,
args,
writer=None,
epoch=args.start_epoch)
return
# Set up directories
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args)
args.ckpt_base_dir = ckpt_base_dir
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
writer = SummaryWriter(log_dir=log_base_dir)
else:
writer = None
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
progress_overall = ProgressMeter(
1, [epoch_time, validation_time, train_time],
prefix="Overall Timing")
end_epoch = time.time()
args.start_epoch = args.start_epoch or 0
acc1 = None
# Save the initial state
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1 if acc1 else "Not evaluated",
},
False,
filename=ckpt_base_dir / f"initial.state",
save=False,
)
# Start training
for epoch in range(args.start_epoch, args.epochs):
# NEW: Distributed data
#if args.distributed:
data.train_sampler.set_epoch(epoch)
data.val_sampler.set_epoch(epoch)
lr_policy(epoch, iteration=None)
#modifier(args, epoch, model)
cur_lr = get_lr(optimizer)
# train for one epoch
start_train = time.time()
train_acc1, train_acc5 = train(data.train_loader,
model,
criterion,
optimizer,
epoch,
args,
writer=writer)
#train_acc1, train_acc5 = train(
# data.train_loader, model, criterion, optimizer, epoch, args, writer=None
#)
train_time.update((time.time() - start_train) / 60)
# evaluate on validation set
start_validation = time.time()
# NEW: Only write values to tensorboard for main processor (one with global rank 0)
if args.rank == 0:
acc1, acc5 = validate(data.val_loader, model, criterion, args,
writer, epoch)
else:
acc1, acc5 = validate(data.val_loader, model, criterion, args,
None, epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
best_acc5 = max(acc5, best_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = ((epoch % args.save_every) == 0) and args.save_every > 0
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
if is_best or save or epoch == args.epochs - 1:
if is_best:
print(
f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}"
)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1,
"curr_acc5": acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_most_recent.state",
save=save,
)
#filename=ckpt_base_dir / f"epoch_{epoch}.state",
epoch_time.update((time.time() - end_epoch) / 60)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(writer,
prefix="diagnostics",
global_step=epoch)
if args.conv_type == "SampleSubnetConv":
count = 0
sum_pr = 0.0
for n, m in model.named_modules():
if isinstance(m, SampleSubnetConv):
# avg pr across 10 samples
pr = 0.0
for _ in range(10):
pr += ((torch.rand_like(m.clamped_scores) >=
m.clamped_scores).float().mean().item())
pr /= 10.0
writer.add_scalar("pr/{}".format(n), pr, epoch)
sum_pr += pr
count += 1
args.prune_rate = sum_pr / count
writer.add_scalar("pr/average", args.prune_rate, epoch)
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
# NEW: Only do for main processor (one with global rank 0)
if args.rank == 0:
write_result_to_csv(
best_acc1=best_acc1,
best_acc5=best_acc5,
best_train_acc1=best_train_acc1,
best_train_acc5=best_train_acc5,
prune_rate=args.prune_rate,
curr_acc1=acc1,
curr_acc5=acc5,
base_config=args.config,
name=args.name,
)
def main_worker(args):
args.gpu = None
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model and optimizer
model = get_model(args)
model = set_gpu(args, model)
# Set up directories
run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args)
# Loading pretrained model
if args.pretrained:
pretrained(args, model)
# Saving a DenseConv (nn.Conv2d) compatible model
if args.dense_conv_model:
print(
f"==> DenseConv compatible model, saving at {ckpt_base_dir / 'model_best.pth'}"
)
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
},
True,
filename=ckpt_base_dir / f"epoch_pretrained.state",
save=True,
)
return
optimizer = get_optimizer(args, model)
data = get_dataset(args)
lr_policy = get_policy(args.lr_policy)(optimizer, args)
if args.label_smoothing is None:
criterion = nn.CrossEntropyLoss().cuda()
else:
criterion = LabelSmoothing(smoothing=args.label_smoothing)
# optionally resume from a checkpoint
best_acc1 = 0.0
best_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if args.resume:
best_acc1 = resume(args, model, optimizer)
# Evaulation of a model
if args.evaluate:
acc1, acc5 = validate(data.val_loader,
model,
criterion,
args,
writer=None,
epoch=args.start_epoch)
return
writer = SummaryWriter(log_dir=log_base_dir)
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
progress_overall = ProgressMeter(1,
[epoch_time, validation_time, train_time],
prefix="Overall Timing")
end_epoch = time.time()
args.start_epoch = args.start_epoch or 0
acc1 = None
# Save the initial state
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1 if acc1 else "Not evaluated",
},
False,
filename=ckpt_base_dir / f"initial.state",
save=False,
)
# Start training
for epoch in range(args.start_epoch, args.epochs):
lr_policy(epoch, iteration=None)
cur_lr = get_lr(optimizer)
# Gradual pruning in GMP experiments
if args.conv_type == "GMPConv" and epoch >= args.init_prune_epoch and epoch <= args.final_prune_epoch:
total_prune_epochs = args.final_prune_epoch - args.init_prune_epoch + 1
for n, m in model.named_modules():
if hasattr(m, 'set_curr_prune_rate'):
prune_decay = (
1 - ((args.curr_prune_epoch - args.init_prune_epoch) /
total_prune_epochs))**3
curr_prune_rate = m.prune_rate - (m.prune_rate *
prune_decay)
m.set_curr_prune_rate(curr_prune_rate)
# train for one epoch
start_train = time.time()
train_acc1, train_acc5 = train(data.train_loader,
model,
criterion,
optimizer,
epoch,
args,
writer=writer)
train_time.update((time.time() - start_train) / 60)
# evaluate on validation set
start_validation = time.time()
acc1, acc5 = validate(data.val_loader, model, criterion, args, writer,
epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
best_acc5 = max(acc5, best_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = ((epoch % args.save_every) == 0) and args.save_every > 0
if is_best or save or epoch == args.epochs - 1:
if is_best:
print(
f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}"
)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1,
"curr_acc5": acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_{epoch}.state",
save=save,
)
epoch_time.update((time.time() - end_epoch) / 60)
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(writer,
prefix="diagnostics",
global_step=epoch)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
# Storing sparsity and threshold statistics for STRConv models
if args.conv_type == "STRConv":
count = 0
sum_sparse = 0.0
for n, m in model.named_modules():
if isinstance(m, STRConv):
sparsity, total_params, thresh = m.getSparsity()
writer.add_scalar("sparsity/{}".format(n), sparsity, epoch)
writer.add_scalar("thresh/{}".format(n), thresh, epoch)
sum_sparse += int(((100 - sparsity) / 100) * total_params)
count += total_params
total_sparsity = 100 - (100 * sum_sparse / count)
writer.add_scalar("sparsity/total", total_sparsity, epoch)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
write_result_to_csv(
best_acc1=best_acc1,
best_acc5=best_acc5,
best_train_acc1=best_train_acc1,
best_train_acc5=best_train_acc5,
prune_rate=args.prune_rate,
curr_acc1=acc1,
curr_acc5=acc5,
base_config=args.config,
name=args.name,
)
if args.conv_type == "STRConv":
json_data = {}
json_thres = {}
for n, m in model.named_modules():
if isinstance(m, STRConv):
sparsity = m.getSparsity()
json_data[n] = sparsity[0]
sum_sparse += int(((100 - sparsity[0]) / 100) * sparsity[1])
count += sparsity[1]
json_thres[n] = sparsity[2]
json_data["total"] = 100 - (100 * sum_sparse / count)
if not os.path.exists("runs/layerwise_sparsity"):
os.mkdir("runs/layerwise_sparsity")
if not os.path.exists("runs/layerwise_threshold"):
os.mkdir("runs/layerwise_threshold")
with open("runs/layerwise_sparsity/{}.json".format(args.name),
"w") as f:
json.dump(json_data, f)
with open("runs/layerwise_threshold/{}.json".format(args.name),
"w") as f:
json.dump(json_thres, f)
def main_worker(args):
args.gpu = None
train, validate, modifier = get_trainer(args)
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
# create model and optimizer
model = get_model(args)
model = set_gpu(args, model)
if args.pretrained:
pretrained(args, model)
# SJT modification:
if args.exp_mode: # pretraining/pruning/funetuning
exp_mode = args.exp_mode
if exp_mode == "pretraining":
# YHT modefication, setting the pruning rate to 0
print(
"Figure out your exp_mode is pretraining, setting prune-rate to 0"
)
args.prune_rate = 0
unfreeze_model_weights(model)
freeze_model_subnet(model)
# End of SJT modification
optimizer = get_optimizer(args, model)
data = get_dataset(args)
lr_policy = get_policy(args.lr_policy)(optimizer, args)
if args.label_smoothing is None:
criterion = nn.CrossEntropyLoss().cuda()
else:
criterion = LabelSmoothing(smoothing=args.label_smoothing)
# optionally resume from a checkpoint
best_acc1 = 0.0
best_acc5 = 0.0
best_train_acc1 = 0.0
best_train_acc5 = 0.0
if args.resume:
# SJT modification
if args.exp_mode:
if args.exp_mode == "pruning":
optimizer = resume_pruning(args, model)
else: # Only can be "finetuning"
if args.exp_mode != "finetuning":
print(
"resume method should be combined with pruning/finetuning exp_mode together!"
)
return
else:
optimizer = resume_finetuning(args, model)
# YHT: not sure whether it is needed
#lr_policy = get_policy(args.lr_policy)(optimizer, args)
#print("#####################DEBUG PRINT : VALIDATE FIRST#####################")
#validate(data.val_loader, model, criterion, args, writer= None, epoch=args.start_epoch)
else:
best_acc1 = resume(args, model, optimizer)
# End of SJT modification
else:
# YHT modification
if args.exp_mode:
if args.exp_mode == "finetuning":
#here, we suppose the user want to use init prun-rate vector to do the finetuning(subnetwork)
print(
"Using finetuning mode without resume, which is supposed to be innit fientune."
)
optimizer = resume_finetuning(args, model)
# YHT: not sure whether it is needed
lr_policy = get_policy(args.lr_policy)(optimizer, args)
# End of modification
# Data loading code
if args.evaluate:
acc1, acc5 = validate(data.val_loader,
model,
criterion,
args,
writer=None,
epoch=args.start_epoch)
return
# Set up directories
run_base_dir, ckpt_base_dir, log_base_dir = get_directories(args)
args.ckpt_base_dir = ckpt_base_dir
writer = SummaryWriter(log_dir=log_base_dir)
epoch_time = AverageMeter("epoch_time", ":.4f", write_avg=False)
validation_time = AverageMeter("validation_time", ":.4f", write_avg=False)
train_time = AverageMeter("train_time", ":.4f", write_avg=False)
progress_overall = ProgressMeter(1,
[epoch_time, validation_time, train_time],
prefix="Overall Timing")
end_epoch = time.time()
args.start_epoch = args.start_epoch or 0
acc1 = None
# Save the initial state
save_checkpoint(
{
"epoch": 0,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1 if acc1 else "Not evaluated",
},
False,
filename=ckpt_base_dir / f"initial.state",
save=False,
)
if args.gp_warm_up:
record_prune_rate = args.prune_rate
if args.print_more:
print_global_layerwise_prune_rate(model, args.prune_rate)
# YHT modification May 20
# till here, we have every prune-rate is accurate
# Now we need to create mask if prandom is true using
if args.prandom:
make_prandom_mask(model)
# End of modification
# Start training
for epoch in range(args.start_epoch, args.epochs):
lr_policy(epoch, iteration=None)
modifier(args, epoch, model)
cur_lr = get_lr(optimizer)
if args.print_more:
print("In epoch{epoch}, lr = {cur_lr}")
# train for one epoch
start_train = time.time()
# WHN modeification add global pruning
if args.pscale == "global":
if args.gp_warm_up:
if epoch < args.gp_warm_up_epochs:
args.prune_rate = 0
else:
args.prune_rate = record_prune_rate
if not args.prandom:
args.score_threshold = get_global_score_threshold(
model, args.prune_rate)
# YHT modification
if args.print_more:
print_global_layerwise_prune_rate(model, args.prune_rate)
# End of modification
train_acc1, train_acc5 = train(data.train_loader,
model,
criterion,
optimizer,
epoch,
args,
writer=writer)
train_time.update((time.time() - start_train) / 60)
# evaluate on validation set
start_validation = time.time()
# if random labeled, evaluate on training set (by yty)
if args.shuffle:
acc1, acc5 = train_acc1, train_acc5
else:
acc1, acc5 = validate(data.val_loader, model, criterion, args,
writer, epoch)
validation_time.update((time.time() - start_validation) / 60)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
best_acc5 = max(acc5, best_acc5)
best_train_acc1 = max(train_acc1, best_train_acc1)
best_train_acc5 = max(train_acc5, best_train_acc5)
save = ((epoch % args.save_every) == 0) and args.save_every > 0
if is_best or save or epoch == args.epochs - 1:
if is_best:
print(
f"==> New best, saving at {ckpt_base_dir / 'model_best.pth'}"
)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"best_acc1": best_acc1,
"best_acc5": best_acc5,
"best_train_acc1": best_train_acc1,
"best_train_acc5": best_train_acc5,
"optimizer": optimizer.state_dict(),
"curr_acc1": acc1,
"curr_acc5": acc5,
},
is_best,
filename=ckpt_base_dir / f"epoch_{epoch}.state",
save=save,
)
epoch_time.update((time.time() - end_epoch) / 60)
progress_overall.display(epoch)
progress_overall.write_to_tensorboard(writer,
prefix="diagnostics",
global_step=epoch)
if args.conv_type == "SampleSubnetConv":
count = 0
sum_pr = 0.0
for n, m in model.named_modules():
if isinstance(m, SampleSubnetConv):
# avg pr across 10 samples
pr = 0.0
for _ in range(10):
pr += ((torch.rand_like(m.clamped_scores) >=
m.clamped_scores).float().mean().item())
pr /= 10.0
writer.add_scalar("pr/{}".format(n), pr, epoch)
sum_pr += pr
count += 1
args.prune_rate = sum_pr / count
writer.add_scalar("pr/average", args.prune_rate, epoch)
writer.add_scalar("test/lr", cur_lr, epoch)
end_epoch = time.time()
write_result_to_csv(
best_acc1=best_acc1,
best_acc5=best_acc5,
best_train_acc1=best_train_acc1,
best_train_acc5=best_train_acc5,
prune_rate=args.prune_rate,
curr_acc1=acc1,
curr_acc5=acc5,
base_config=args.config,
name=args.name,
)
def train(params):
# history logs
best_valid_loss = float('inf')
log_info = ""
hist_train_loss, hist_valid_loss, hist_lr = [], [], []
# dataset
# ds_csv = "/home/ysheng/Dataset/new_dataset/meta_data.csv"
# ds_folder = './dataset/new_dataset'
ds_folder = params.ds_folder
train_set = SSN_Dataset(ds_folder, True)
train_dataloder = DataLoader(train_set,
batch_size=min(len(train_set),
params.batch_size),
shuffle=True,
num_workers=params.workers,
drop_last=True)
valid_set = SSN_Dataset(ds_folder, False)
valid_dataloader = DataLoader(valid_set,
batch_size=min(len(valid_set),
params.batch_size),
shuffle=False,
num_workers=params.workers,
drop_last=True)
best_weight = ''
# model & optimizer & scheduler & loss function
if not params.from_baseline:
input_channel = params.input_channel
model = Relight_SSN(input_channel, 1) # input is mask + human
model.to(device)
else:
model = Relight_SSN(1, 1)
model.to(device)
baseline_checkpoint = torch.load("weights/human_baseline.pt",
map_location=device)
model.load_state_dict(baseline_checkpoint['model_state_dict'])
if params.tbaseline:
params.input_channel = 2
model = baseline_2_tbaseline(model)
model.to(device)
if params.touch_loss:
params.input_channel = 1
model = baseline_2_touchloss(model)
model.to(device)
# resume from last saved points
if params.resume:
best_weight = os.path.join("weights", params.weight_file)
checkpoint = torch.load(best_weight, map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
best_valid_loss = checkpoint['best_loss']
hist_train_loss = checkpoint['hist_train_loss']
hist_valid_loss = checkpoint['hist_valid_loss']
if 'hist_lr' in checkpoint.keys():
hist_lr = checkpoint['hist_lr']
print("resuming from: {}".format(best_weight))
del checkpoint
# tensorboard writer update history
for i in range(0, len(hist_train_loss)):
tensorboard_plot_loss("history train loss",
hist_train_loss[:i + 1], writer)
for i in range(0, len(hist_valid_loss)):
tensorboard_plot_loss("history valid loss",
hist_valid_loss[:i + 1], writer)
if params.relearn:
best_valid_loss = float('inf')
print(torch.cuda.device_count())
# test multiple GPUs
if torch.cuda.device_count() > 1 and params.multi_gpu:
print("Let's use ", torch.cuda.device_count(), "GPUs")
model = nn.DataParallel(model)
optimizer = set_model_optimizer(model, params.weight_decay)
set_lr(optimizer, params.lr)
print("Current LR: {}".format(get_lr(optimizer)))
# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=params.patience)
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=30,
gamma=0.1,
last_epoch=-1)
# training states
train_loss, valid_loss = [], []
# training iterations
for epoch in range(params.epochs):
# training
cur_train_loss = training_iteration(model, train_dataloder, optimizer,
train_loss, epoch)
# # validation
cur_valid_loss = validation_iteration(model, valid_dataloader,
valid_loss, epoch)
if params.use_schedule:
scheduler.step()
log_info += "Current epoch: {} Learning Rate: {} <br>".format(
epoch, get_lr(optimizer))
tensorboard_log(log_info, writer, step=epoch)
hist_train_loss.append(cur_train_loss)
hist_valid_loss.append(cur_valid_loss)
tensorboard_plot_loss("history train loss", hist_train_loss, writer)
tensorboard_plot_loss("history valid loss", hist_valid_loss, writer)
log_info += "Epoch: {} training loss: {}, valid loss: {} <br>".format(
epoch, cur_train_loss, cur_valid_loss)
# save results
if best_valid_loss > cur_valid_loss:
log_info += "<br> ---------- Exp: {} Find better loss: {} at {} -------- <br>".format(
exp_name, cur_valid_loss, datetime.datetime.now())
tensorboard_log(log_info, writer, step=epoch)
best_valid_loss = cur_valid_loss
outfname = '{}_{}.pt'.format(exp_name, get_cur_time_stamp())
best_weight = save_model("weights", model, optimizer, epoch,
best_valid_loss, outfname,
hist_train_loss, hist_valid_loss, hist_lr,
params)
outfname = '{}.pt'.format(exp_name)
save_model("weights", model, optimizer, epoch, best_valid_loss,
outfname, hist_train_loss, hist_valid_loss, hist_lr, params)
# saving loss to local directory
plt.figure()
plt.plot(hist_train_loss, label='train loss')
plt.plot(hist_train_loss, label='valid loss')
plt.legend()
plt.savefig('{}_loss_plot.png'.format(params.exp_name))
plt.close()
# termination
if get_lr(optimizer) < 1e-7:
break
print("Training finished")
return best_weight
