def main():
"""
Main Function
"""
if AutoResume:
AutoResume.init()
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True,
hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
train_loader, val_loader, train_obj = \
datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
auto_resume_details = None
if AutoResume:
auto_resume_details = AutoResume.get_resume_details()
if auto_resume_details:
checkpoint_fn = auto_resume_details.get("RESUME_FILE", None)
checkpoint = torch.load(checkpoint_fn,
map_location=torch.device('cpu'))
args.result_dir = auto_resume_details.get("TENSORBOARD_DIR", None)
args.start_epoch = int(auto_resume_details.get("EPOCH", None)) + 1
args.restore_net = True
args.restore_optimizer = True
msg = ("Found details of a requested auto-resume: checkpoint={}"
" tensorboard={} at epoch {}")
logx.msg(msg.format(checkpoint_fn, args.result_dir, args.start_epoch))
elif args.resume:
checkpoint = torch.load(args.resume, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
msg = "Resuming from: checkpoint={}, epoch {}, arch {}"
logx.msg(msg.format(args.resume, args.start_epoch, args.arch))
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
msg = "Loading weights from: checkpoint={}".format(args.snapshot)
logx.msg(msg)
#define the NASA optimizer parameter
iter_tot = len(train_loader) * args.max_epoch
# tau = args.tau_factor/sqrt(iter_tot)
tau = 1
net = network.get_net(args, criterion)
k = 1
# optim, scheduler = get_optimizer(args, net)
optim, scheduler = get_optimizer(args, net, tau, k)
# Visualize feature maps
#activation = {}
#def get_activation(name):
#def hook(model, input, output):
#activation[name] = output.detach()
#return hook
#net.layer[0].register_forward_hook(get_activation('conv1'))
#data, _ = dataset[0]
#data.unsqueeze_(0)
#output = model(data)
#act = activation['conv1'].squeeze()
#fig, axarr = plt.subplots(act.size(0))
#for idx in range(act.size(0)):
#axarr[idx].imshow(act[idx])
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level=args.amp_opt_level)
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.summary:
from thop import profile
img = torch.randn(1, 3, 640, 640).cuda()
mask = torch.randn(1, 1, 640, 640).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
if args.eval == 'test':
validate(val_loader,
net,
criterion=None,
optim=None,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True,
testing=True,
grid=city)
return 0
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
for epoch in range(args.start_epoch, args.max_epoch):
update_epoch(epoch)
if args.only_coarse:
train_obj.only_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
else:
pass
train(train_loader, net, optim, epoch)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
if epoch % args.val_freq == 0:
validate(val_loader, net, criterion_val, optim, epoch)
scheduler.step()
if check_termination(epoch):
return 0
def main():
"""
Main Function
"""
rank = args.rank
cfg.GLOBAL_RANK = rank
args.gpus = torch.cuda.device_count()
device = torch.device("cpu")
loc_dist = True if args.gpus > 1 else False
loc_rank = rank % args.gpus
args.gpu = loc_rank
args.local_rank = loc_rank
if loc_dist:
device = "cuda:" + str(loc_rank)
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "19500"
os.environ["NCCL_SOCKET_IFNAME"] = "ib"
torch.cuda.set_device(device)
torch.distributed.init_process_group(backend="nccl",
rank=loc_rank,
world_size=args.gpus)
# torch.cuda.set_device(device)
elif args.gpus == 1:
args.gpus = torch.cuda.device_count()
device = "cuda:0"
args.local_rank = 0
torch.cuda.set_device(device)
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True,
hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
# args.ngpu = torch.cuda.device_count()
# args.best_record = {'mean_iu': -1, 'epoch': 0}
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
cwd = os.getcwd()
sz = ht.MPI_WORLD.size
filename = cwd + "/citys-heat-checkpoint-" + str(sz) + ".pth.tar"
if args.resume and os.path.isfile(filename):
checkpoint = torch.load(filename, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
logx.msg(f"Resuming from: checkpoint={args.resume}, "
f"epoch {args.start_epoch}, arch {args.arch}")
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
logx.msg(f"Loading weights from: checkpoint={args.snapshot}")
net = network.get_net(args, criterion)
net = net.to(device)
# args.lr = (1. / args.world_size * (5 * (args.world_size - 1) / 6.)) * 0.0125 * args.world_size
optim, scheduler = get_optimizer(args, net)
# the scheduler in this code is only run at the end of each epoch
# todo: make heat an option not this whole file
# if args.heat:
dp_optim = ht.optim.DASO(
local_optimizer=optim,
total_epochs=args.max_epoch,
max_global_skips=4,
)
#if args.no_cycling:
dp_optim.disable_cycling(global_skips=args.batch_skip,
batches_to_wait=args.gs)
# this is where the network is wrapped with DDDP (w/apex) or DP
htnet = ht.nn.DataParallelMultiGPU(net,
comm=ht.MPI_WORLD,
optimizer=dp_optim)
if args.summary:
print(str(net))
from thop import profile
img = torch.randn(1, 3, 1024, 2048).cuda()
mask = torch.randn(1, 1, 1024, 2048).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print0(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
dp_optim.stability.load_dict(checkpoint["skip_stable"])
if args.restore_net:
#restore_net(net, checkpoint)
htnet.load_state_dict(checkpoint["state_dict"])
#dp_optim.module.load_state_dist(checkpoint["state_dict"])
# htnet = ht.nn.DataParallelMultiGPU(net, ht.MPI_WORLD, dp_optim)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
# TODO: need a loss value for the restart at a certain epoch...
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
# todo: HeAT fixes -- not urgent --
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader,
net,
criterion=None,
optim=None,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
scaler = amp.GradScaler()
if dp_optim.comm.rank == 0:
print("scheduler", args.lr_schedule)
dp_optim.add_scaler(scaler)
nodes = str(int(dp_optim.comm.size / torch.cuda.device_count()))
cwd = os.getcwd()
fname = cwd + "/" + nodes + "-heat-citys-benchmark"
if args.resume and rank == 0 and os.path.isfile(fname + ".pkl"):
with open(fname + ".pkl", "rb") as f:
out_dict = pickle.load(f)
else:
out_dict = {
"epochs": [],
nodes + "-avg-batch-time": [],
nodes + "-total-train-time": [],
nodes + "-train-loss": [],
nodes + "-val-loss": [],
nodes + "-val-iou": [],
nodes + "-val-time": [],
}
print0("Output dict:", fname)
for epoch in range(args.start_epoch, args.max_epoch):
# todo: HeAT fixes -- possible conflict between processes
update_epoch(epoch)
if args.only_coarse: # default: false
train_obj.only_coarse()
train_obj.build_epoch()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
else:
train_obj.build_epoch()
else:
pass
ls, bt, btt = train(train_loader, htnet, dp_optim, epoch, scaler)
dp_optim.epoch_loss_logic(ls, loss_globally_averaged=True)
# if epoch % args.val_freq == 0:
vls, iu, vtt = validate(val_loader, htnet, criterion_val, dp_optim,
epoch)
if args.lr_schedule == "plateau":
if dp_optim.comm.rank == 0:
print("loss", ls, 'best:',
scheduler.best * (1. - scheduler.threshold),
scheduler.num_bad_epochs)
scheduler.step(ls) # val_loss)
else:
scheduler.step()
if args.rank == 0:
save_checkpoint({
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": htnet.state_dict(),
"optimizer": optim.state_dict(),
"skip_stable": dp_optim.stability.get_dict()
})
out_dict["epochs"].append(epoch)
out_dict[nodes + "-train-loss"].append(ls)
out_dict[nodes + "-avg-batch-time"].append(bt)
out_dict[nodes + "-total-train-time"].append(btt)
out_dict[nodes + "-val-loss"].append(vls)
out_dict[nodes + "-val-iou"].append(iu)
out_dict[nodes + "-val-time"].append(vtt)
if args.rank == 0:
save_obj(out_dict, fname)
if args.rank == 0:
print("\nRESULTS\n")
import pandas as pd
df = pd.DataFrame.from_dict(out_dict).set_index("epochs")
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
# more options can be specified also
print(df)
if args.benchmarking:
try:
fulldf = pd.read_csv(cwd + "/heat-bench-results.csv")
fulldf = pd.concat([df, fulldf], axis=1)
except FileNotFoundError:
fulldf = df
fulldf.to_csv(cwd + "/heat-bench-results.csv")
def main():
"""
Main Function
"""
if AutoResume:
AutoResume.init()
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True, hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
train_loader, val_loader, train_obj = \
datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
auto_resume_details = None
if AutoResume:
auto_resume_details = AutoResume.get_resume_details()
if auto_resume_details:
checkpoint_fn = auto_resume_details.get("RESUME_FILE", None)
checkpoint = torch.load(checkpoint_fn,
map_location=torch.device('cpu'))
args.result_dir = auto_resume_details.get("TENSORBOARD_DIR", None)
args.start_epoch = int(auto_resume_details.get("EPOCH", None)) + 1
args.restore_net = True
args.restore_optimizer = True
msg = ("Found details of a requested auto-resume: checkpoint={}"
" tensorboard={} at epoch {}")
logx.msg(msg.format(checkpoint_fn, args.result_dir,
args.start_epoch))
elif args.resume:
checkpoint = torch.load(args.resume,
map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
msg = "Resuming from: checkpoint={}, epoch {}, arch {}"
logx.msg(msg.format(args.resume, args.start_epoch, args.arch))
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH', cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
msg = "Loading weights from: checkpoint={}".format(args.snapshot)
logx.msg(msg)
net = network.get_net(args, criterion)
optim, scheduler = get_optimizer(args, net)
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level=args.amp_opt_level)
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.summary:
print(str(net))
from pytorchOpCounter.thop import profile
img = torch.randn(1, 3, 1024, 2048).cuda()
mask = torch.randn(1, 1, 1024, 2048).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader, net, criterion=criterion_val, optim=optim, epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader, net, criterion=None, optim=None, epoch=0,
calc_metrics=False, dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
for epoch in range(args.start_epoch, args.max_epoch):
update_epoch(epoch)
if args.only_coarse:
train_obj.only_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
else:
pass
train(train_loader, net, optim, epoch)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
if epoch % args.val_freq == 0:
validate(val_loader, net, criterion_val, optim, epoch)
scheduler.step()
if check_termination(epoch):
return 0