def main():
create_exp_dir(config.save,
scripts_to_save=glob.glob('*.py') + glob.glob('*.sh'))
logger = SummaryWriter(config.save)
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(config.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
logging.info("args = %s", str(config))
# preparation ################
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
seed = config.seed
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# config network and criterion ################
min_kept = int(config.batch_size * config.image_height *
config.image_width // (16 * config.gt_down_sampling**2))
ohem_criterion = ProbOhemCrossEntropy2d(ignore_label=255,
thresh=0.7,
min_kept=min_kept,
use_weight=False)
distill_criterion = nn.KLDivLoss()
# data loader ###########################
if config.is_test:
data_setting = {
'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_eval_source,
'eval_source': config.eval_source,
'test_source': config.test_source,
'down_sampling': config.down_sampling
}
else:
data_setting = {
'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source,
'test_source': config.test_source,
'down_sampling': config.down_sampling
}
train_loader = get_train_loader(config, Cityscapes, test=config.is_test)
# Model #######################################
models = []
evaluators = []
testers = []
lasts = []
for idx, arch_idx in enumerate(config.arch_idx):
if config.load_epoch == "last":
state = torch.load(
os.path.join(config.load_path, "arch_%d.pt" % arch_idx))
else:
state = torch.load(
os.path.join(
config.load_path,
"arch_%d_%d.pt" % (arch_idx, int(config.load_epoch))))
model = Network([
state["alpha_%d_0" % arch_idx].detach(),
state["alpha_%d_1" % arch_idx].detach(),
state["alpha_%d_2" % arch_idx].detach()
], [
None, state["beta_%d_1" % arch_idx].detach(),
state["beta_%d_2" % arch_idx].detach()
], [
state["ratio_%d_0" % arch_idx].detach(),
state["ratio_%d_1" % arch_idx].detach(),
state["ratio_%d_2" % arch_idx].detach()
],
num_classes=config.num_classes,
layers=config.layers,
Fch=config.Fch,
width_mult_list=config.width_mult_list,
stem_head_width=config.stem_head_width[idx],
ignore_skip=arch_idx == 0)
mIoU02 = state["mIoU02"]
latency02 = state["latency02"]
obj02 = objective_acc_lat(mIoU02, latency02)
mIoU12 = state["mIoU12"]
latency12 = state["latency12"]
obj12 = objective_acc_lat(mIoU12, latency12)
if obj02 > obj12: last = [2, 0]
else: last = [2, 1]
lasts.append(last)
model.build_structure(last)
logging.info("net: " + str(model))
for b in last:
if len(config.width_mult_list) > 1:
plot_op(getattr(model, "ops%d" % b),
getattr(model, "path%d" % b),
width=getattr(model, "widths%d" % b),
head_width=config.stem_head_width[idx][1],
F_base=config.Fch).savefig(os.path.join(
config.save, "ops_%d_%d.png" % (arch_idx, b)),
bbox_inches="tight")
else:
plot_op(getattr(model, "ops%d" % b),
getattr(model, "path%d" % b),
F_base=config.Fch).savefig(os.path.join(
config.save, "ops_%d_%d.png" % (arch_idx, b)),
bbox_inches="tight")
plot_path_width(model.lasts, model.paths, model.widths).savefig(
os.path.join(config.save, "path_width%d.png" % arch_idx))
plot_path_width([2, 1, 0], [model.path2, model.path1, model.path0],
[model.widths2, model.widths1, model.widths0]).savefig(
os.path.join(config.save,
"path_width_all%d.png" % arch_idx))
flops, params = profile(model,
inputs=(torch.randn(1, 3, 1024, 2048), ))
logging.info("params = %fMB, FLOPs = %fGB", params / 1e6, flops / 1e9)
logging.info("ops:" + str(model.ops))
logging.info("path:" + str(model.paths))
logging.info("last:" + str(model.lasts))
model = model.cuda()
init_weight(model,
nn.init.kaiming_normal_,
torch.nn.BatchNorm2d,
config.bn_eps,
config.bn_momentum,
mode='fan_in',
nonlinearity='relu')
if arch_idx == 0 and len(config.arch_idx) > 1:
partial = torch.load(
os.path.join(config.teacher_path, "weights%d.pt" % arch_idx))
state = model.state_dict()
pretrained_dict = {k: v for k, v in partial.items() if k in state}
state.update(pretrained_dict)
model.load_state_dict(state)
elif config.is_eval:
partial = torch.load(
os.path.join(config.eval_path, "weights%d.pt" % arch_idx))
state = model.state_dict()
pretrained_dict = {k: v for k, v in partial.items() if k in state}
state.update(pretrained_dict)
model.load_state_dict(state)
evaluator = SegEvaluator(Cityscapes(data_setting, 'val', None),
config.num_classes,
config.image_mean,
config.image_std,
model,
config.eval_scale_array,
config.eval_flip,
0,
out_idx=0,
config=config,
verbose=False,
save_path=None,
show_image=False)
evaluators.append(evaluator)
tester = SegTester(Cityscapes(data_setting, 'test', None),
config.num_classes,
config.image_mean,
config.image_std,
model,
config.eval_scale_array,
config.eval_flip,
0,
out_idx=0,
config=config,
verbose=False,
save_path=None,
show_image=False)
testers.append(tester)
# Optimizer ###################################
base_lr = config.lr
if arch_idx == 1 or len(config.arch_idx) == 1:
# optimize teacher solo OR student (w. distill from teacher)
optimizer = torch.optim.SGD(model.parameters(),
lr=base_lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
models.append(model)
# Cityscapes ###########################################
if config.is_eval:
logging.info(config.load_path)
logging.info(config.eval_path)
logging.info(config.save)
# validation
print("[validation...]")
with torch.no_grad():
valid_mIoUs = infer(models, evaluators, logger)
for idx, arch_idx in enumerate(config.arch_idx):
if arch_idx == 0:
logger.add_scalar("mIoU/val_teacher", valid_mIoUs[idx], 0)
logging.info("teacher's valid_mIoU %.3f" %
(valid_mIoUs[idx]))
else:
logger.add_scalar("mIoU/val_student", valid_mIoUs[idx], 0)
logging.info("student's valid_mIoU %.3f" %
(valid_mIoUs[idx]))
exit(0)
tbar = tqdm(range(config.nepochs), ncols=80)
for epoch in tbar:
logging.info(config.load_path)
logging.info(config.save)
logging.info("lr: " + str(optimizer.param_groups[0]['lr']))
# training
tbar.set_description("[Epoch %d/%d][train...]" %
(epoch + 1, config.nepochs))
train_mIoUs = train(train_loader, models, ohem_criterion,
distill_criterion, optimizer, logger, epoch)
torch.cuda.empty_cache()
for idx, arch_idx in enumerate(config.arch_idx):
if arch_idx == 0:
logger.add_scalar("mIoU/train_teacher", train_mIoUs[idx],
epoch)
logging.info("teacher's train_mIoU %.3f" % (train_mIoUs[idx]))
else:
logger.add_scalar("mIoU/train_student", train_mIoUs[idx],
epoch)
logging.info("student's train_mIoU %.3f" % (train_mIoUs[idx]))
adjust_learning_rate(base_lr, 0.992, optimizer, epoch + 1,
config.nepochs)
# validation
if not config.is_test and ((epoch + 1) % 10 == 0 or epoch == 0):
tbar.set_description("[Epoch %d/%d][validation...]" %
(epoch + 1, config.nepochs))
with torch.no_grad():
valid_mIoUs = infer(models, evaluators, logger)
for idx, arch_idx in enumerate(config.arch_idx):
if arch_idx == 0:
logger.add_scalar("mIoU/val_teacher", valid_mIoUs[idx],
epoch)
logging.info("teacher's valid_mIoU %.3f" %
(valid_mIoUs[idx]))
else:
logger.add_scalar("mIoU/val_student", valid_mIoUs[idx],
epoch)
logging.info("student's valid_mIoU %.3f" %
(valid_mIoUs[idx]))
save(models[idx],
os.path.join(config.save, "weights%d.pt" % arch_idx))
# test
if config.is_test and (epoch + 1) >= 250 and (epoch + 1) % 10 == 0:
tbar.set_description("[Epoch %d/%d][test...]" %
(epoch + 1, config.nepochs))
with torch.no_grad():
test(epoch, models, testers, logger)
for idx, arch_idx in enumerate(config.arch_idx):
save(models[idx],
os.path.join(config.save, "weights%d.pt" % arch_idx))
with Engine(custom_parser=parser) as engine:
args = parser.parse_args()
cudnn.benchmark = True
if engine.distributed:
torch.cuda.set_device(engine.local_rank)
# data loader
train_loader, train_sampler = get_train_loader(engine, Cityscapes)
# config network and criterion
min_kept = int(config.batch_size // len(engine.devices) *
config.image_height * config.image_width // 64)
criterion = ProbOhemCrossEntropy2d(ignore_label=255,
thresh=0.7,
min_kept=min_kept,
use_weight=False)
if engine.distributed:
logger.info('Use the Multi-Process-SyncBatchNorm')
BatchNorm2d = SyncBatchNorm
# else:
# BatchNorm2d = BatchNorm2d
model = CPNet(config.num_classes,
criterion=criterion,
pretrained_model=config.pretrained_model,
norm_layer=BatchNorm2d)
init_weight(model.business_layer,
nn.init.kaiming_normal_,
BatchNorm2d,
config.bn_eps,
def main(pretrain=True):
config.save = 'search-{}-{}'.format(config.save,
time.strftime("%Y%m%d-%H%M%S"))
create_exp_dir(config.save,
scripts_to_save=glob.glob('*.py') + glob.glob('*.sh'))
logger = SummaryWriter(config.save)
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(config.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
assert type(pretrain) == bool or type(pretrain) == str
update_arch = True
if pretrain == True:
update_arch = False
logging.info("args = %s", str(config))
# preparation ################
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
seed = config.seed
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# config network and criterion ################
min_kept = int(config.batch_size * config.image_height *
config.image_width // (16 * config.gt_down_sampling**2))
ohem_criterion = ProbOhemCrossEntropy2d(ignore_label=255,
thresh=0.7,
min_kept=min_kept,
use_weight=False)
# Model #######################################
model = Network(config.num_classes,
config.layers,
ohem_criterion,
Fch=config.Fch,
width_mult_list=config.width_mult_list,
prun_modes=config.prun_modes,
stem_head_width=config.stem_head_width)
flops, params = profile(model,
inputs=(torch.randn(1, 3, 1024, 2048), ),
verbose=False)
logging.info("params = %fMB, FLOPs = %fGB", params / 1e6, flops / 1e9)
model = model.cuda()
if type(pretrain) == str:
partial = torch.load(pretrain + "/weights.pt", map_location='cuda:0')
state = model.state_dict()
pretrained_dict = {
k: v
for k, v in partial.items()
if k in state and state[k].size() == partial[k].size()
}
state.update(pretrained_dict)
model.load_state_dict(state)
else:
init_weight(model,
nn.init.kaiming_normal_,
nn.BatchNorm2d,
config.bn_eps,
config.bn_momentum,
mode='fan_in',
nonlinearity='relu')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
architect = Architect(model, config)
# Optimizer ###################################
base_lr = config.lr
parameters = []
parameters += list(model.stem.parameters())
parameters += list(model.cells.parameters())
parameters += list(model.refine32.parameters())
parameters += list(model.refine16.parameters())
parameters += list(model.head0.parameters())
parameters += list(model.head1.parameters())
parameters += list(model.head2.parameters())
parameters += list(model.head02.parameters())
parameters += list(model.head12.parameters())
optimizer = torch.optim.SGD(parameters,
lr=base_lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
# lr policy ##############################
lr_policy = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.978)
# data loader ###########################
data_setting = {
'img_root': config.img_root_folder,
'gt_root': config.gt_root_folder,
'train_source': config.train_source,
'eval_source': config.eval_source,
'down_sampling': config.down_sampling
}
train_loader_model = get_train_loader(config,
EGTEA,
portion=config.train_portion)
train_loader_arch = get_train_loader(config,
EGTEA,
portion=config.train_portion - 1)
evaluator = SegEvaluator(EGTEA(data_setting, 'val', None),
config.num_classes,
config.image_mean,
config.image_std,
model,
config.eval_scale_array,
config.eval_flip,
0,
config=config,
verbose=False,
save_path=None,
show_image=False)
if update_arch:
for idx in range(len(config.latency_weight)):
logger.add_scalar("arch/latency_weight%d" % idx,
config.latency_weight[idx], 0)
logging.info("arch_latency_weight%d = " % idx +
str(config.latency_weight[idx]))
tbar = tqdm(range(config.nepochs), ncols=80)
valid_mIoU_history = []
FPSs_history = []
latency_supernet_history = []
latency_weight_history = []
valid_names = ["8s", "16s", "32s", "8s_32s", "16s_32s"]
arch_names = {0: "teacher", 1: "student"}
for epoch in tbar:
logging.info(pretrain)
logging.info(config.save)
logging.info("lr: " + str(optimizer.param_groups[0]['lr']))
logging.info("update arch: " + str(update_arch))
# training
tbar.set_description("[Epoch %d/%d][train...]" %
(epoch + 1, config.nepochs))
train(pretrain,
train_loader_model,
train_loader_arch,
model,
architect,
ohem_criterion,
optimizer,
lr_policy,
logger,
epoch,
update_arch=update_arch)
torch.cuda.empty_cache()
lr_policy.step()
# validation
tbar.set_description("[Epoch %d/%d][validation...]" %
(epoch + 1, config.nepochs))
with torch.no_grad():
if pretrain == True:
model.prun_mode = "min"
valid_mIoUs = infer(epoch, model, evaluator, logger, FPS=False)
for i in range(5):
logger.add_scalar('mIoU/val_min_%s' % valid_names[i],
valid_mIoUs[i], epoch)
logging.info("Epoch %d: valid_mIoU_min_%s %.3f" %
(epoch, valid_names[i], valid_mIoUs[i]))
if len(model._width_mult_list) > 1:
model.prun_mode = "max"
valid_mIoUs = infer(epoch,
model,
evaluator,
logger,
FPS=False)
for i in range(5):
logger.add_scalar('mIoU/val_max_%s' % valid_names[i],
valid_mIoUs[i], epoch)
logging.info("Epoch %d: valid_mIoU_max_%s %.3f" %
(epoch, valid_names[i], valid_mIoUs[i]))
model.prun_mode = "random"
valid_mIoUs = infer(epoch,
model,
evaluator,
logger,
FPS=False)
for i in range(5):
logger.add_scalar(
'mIoU/val_random_%s' % valid_names[i],
valid_mIoUs[i], epoch)
logging.info("Epoch %d: valid_mIoU_random_%s %.3f" %
(epoch, valid_names[i], valid_mIoUs[i]))
else:
valid_mIoUss = []
FPSs = []
model.prun_mode = None
for idx in range(len(model._arch_names)):
# arch_idx
model.arch_idx = idx
valid_mIoUs, fps0, fps1 = infer(epoch, model, evaluator,
logger)
valid_mIoUss.append(valid_mIoUs)
FPSs.append([fps0, fps1])
for i in range(5):
# preds
logger.add_scalar(
'mIoU/val_%s_%s' %
(arch_names[idx], valid_names[i]), valid_mIoUs[i],
epoch)
logging.info("Epoch %d: valid_mIoU_%s_%s %.3f" %
(epoch, arch_names[idx], valid_names[i],
valid_mIoUs[i]))
if config.latency_weight[idx] > 0:
logger.add_scalar(
'Objective/val_%s_8s_32s' % arch_names[idx],
objective_acc_lat(valid_mIoUs[3], 1000. / fps0),
epoch)
logging.info(
"Epoch %d: Objective_%s_8s_32s %.3f" %
(epoch, arch_names[idx],
objective_acc_lat(valid_mIoUs[3], 1000. / fps0)))
logger.add_scalar(
'Objective/val_%s_16s_32s' % arch_names[idx],
objective_acc_lat(valid_mIoUs[4], 1000. / fps1),
epoch)
logging.info(
"Epoch %d: Objective_%s_16s_32s %.3f" %
(epoch, arch_names[idx],
objective_acc_lat(valid_mIoUs[4], 1000. / fps1)))
valid_mIoU_history.append(valid_mIoUss)
FPSs_history.append(FPSs)
if update_arch:
latency_supernet_history.append(architect.latency_supernet)
latency_weight_history.append(architect.latency_weight)
save(model, os.path.join(config.save, 'weights.pt'))
if type(pretrain) == str:
# contains arch_param names: {"alphas": alphas, "betas": betas, "gammas": gammas, "ratios": ratios}
for idx, arch_name in enumerate(model._arch_names):
state = {}
for name in arch_name['alphas']:
state[name] = getattr(model, name)
for name in arch_name['betas']:
state[name] = getattr(model, name)
for name in arch_name['ratios']:
state[name] = getattr(model, name)
state["mIoU02"] = valid_mIoUs[3]
state["mIoU12"] = valid_mIoUs[4]
if pretrain is not True:
state["latency02"] = 1000. / fps0
state["latency12"] = 1000. / fps1
torch.save(
state,
os.path.join(config.save, "arch_%d_%d.pt" % (idx, epoch)))
torch.save(state,
os.path.join(config.save, "arch_%d.pt" % (idx)))
if update_arch:
for idx in range(len(config.latency_weight)):
if config.latency_weight[idx] > 0:
if (int(FPSs[idx][0] >= config.FPS_max[idx]) +
int(FPSs[idx][1] >= config.FPS_max[idx])) >= 1:
architect.latency_weight[idx] /= 2
elif (int(FPSs[idx][0] <= config.FPS_min[idx]) +
int(FPSs[idx][1] <= config.FPS_min[idx])) > 0:
architect.latency_weight[idx] *= 2
logger.add_scalar(
"arch/latency_weight_%s" % arch_names[idx],
architect.latency_weight[idx], epoch + 1)
logging.info("arch_latency_weight_%s = " %
arch_names[idx] +
str(architect.latency_weight[idx]))
seed = config.seed
if engine.distributed:
seed = engine.local_rank
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
# data loader
train_loader, train_sampler = get_train_loader(engine, Cityscapes)
# config network and criterion
criterion = nn.CrossEntropyLoss(reduction='mean', ignore_index=255)
ohem_criterion = ProbOhemCrossEntropy2d(
ignore_label=255,
thresh=0.7,
min_kept=int(config.batch_size // len(engine.devices) *
config.image_height * config.image_width //
(16 * config.gt_down_sampling**2)),
use_weight=False)
if engine.distributed:
BatchNorm2d = SyncBatchNorm
model = BiSeNet(config.num_classes,
is_training=True,
criterion=criterion,
ohem_criterion=ohem_criterion,
pretrained_model=config.pretrained_model,
norm_layer=BatchNorm2d)
init_weight(model.business_layer,
nn.init.kaiming_normal_,
def main():
args, args_text = _parse_args()
# dist init
torch.distributed.init_process_group(backend='nccl', init_method='env://')
torch.cuda.set_device(args.local_rank)
args.world_size = torch.distributed.get_world_size()
args.local_rank = torch.distributed.get_rank()
args.save = args.save + args.exp_name
# detectron2 data loader ###########################
# det2_args = default_argument_parser().parse_args()
det2_args = args
det2_args.config_file = args.det2_cfg
cfg = setup(det2_args)
mapper = DatasetMapper(cfg, augmentations=build_sem_seg_train_aug(cfg))
det2_dataset = iter(build_detection_train_loader(cfg, mapper=mapper))
det2_val = build_batch_test_loader(cfg, cfg.DATASETS.TEST[0])
len_det2_train = 20210 // cfg.SOLVER.IMS_PER_BATCH
if args.local_rank == 0:
create_exp_dir(args.save,
scripts_to_save=glob.glob('*.py') + glob.glob('*.sh'))
logger = SummaryWriter(args.save)
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
logging.info("args = %s", str(args))
else:
logger = None
# preparation ################
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# config network and criterion ################
gt_down_sampling = 1
min_kept = int(args.batch_size * args.image_height * args.image_width //
(16 * gt_down_sampling**2))
ohem_criterion = ProbOhemCrossEntropy2d(ignore_label=255,
thresh=0.7,
min_kept=min_kept,
use_weight=False)
# data loader ###########################
num_classes = args.num_classes
with open(args.json_file, 'r') as f:
# dict_a = json.loads(f, cls=NpEncoder)
model_dict = json.loads(f.read())
width_mult_list = [
4. / 12,
6. / 12,
8. / 12,
10. / 12,
1.,
]
model = Network(Fch=args.Fch,
num_classes=num_classes,
stem_head_width=(args.stem_head_width,
args.stem_head_width))
last = model_dict["lasts"]
if args.local_rank == 0:
with torch.cuda.device(0):
macs, params = get_model_complexity_info(
model, (3, args.eval_height, args.eval_width),
as_strings=True,
print_per_layer_stat=True,
verbose=True)
logging.info('{:<30} {:<8}'.format('Computational complexity: ',
macs))
logging.info('{:<30} {:<8}'.format('Number of parameters: ',
params))
with open(os.path.join(args.save, 'args.yaml'), 'w') as f:
f.write(args_text)
init_weight(model,
nn.init.kaiming_normal_,
torch.nn.BatchNorm2d,
args.bn_eps,
args.bn_momentum,
mode='fan_in',
nonlinearity='relu')
if args.pretrain:
model.backbone = load_pretrain(model.backbone, args.pretrain)
model = model.cuda()
# if args.sync_bn:
# if has_apex:
# model = apex.parallel.convert_syncbn_model(model)
# else:
# model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
# Optimizer ###################################
base_lr = args.base_lr
if args.opt == "sgd":
optimizer = torch.optim.SGD(model.parameters(),
lr=base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.opt == "adam":
optimizer = torch.optim.Adam(model.parameters(),
lr=base_lr,
betas=(0.9, 0.999),
eps=1e-08)
elif args.opt == "adamw":
optimizer = torch.optim.AdamW(model.parameters(),
lr=base_lr,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.weight_decay)
else:
optimizer = create_optimizer(args, model)
if args.sched == "raw":
lr_scheduler = None
else:
max_iteration = args.epochs * len_det2_train
lr_scheduler = Iter_LR_Scheduler(args, max_iteration, len_det2_train)
start_epoch = 0
if os.path.exists(os.path.join(args.save, 'last.pth.tar')):
args.resume = os.path.join(args.save, 'last.pth.tar')
if args.resume:
model_state_file = args.resume
if os.path.isfile(model_state_file):
checkpoint = torch.load(model_state_file,
map_location=torch.device('cpu'))
start_epoch = checkpoint['start_epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
logging.info('Loaded checkpoint (starting from iter {})'.format(
checkpoint['start_epoch']))
model_ema = None
if args.model_ema:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEma(model,
decay=args.model_ema_decay,
device='cpu' if args.model_ema_force_cpu else '',
resume=None)
if model_ema:
eval_model = model_ema.ema
else:
eval_model = model
if has_apex:
model = DDP(model, delay_allreduce=True)
else:
model = DDP(model, device_ids=[args.local_rank])
best_valid_iou = 0.
best_epoch = 0
temp_iou = 0.
avg_loss = -1
logging.info("rank: {} world_size: {}".format(args.local_rank,
args.world_size))
for epoch in range(start_epoch, args.epochs):
if args.local_rank == 0:
logging.info(args.load_path)
logging.info(args.save)
logging.info("lr: " + str(optimizer.param_groups[0]['lr']))
# training
drop_prob = args.drop_path_prob * epoch / args.epochs
# model.module.drop_path_prob(drop_prob)
train_mIoU = train(len_det2_train, det2_dataset, model, model_ema,
ohem_criterion, num_classes, lr_scheduler,
optimizer, logger, epoch, args, cfg)
# torch.cuda.empty_cache()
# if epoch > args.epochs // 3:
if epoch >= 0:
temp_iou, avg_loss = validation(det2_val, eval_model,
ohem_criterion, num_classes, args,
cfg)
torch.cuda.empty_cache()
if args.local_rank == 0:
logging.info("Epoch: {} train miou: {:.2f}".format(
epoch + 1, 100 * train_mIoU))
if temp_iou > best_valid_iou:
best_valid_iou = temp_iou
best_epoch = epoch
if model_ema is not None:
torch.save(
{
'start_epoch': epoch + 1,
'state_dict': model_ema.ema.state_dict(),
'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
},
os.path.join(args.save, 'best_checkpoint.pth.tar'))
else:
torch.save(
{
'start_epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
},
os.path.join(args.save, 'best_checkpoint.pth.tar'))
logger.add_scalar("mIoU/val", temp_iou, epoch)
logging.info("[Epoch %d/%d] valid mIoU %.4f eval loss %.4f" %
(epoch + 1, args.epochs, temp_iou, avg_loss))
logging.info("Best valid mIoU %.4f Epoch %d" %
(best_valid_iou, best_epoch))
if model_ema is not None:
torch.save(
{
'start_epoch': epoch + 1,
'state_dict': model_ema.ema.state_dict(),
'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
},
os.path.join(args.save, 'last.pth.tar'))
else:
torch.save(
{
'start_epoch': epoch + 1,
'state_dict': model.module.state_dict(),
'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
},
os.path.join(args.save, 'last.pth.tar'))