def exp_htcn_mixed(cfg_file, output_dir, dataset_source, dataset_target,
val_datasets, device, net, optimizer, num_workers, lr,
batch_size, start_epoch, max_epochs, lr_decay_gamma,
lr_decay_step, mask_load_p, resume, load_name, pretrained,
model_type, eta, gamma, ef, class_agnostic, lc, gc, LA_ATT,
MID_ATT, debug, _run):
args = Args(dataset=dataset_source,
dataset_t=dataset_target,
cfg_file=cfg_file,
net=net)
args = set_dataset_args(args)
is_bgr = False
if net in ['res101', 'res50', 'res152', 'vgg16']:
is_bgr = True
logger = LoggerForSacred(None, ex, False)
if cfg_file is not None:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
np.random.seed(cfg.RNG_SEED)
cfg.TRAIN.USE_FLIPPED = True
cfg.USE_GPU_NMS = True if device == 'cuda' else False
device = torch.device(device)
backbone_fn = htcn_resnet
if 'res' in net:
if model_type == 'normal':
backbone_fn = n_resnet
elif model_type == 'saitp':
backbone_fn = s_resnet
else:
if model_type == 'normal':
backbone_fn = n_vgg16
elif model_type == 'htcn':
backbone_fn = htcn_vgg16
elif model_type == 'saitp':
backbone_fn = None
dataloader_s, dataloader_t, imdb, imdb_t = init_dataloaders_1s_1t(
args, batch_size, num_workers, is_bgr, False)
model = init_frcnn_utils.init_model_only(device,
net,
backbone_fn,
imdb_t,
'',
class_agnostic=class_agnostic,
lc=lc,
gc=gc,
la_attention=LA_ATT,
mid_attention=MID_ATT)
model.eval()
im_data = torch.randn(1, 3, 600, 1200).to(device)
im_info = torch.FloatTensor([[600, 900, 2]]).to(device)
gt_boxes = torch.zeros((1, 1, 5)).to(device)
num_boxes = torch.zeros([1]).to(device)
macs, params = profile(model,
inputs=(im_data, im_info, gt_boxes, num_boxes))
macs, params = clever_format([macs, params], "%.3f")
print("Model CFLOPS: {}".format(macs))
print("Model Cparams: {}".format(params))
random_mask = nn.Sequential(
nn.Conv2d(3, 256, 1, stride=1, padding=0, bias=False), nn.ReLU(),
nn.Conv2d(256, 3, 1)).to(device)
macs, params = profile(random_mask, inputs=(im_data, ))
macs, params = clever_format([macs, params], "%.3f")
print("Mask CFLOPS: {}".format(macs))
print("Mask Cparams: {}".format(params))
iters_per_epoch = int(1000 / batch_size)
data_iter_s = iter(dataloader_s)
for step in range(1, iters_per_epoch + 1):
try:
data_s = next(data_iter_s)
except:
data_iter_s = iter(dataloader_s)
data_s = next(data_iter_s)
im_data = data_s[0].to(device)
im_info = data_s[1].to(device)
gt_boxes = data_s[2].to(device)
num_boxes = data_s[3].to(device)
pass
def exp_htcn_mixed(cfg_file, output_dir, dataset_source, val_datasets, device,
net, optimizer, num_workers, lr, batch_size, start_epoch,
max_epochs, lr_decay_gamma, lr_decay_step, resume,
load_name, class_agnostic, debug, _run):
args = Args(dataset=dataset_source,
dataset_t="",
cfg_file=cfg_file,
net=net)
args = set_dataset_args(args)
args_val = Args(dataset=dataset_source,
dataset_t=val_datasets,
imdb_name_target=[],
cfg_file=cfg_file,
net=net)
args_val = set_dataset_args(args_val, test=True)
logger = LoggerForSacred(None, ex, True)
if cfg_file is not None:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
np.random.seed(cfg.RNG_SEED)
cfg.TRAIN.USE_FLIPPED = True
cfg.USE_GPU_NMS = True if device == 'cuda' else False
device = torch.device(device)
output_dir = output_dir + "_{}".format(_run._id)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
dataloader_s, _, imdb, imdb_t = init_dataloaders_1s_1t(
args, batch_size, num_workers)
val_dataloader_ts, val_imdb_ts = init_val_dataloaders_mt(
args_val, 1, num_workers)
session = 1
fasterRCNN, lr, optimizer, session, start_epoch, _ = init_frcnn_utils.init_non_damodel_optimizer(
lr,
class_agnostic,
device,
imdb,
load_name,
net,
optimizer,
resume,
session,
start_epoch,
is_all_params=True)
if torch.cuda.device_count() > 1:
fasterRCNN = nn.DataParallel(fasterRCNN)
iters_per_epoch = int(10000 / batch_size)
total_step = 0
for epoch in range(start_epoch, max_epochs + 1):
# setting to train mode
fasterRCNN.train()
if epoch - 1 in lr_decay_step:
adjust_learning_rate(optimizer, lr_decay_gamma)
lr *= lr_decay_gamma
total_step = frcnn_utils.train_no_da_frcnn_one_epoch(
args, total_step, dataloader_s, iters_per_epoch, fasterRCNN,
optimizer, device, logger)
save_name = os.path.join(
output_dir,
'source_train_{}_session_{}_epoch_{}_total_step_{}.pth'.format(
dataset_source, session, epoch, total_step))
save_checkpoint(
{
'session':
session,
'epoch':
epoch + 1,
'model':
fasterRCNN.module.state_dict()
if torch.cuda.device_count() > 1 else fasterRCNN.state_dict(),
'optimizer':
optimizer.state_dict(),
'pooling_mode':
cfg.POOLING_MODE,
'class_agnostic':
class_agnostic,
}, save_name)
return 0
def exp_htcn_mixed(cfg_file, output_dir, dataset_source, dataset_target,
val_datasets, device, net, optimizer, num_workers, lr,
batch_size, start_epoch, max_epochs, lr_decay_gamma,
lr_decay_step, dtm_load_p, resume, load_name, pretrained,
eta, gamma, ef, class_agnostic, lc, gc, LA_ATT, MID_ATT,
alpha, debug, _run):
args = Args(dataset=dataset_source,
dataset_t=dataset_target,
cfg_file=cfg_file,
net=net)
args = set_dataset_args(args)
args_val = Args(dataset=dataset_source,
dataset_t=val_datasets,
imdb_name_target=[],
cfg_file=cfg_file,
net=net)
args_val = set_dataset_args(args_val, test=True)
is_bgr = False
if net in ['res101', 'res50', 'res152', 'vgg16']:
is_bgr = True
logger = LoggerForSacred(None, ex, True)
if cfg_file is not None:
cfg_from_file(cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
np.random.seed(cfg.RNG_SEED)
cfg.TRAIN.USE_FLIPPED = True
cfg.USE_GPU_NMS = True if device == 'cuda' else False
device = torch.device(device)
output_dir = output_dir + "_{}".format(_run._id)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
dataloader_s, dataloader_t, imdb, imdb_t = init_dataloaders_1s_1t(
args, batch_size, num_workers, is_bgr)
session = 1
fasterRCNN = init_htcn_model(LA_ATT,
MID_ATT,
class_agnostic,
device,
gc,
imdb,
lc,
load_name,
net,
strict=False,
pretrained=pretrained)
lr, optimizer, session, start_epoch = init_optimizer(lr,
fasterRCNN,
optimizer,
resume,
load_name,
session,
start_epoch,
is_all_params=True)
dtm = torch.load(dtm_load_p)
dtm = dtm.to(device)
dtm_dict = {}
dtm_dict['dtm'] = dtm
if torch.cuda.device_count() > 1:
fasterRCNN = nn.DataParallel(fasterRCNN)
iters_per_epoch = int(10000 / batch_size)
if ef:
FL = EFocalLoss(class_num=2, gamma=gamma)
else:
FL = FocalLoss(class_num=2, gamma=gamma)
total_step = 0
for epoch in range(start_epoch, max_epochs + 1):
# setting to train mode
fasterRCNN.train()
if epoch - 1 in lr_decay_step:
adjust_learning_rate(optimizer, lr_decay_gamma)
lr *= lr_decay_gamma
total_step = dtm_util.train_htcn_one_epoch_ida_with_dtm(
args, FL, total_step, dataloader_s, dtm_dict, dataloader_t,
iters_per_epoch, fasterRCNN, optimizer, device, eta, alpha, logger)
save_name = os.path.join(
output_dir,
'target_{}_eta_{}_local_{}_global_{}_gamma_{}_session_{}_epoch_{}_total_step_{}.pth'
.format(args.dataset_t, args.eta, lc, gc, gamma, session, epoch,
total_step))
save_checkpoint(
{
'session':
session,
'epoch':
epoch + 1,
'model':
fasterRCNN.module.state_dict()
if torch.cuda.device_count() > 1 else fasterRCNN.state_dict(),
'optimizer':
optimizer.state_dict(),
'pooling_mode':
cfg.POOLING_MODE,
'class_agnostic':
class_agnostic,
}, save_name)