def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
print(model)
print("")
print("##############################################################")
print("")
#summary(model, (3, 2048, 1024))
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
#print(f"{bcolors.WARNING}Warning: No active frommets remain. Continue?{bcolors.ENDC}")
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
#summary(model,input_size=(2,3,1333,800))
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, cfg.MODEL.CLS_WEIGHT, cfg.MODEL.REG_WEIGHT, init_div=True, init_opti=False, init_model=True)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed): #cfg 0 False
model = build_detection_model(cfg) #实例化模型
device = torch.device(cfg.MODEL.DEVICE) #cfg.MODEL.DEVICE="cuda" 将torch.tensor分配到cuda 即GPU上
model.to(device) #将模型放在gpu上运行
if cfg.MODEL.USE_SYNCBN: #False
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model) # 定义网络训练优化器
scheduler = make_lr_scheduler(cfg, optimizer) #设置学习率
if distributed: #False
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR #"."
save_to_disk = get_rank() == 0 #True
#checkpoint为网络的预训练模型
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data) #将extra_checkpoint_data字典里的数值加入到arguments字典中
#make_data_loader
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed, #False
start_iter=arguments["iteration"], # 0
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD #SOLVER.CHECKPOINT_PERIOD = 2500
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def __init__(self, cfg, local_rank, distributed):
self.writer = SummaryWriter(log_dir=cfg.OUTPUT_DIR)
self.start_epoch = 0
# self.epochs = cfg.MAX_ITER / len()
self.epochs = 5
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# 核心修改在于dataset，dataloader都是torch.utils.data.data_loader
# import pdb; pdb.set_trace()
# train_loader = build_single_data_loader(cfg)
self.train_loader = make_train_loader(
cfg, start_iter=arguments["iteration"])
# self.val_loader = make_val_loader(cfg)
# train_data_loader = make_data_loader(
# cfg,
# is_train=True,
# is_distributed=distributed,
# start_iter=arguments["iteration"],
# )
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.checkpointer = checkpointer
self.scheduler = scheduler
self.device = device
self.checkpoint_period = checkpoint_period
self.arguments = arguments
self.distributed = distributed
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
# import matplotlib.pyplot as plt
# import numpy as np
#
# def imshow(img):
# #img = img / 2 + 0.5 # unnormalize
# img = img + 115
# img = img[[2, 1, 0]]
# npimg = img.numpy().astype(np.int)
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
#
# import torchvision
# dataiter = iter(data_loader)
# images, target, _ = dataiter.next() #chwangteg target and pixel is hundreds
#
# imshow(torchvision.utils.make_grid(images.tensors))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed, labelenc_fpath):
model = LabelEncStep2Network(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# Load LabelEncodingFunction
# Initialize FPN and Head from Step1 weights
if not checkpointer.has_checkpoint():
labelenc_weights = torch.load(labelenc_fpath, map_location=torch.device('cpu'))
# load LabelEncodingFunction
model.module.label_encoding_function.load_state_dict(
labelenc_weights['label_encoding_function'], strict=True)
# Initialize Head
model.module.rpn.load_state_dict(
labelenc_weights['rpn'], strict=True)
if model.module.roi_heads:
model.module.roi_heads.load_state_dict(
labelenc_weights['roi_heads'], strict=True)
# Initialize FPN
fpn_weight = model.module.label_encoding_function.fpn.state_dict()
model.module.backbone.fpn.load_state_dict(fpn_weight, strict=True)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg) # 利用build_detection_model构建model
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN: # syncbn是什么，SyncBatchNorm
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
model) # 对model进行转换，转换成sync的
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed: # 是否使用分布式训练，distributed 分布式的
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {} # 创建字典
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg,
model,
optimizer,
scheduler,
output_dir,
save_to_disk # checkpoint
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader( # 利用make_data_loader读取数据
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed, device_ids, use_tensorboard=False):
# ------------------------------- more configs
half_data = [0, 0] # do not split
first_order = cfg.SOLVER.SEARCH.FIRST_ORDER
alpha_lr = cfg.SOLVER.SEARCH.BASE_LR_ALPHA
alpha_weight_decay = 1e-3
device_ids = [int(x) for x in device_ids]
if cfg.MODEL.FAD.CLSTOWER or cfg.MODEL.FAD.BOXTOWER:
n_cells = cfg.MODEL.FAD.NUM_CELLS_CLS
if cfg.MODEL.FAD.CLSTOWER and cfg.MODEL.FAD.BOXTOWER:
n_nodes = cfg.MODEL.FAD.NUM_NODES_CLS
n_module = 2
elif cfg.MODEL.FAD.CLSTOWER:
n_nodes = cfg.MODEL.FAD.NUM_NODES_CLS
n_module = 1
else:
n_nodes = cfg.MODEL.FAD.NUM_NODES_BOX
n_module = 1
else:
pdb.set_trace()
# build model
model = SearchRCNNController(n_cells,
n_nodes=n_nodes,
device_ids=device_ids,
cfg_det=cfg,
n_module=n_module)
device = torch.device(cfg.MODEL.DEVICE)
model = model.to(device)
torch.cuda.set_device(0)
distributed = False
if first_order: print('Using 1st order approximationfor the search')
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# ---------------------- optimize alpha
arch = Architect(model, cfg.SOLVER.MOMENTUM, cfg.SOLVER.WEIGHT_DECAY)
alpha_optim = torch.optim.Adam(model.alphas(),
alpha_lr,
betas=(0.5, 0.999),
weight_decay=alpha_weight_decay)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
# ------ tensorboard
tb_info = {"tb_logger": None}
if use_tensorboard:
tb_logger = get_tensorboard_writer(output_dir)
tb_info['tb_logger'] = tb_logger
tb_info['prefix'] = cfg.TENSORBOARD.PREFIX
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
half=half_data[0])
val_loader = make_data_loader(cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
half=half_data[1])
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
arch,
data_loader,
val_loader,
optimizer,
alpha_optim,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
cfg,
tb_info=tb_info,
first_order=first_order,
)
return model
def train(cfg, local_rank, distributed):
writer = SummaryWriter('runs/{}'.format(cfg.OUTPUT_DIR))
##########################################################################
############################# Initial Model ##############################
##########################################################################
model = {}
device = torch.device(cfg.MODEL.DEVICE)
backbone = build_backbone(cfg).to(device)
fcos = build_rpn(cfg, backbone.out_channels).to(device)
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7 = FCOSDiscriminator(
num_convs=cfg.MODEL.ADV.DIS_P7_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.GRL_WEIGHT_P7,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6 = FCOSDiscriminator(
num_convs=cfg.MODEL.ADV.DIS_P6_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.GRL_WEIGHT_P6,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5 = FCOSDiscriminator(
num_convs=cfg.MODEL.ADV.DIS_P5_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.GRL_WEIGHT_P5,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4 = FCOSDiscriminator(
num_convs=cfg.MODEL.ADV.DIS_P4_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.GRL_WEIGHT_P4,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3 = FCOSDiscriminator(
num_convs=cfg.MODEL.ADV.DIS_P3_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.GRL_WEIGHT_P3,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_CA = FCOSDiscriminator_CA(
num_convs=cfg.MODEL.ADV.CA_DIS_P7_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.CA_GRL_WEIGHT_P7,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_CA = FCOSDiscriminator_CA(
num_convs=cfg.MODEL.ADV.CA_DIS_P6_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.CA_GRL_WEIGHT_P6,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_CA = FCOSDiscriminator_CA(
num_convs=cfg.MODEL.ADV.CA_DIS_P5_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.CA_GRL_WEIGHT_P5,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_CA = FCOSDiscriminator_CA(
num_convs=cfg.MODEL.ADV.CA_DIS_P4_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.CA_GRL_WEIGHT_P4,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_CA = FCOSDiscriminator_CA(
num_convs=cfg.MODEL.ADV.CA_DIS_P3_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.CA_GRL_WEIGHT_P3,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_Cond = FCOSDiscriminator_CondA(
num_convs=cfg.MODEL.ADV.COND_DIS_P7_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.COND_GRL_WEIGHT_P7,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
# center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN,
class_align=cfg.MODEL.ADV.COND_CLASS,
reg_left_align=cfg.MODEL.ADV.COND_REG.LEFT,
reg_top_align=cfg.MODEL.ADV.COND_REG.TOP,
expand_dim=cfg.MODEL.ADV.COND_EXPAND).to(device)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_Cond = FCOSDiscriminator_CondA(
num_convs=cfg.MODEL.ADV.COND_DIS_P6_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.COND_GRL_WEIGHT_P6,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
# center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN,
class_align=cfg.MODEL.ADV.COND_CLASS,
reg_left_align=cfg.MODEL.ADV.COND_REG.LEFT,
reg_top_align=cfg.MODEL.ADV.COND_REG.TOP,
expand_dim=cfg.MODEL.ADV.COND_EXPAND).to(device)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_Cond = FCOSDiscriminator_CondA(
num_convs=cfg.MODEL.ADV.COND_DIS_P5_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.COND_GRL_WEIGHT_P5,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
# center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN,
class_align=cfg.MODEL.ADV.COND_CLASS,
reg_left_align=cfg.MODEL.ADV.COND_REG.LEFT,
reg_top_align=cfg.MODEL.ADV.COND_REG.TOP,
expand_dim=cfg.MODEL.ADV.COND_EXPAND).to(device)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_Cond = FCOSDiscriminator_CondA(
num_convs=cfg.MODEL.ADV.COND_DIS_P4_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.COND_GRL_WEIGHT_P4,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
# center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN,
class_align=cfg.MODEL.ADV.COND_CLASS,
reg_left_align=cfg.MODEL.ADV.COND_REG.LEFT,
reg_top_align=cfg.MODEL.ADV.COND_REG.TOP,
expand_dim=cfg.MODEL.ADV.COND_EXPAND).to(device)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_Cond = FCOSDiscriminator_CondA(
num_convs=cfg.MODEL.ADV.COND_DIS_P3_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.COND_GRL_WEIGHT_P3,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
# center_aware_type=cfg.MODEL.ADV.CENTER_AWARE_TYPE,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN,
class_align=cfg.MODEL.ADV.COND_CLASS,
reg_left_align=cfg.MODEL.ADV.COND_REG.LEFT,
reg_top_align=cfg.MODEL.ADV.COND_REG.TOP,
expand_dim=cfg.MODEL.ADV.COND_EXPAND).to(device)
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_HA = FCOSDiscriminator_HA(
num_convs=cfg.MODEL.ADV.HA_DIS_P7_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.HA_GRL_WEIGHT_P7,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_HA = FCOSDiscriminator_HA(
num_convs=cfg.MODEL.ADV.HA_DIS_P6_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.HA_GRL_WEIGHT_P6,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_HA = FCOSDiscriminator_HA(
num_convs=cfg.MODEL.ADV.HA_DIS_P5_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.HA_GRL_WEIGHT_P5,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_HA = FCOSDiscriminator_HA(
num_convs=cfg.MODEL.ADV.HA_DIS_P4_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.HA_GRL_WEIGHT_P4,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_HA = FCOSDiscriminator_HA(
num_convs=cfg.MODEL.ADV.HA_DIS_P3_NUM_CONVS,
grad_reverse_lambda=cfg.MODEL.ADV.HA_GRL_WEIGHT_P3,
center_aware_weight=cfg.MODEL.ADV.CENTER_AWARE_WEIGHT,
grl_applied_domain=cfg.MODEL.ADV.GRL_APPLIED_DOMAIN).to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
backbone = torch.nn.SyncBatchNorm.convert_sync_batchnorm(backbone)
fcos = torch.nn.SyncBatchNorm.convert_sync_batchnorm(fcos)
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7 = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dis_P7)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6 = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dis_P6)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5 = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dis_P5)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4 = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dis_P4)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3 = torch.nn.SyncBatchNorm.convert_sync_batchnorm(dis_P3)
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_CA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P7_CA)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_CA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P6_CA)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_CA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P5_CA)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_CA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P4_CA)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_CA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P3_CA)
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_Cond = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P7_Cond)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_Cond = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P6_Cond)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_Cond = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P5_Cond)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_Cond = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P4_Cond)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_Cond = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P3_Cond)
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_HA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P7_HA)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_HA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P6_HA)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_HA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P5_HA)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_HA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P4_HA)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_HA = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
dis_P3_HA)
##########################################################################
#################### Initial Optimizer and Scheduler #####################
##########################################################################
optimizer = {}
optimizer["backbone"] = make_optimizer(cfg, backbone, name='backbone')
optimizer["fcos"] = make_optimizer(cfg, fcos, name='fcos')
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
optimizer["dis_P7"] = make_optimizer(cfg,
dis_P7,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
optimizer["dis_P6"] = make_optimizer(cfg,
dis_P6,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
optimizer["dis_P5"] = make_optimizer(cfg,
dis_P5,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
optimizer["dis_P4"] = make_optimizer(cfg,
dis_P4,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
optimizer["dis_P3"] = make_optimizer(cfg,
dis_P3,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
optimizer["dis_P7_CA"] = make_optimizer(cfg,
dis_P7_CA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
optimizer["dis_P6_CA"] = make_optimizer(cfg,
dis_P6_CA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
optimizer["dis_P5_CA"] = make_optimizer(cfg,
dis_P5_CA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
optimizer["dis_P4_CA"] = make_optimizer(cfg,
dis_P4_CA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
optimizer["dis_P3_CA"] = make_optimizer(cfg,
dis_P3_CA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
optimizer["dis_P7_Cond"] = make_optimizer(cfg,
dis_P7_Cond,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
optimizer["dis_P6_Cond"] = make_optimizer(cfg,
dis_P6_Cond,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
optimizer["dis_P5_Cond"] = make_optimizer(cfg,
dis_P5_Cond,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
optimizer["dis_P4_Cond"] = make_optimizer(cfg,
dis_P4_Cond,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
optimizer["dis_P3_Cond"] = make_optimizer(cfg,
dis_P3_Cond,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
optimizer["dis_P7_HA"] = make_optimizer(cfg,
dis_P7_HA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
optimizer["dis_P6_HA"] = make_optimizer(cfg,
dis_P6_HA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
optimizer["dis_P5_HA"] = make_optimizer(cfg,
dis_P5_HA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
optimizer["dis_P4_HA"] = make_optimizer(cfg,
dis_P4_HA,
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
optimizer["dis_P3_HA"] = make_optimizer(cfg,
dis_P3_HA,
name='discriminator')
scheduler = {}
scheduler["backbone"] = make_lr_scheduler(cfg,
optimizer["backbone"],
name='backbone')
scheduler["fcos"] = make_lr_scheduler(cfg, optimizer["fcos"], name='fcos')
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
scheduler["dis_P7"] = make_lr_scheduler(cfg,
optimizer["dis_P7"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
scheduler["dis_P6"] = make_lr_scheduler(cfg,
optimizer["dis_P6"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
scheduler["dis_P5"] = make_lr_scheduler(cfg,
optimizer["dis_P5"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
scheduler["dis_P4"] = make_lr_scheduler(cfg,
optimizer["dis_P4"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
scheduler["dis_P3"] = make_lr_scheduler(cfg,
optimizer["dis_P3"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
scheduler["dis_P7_CA"] = make_lr_scheduler(cfg,
optimizer["dis_P7_CA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
scheduler["dis_P6_CA"] = make_lr_scheduler(cfg,
optimizer["dis_P6_CA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
scheduler["dis_P5_CA"] = make_lr_scheduler(cfg,
optimizer["dis_P5_CA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
scheduler["dis_P4_CA"] = make_lr_scheduler(cfg,
optimizer["dis_P4_CA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
scheduler["dis_P3_CA"] = make_lr_scheduler(cfg,
optimizer["dis_P3_CA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
scheduler["dis_P7_Cond"] = make_lr_scheduler(
cfg, optimizer["dis_P7_Cond"], name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
scheduler["dis_P6_Cond"] = make_lr_scheduler(
cfg, optimizer["dis_P6_Cond"], name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
scheduler["dis_P5_Cond"] = make_lr_scheduler(
cfg, optimizer["dis_P5_Cond"], name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
scheduler["dis_P4_Cond"] = make_lr_scheduler(
cfg, optimizer["dis_P4_Cond"], name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
scheduler["dis_P3_Cond"] = make_lr_scheduler(
cfg, optimizer["dis_P3_Cond"], name='discriminator')
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
scheduler["dis_P7_HA"] = make_lr_scheduler(cfg,
optimizer["dis_P7_HA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P6:
scheduler["dis_P6_HA"] = make_lr_scheduler(cfg,
optimizer["dis_P6_HA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P5:
scheduler["dis_P5_HA"] = make_lr_scheduler(cfg,
optimizer["dis_P5_HA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P4:
scheduler["dis_P4_HA"] = make_lr_scheduler(cfg,
optimizer["dis_P4_HA"],
name='discriminator')
if cfg.MODEL.ADV.USE_DIS_P3:
scheduler["dis_P3_HA"] = make_lr_scheduler(cfg,
optimizer["dis_P3_HA"],
name='discriminator')
##########################################################################
######################## DistributedDataParallel #########################
##########################################################################
if distributed:
backbone = torch.nn.parallel.DistributedDataParallel(
backbone,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
fcos = torch.nn.parallel.DistributedDataParallel(
fcos,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7 = torch.nn.parallel.DistributedDataParallel(
dis_P7,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6 = torch.nn.parallel.DistributedDataParallel(
dis_P6,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5 = torch.nn.parallel.DistributedDataParallel(
dis_P5,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4 = torch.nn.parallel.DistributedDataParallel(
dis_P4,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3 = torch.nn.parallel.DistributedDataParallel(
dis_P3,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_CA = torch.nn.parallel.DistributedDataParallel(
dis_P7_CA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_CA = torch.nn.parallel.DistributedDataParallel(
dis_P6_CA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_CA = torch.nn.parallel.DistributedDataParallel(
dis_P5_CA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_CA = torch.nn.parallel.DistributedDataParallel(
dis_P4_CA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_CA = torch.nn.parallel.DistributedDataParallel(
dis_P3_CA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_Cond = torch.nn.parallel.DistributedDataParallel(
dis_P7_Cond,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_Cond = torch.nn.parallel.DistributedDataParallel(
dis_P6_Cond,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_Cond = torch.nn.parallel.DistributedDataParallel(
dis_P5_Cond,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_Cond = torch.nn.parallel.DistributedDataParallel(
dis_P4_Cond,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_Cond = torch.nn.parallel.DistributedDataParallel(
dis_P3_Cond,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
dis_P7_HA = torch.nn.parallel.DistributedDataParallel(
dis_P7_HA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P6:
dis_P6_HA = torch.nn.parallel.DistributedDataParallel(
dis_P6_HA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P5:
dis_P5_HA = torch.nn.parallel.DistributedDataParallel(
dis_P5_HA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P4:
dis_P4_HA = torch.nn.parallel.DistributedDataParallel(
dis_P4_HA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
if cfg.MODEL.ADV.USE_DIS_P3:
dis_P3_HA = torch.nn.parallel.DistributedDataParallel(
dis_P3_HA,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False)
##########################################################################
########################### Save Model to Dict ###########################
##########################################################################
model["backbone"] = backbone
model["fcos"] = fcos
if cfg.MODEL.ADV.USE_DIS_GLOBAL:
if cfg.MODEL.ADV.USE_DIS_P7:
model["dis_P7"] = dis_P7
if cfg.MODEL.ADV.USE_DIS_P6:
model["dis_P6"] = dis_P6
if cfg.MODEL.ADV.USE_DIS_P5:
model["dis_P5"] = dis_P5
if cfg.MODEL.ADV.USE_DIS_P4:
model["dis_P4"] = dis_P4
if cfg.MODEL.ADV.USE_DIS_P3:
model["dis_P3"] = dis_P3
if cfg.MODEL.ADV.USE_DIS_CENTER_AWARE:
if cfg.MODEL.ADV.USE_DIS_P7:
model["dis_P7_CA"] = dis_P7_CA
if cfg.MODEL.ADV.USE_DIS_P6:
model["dis_P6_CA"] = dis_P6_CA
if cfg.MODEL.ADV.USE_DIS_P5:
model["dis_P5_CA"] = dis_P5_CA
if cfg.MODEL.ADV.USE_DIS_P4:
model["dis_P4_CA"] = dis_P4_CA
if cfg.MODEL.ADV.USE_DIS_P3:
model["dis_P3_CA"] = dis_P3_CA
if cfg.MODEL.ADV.USE_DIS_CONDITIONAL:
if cfg.MODEL.ADV.USE_DIS_P7:
model["dis_P7_Cond"] = dis_P7_Cond
if cfg.MODEL.ADV.USE_DIS_P6:
model["dis_P6_Cond"] = dis_P6_Cond
if cfg.MODEL.ADV.USE_DIS_P5:
model["dis_P5_Cond"] = dis_P5_Cond
if cfg.MODEL.ADV.USE_DIS_P4:
model["dis_P4_Cond"] = dis_P4_Cond
if cfg.MODEL.ADV.USE_DIS_P3:
model["dis_P3_Cond"] = dis_P3_Cond
if cfg.MODEL.ADV.USE_DIS_HEAD:
if cfg.MODEL.ADV.USE_DIS_P7:
model["dis_P7_HA"] = dis_P7_HA
if cfg.MODEL.ADV.USE_DIS_P6:
model["dis_P6_HA"] = dis_P6_HA
if cfg.MODEL.ADV.USE_DIS_P5:
model["dis_P5_HA"] = dis_P5_HA
if cfg.MODEL.ADV.USE_DIS_P4:
model["dis_P4_HA"] = dis_P4_HA
if cfg.MODEL.ADV.USE_DIS_P3:
model["dis_P3_HA"] = dis_P3_HA
##########################################################################
################################ Training ################################
##########################################################################
arguments = {}
arguments["iteration"] = 0
arguments["use_dis_global"] = cfg.MODEL.ADV.USE_DIS_GLOBAL
arguments["use_dis_ca"] = cfg.MODEL.ADV.USE_DIS_CENTER_AWARE
arguments["use_dis_conditional"] = cfg.MODEL.ADV.USE_DIS_CONDITIONAL
arguments["use_dis_ha"] = cfg.MODEL.ADV.USE_DIS_HEAD
arguments["ga_dis_lambda"] = cfg.MODEL.ADV.GA_DIS_LAMBDA
arguments["ca_dis_lambda"] = cfg.MODEL.ADV.CA_DIS_LAMBDA
arguments["cond_dis_lambda"] = cfg.MODEL.ADV.COND_DIS_LAMBDA
arguments["ha_dis_lambda"] = cfg.MODEL.ADV.HA_DIS_LAMBDA
arguments["use_feature_layers"] = []
if cfg.MODEL.ADV.USE_DIS_P7:
arguments["use_feature_layers"].append("P7")
if cfg.MODEL.ADV.USE_DIS_P6:
arguments["use_feature_layers"].append("P6")
if cfg.MODEL.ADV.USE_DIS_P5:
arguments["use_feature_layers"].append("P5")
if cfg.MODEL.ADV.USE_DIS_P4:
arguments["use_feature_layers"].append("P4")
if cfg.MODEL.ADV.USE_DIS_P3:
arguments["use_feature_layers"].append("P3")
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(f=cfg.MODEL.WEIGHT,
load_dis=True,
load_opt_sch=False)
# arguments.update(extra_checkpoint_data)
# Initial dataloader (both target and source domain)
data_loader = {}
data_loader["source"] = make_data_loader_source(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
data_loader["target"] = make_data_loader_target(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(model, data_loader, optimizer, scheduler, checkpointer, device,
checkpoint_period, arguments, cfg, run_test, distributed, writer)
return model
def train(cfg, local_rank, distributed, iter_clear, ignore_head):
model = build_detection_model(cfg)
# model, conversion_count = convert_to_shift_dbg(
# model,
# cfg.DEEPSHIFT_DEPTH,
# cfg.DEEPSHIFT_TYPE,
# convert_weights=True,
# use_kernel=cfg.DEEPSHIFT_USEKERNEL,
# rounding=cfg.DEEPSHIFT_ROUNDING,
# shift_range=cfg.DEEPSHIFT_RANGE)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
if iter_clear:
load_opt = False
load_sch = False
else:
load_opt = True
load_sch = True
if ignore_head:
load_body = True
load_fpn = True
load_head = False
else:
load_body = True
load_fpn = True
load_head = True
# 预加载模型或者是通常的模型，或者是deepshift模型
if cfg.MODEL.WEIGHT:
checkpointer = DetectronCheckpointer(
cfg, model, None, None, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT, load_opt=False, load_sch=False,
load_body=load_body, load_fpn=load_fpn, load_head=load_head)
model, conversion_count = convert_to_shift(
model,
cfg.DEEPSHIFT_DEPTH,
cfg.DEEPSHIFT_TYPE,
convert_weights=True,
use_kernel=cfg.DEEPSHIFT_USEKERNEL,
rounding=cfg.DEEPSHIFT_ROUNDING,
shift_range=cfg.DEEPSHIFT_RANGE)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
else:
model, conversion_count = convert_to_shift(
model,
cfg.DEEPSHIFT_DEPTH,
cfg.DEEPSHIFT_TYPE,
convert_weights=True,
use_kernel=cfg.DEEPSHIFT_USEKERNEL,
rounding=cfg.DEEPSHIFT_ROUNDING,
shift_range=cfg.DEEPSHIFT_RANGE)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT, load_opt=False, load_sch=False,
load_body=load_body, load_fpn=load_fpn, load_head=load_head)
conv2d_layers_count = count_layer_type(model, torch.nn.Conv2d)
linear_layers_count = count_layer_type(model, torch.nn.Linear)
print("###### conversion_count: {}, not convert conv2d layer: {}, linear layer: {}".format(
conversion_count, conv2d_layers_count, linear_layers_count))
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
arguments.update(extra_checkpoint_data)
if iter_clear:
arguments["iteration"] = 0
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
model = round_shift_weights(model)
torch.save({"model": model.state_dict()}, os.path.join(output_dir, "model_final_round.pth"))
return model
def train(cfg, local_rank, distributed, iter_clear, ignore_head):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
if iter_clear:
load_opt = False
load_sch = False
else:
load_opt = True
load_sch = True
if ignore_head:
load_body = True
load_fpn = True
load_head = False
else:
load_body = True
load_fpn = True
load_head = True
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT,
load_opt=load_opt,
load_sch=load_sch,
load_body=load_body,
load_fpn=load_fpn,
load_head=load_head)
arguments.update(extra_checkpoint_data)
if iter_clear:
arguments["iteration"] = 0
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
writer = SummaryWriter('runs/{}'.format(cfg.OUTPUT_DIR))
##########################################################################
############################# Initial Model ##############################
##########################################################################
model = {}
device = torch.device(cfg.MODEL.DEVICE)
backbone = build_backbone(cfg).to(device)
fcos = build_rpn(cfg, backbone.out_channels).to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
backbone = torch.nn.SyncBatchNorm.convert_sync_batchnorm(backbone)
fcos = torch.nn.SyncBatchNorm.convert_sync_batchnorm(fcos)
##########################################################################
#################### Initial Optimizer and Scheduler #####################
##########################################################################
optimizer = {}
optimizer["backbone"] = make_optimizer(cfg, backbone, name='backbone')
optimizer["fcos"] = make_optimizer(cfg, fcos, name='fcos')
scheduler = {}
scheduler["backbone"] = make_lr_scheduler(cfg, optimizer["backbone"], name='backbone')
scheduler["fcos"] = make_lr_scheduler(cfg, optimizer["fcos"], name='fcos')
if distributed:
backbone = torch.nn.parallel.DistributedDataParallel(
backbone, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False
)
fcos = torch.nn.parallel.DistributedDataParallel(
fcos, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False
)
########################### Save Model to Dict ###########################
##########################################################################
model["backbone"] = backbone
model["fcos"] = fcos
##########################################################################
################################ Training ################################
##########################################################################
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader_source(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train_base(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
cfg,
run_test,
distributed,
writer
)
return model
