def init_discriminator(self, args):
# init D
self.discriminator_model = FCDiscriminator(num_classes=2).cuda()
self.interp = nn.Upsample(size=400, mode='bilinear')
self.disc_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(mode=args.loss_type)
return
def load_models(mode, device, args):
"""
:param mode: "SS" or "Discriminator"
:param args:
:return:
"""
if mode == "SS":
if args.network == "segnet_small":
from models.SegNet import SegNet_Small
model = SegNet_Small(args.channels,
args.classes,
args.skip_type,
BR_bool=args.BR,
separable_conv=args.SC)
model = model.to(device)
summary(model, (args.channels, args.image_size, args.image_size), args)
elif mode == "Discriminator":
from models.discriminator import FCDiscriminator
model = FCDiscriminator(num_classes=NUM_CLASSES, )
else:
raise ValueError("Invalid mode {}!".format(mode))
try:
if args.checkpoint_SS:
model.load_state_dict(torch.load(args.checkpoint_SS))
if args.checkpoint_DNet:
model.load_state_dict(torch.load(args.checkpoint_DNet))
except Exception as e:
print(e)
sys.exit(0)
return model
def __init__(self, cfg, writer, logger, use_pseudo_label=False, modal_num=3, multimodal_merger=multimodal_merger):
self.cfg = cfg
self.writer = writer
self.class_numbers = 19
self.logger = logger
cfg_model = cfg['model']
self.cfg_model = cfg_model
self.best_iou = -100
self.iter = 0
self.nets = []
self.split_gpu = 0
self.default_gpu = cfg['model']['default_gpu']
self.PredNet_Dir = None
self.valid_classes = cfg['training']['valid_classes']
self.G_train = True
self.cls_feature_weight = cfg['training']['cls_feature_weight']
self.use_pseudo_label = use_pseudo_label
self.modal_num = modal_num
# cluster vectors & cuda initialization
self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda()
self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda()
self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda()
self.class_threshold_group = torch.full([self.modal_num + 1, 19], 0.6).cuda()
self.disc_T = torch.FloatTensor([0.0]).cuda()
#self.metrics = CustomMetrics(self.class_numbers)
self.metrics = CustomMetrics(self.class_numbers, modal_num=self.modal_num, model=self)
# multimodal / multi-branch merger
self.multimodal_merger = multimodal_merger
bn = cfg_model['bn']
if bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
elif bn == 'bn':
BatchNorm = nn.BatchNorm2d
elif bn == 'gn':
BatchNorm = nn.GroupNorm
else:
raise NotImplementedError('batch norm choice {} is not implemented'.format(bn))
if True:
self.PredNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
).cuda()
self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet)
self.PredNet_DP = self.init_device(self.PredNet, gpu_id=self.default_gpu, whether_DP=True)
self.PredNet.eval()
self.PredNet_num = 0
self.PredDnet = FCDiscriminator(inplanes=19)
self.load_PredDnet(cfg, writer, logger, dir=None, net=self.PredDnet)
self.PredDnet_DP = self.init_device(self.PredDnet, gpu_id=self.default_gpu, whether_DP=True)
self.PredDnet.eval()
self.BaseNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
)
logger.info('the backbone is {}'.format(cfg_model['basenet']['version']))
self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True)
self.nets.extend([self.BaseNet])
self.nets_DP = [self.BaseNet_DP]
# Discriminator
self.SOURCE_LABEL = 0
self.TARGET_LABEL = 1
self.DNets = []
self.DNets_DP = []
for _ in range(self.modal_num+1):
_net_d = FCDiscriminator(inplanes=19)
self.DNets.append(_net_d)
_net_d_DP = self.init_device(_net_d, gpu_id=self.default_gpu, whether_DP=True)
self.DNets_DP.append(_net_d_DP)
self.nets.extend(self.DNets)
self.nets_DP.extend(self.DNets_DP)
self.optimizers = []
self.schedulers = []
optimizer_cls = torch.optim.SGD
optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items()
if k != 'name'}
optimizer_cls_D = torch.optim.Adam
optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items()
if k != 'name'}
if False:
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params)
else:
self.BaseOpti = optimizer_cls(self.BaseNet.optim_parameters(cfg['training']['optimizer']['lr']), **optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.DiscOptis = []
for _d_net in self.DNets:
self.DiscOptis.append(
optimizer_cls_D(_d_net.parameters(), **optimizer_params_D)
)
self.optimizers.extend(self.DiscOptis)
self.schedulers = []
if False:
self.BaseSchedule = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self.BaseSchedule])
else:
"""BaseSchedule detail see FUNC: scheduler_step()"""
self.learning_rate = cfg['training']['optimizer']['lr']
self.gamma = cfg['training']['lr_schedule']['gamma']
self.num_steps = cfg['training']['lr_schedule']['max_iter']
self._BaseSchedule_nouse = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self._BaseSchedule_nouse])
self.DiscSchedules = []
for _disc_opt in self.DiscOptis:
self.DiscSchedules.append(
get_scheduler(_disc_opt, cfg['training']['lr_schedule'])
)
self.schedulers.extend(self.DiscSchedules)
self.setup(cfg, writer, logger)
self.adv_source_label = 0
self.adv_target_label = 1
self.bceloss = nn.BCEWithLogitsLoss(reduce=False)
self.loss_fn = get_loss_function(cfg)
pseudo_cfg = copy.deepcopy(cfg)
pseudo_cfg['training']['loss']['name'] = 'cross_entropy4d'
self.pseudo_loss_fn = get_loss_function(pseudo_cfg)
self.mseloss = nn.MSELoss()
self.l1loss = nn.L1Loss()
self.smoothloss = nn.SmoothL1Loss()
self.triplet_loss = nn.TripletMarginLoss()
self.kl_distance = nn.KLDivLoss(reduction='none')
class CustomModel():
def __init__(self, cfg, writer, logger, use_pseudo_label=False, modal_num=3, multimodal_merger=multimodal_merger):
self.cfg = cfg
self.writer = writer
self.class_numbers = 19
self.logger = logger
cfg_model = cfg['model']
self.cfg_model = cfg_model
self.best_iou = -100
self.iter = 0
self.nets = []
self.split_gpu = 0
self.default_gpu = cfg['model']['default_gpu']
self.PredNet_Dir = None
self.valid_classes = cfg['training']['valid_classes']
self.G_train = True
self.cls_feature_weight = cfg['training']['cls_feature_weight']
self.use_pseudo_label = use_pseudo_label
self.modal_num = modal_num
# cluster vectors & cuda initialization
self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda()
self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda()
self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda()
self.class_threshold_group = torch.full([self.modal_num + 1, 19], 0.6).cuda()
self.disc_T = torch.FloatTensor([0.0]).cuda()
#self.metrics = CustomMetrics(self.class_numbers)
self.metrics = CustomMetrics(self.class_numbers, modal_num=self.modal_num, model=self)
# multimodal / multi-branch merger
self.multimodal_merger = multimodal_merger
bn = cfg_model['bn']
if bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
elif bn == 'bn':
BatchNorm = nn.BatchNorm2d
elif bn == 'gn':
BatchNorm = nn.GroupNorm
else:
raise NotImplementedError('batch norm choice {} is not implemented'.format(bn))
if True:
self.PredNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
).cuda()
self.load_PredNet(cfg, writer, logger, dir=None, net=self.PredNet)
self.PredNet_DP = self.init_device(self.PredNet, gpu_id=self.default_gpu, whether_DP=True)
self.PredNet.eval()
self.PredNet_num = 0
self.PredDnet = FCDiscriminator(inplanes=19)
self.load_PredDnet(cfg, writer, logger, dir=None, net=self.PredDnet)
self.PredDnet_DP = self.init_device(self.PredDnet, gpu_id=self.default_gpu, whether_DP=True)
self.PredDnet.eval()
self.BaseNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num
)
logger.info('the backbone is {}'.format(cfg_model['basenet']['version']))
self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True)
self.nets.extend([self.BaseNet])
self.nets_DP = [self.BaseNet_DP]
# Discriminator
self.SOURCE_LABEL = 0
self.TARGET_LABEL = 1
self.DNets = []
self.DNets_DP = []
for _ in range(self.modal_num+1):
_net_d = FCDiscriminator(inplanes=19)
self.DNets.append(_net_d)
_net_d_DP = self.init_device(_net_d, gpu_id=self.default_gpu, whether_DP=True)
self.DNets_DP.append(_net_d_DP)
self.nets.extend(self.DNets)
self.nets_DP.extend(self.DNets_DP)
self.optimizers = []
self.schedulers = []
optimizer_cls = torch.optim.SGD
optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items()
if k != 'name'}
optimizer_cls_D = torch.optim.Adam
optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items()
if k != 'name'}
if False:
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params)
else:
self.BaseOpti = optimizer_cls(self.BaseNet.optim_parameters(cfg['training']['optimizer']['lr']), **optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.DiscOptis = []
for _d_net in self.DNets:
self.DiscOptis.append(
optimizer_cls_D(_d_net.parameters(), **optimizer_params_D)
)
self.optimizers.extend(self.DiscOptis)
self.schedulers = []
if False:
self.BaseSchedule = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self.BaseSchedule])
else:
"""BaseSchedule detail see FUNC: scheduler_step()"""
self.learning_rate = cfg['training']['optimizer']['lr']
self.gamma = cfg['training']['lr_schedule']['gamma']
self.num_steps = cfg['training']['lr_schedule']['max_iter']
self._BaseSchedule_nouse = get_scheduler(self.BaseOpti, cfg['training']['lr_schedule'])
self.schedulers.extend([self._BaseSchedule_nouse])
self.DiscSchedules = []
for _disc_opt in self.DiscOptis:
self.DiscSchedules.append(
get_scheduler(_disc_opt, cfg['training']['lr_schedule'])
)
self.schedulers.extend(self.DiscSchedules)
self.setup(cfg, writer, logger)
self.adv_source_label = 0
self.adv_target_label = 1
self.bceloss = nn.BCEWithLogitsLoss(reduce=False)
self.loss_fn = get_loss_function(cfg)
pseudo_cfg = copy.deepcopy(cfg)
pseudo_cfg['training']['loss']['name'] = 'cross_entropy4d'
self.pseudo_loss_fn = get_loss_function(pseudo_cfg)
self.mseloss = nn.MSELoss()
self.l1loss = nn.L1Loss()
self.smoothloss = nn.SmoothL1Loss()
self.triplet_loss = nn.TripletMarginLoss()
self.kl_distance = nn.KLDivLoss(reduction='none')
def create_PredNet(self,):
ss = DeepLab(
num_classes=19,
backbone=self.cfg_model['basenet']['version'],
output_stride=16,
bn=self.cfg_model['bn'],
freeze_bn=True,
modal_num=self.modal_num,
).cuda()
ss.eval()
return ss
def setup(self, cfg, writer, logger):
'''
set optimizer and load pretrained model
'''
for net in self.nets:
# name = net.__class__.__name__
self.init_weights(cfg['model']['init'], logger, net)
print("Initializition completed")
if hasattr(net, '_load_pretrained_model') and cfg['model']['pretrained']:
print("loading pretrained model for {}".format(net.__class__.__name__))
net._load_pretrained_model()
'''load pretrained model
'''
if cfg['training']['resume_flag']:
self.load_nets(cfg, writer, logger)
pass
def lr_poly(self):
return self.learning_rate * ((1 - float(self.iter) / self.num_steps) ** (self.gamma))
def adjust_basenet_learning_rate(self):
lr = self.lr_poly()
self.BaseOpti.param_groups[0]['lr'] = lr
if len(self.BaseOpti.param_groups) > 1:
self.BaseOpti.param_groups[1]['lr'] = lr * 10
def forward(self, input):
feat, feat_low, att_mask, feat_cls, output = self.BaseNet_DP(input)
return feat, feat_low, feat_cls, output
def forward_Up(self, input):
feat, feat_low, feat_cls, outputs = self.forward(input)
merge_out = self.multimodal_merger(
{
'feat_cls': feat_cls,
'output': output,
},
is_upsample=True,
size=input.size()[2:],
)
return feat, feat_low, merge_out['feat_cls'], merge_out['output_comb']
def PredNet_Forward(self, input):
with torch.no_grad():
_, _, att_mask, feat_cls, output_result = self.PredNet_DP(input)
return _, _, att_mask, feat_cls, output_result
def calculate_mean_vector(self, feat_cls, outputs, labels, ):
outputs_softmax = F.softmax(outputs, dim=1)
outputs_argmax = outputs_softmax.argmax(dim=1, keepdim=True)
outputs_argmax = self.process_label(outputs_argmax.float())
labels_expanded = self.process_label(labels)
outputs_pred = labels_expanded * outputs_argmax
scale_factor = F.adaptive_avg_pool2d(outputs_pred, 1)
vectors = []
ids = []
for n in range(feat_cls.size()[0]):
for t in range(self.class_numbers):
if scale_factor[n][t].item()==0:
continue
if (outputs_pred[n][t] > 0).sum() < 10:
continue
s = feat_cls[n] * outputs_pred[n][t]
scale = torch.sum(outputs_pred[n][t]) / labels.shape[2] / labels.shape[3] * 2
s = normalisation_pooling()(s, scale)
s = F.adaptive_avg_pool2d(s, 1) / scale_factor[n][t]
vectors.append(s)
ids.append(t)
return vectors, ids
def step(self, source_x, source_label, source_modal_ids, target_x, target_label, target_modal_ids, use_pseudo_loss=False):
assert len(source_modal_ids) == source_x.size(0), "modal_ids' batchsize != source_x's batchsize"
_, _, source_feat_cls, source_output = self.forward(input=source_x)
"""source_output: [B x 19 x W x H, ...]
select modal-branch output in each batchsize
Specific-modal output
"""
source_output_modal_k = torch.stack(
[
source_output[_modal_i][_batch_i]
for _batch_i, _modal_i in enumerate(source_modal_ids)
],
dim=0,
)
# attention output & specific-modal output
source_output_comb = torch.cat([source_output_modal_k, source_output[-1]], dim=0)
source_label_comb = torch.cat([source_label, source_label.clone()], dim=0)
source_outputUp = F.interpolate(source_output_comb, size=source_x.size()[-2:], mode='bilinear', align_corners=True)
loss_GTA = self.loss_fn(input=source_outputUp, target=source_label_comb)
self.PredNet.eval()
with torch.no_grad():
_, _, att_mask, feat_cls, output = self.PredNet_Forward(target_x)
threshold_args_comb, cluster_args_comb = self.metrics.update(feat_cls, output, target_label, modal_ids=[_i for _i in range(self.modal_num+1)], att_mask=att_mask)
""" Discriminator-guided easy/hard training """
target_label_size = target_label.size()
t_out = output[-1]
_t_out = F.interpolate(t_out.detach(), size=(target_label_size[1]*4, target_label_size[2]*4), mode='bilinear', align_corners=True)
_t_D_out = self.PredDnet_DP(F.softmax(_t_out))
_t_D_out_prob = F.sigmoid(_t_D_out)
disc_easy_weight = torch.where(_t_D_out_prob > self.disc_T, _t_D_out_prob, torch.FloatTensor([0.0]).cuda())
disc_easy_weight = torch.where(threshold_args_comb != 250, disc_easy_weight, torch.FloatTensor([0.0]).cuda()).squeeze(1)
disc_hard_mask = torch.where(_t_D_out_prob < self.disc_T, torch.Tensor([1]).cuda(), torch.Tensor([0]).cuda())
disc_hard_mask = torch.where(threshold_args_comb == 250, torch.Tensor([1]).cuda(), disc_hard_mask)
loss_L2_source_cls = torch.Tensor([0]).cuda(self.split_gpu)
loss_L2_target_cls = torch.Tensor([0]).cuda(self.split_gpu)
_, _, target_feat_cls, target_output = self.forward(target_x)
if self.cfg['training']['loss_L2_cls']: # distance loss
_batch, _w, _h = source_label.shape
source_label_downsampled = source_label.reshape([_batch,1,_w, _h]).float()
source_label_downsampled = F.interpolate(source_label_downsampled.float(), size=source_feat_cls[0].size()[-2:], mode='nearest') #or F.softmax(input=source_output, dim=1)
loss_L2_source_cls = torch.Tensor([0]).cuda()
loss_L2_target_cls = torch.Tensor([0]).cuda()
for _modal_i, _source_feat_i, _source_out_i, _target_feat_i, _target_out_i in zip(range(self.modal_num + 1), source_feat_cls, source_output, target_feat_cls, target_output):
if _modal_i < 2:
continue
source_vectors, source_ids = self.calculate_mean_vector(_source_feat_i, _source_out_i, source_label_downsampled)
loss_L2_source_cls += self.class_vectors_alignment(source_ids, source_vectors, modal_ids=[_modal_i,])
target_vectors, target_ids = self.calculate_mean_vector(_target_feat_i, _target_out_i, cluster_args_comb.float())
loss_L2_target_cls += self.class_vectors_alignment(target_ids, target_vectors, modal_ids=[_modal_i,])
loss_L2_cls = self.cls_feature_weight * (loss_L2_source_cls + loss_L2_target_cls)
if loss_L2_cls.item() > 1.0:
loss_L2_cls = loss_L2_cls / 10.0
if loss_L2_cls.item() > 0.5:
loss_L2_cls = loss_L2_cls / 3.0
target_label_size = target_label.size()
loss = torch.Tensor([0]).cuda()
batch, _, w, h = threshold_args_comb.shape
_cluster_args_comb = cluster_args_comb.reshape([batch, w, h])
_threshold_args_comb = threshold_args_comb.reshape([batch, w, h])
_target_output = target_output[-1]
_loss_CTS = self.pseudo_loss_fn(input=_target_output, target=_threshold_args_comb) # CAG-based and probability-based PLA
loss_CTS = torch.sum(_loss_CTS * disc_easy_weight) / (1 + (disc_easy_weight > 0).sum())
if self.G_train and self.cfg['training']['loss_pseudo_label']:
loss = loss + loss_CTS
if self.G_train and self.cfg['training']['loss_source_seg']:
loss = loss + loss_GTA
if self.cfg['training']['loss_L2_cls']:
loss = loss + torch.sum(loss_L2_cls)
# adversarial loss
# -----------------------------
"""Generator (segmentation)"""
# -----------------------------
# On Source Domain
loss_adv = torch.Tensor([0]).cuda()
_batch_size = 0
source_modal_ids_tensor = torch.Tensor(source_modal_ids).cuda()
target_modal_ids_tensor = torch.Tensor(target_modal_ids).cuda()
for t_out, _d_net_DP, _d_net, modal_idx in zip(target_output, self.DNets_DP, self.DNets, range(len(target_output))):
# set grad false
self.set_requires_grad(self.logger, _d_net, requires_grad = False)
t_D_out = _d_net_DP(F.softmax(t_out))
_disc_hard_mask = F.interpolate(disc_hard_mask, size=(t_D_out.size(2), t_D_out.size(3)), mode='nearest')
#source_modal_ids
loss_temp = torch.sum(self.bceloss(
t_D_out,
torch.FloatTensor(t_D_out.data.size()).fill_(1.0).cuda()
) * _disc_hard_mask, [1,2,3]) / (torch.sum(disc_hard_mask, [1,2,3]) + 1)
if modal_idx >= self.modal_num:
loss_adv += torch.mean(loss_temp)
elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0:
loss_adv += 0.0
else:
loss_adv += torch.mean(torch.masked_select(loss_temp, target_modal_ids_tensor==modal_idx))
_batch_size += t_out.size(0)
loss_adv *= self.cfg['training']['loss_adv_lambda']
loss_G = torch.Tensor([0]).cuda()
loss_G = loss_G + loss_adv
loss = loss + loss_G
if loss.item() != 0:
loss.backward()
self.BaseOpti.step()
self.BaseOpti.zero_grad()
# -----------------------------
"""Discriminator """
# -----------------------------
_batch_size = 0
loss_D_comb = torch.Tensor([0]).cuda()
source_label_size = source_label.size()
for s_out, t_out, _d_net_DP, _d_net, _disc_opt, modal_idx in zip(source_output, target_output, self.DNets_DP, self.DNets, self.DiscOptis, range(len(source_output))):
self.set_requires_grad(self.logger, _d_net, requires_grad = True)
_batch_size = 0
loss_D = torch.Tensor([0]).cuda()
# source domain
s_D_out = _d_net_DP(F.softmax(s_out.detach()))
loss_temp_s = torch.mean(self.bceloss(
s_D_out,
torch.FloatTensor(s_D_out.data.size()).fill_(1.0).cuda()
), [1,2,3])
if modal_idx >= self.modal_num:
loss_D += torch.mean(loss_temp_s)
elif torch.mean(torch.as_tensor((modal_idx==source_modal_ids_tensor), dtype=torch.float32)) == 0:
loss_D += 0.0
else:
loss_D += torch.mean(torch.masked_select(loss_temp_s, source_modal_ids_tensor==modal_idx))
# target domain
_batch_size += (s_out.size(0) + t_out.size(0))
t_D_out = _d_net_DP(F.softmax(t_out.detach()))
loss_temp_t = torch.mean(self.bceloss(
t_D_out,
torch.FloatTensor(t_D_out.data.size()).fill_(0.0).cuda()
), [1,2,3])
if modal_idx >= self.modal_num:
loss_D += torch.mean(loss_temp_t)
elif torch.mean(torch.as_tensor((modal_idx==target_modal_ids_tensor), dtype=torch.float32)) == 0:
loss_D += 0.0
else:
loss_D += torch.mean(torch.masked_select(loss_temp_t, target_modal_ids_tensor==modal_idx))
loss_D *= self.cfg['training']['loss_adv_lambda']*0.5
if loss_D.item() != 0:
loss_D.backward()
_disc_opt.step()
_disc_opt.zero_grad()
loss_D_comb += loss_D
return loss, loss_adv, loss_D_comb
def process_label(self, label):
batch, channel, w, h = label.size()
pred1 = torch.zeros(batch, 20, w, h).cuda()
id = torch.where(label < 19, label, torch.Tensor([19]).cuda())
pred1 = pred1.scatter_(1, id.long(), 1)
return pred1
def class_vectors_alignment(self, ids, vectors, modal_ids=[0,]):
loss = torch.Tensor([0]).cuda()
"""construct category objective vectors"""
# objective_vectors_group 2 x 19 x 256 --> 19 x 512
_objective_vectors_set = self.metrics.multimodal_merger.merge_objective_vectors(modal_ids=modal_ids)
for i in range(len(ids)):
if ids[i] not in self.valid_classes:
continue
new_loss = self.smoothloss(vectors[i].squeeze().cuda(), _objective_vectors_set[ids[i]])
while (new_loss.item() > 5):
new_loss = new_loss / 10
loss = loss + new_loss
loss = loss / len(ids) * 10
return loss
def freeze_bn_apply(self):
for net in self.nets:
net.apply(freeze_bn)
for net in self.nets_DP:
net.apply(freeze_bn)
def scheduler_step(self):
if self.use_pseudo_label:
for scheduler in self.schedulers:
scheduler.step()
else:
"""skipped _BaseScheduler_nouse"""
for scheduler in self.schedulers[1:]:
scheduler.step()
self.adjust_basenet_learning_rate()
def optimizer_zerograd(self):
for optimizer in self.optimizers:
optimizer.zero_grad()
def optimizer_step(self):
for opt in self.optimizers:
opt.step()
def init_device(self, net, gpu_id=None, whether_DP=False):
gpu_id = gpu_id or self.default_gpu
device = torch.device("cuda:{}".format(gpu_id) if torch.cuda.is_available() else 'cpu')
net = net.to(device)
if whether_DP:
net = DataParallelWithCallback(net, device_ids=range(torch.cuda.device_count()))
return net
def eval(self, net=None, logger=None):
"""Make specific models eval mode during test time"""
if net == None:
for net in self.nets:
net.eval()
for net in self.nets_DP:
net.eval()
if logger!=None:
logger.info("Successfully set the model eval mode")
else:
net.eval()
if logger!=None:
logger("Successfully set {} eval mode".format(net.__class__.__name__))
return
def train(self, net=None, logger=None):
if net==None:
for net in self.nets:
net.train()
for net in self.nets_DP:
net.train()
else:
net.train()
return
def set_requires_grad(self, logger, net, requires_grad = False):
"""Set requires_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
net (BaseModel) -- the network which will be operated on
requires_grad (bool) -- whether the networks require gradients or not
"""
for parameter in net.parameters():
parameter.requires_grad = requires_grad
def set_requires_grad_layer(self, logger, net, layer_type='batchnorm', requires_grad=False):
''' set specific type of layers whether needing grad
'''
# print('Warning: all the BatchNorm params are fixed!')
# logger.info('Warning: all the BatchNorm params are fixed!')
for net in self.nets:
for _i in net.modules():
if _i.__class__.__name__.lower().find(layer_type.lower()) != -1:
_i.weight.requires_grad = requires_grad
return
def init_weights(self, cfg, logger, net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
init_type = cfg.get('init_type', init_type)
init_gain = cfg.get('init_gain', init_gain)
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
nn.init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
nn.init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
nn.init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
nn.init.constant_(m.bias.data, 0.0)
elif isinstance(m, SynchronizedBatchNorm2d) or classname.find('BatchNorm2d') != -1 \
or isinstance(m, nn.GroupNorm):
# or isinstance(m, InPlaceABN) or isinstance(m, InPlaceABNSync):
m.weight.data.fill_(1)
m.bias.data.zero_() # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
print('initialize {} with {}'.format(init_type, net.__class__.__name__))
logger.info('initialize {} with {}'.format(init_type, net.__class__.__name__))
net.apply(init_func) # apply the initialization function <init_func>
pass
def adaptive_load_nets(self, net, model_weight):
model_dict = net.state_dict()
pretrained_dict = {k : v for k, v in model_weight.items() if k in model_dict}
print("[INFO] Pretrained dict:", pretrained_dict.keys())
model_dict.update(pretrained_dict)
net.load_state_dict(model_dict)
def load_nets(self, cfg, writer, logger): # load pretrained weights on the net
if os.path.isfile(cfg['training']['resume']):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(cfg['training']['resume'])
)
checkpoint = torch.load(cfg['training']['resume'])
_k = -1
net_state_no = {}
for net in self.nets:
name = net.__class__.__name__
if name not in net_state_no:
net_state_no[name] = 0
else:
net_state_no[name] += 1
_k += 1
if checkpoint.get(name) == None:
continue
if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False:
continue
#self.adaptive_load_nets(net, checkpoint[name]["model_state"])
if isinstance(checkpoint[name], list):
self.adaptive_load_nets(net, checkpoint[name][net_state_no[name]]["model_state"])
else:
print("*****************************************")
print("[WARNING] Using depreciated load version! Model {}".format(name))
print("*****************************************")
self.adaptive_load_nets(net, checkpoint[name]["model_state"])
if cfg['training']['optimizer_resume']:
if isinstance(checkpoint[name], list):
self.adaptive_load_nets(self.optimizers[_k], checkpoint[name][net_state_no[name]]["optimizer_state"])
self.adaptive_load_nets(self.schedulers[_k], checkpoint[name][net_state_no[name]]["scheduler_state"])
else:
self.adaptive_load_nets(self.optimizers[_k], checkpoint[name]["optimizer_state"])
self.adaptive_load_nets(self.schedulers[_k], checkpoint[name]["scheduler_state"])
self.iter = checkpoint["iter"] if 'iter' in checkpoint else 0
logger.info(
"Loaded checkpoint '{}' (iter {})".format(
cfg['training']['resume'], self.iter
)
)
else:
raise Exception("No checkpoint found at '{}'".format(cfg['training']['resume']))
def load_PredNet(self, cfg, writer, logger, dir=None, net=None): # load pretrained weights on the net
dir = dir or cfg['training']['Pred_resume']
best_iou = 0
if os.path.isfile(dir):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(dir)
)
checkpoint = torch.load(dir)
name = net.__class__.__name__
if checkpoint.get(name) == None:
return
if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False:
return
if isinstance(checkpoint[name], list):
self.adaptive_load_nets(net, checkpoint[name][0]["model_state"])
else:
self.adaptive_load_nets(net, checkpoint[name]["model_state"])
if 'iter' in checkpoint:
checkpoint_iter = checkpoint["iter"]
else:
checkpoint_iter = 0
if 'best_iou' in checkpoint:
best_iou = checkpoint['best_iou']
else:
best_iou = 0
logger.info(
"Loaded checkpoint '{}' (iter {}) (best iou {}) for PredNet".format(
dir, checkpoint_iter, best_iou
)
)
else:
raise Exception("No checkpoint found at '{}'".format(dir))
if hasattr(net, 'best_iou'):
pass
return best_iou
def load_PredDnet(self, cfg, writer, logger, dir=None, net=None): # load pretrained weights on the net
dir = dir or cfg['training']['Pred_resume']
best_iou = 0
if os.path.isfile(dir):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(dir)
)
checkpoint = torch.load(dir)
name = net.__class__.__name__
if checkpoint.get(name) == None:
return
if name.find('FCDiscriminator') != -1 and cfg['training']['gan_resume'] == False:
return
if isinstance(checkpoint[name], list):
self.adaptive_load_nets(net, checkpoint[name][-1]["model_state"]) # attention-branch discriminator
else:
print("[WARNING] load discriminator maybe error!")
self.adaptive_load_nets(net, checkpoint[name]["model_state"])
print("[INFO] {}: {}".format(name, net))
iter = checkpoint["iter"]
logger.info(
"Loaded checkpoint '{}' (iter {}) for PredNet".format(
dir, checkpoint["iter"]
)
)
else:
raise Exception("No checkpoint found at '{}'".format(dir))
return best_iou
def set_optimizer(self, optimizer): #set optimizer to all nets
pass
def reset_objective_SingleVector(self,):
self.objective_vectors_group = torch.zeros(self.modal_num + 1, 19, 256).cuda()
self.objective_vectors_num_group = torch.zeros(self.modal_num + 1, 19).cuda()
self.objective_vectors_dis_group = torch.zeros(self.modal_num + 1, 19, 19).cuda()
def update_objective_SingleVector(self, vectors, vectors_num, name='moving_average'):
if torch.sum(vectors) == 0:
return
"""
if self.objective_vectors_num_group[modal_idx][id] < 100:
name = 'mean'
"""
if name == 'moving_average':
self.objective_vectors_group = self.objective_vectors_group * 0.9999 + 0.0001 * vectors
self.objective_vectors_num_group += vectors_num
self.objective_vectors_num_group = min(self.objective_vectors_num_group, 3000)
elif name == 'mean':
self.objective_vectors_group = self.objective_vectors_group * self.objective_vectors_num_group.view(-1, 19, 1).expand(self.modal_num+1, 19, 256) + vectors
self.objective_vectors_num_group = self.objective_vectors_num_group + vectors_num
_objective_vectors_num_group = self.objective_vectors_num_group.clone()
_ids = torch.where(_objective_vectors_num_group == 0)
_objective_vectors_num_group[_ids] = 1.0
self.objective_vectors_group = self.objective_vectors_group / _objective_vectors_num_group.view(-1, 19, 1).expand(self.modal_num+1, 19, 256)
self.objective_vectors_num_group = torch.min(self.objective_vectors_num_group, torch.Tensor([3000]).cuda())
else:
raise NotImplementedError('no such updating way of objective vectors {}'.format(name))
class multisource_metatrainer(object):
def __init__(self,
args,
nnclass,
meta_update_lr,
meta_update_step,
beta,
pretrain_mode='meta'):
self.device = 1
self.generator_model = None
self.generator_optim = None
self.generator_criterion = None
self.pretrain_mode = pretrain_mode
self.batch_size = args.batch_size
self.nnclass = nnclass
self.init_generator(args)
self.init_discriminator(args)
self.init_optimizer(args)
self.meta_update_lr = meta_update_lr
self.meta_update_step = meta_update_step
self.beta = beta
def init_generator(self, args):
self.generator_model = DeepLab(num_classes=self.nnclass,
backbone='resnet',
output_stride=16,
sync_bn=None,
freeze_bn=False).cuda()
self.generator_model = torch.nn.DataParallel(
self.generator_model).cuda()
patch_replication_callback(self.generator_model)
if args.resume:
print('#--------- load pretrained model --------------#')
model_dict = self.generator_model.module.state_dict()
checkpoint = torch.load(args.resume)
pretrained_dict = {
k: v
for k, v in checkpoint['state_dict'].items()
if 'last_conv' not in k and k in model_dict.keys()
}
#pretrained_dict = {k.replace('module.',''):v for k,v in checkpoint['state_dict'].items() if 'last_conv' not in k}
model_dict.update(pretrained_dict)
self.generator_model.module.load_state_dict(model_dict)
for param in self.generator_model.parameters():
param.requires_grad = True
def init_discriminator(self, args):
# init D
self.discriminator_model = FCDiscriminator(num_classes=2).cuda()
self.interp = nn.Upsample(size=400, mode='bilinear')
self.disc_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(mode=args.loss_type)
return
def init_optimizer(self, args):
self.generator_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(
mode='bce') #torch.nn.BCELoss(reduce ='mean')
self.generator_params = [{
'params':
self.generator_model.module.get_1x_lr_params(),
'lr':
args.lr
}, {
'params':
self.generator_model.module.get_10x_lr_params(),
'lr':
args.lr * 10
}]
self.discriminator_params = [{
'params':
self.discriminator_model.parameters(),
'lr':
args.lr * 5
}]
self.model_optim = torch.optim.Adadelta(self.generator_params +
self.discriminator_params)
self.scheduler = LR_Scheduler(args.lr_scheduler,
args.lr,
args.epochs,
lr_step=30,
iters_per_epoch=100)
# for madan the src_image has shape B x source_index x channel x H x W
def update_weights(self, srca, srca_labels, src_b, srcb_labels, target_img,
target_label):
#self.pretrain_mode = 'meta'
src_labels = torch.cat([srca_labels.squeeze(),
srcb_labels.squeeze()],
0).type(torch.LongTensor).cuda()
src_image = torch.cat([srca.squeeze(), src_b.squeeze()])
if self.pretrain_mode == 'meta':
seg_loss = self.meta_mldg(src_image, src_labels, self.batch_size)
else:
print('a default training is enabled')
src_out, source_feature = self.generator_model(src_image)
seg_loss = self.generator_criterion(src_out, src_labels)
self.model_optim.zero_grad()
seg_loss.backward()
self.model_optim.step()
target_logit, _ = self.generator_model(target_img.cuda())
tgt_loss = self.generator_criterion(target_logit, target_label)
tgt_loss = tgt_loss.detach()
seg_loss = seg_loss.detach()
return seg_loss, tgt_loss
def meta_mldg(self, src_image, src_labels, batch_size):
batch_size = 4
num_src = 2
S = np.random.choice(num_src)
V = abs(S - 1)
source_out, _ = self.generator_model(src_image[S * batch_size:(S + 1) *
batch_size].squeeze())
losses = self.generator_criterion(
source_out, src_labels[S * batch_size:(S + 1) * batch_size])
for k in range(1, self.meta_update_step):
source_out, _ = self.generator_model(
src_image[S * batch_size:(S + 1) * batch_size].squeeze())
loss = self.generator_criterion(
source_out, src_labels[S * batch_size:(S + 1) * batch_size])
grad = torch.autograd.grad(loss, self.generator_model.parameters())
fast_weights = list(
map(lambda p: p[1] - self.meta_update_lr * p[0],
zip(grad, self.generator_model.parameters())))
# compute the test loss on the fast weights
Grad_test = self.generator_model(src_image[V * batch_size:(V + 1) *
batch_size],
fast_weights,
bn_training=True)
# compute the gradient on generator_model
losses += self.beta * Grad_test
return losses
class madan_trainer(object):
def __init__(self, args, nnclass, ndomains):
self.device = 1
self.generator_model = None
self.generator_optim = None
self.generator_criterion = None
self.batch_size = args.batch_size
self.nnclass = nnclass
self.num_domains = ndomains
self.init_wasserstein = Wasserstein()
self.init_generator(args)
self.init_discriminator(args)
self.init_optimizer(args)
def init_generator(self, args):
self.generator_model = DeepLab(num_classes=self.nnclass,
backbone='resnet',
output_stride=16,
sync_bn=None,
freeze_bn=False).cuda()
self.generator_model = torch.nn.DataParallel(
self.generator_model).cuda()
patch_replication_callback(self.generator_model)
if args.resume:
print('#--------- load pretrained model --------------#')
model_dict = self.generator_model.module.state_dict()
checkpoint = torch.load(args.resume)
pretrained_dict = {
k: v
for k, v in checkpoint['state_dict'].items()
if 'last_conv' not in k and k in model_dict.keys()
}
#pretrained_dict = {k.replace('module.',''):v for k,v in checkpoint['state_dict'].items() if 'last_conv' not in k}
model_dict.update(pretrained_dict)
self.generator_model.module.load_state_dict(model_dict)
for param in self.generator_model.parameters():
param.requires_grad = True
def init_discriminator(self, args):
# init D
self.discriminator_model = FCDiscriminator(num_classes=2).cuda()
self.interp = nn.Upsample(size=400, mode='bilinear')
self.disc_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(mode=args.loss_type)
return
def init_optimizer(self, args):
self.generator_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(
mode='bce') #torch.nn.BCELoss(reduce ='mean')
self.generator_params = [{
'params':
self.generator_model.module.get_1x_lr_params(),
'lr':
args.lr
}, {
'params':
self.generator_model.module.get_10x_lr_params(),
'lr':
args.lr * 10
}]
self.discriminator_params = [{
'params':
self.discriminator_model.parameters(),
'lr':
args.lr * 5
}]
self.model_optim = torch.optim.Adadelta(self.generator_params +
self.discriminator_params)
self.scheduler = LR_Scheduler(args.lr_scheduler,
args.lr,
args.epochs,
lr_step=30,
iters_per_epoch=100)
# for madan the src_image has shape B x source_index x channel x H x W
def update_weights(self, src_image, src_labels, targ_image, targ_labels,
options):
running_loss = 0.0
src_labels = torch.cat(
[src_labels[:, 0].squeeze(), src_labels[:, 1].squeeze()],
0).type(torch.LongTensor).cuda()
self.model_optim.zero_grad()
# src image shape batch_size x domain x 3 channels x height x width
src_out, source_feature = self.generator_model(
torch.cat([src_image[:, 0].squeeze(), src_image[:, 1].squeeze()]))
targ_out, target_feature = self.generator_model(targ_image)
# Discriminator
discriminator_x = torch.cat([source_feature, target_feature]).squeeze()
disc_clf = self.discriminator_model(discriminator_x)
# Losses
losses = torch.stack([
self.generator_criterion(
src_out[j * self.batch_size:j + self.batch_size],
src_labels[j * self.batch_size:j + self.batch_size])
for j in range(self.num_domains)
])
slabels = torch.ones(self.batch_size,
disc_clf.shape[2],
disc_clf.shape[3],
requires_grad=False).type(
torch.LongTensor).cuda()
tlabels = torch.zeros(self.batch_size * 2,
disc_clf.shape[2],
disc_clf.shape[3],
requires_grad=False).type(
torch.LongTensor).cuda()
domain_losses = torch.stack([
self.generator_criterion(
disc_clf[j * self.batch_size:j + self.batch_size].squeeze(),
slabels) for j in range(self.num_domains)
])
domain_losses = torch.cat([
domain_losses,
self.generator_criterion(
disc_clf[2 * self.batch_size:2 * self.batch_size +
2 * self.batch_size].squeeze(), tlabels).view(-1)
])
# Different final loss function depending on different training modes.
if options['mode'] == "maxmin":
loss = torch.max(losses) + options['mu'] * torch.min(domain_losses)
elif options['mode'] == "dynamic":
loss = torch.log(
torch.sum(
torch.exp(options['gamma'] *
(losses + options['mu'] * domain_losses)))
) / options['gamma']
else:
raise ValueError(
"No support for the training mode on madnNet: {}.".format(
options['mode']))
loss.backward()
self.model_optim.step()
running_loss += loss.detach().cpu().numpy()
# compute target loss
target_loss = self.generator_criterion(
targ_out, targ_labels).detach().cpu().numpy()
return running_loss, target_loss
def update_wasserstein(self, src_image, src_labels, targ_image,
targ_labels, options):
running_loss = 0.0
src_labels = torch.cat(
[src_labels[:, 0].squeeze(), src_labels[:, 1].squeeze()],
0).type(torch.LongTensor).cuda()
self.model_optim.zero_grad()
# src image shape batch_size x domain x 3 channels x height x width
src_out, source_feature = self.generator_model(
torch.cat([src_image[:, 0].squeeze(), src_image[:, 1].squeeze()]))
targ_out, target_feature = self.generator_model(targ_image)
# Discriminator
discriminator_x = torch.cat([source_feature, target_feature]).squeeze()
disc_clf = self.discriminator_model(discriminator_x)
# Losses
losses = torch.stack([
self.generator_criterion(
src_out[j * self.batch_size:j * self.batch_size +
self.batch_size],
src_labels[j * self.batch_size:j * self.batch_size +
self.batch_size]) for j in range(self.num_domains)
])
wass_loss = [
self.init_wasserstein.update_wasserstein_dual_source(
disc_clf[j * self.batch_size:j * self.batch_size +
self.batch_size].squeeze(),
disc_clf[self.num_domains *
self.batch_size:self.num_domains * self.batch_size +
self.batch_size].squeeze())
for j in range(self.num_domains)
]
domain_losses = torch.stack(wass_loss)
# compute gradient penalty
penalty_cup, penalty_disc = self.init_wasserstein.gradient_regularization_dual_source(
self.discriminator_model, source_feature.detach(),
target_feature.detach(), options['batch_size'],
options['num_domains'])
# Different final loss function depending on different training modes.
if options['mode'] == "maxmin":
loss = torch.max(
losses) + options['mu'] * torch.min(domain_losses) + options[
'gamma'] * penalty_cup + options['gamma'] * penalty_disc
elif options['mode'] == "dynamic":
# TODO Wasserstein not implemented yet for this
loss = torch.log(
torch.sum(
torch.exp(options['gamma'] *
(losses + options['mu'] * domain_losses)))
) / options['gamma']
else:
raise ValueError(
"No support for the training mode on madnNet: {}.".format(
options['mode']))
loss.backward()
self.model_optim.step()
for p in self.discriminator_model.parameters():
p.data.clamp_(-0.01, 0.01)
running_loss += loss.detach().cpu().numpy()
# compute target loss
target_loss = self.generator_criterion(
targ_out, targ_labels).detach().cpu().numpy()
return running_loss, target_loss
def main():
"""Create the model and start the training."""
args = get_arguments()
cudnn.enabled = True
n_discriminators = 5
# create teacher & student
student_net = UNet(3, n_classes=args.num_classes)
teacher_net = UNet(3, n_classes=args.num_classes)
student_params = list(student_net.parameters())
# teacher doesn't need gradient as it's just a EMA of the student
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
student_net.train()
student_net.cuda(args.gpu)
teacher_net.train()
teacher_net.cuda(args.gpu)
cudnn.benchmark = True
unsup_weights = [
args.unsup_weight5, args.unsup_weight6, args.unsup_weight7,
args.unsup_weight8, args.unsup_weight9
]
lambda_adv_tgts = [
args.lambda_adv_tgt5, args.lambda_adv_tgt6, args.lambda_adv_tgt7,
args.lambda_adv_tgt8, args.lambda_adv_tgt9
]
# create a list of discriminators
discriminators = []
for dis_idx in range(n_discriminators):
discriminators.append(FCDiscriminator(num_classes=args.num_classes))
discriminators[dis_idx].train()
discriminators[dis_idx].cuda(args.gpu)
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
max_iters = args.num_steps * args.iter_size * args.batch_size
src_set = REFUGE(True,
domain='REFUGE_SRC',
is_transform=True,
augmentations=aug_student,
aug_for_target=aug_teacher,
max_iters=max_iters)
src_loader = data.DataLoader(src_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
src_loader_iter = enumerate(src_loader)
tgt_set = REFUGE(True,
domain='REFUGE_DST',
is_transform=True,
augmentations=aug_student,
aug_for_target=aug_teacher,
max_iters=max_iters)
tgt_loader = data.DataLoader(tgt_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
tgt_loader_iter = enumerate(tgt_loader)
student_optimizer = optim.SGD(student_params,
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
teacher_optimizer = optim_weight_ema.WeightEMA(teacher_params,
student_params,
alpha=args.teacher_alpha)
d_optimizers = []
for idx in range(n_discriminators):
optimizer = optim.Adam(discriminators[idx].parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
d_optimizers.append(optimizer)
calc_bce_loss = torch.nn.BCEWithLogitsLoss()
# labels for adversarial training
source_label, tgt_label = 0, 1
for i_iter in range(args.num_steps):
total_seg_loss = 0
seg_loss_vals = [0] * n_discriminators
adv_tgt_loss_vals = [0] * n_discriminators
d_loss_vals = [0] * n_discriminators
unsup_loss_vals = [0] * n_discriminators
for d_optimizer in d_optimizers:
d_optimizer.zero_grad()
adjust_learning_rate_D(d_optimizer, i_iter, args)
student_optimizer.zero_grad()
adjust_learning_rate(student_optimizer, i_iter, args)
for sub_i in range(args.iter_size):
# ******** Optimize source network with segmentation loss ********
# As we don't change the discriminators, their parameters are fixed
for discriminator in discriminators:
for param in discriminator.parameters():
param.requires_grad = False
_, src_batch = src_loader_iter.__next__()
_, _, src_images, src_labels, _ = src_batch
src_images = Variable(src_images).cuda(args.gpu)
# calculate the segmentation losses
sup_preds = list(student_net(src_images))
seg_losses, total_seg_loss = [], 0
for idx, sup_pred in enumerate(sup_preds):
sup_interp_pred = (sup_pred)
# you also can use dice loss like: dice_loss(src_labels, sup_interp_pred)
seg_loss = Weighted_Jaccard_loss(src_labels, sup_interp_pred,
args.class_weights, args.gpu)
seg_losses.append(seg_loss)
total_seg_loss += seg_loss * unsup_weights[idx]
seg_loss_vals[idx] += seg_loss.item() / args.iter_size
_, tgt_batch = tgt_loader_iter.__next__()
tgt_images0, tgt_lbl0, tgt_images1, tgt_lbl1, _ = tgt_batch
tgt_images0 = Variable(tgt_images0).cuda(args.gpu)
tgt_images1 = Variable(tgt_images1).cuda(args.gpu)
# calculate ensemble losses
stu_unsup_preds = list(student_net(tgt_images1))
tea_unsup_preds = teacher_net(tgt_images0)
total_mse_loss = 0
for idx in range(n_discriminators):
stu_unsup_probs = F.softmax(stu_unsup_preds[idx], dim=-1)
tea_unsup_probs = F.softmax(tea_unsup_preds[idx], dim=-1)
unsup_loss = calc_mse_loss(stu_unsup_probs, tea_unsup_probs,
args.batch_size)
unsup_loss_vals[idx] += unsup_loss.item() / args.iter_size
total_mse_loss += unsup_loss * unsup_weights[idx]
total_mse_loss = total_mse_loss / args.iter_size
# As the requires_grad is set to False in the discriminator, the
# gradients are only accumulated in the generator, the target
# student network is optimized to make the outputs of target domain
# images close to the outputs of source domain images
stu_unsup_preds = list(student_net(tgt_images0))
d_outs, total_adv_loss = [], 0
for idx in range(n_discriminators):
stu_unsup_interp_pred = (stu_unsup_preds[idx])
d_outs.append(discriminators[idx](stu_unsup_interp_pred))
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
adv_tgt_loss = calc_bce_loss(d_outs[idx], labels)
total_adv_loss += lambda_adv_tgts[idx] * adv_tgt_loss
adv_tgt_loss_vals[idx] += adv_tgt_loss.item() / args.iter_size
total_adv_loss = total_adv_loss / args.iter_size
# requires_grad is set to True in the discriminator, we only
# accumulate gradients in the discriminators, the discriminators are
# optimized to make true predictions
d_losses = []
for idx in range(n_discriminators):
discriminator = discriminators[idx]
for param in discriminator.parameters():
param.requires_grad = True
sup_preds[idx] = sup_preds[idx].detach()
d_outs[idx] = discriminators[idx](sup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(source_label)
labels = Variable(labels).cuda(args.gpu)
d_losses.append(calc_bce_loss(d_outs[idx], labels))
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for idx in range(n_discriminators):
stu_unsup_preds[idx] = stu_unsup_preds[idx].detach()
d_outs[idx] = discriminators[idx](stu_unsup_preds[idx])
label_size = d_outs[idx].data.size()
labels = torch.FloatTensor(label_size).fill_(tgt_label)
labels = Variable(labels).cuda(args.gpu)
d_losses[idx] = calc_bce_loss(d_outs[idx], labels)
d_losses[idx] = d_losses[idx] / args.iter_size / 2
d_losses[idx].backward()
d_loss_vals[idx] += d_losses[idx].item()
for d_optimizer in d_optimizers:
d_optimizer.step()
total_loss = total_seg_loss + total_adv_loss + total_mse_loss
total_loss.backward()
student_optimizer.step()
teacher_optimizer.step()
log_str = 'iter = {0:7d}/{1:7d}'.format(i_iter, args.num_steps)
log_str += ', total_seg_loss = {0:.3f} '.format(total_seg_loss)
templ = 'seg_losses = [' + ', '.join(['%.2f'] * len(seg_loss_vals))
log_str += templ % tuple(seg_loss_vals) + '] '
templ = 'ens_losses = [' + ', '.join(['%.5f'] * len(unsup_loss_vals))
log_str += templ % tuple(unsup_loss_vals) + '] '
templ = 'adv_losses = [' + ', '.join(['%.2f'] * len(adv_tgt_loss_vals))
log_str += templ % tuple(adv_tgt_loss_vals) + '] '
templ = 'd_losses = [' + ', '.join(['%.2f'] * len(d_loss_vals))
log_str += templ % tuple(d_loss_vals) + '] '
print(log_str)
if i_iter >= args.num_steps_stop - 1:
print('save model ...')
filename = 'UNet' + str(
args.num_steps_stop) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('taking snapshot ...')
filename = 'UNet' + str(i_iter) + '_v18_weightedclass.pth'
torch.save(teacher_net.cpu().state_dict(),
os.path.join(args.snapshot_dir, filename))
teacher_net.cuda(args.gpu)
class adda_trainer(object):
def __init__(self, args, nnclass):
self.target_model = None
self.target_optim = None
self.target_criterion = None
self.batch_size = args.batch_size
self.nnclass = nnclass
self.init_target(args)
self.init_discriminator(args)
self.scheduler = LR_Scheduler(args.lr_scheduler,
args.lr,
args.epochs,
lr_step=40,
iters_per_epoch=100)
self.disc_params = [{
'params': self.disc_model.parameters(),
'lr': args.lr * 5
}]
self.dda_optim = torch.optim.Adam(self.train_params)
self.discriminator_optim = torch.optim.Adam(self.disc_params)
#self.dda_optim = torch.optim.SGD(self.train_params, momentum=args.momentum,
# weight_decay=args.weight_decay, nesterov=args.nesterov)
#self.discriminator_optim = torch.optim.SGD(self.disc_params, momentum=args.momentum,
# weight_decay=args.weight_decay, nesterov=args.nesterov)
self.adv_aug = FastGradientSignUntargeted(self.target_model,
0.0157,
0.00784,
min_val=0,
max_val=1,
max_iters=2,
_type='linf')
def init_target(self, args):
self.target_model = DeepLab(num_classes=self.nnclass,
backbone='resnet',
output_stride=16,
sync_bn=None,
freeze_bn=False)
self.train_params = [{
'params': self.target_model.get_1x_lr_params(),
'lr': args.lr
}, {
'params': self.target_model.get_10x_lr_params(),
'lr': args.lr * 10
}]
self.target_model = torch.nn.DataParallel(self.target_model)
self.target_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(
mode='bce') #torch.nn.BCELoss(reduce ='mean')
patch_replication_callback(self.target_model)
model_dict = self.target_model.module.state_dict()
checkpoint = torch.load(args.resume)
pretrained_dict = {
k.replace('module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
#pretrained_dict = {k:v for k,v in checkpoint['state_dict'].items() if 'last_conv' not in k }
model_dict.update(pretrained_dict)
self.target_model.module.load_state_dict(model_dict)
self.target_model = self.target_model.cuda()
return
def init_discriminator(self, args):
# init D
self.disc_model = FCDiscriminator(num_classes=2).cuda()
self.interp = nn.Upsample(size=400, mode='bilinear')
self.disc_criterion = SegmentationLosses(
weight=None, cuda=args.cuda).build_loss(mode=args.loss_type)
return
def update_weights(self, input_, src_labels, target, tgt_labels, lamda_g,
trainmodel):
self.dda_optim.zero_grad()
self.discriminator_optim.zero_grad()
if trainmodel == 'train_gen':
for param in self.target_model.parameters():
param.requires_grad = True
for param in self.disc_model.parameters():
param.requires_grad = False
self.disc_model.eval()
self.target_model.train()
else:
for param in self.target_model.parameters():
param.requires_grad = False
for param in self.disc_model.parameters():
param.requires_grad = True
self.disc_model.train()
self.target_model.eval()
#tot_input = torch.cat([input_, target])
#import pdb
#pdb.set_trace()
src_out, source_feature = self.target_model(input_)
seg_loss = self.target_criterion(src_out, src_labels)
#print(target.shape)
targ_out, target_feature = self.target_model(target)
# discriminator
discriminator_x = torch.cat([source_feature, target_feature]).squeeze()
discriminator_adv_logit = torch.cat([
torch.zeros(source_feature.shape),
torch.ones(target_feature.shape)
])
discriminator_real_logit = torch.cat([
torch.ones(source_feature.shape),
torch.zeros(target_feature.shape)
])
disc_out = self.disc_model(discriminator_x)
#print(source_feature.shape, input_.shape,discriminator_adv_logit.shape, disc_out.shape)
adv_loss = self.target_criterion(
disc_out, discriminator_adv_logit[:, 0, :, :].cuda())
adv_loss += self.target_criterion(
disc_out, discriminator_adv_logit[:, 1, :, :].cuda())
disc_loss = self.disc_criterion(
disc_out, discriminator_real_logit[:, 0, :, :].cuda())
disc_loss += self.disc_criterion(
disc_out, discriminator_real_logit[:, 1, :, :].cuda())
if trainmodel == 'train_gen':
loss_seg = seg_loss + lamda_g * adv_loss
loss_seg.backward()
self.dda_optim.step()
else:
disc_loss.backward()
self.discriminator_optim.step()
tgt_loss = self.target_criterion(targ_out, tgt_labels)
return seg_loss.data.cpu().numpy(), tgt_loss.data.cpu().numpy()
def __init__(self, opt, logger, isTrain=True):
self.opt = opt
self.class_numbers = opt.n_class
self.logger = logger
self.best_iou = -100
self.nets = []
self.nets_DP = []
self.default_gpu = 0
self.objective_vectors = torch.zeros([self.class_numbers, 256])
self.objective_vectors_num = torch.zeros([self.class_numbers])
if opt.bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
elif opt.bn == 'bn':
BatchNorm = nn.BatchNorm2d
else:
raise NotImplementedError('batch norm choice {} is not implemented'.format(opt.bn))
if self.opt.no_resume:
restore_from = None
else:
restore_from= opt.resume_path
self.best_iou = 0
if self.opt.student_init == 'imagenet':
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from)
elif self.opt.student_init == 'simclr':
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from,
initialization=os.path.join(opt.root, 'Code/ProDA', 'pretrained/simclr/r101_1x_sk0.pth'), bn_clr=opt.bn_clr)
else:
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from)
logger.info('the backbone is {}'.format(opt.model_name))
self.nets.extend([self.BaseNet])
self.optimizers = []
self.schedulers = []
optimizer_cls = torch.optim.SGD
optimizer_params = {'lr':opt.lr, 'weight_decay':2e-4, 'momentum':0.9}
if self.opt.stage == 'warm_up':
self.net_D = FCDiscriminator(inplanes=self.class_numbers)
self.net_D_DP = self.init_device(self.net_D, gpu_id=self.default_gpu, whether_DP=True)
self.nets.extend([self.net_D])
self.nets_DP.append(self.net_D_DP)
self.optimizer_D = torch.optim.Adam(self.net_D.parameters(), lr=1e-4, betas=(0.9, 0.99))
self.optimizers.extend([self.optimizer_D])
self.DSchedule = get_scheduler(self.optimizer_D, opt)
self.schedulers.extend([self.DSchedule])
if self.opt.finetune or self.opt.stage == 'warm_up':
self.BaseOpti = optimizer_cls([{'params':self.BaseNet.get_1x_lr_params(), 'lr':optimizer_params['lr']},
{'params':self.BaseNet.get_10x_lr_params(), 'lr':optimizer_params['lr']*10}], **optimizer_params)
else:
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.BaseSchedule = get_scheduler(self.BaseOpti, opt)
self.schedulers.extend([self.BaseSchedule])
if self.opt.ema:
self.BaseNet_ema = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from, bn_clr=opt.ema_bn)
self.BaseNet_ema.load_state_dict(self.BaseNet.state_dict().copy())
if self.opt.distillation > 0:
self.teacher = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=opt.resume_path, bn_clr=opt.ema_bn)
self.teacher.eval()
self.teacher_DP = self.init_device(self.teacher, gpu_id=self.default_gpu, whether_DP=True)
self.adv_source_label = 0
self.adv_target_label = 1
if self.opt.gan == 'Vanilla':
self.bceloss = nn.BCEWithLogitsLoss(size_average=True)
elif self.opt.gan == 'LS':
self.bceloss = torch.nn.MSELoss()
self.feat_prototype_distance_DP = self.init_device(feat_prototype_distance_module(), gpu_id=self.default_gpu, whether_DP=True)
self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True)
self.nets_DP.append(self.BaseNet_DP)
if self.opt.ema:
self.BaseNet_ema_DP = self.init_device(self.BaseNet_ema, gpu_id=self.default_gpu, whether_DP=True)
class CustomModel():
def __init__(self, opt, logger, isTrain=True):
self.opt = opt
self.class_numbers = opt.n_class
self.logger = logger
self.best_iou = -100
self.nets = []
self.nets_DP = []
self.default_gpu = 0
self.objective_vectors = torch.zeros([self.class_numbers, 256])
self.objective_vectors_num = torch.zeros([self.class_numbers])
if opt.bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
elif opt.bn == 'bn':
BatchNorm = nn.BatchNorm2d
else:
raise NotImplementedError('batch norm choice {} is not implemented'.format(opt.bn))
if self.opt.no_resume:
restore_from = None
else:
restore_from= opt.resume_path
self.best_iou = 0
if self.opt.student_init == 'imagenet':
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from)
elif self.opt.student_init == 'simclr':
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from,
initialization=os.path.join(opt.root, 'Code/ProDA', 'pretrained/simclr/r101_1x_sk0.pth'), bn_clr=opt.bn_clr)
else:
self.BaseNet = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from)
logger.info('the backbone is {}'.format(opt.model_name))
self.nets.extend([self.BaseNet])
self.optimizers = []
self.schedulers = []
optimizer_cls = torch.optim.SGD
optimizer_params = {'lr':opt.lr, 'weight_decay':2e-4, 'momentum':0.9}
if self.opt.stage == 'warm_up':
self.net_D = FCDiscriminator(inplanes=self.class_numbers)
self.net_D_DP = self.init_device(self.net_D, gpu_id=self.default_gpu, whether_DP=True)
self.nets.extend([self.net_D])
self.nets_DP.append(self.net_D_DP)
self.optimizer_D = torch.optim.Adam(self.net_D.parameters(), lr=1e-4, betas=(0.9, 0.99))
self.optimizers.extend([self.optimizer_D])
self.DSchedule = get_scheduler(self.optimizer_D, opt)
self.schedulers.extend([self.DSchedule])
if self.opt.finetune or self.opt.stage == 'warm_up':
self.BaseOpti = optimizer_cls([{'params':self.BaseNet.get_1x_lr_params(), 'lr':optimizer_params['lr']},
{'params':self.BaseNet.get_10x_lr_params(), 'lr':optimizer_params['lr']*10}], **optimizer_params)
else:
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(), **optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.BaseSchedule = get_scheduler(self.BaseOpti, opt)
self.schedulers.extend([self.BaseSchedule])
if self.opt.ema:
self.BaseNet_ema = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=restore_from, bn_clr=opt.ema_bn)
self.BaseNet_ema.load_state_dict(self.BaseNet.state_dict().copy())
if self.opt.distillation > 0:
self.teacher = Deeplab(BatchNorm, num_classes=self.class_numbers, freeze_bn=False, restore_from=opt.resume_path, bn_clr=opt.ema_bn)
self.teacher.eval()
self.teacher_DP = self.init_device(self.teacher, gpu_id=self.default_gpu, whether_DP=True)
self.adv_source_label = 0
self.adv_target_label = 1
if self.opt.gan == 'Vanilla':
self.bceloss = nn.BCEWithLogitsLoss(size_average=True)
elif self.opt.gan == 'LS':
self.bceloss = torch.nn.MSELoss()
self.feat_prototype_distance_DP = self.init_device(feat_prototype_distance_module(), gpu_id=self.default_gpu, whether_DP=True)
self.BaseNet_DP = self.init_device(self.BaseNet, gpu_id=self.default_gpu, whether_DP=True)
self.nets_DP.append(self.BaseNet_DP)
if self.opt.ema:
self.BaseNet_ema_DP = self.init_device(self.BaseNet_ema, gpu_id=self.default_gpu, whether_DP=True)
def calculate_mean_vector(self, feat_cls, outputs, labels=None, thresh=None):
outputs_softmax = F.softmax(outputs, dim=1)
if thresh is None:
thresh = -1
conf = outputs_softmax.max(dim=1, keepdim=True)[0]
mask = conf.ge(thresh)
outputs_argmax = outputs_softmax.argmax(dim=1, keepdim=True)
outputs_argmax = self.process_label(outputs_argmax.float())
if labels is None:
outputs_pred = outputs_argmax
else:
labels_expanded = self.process_label(labels)
outputs_pred = labels_expanded * outputs_argmax
scale_factor = F.adaptive_avg_pool2d(outputs_pred * mask, 1)
vectors = []
ids = []
for n in range(feat_cls.size()[0]):
for t in range(self.class_numbers):
if scale_factor[n][t].item()==0:
continue
if (outputs_pred[n][t] > 0).sum() < 10:
continue
s = feat_cls[n] * outputs_pred[n][t] * mask[n]
# scale = torch.sum(outputs_pred[n][t]) / labels.shape[2] / labels.shape[3] * 2
# s = normalisation_pooling()(s, scale)
s = F.adaptive_avg_pool2d(s, 1) / scale_factor[n][t]
vectors.append(s)
ids.append(t)
return vectors, ids
def step_adv(self, source_x, source_label, target_x, source_imageS, source_params):
for param in self.net_D.parameters():
param.requires_grad = False
self.BaseOpti.zero_grad()
if self.opt.S_pseudo_src > 0:
source_output = self.BaseNet_DP(source_imageS)
source_label_d4 = F.interpolate(source_label.unsqueeze(1).float(), size=source_output['out'].size()[2:])
source_labelS = self.label_strong_T(source_label_d4.clone().float(), source_params, padding=250, scale=4).to(torch.int64)
loss_ = cross_entropy2d(input=source_output['out'], target=source_labelS.squeeze(1))
loss_GTA = loss_ * self.opt.S_pseudo_src
source_outputUp = F.interpolate(source_output['out'], size=source_x.size()[2:], mode='bilinear', align_corners=True)
else:
source_output = self.BaseNet_DP(source_x, ssl=True)
source_outputUp = F.interpolate(source_output['out'], size=source_x.size()[2:], mode='bilinear', align_corners=True)
loss_GTA = cross_entropy2d(input=source_outputUp, target=source_label, size_average=True, reduction='mean')
target_output = self.BaseNet_DP(target_x, ssl=True)
target_outputUp = F.interpolate(target_output['out'], size=target_x.size()[2:], mode='bilinear', align_corners=True)
target_D_out = self.net_D_DP(F.softmax(target_outputUp, dim=1))
loss_adv_G = self.bceloss(target_D_out, torch.FloatTensor(target_D_out.data.size()).fill_(self.adv_source_label).to(target_D_out.device)) * self.opt.adv
loss_G = loss_adv_G + loss_GTA
loss_G.backward()
self.BaseOpti.step()
for param in self.net_D.parameters():
param.requires_grad = True
self.optimizer_D.zero_grad()
source_D_out = self.net_D_DP(F.softmax(source_outputUp.detach(), dim=1))
target_D_out = self.net_D_DP(F.softmax(target_outputUp.detach(), dim=1))
loss_D = self.bceloss(source_D_out, torch.FloatTensor(source_D_out.data.size()).fill_(self.adv_source_label).to(source_D_out.device)) + \
self.bceloss(target_D_out, torch.FloatTensor(target_D_out.data.size()).fill_(self.adv_target_label).to(target_D_out.device))
loss_D.backward()
self.optimizer_D.step()
return loss_GTA.item(), loss_adv_G.item(), loss_D.item()
def step(self, source_x, source_label, target_x, target_imageS=None, target_params=None, target_lp=None,
target_lpsoft=None, target_image_full=None, target_weak_params=None):
source_out = self.BaseNet_DP(source_x, ssl=True)
source_outputUp = F.interpolate(source_out['out'], size=source_x.size()[2:], mode='bilinear', align_corners=True)
loss_GTA = cross_entropy2d(input=source_outputUp, target=source_label)
loss_GTA.backward()
if self.opt.proto_rectify:
threshold_arg = F.interpolate(target_lpsoft, scale_factor=0.25, mode='bilinear', align_corners=True)
else:
threshold_arg = F.interpolate(target_lp.unsqueeze(1).float(), scale_factor=0.25).long()
if self.opt.ema:
ema_input = target_image_full
with torch.no_grad():
ema_out = self.BaseNet_ema_DP(ema_input)
ema_out['feat'] = F.interpolate(ema_out['feat'], size=(int(ema_input.shape[2]/4), int(ema_input.shape[3]/4)), mode='bilinear', align_corners=True)
ema_out['out'] = F.interpolate(ema_out['out'], size=(int(ema_input.shape[2]/4), int(ema_input.shape[3]/4)), mode='bilinear', align_corners=True)
target_out = self.BaseNet_DP(target_imageS) if self.opt.S_pseudo > 0 else self.BaseNet_DP(target_x)
target_out['out'] = F.interpolate(target_out['out'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
target_out['feat'] = F.interpolate(target_out['feat'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
loss = torch.Tensor([0]).to(self.default_gpu)
batch, _, w, h = threshold_arg.shape
if self.opt.proto_rectify:
weights = self.get_prototype_weight(ema_out['feat'], target_weak_params=target_weak_params)
rectified = weights * threshold_arg
threshold_arg = rectified.max(1, keepdim=True)[1]
rectified = rectified / rectified.sum(1, keepdim=True)
argmax = rectified.max(1, keepdim=True)[0]
threshold_arg[argmax < self.opt.train_thred] = 250
if self.opt.S_pseudo > 0:
threshold_argS = self.label_strong_T(threshold_arg.clone().float(), target_params, padding=250, scale=4).to(torch.int64)
cluster_argS = self.label_strong_T(cluster_arg.clone().float(), target_params, padding=250, scale=4).to(torch.int64)
threshold_arg = threshold_argS
loss_CTS = cross_entropy2d(input=target_out['out'], target=threshold_arg.reshape([batch, w, h]))
if self.opt.rce:
rce = self.rce(target_out['out'], threshold_arg.reshape([batch, w, h]).clone())
loss_CTS = self.opt.rce_alpha * loss_CTS + self.opt.rce_beta * rce
if self.opt.regular_w > 0:
regular_loss = self.regular_loss(target_out['out'])
loss_CTS = loss_CTS + regular_loss * self.opt.regular_w
cluster_argS = None
loss_consist = torch.Tensor([0]).to(self.default_gpu)
if self.opt.proto_consistW > 0:
ema2weak_feat = self.full2weak(ema_out['feat'], target_weak_params) #N*256*H*W
ema2weak_feat_proto_distance = self.feat_prototype_distance(ema2weak_feat) #N*19*H*W
ema2strong_feat_proto_distance = self.label_strong_T(ema2weak_feat_proto_distance, target_params, padding=250, scale=4)
mask = (ema2strong_feat_proto_distance != 250).float()
teacher = F.softmax(-ema2strong_feat_proto_distance * self.opt.proto_temperature, dim=1)
targetS_out = target_out if self.opt.S_pseudo > 0 else self.BaseNet_DP(target_imageS)
targetS_out['out'] = F.interpolate(targetS_out['out'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
targetS_out['feat'] = F.interpolate(targetS_out['feat'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
prototype_tmp = self.objective_vectors.expand(4, -1, -1) #gpu memory limitation
strong_feat_proto_distance = self.feat_prototype_distance_DP(targetS_out['feat'], prototype_tmp, self.class_numbers)
student = F.log_softmax(-strong_feat_proto_distance * self.opt.proto_temperature, dim=1)
loss_consist = F.kl_div(student, teacher, reduction='none')
loss_consist = (loss_consist * mask).sum() / mask.sum()
loss = loss + self.opt.proto_consistW * loss_consist
loss = loss + loss_CTS
loss.backward()
self.BaseOpti.step()
self.BaseOpti.zero_grad()
if self.opt.moving_prototype: #update prototype
ema_vectors, ema_ids = self.calculate_mean_vector(ema_out['feat'].detach(), ema_out['out'].detach())
for t in range(len(ema_ids)):
self.update_objective_SingleVector(ema_ids[t], ema_vectors[t].detach(), start_mean=False)
if self.opt.ema: #update ema model
for param_q, param_k in zip(self.BaseNet.parameters(), self.BaseNet_ema.parameters()):
param_k.data = param_k.data.clone() * 0.999 + param_q.data.clone() * (1. - 0.999)
for buffer_q, buffer_k in zip(self.BaseNet.buffers(), self.BaseNet_ema.buffers()):
buffer_k.data = buffer_q.data.clone()
return loss.item(), loss_CTS.item(), loss_consist.item()
def regular_loss(self, activation):
logp = F.log_softmax(activation, dim=1)
if self.opt.regular_type == 'MRENT':
p = F.softmax(activation, dim=1)
loss = (p * logp).sum() / (p.shape[0]*p.shape[2]*p.shape[3])
elif self.opt.regular_type == 'MRKLD':
loss = - logp.sum() / (logp.shape[0]*logp.shape[1]*logp.shape[2]*logp.shape[3])
return loss
def rce(self, pred, labels):
pred = F.softmax(pred, dim=1)
pred = torch.clamp(pred, min=1e-7, max=1.0)
mask = (labels != 250).float()
labels[labels==250] = self.class_numbers
label_one_hot = torch.nn.functional.one_hot(labels, self.class_numbers + 1).float().to(self.default_gpu)
label_one_hot = torch.clamp(label_one_hot.permute(0,3,1,2)[:,:-1,:,:], min=1e-4, max=1.0)
rce = -(torch.sum(pred * torch.log(label_one_hot), dim=1) * mask).sum() / (mask.sum() + 1e-6)
return rce
def step_distillation(self, source_x, source_label, target_x, target_imageS=None, target_params=None, target_lp=None):
source_out = self.BaseNet_DP(source_x, ssl=True)
source_outputUp = F.interpolate(source_out['out'], size=source_x.size()[2:], mode='bilinear', align_corners=True)
loss_GTA = cross_entropy2d(input=source_outputUp, target=source_label)
loss_GTA.backward()
threshold_arg = F.interpolate(target_lp.unsqueeze(1).float(), scale_factor=0.25).long()
if self.opt.S_pseudo > 0:
threshold_arg = self.label_strong_T(threshold_arg.clone().float(), target_params, padding=250, scale=4).to(torch.int64)
target_out = self.BaseNet_DP(target_imageS)
else:
target_out = self.BaseNet_DP(target_x)
target_out['out'] = F.interpolate(target_out['out'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
batch, _, w, h = threshold_arg.shape
loss = cross_entropy2d(input=target_out['out'], target=threshold_arg.reshape([batch, w, h]), size_average=True, reduction='mean')
if self.opt.rce:
rce = self.rce(target_out['out'], threshold_arg.reshape([batch, w, h]).clone())
loss = self.opt.rce_alpha * loss + self.opt.rce_beta * rce
if self.opt.distillation > 0:
student = F.softmax(target_out['out'], dim=1)
with torch.no_grad():
teacher_out = self.teacher_DP(target_imageS)
teacher_out['out'] = F.interpolate(teacher_out['out'], size=threshold_arg.shape[2:], mode='bilinear', align_corners=True)
teacher = F.softmax(teacher_out['out'], dim=1)
loss_kd = F.kl_div(student, teacher, reduction='none')
mask = (teacher != 250).float()
loss_kd = (loss_kd * mask).sum() / mask.sum()
loss = loss + self.opt.distillation * loss_kd
loss.backward()
self.BaseOpti.step()
self.BaseOpti.zero_grad()
return loss_GTA.item(), loss.item()
def full2weak(self, feat, target_weak_params):
tmp = []
for i in range(feat.shape[0]):
h, w = target_weak_params['RandomSized'][0][i], target_weak_params['RandomSized'][1][i]
feat_ = F.interpolate(feat[i:i+1], size=[int(h/4), int(w/4)], mode='bilinear', align_corners=True)
y1, y2, x1, x2 = target_weak_params['RandomCrop'][0][i], target_weak_params['RandomCrop'][1][i], target_weak_params['RandomCrop'][2][i], target_weak_params['RandomCrop'][3][i]
y1, th, x1, tw = int(y1/4), int((y2-y1)/4), int(x1/4), int((x2-x1)/4)
feat_ = feat_[:, :, y1:y1+th, x1:x1+tw]
if target_weak_params['RandomHorizontallyFlip'][i]:
inv_idx = torch.arange(feat_.size(3)-1,-1,-1).long().to(feat_.device)
feat_ = feat_.index_select(3,inv_idx)
tmp.append(feat_)
feat = torch.cat(tmp, 0)
return feat
def feat_prototype_distance(self, feat):
N, C, H, W = feat.shape
feat_proto_distance = -torch.ones((N, self.class_numbers, H, W)).to(feat.device)
for i in range(self.class_numbers):
#feat_proto_distance[:, i, :, :] = torch.norm(torch.Tensor(self.objective_vectors[i]).reshape(-1,1,1).expand(-1, H, W).to(feat.device) - feat, 2, dim=1,)
feat_proto_distance[:, i, :, :] = torch.norm(self.objective_vectors[i].reshape(-1,1,1).expand(-1, H, W) - feat, 2, dim=1,)
return feat_proto_distance
def get_prototype_weight(self, feat, label=None, target_weak_params=None):
feat = self.full2weak(feat, target_weak_params)
feat_proto_distance = self.feat_prototype_distance(feat)
feat_nearest_proto_distance, feat_nearest_proto = feat_proto_distance.min(dim=1, keepdim=True)
feat_proto_distance = feat_proto_distance - feat_nearest_proto_distance
weight = F.softmax(-feat_proto_distance * self.opt.proto_temperature, dim=1)
return weight
def label_strong_T(self, label, params, padding, scale=1):
label = label + 1
for i in range(label.shape[0]):
for (Tform, param) in params.items():
if Tform == 'Hflip' and param[i].item() == 1:
label[i] = label[i].clone().flip(-1)
elif (Tform == 'ShearX' or Tform == 'ShearY' or Tform == 'TranslateX' or Tform == 'TranslateY' or Tform == 'Rotate') and param[i].item() != 1e4:
v = int(param[i].item() // scale) if Tform == 'TranslateX' or Tform == 'TranslateY' else param[i].item()
label[i:i+1] = affine_sample(label[i:i+1].clone(), v, Tform)
elif Tform == 'CutoutAbs' and isinstance(param, list):
x0 = int(param[0][i].item() // scale)
y0 = int(param[1][i].item() // scale)
x1 = int(param[2][i].item() // scale)
y1 = int(param[3][i].item() // scale)
label[i, :, y0:y1, x0:x1] = 0
label[label == 0] = padding + 1 # for strong augmentation, constant padding
label = label - 1
return label
def process_label(self, label):
batch, channel, w, h = label.size()
pred1 = torch.zeros(batch, self.class_numbers + 1, w, h).to(self.default_gpu)
id = torch.where(label < self.class_numbers, label, torch.Tensor([self.class_numbers]).to(self.default_gpu))
pred1 = pred1.scatter_(1, id.long(), 1)
return pred1
def freeze_bn_apply(self):
for net in self.nets:
net.apply(freeze_bn)
for net in self.nets_DP:
net.apply(freeze_bn)
def scheduler_step(self):
for scheduler in self.schedulers:
scheduler.step()
def optimizer_zerograd(self):
for optimizer in self.optimizers:
optimizer.zero_grad()
def init_device(self, net, gpu_id=None, whether_DP=False):
gpu_id = gpu_id or self.default_gpu
device = torch.device("cuda:{}".format(gpu_id) if torch.cuda.is_available() else 'cpu')
net = net.to(device)
# if torch.cuda.is_available():
if whether_DP:
#net = DataParallelWithCallback(net, device_ids=[0])
net = DataParallelWithCallback(net, device_ids=range(torch.cuda.device_count()))
return net
def eval(self, net=None, logger=None):
"""Make specific models eval mode during test time"""
# if issubclass(net, nn.Module) or issubclass(net, BaseModel):
if net == None:
for net in self.nets:
net.eval()
for net in self.nets_DP:
net.eval()
if logger!=None:
logger.info("Successfully set the model eval mode")
else:
net.eval()
if logger!=None:
logger("Successfully set {} eval mode".format(net.__class__.__name__))
return
def train(self, net=None, logger=None):
if net==None:
for net in self.nets:
net.train()
for net in self.nets_DP:
net.train()
else:
net.train()
return
def update_objective_SingleVector(self, id, vector, name='moving_average', start_mean=True):
if vector.sum().item() == 0:
return
if start_mean and self.objective_vectors_num[id].item() < 100:
name = 'mean'
if name == 'moving_average':
self.objective_vectors[id] = self.objective_vectors[id] * (1 - self.opt.proto_momentum) + self.opt.proto_momentum * vector.squeeze()
self.objective_vectors_num[id] += 1
self.objective_vectors_num[id] = min(self.objective_vectors_num[id], 3000)
elif name == 'mean':
self.objective_vectors[id] = self.objective_vectors[id] * self.objective_vectors_num[id] + vector.squeeze()
self.objective_vectors_num[id] += 1
self.objective_vectors[id] = self.objective_vectors[id] / self.objective_vectors_num[id]
self.objective_vectors_num[id] = min(self.objective_vectors_num[id], 3000)
pass
else:
raise NotImplementedError('no such updating way of objective vectors {}'.format(name))