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,
).cuda()
ss.eval()
return ss
def _load_classifier(self, modelName):
models_dir = "models/"
network_name = os.path.join(models_dir, modelName)
classifier_pocillopora = DeepLab(backbone='resnet', output_stride=16, num_classes=self.nclasses)
classifier_pocillopora.load_state_dict(torch.load(network_name))
classifier_pocillopora.eval()
return classifier_pocillopora
import torch
from torchscope import scope
from config import num_classes
from models.deeplab import DeepLab
if __name__ == "__main__":
model = DeepLab(backbone='mobilenet',
output_stride=16,
num_classes=num_classes)
model.eval()
input = torch.rand(1, 1, 256, 256)
output = model(input)
print(output.size())
scope(model, (1, 256, 256))
# model = models.segmentation.deeplabv3_resnet101(pretrained=True, num_classes=num_classes)
# model.eval()
# input = torch.rand(1, 3, 256, 256)
# output = model(input)['out']
# print(output.size())
# scope(model, (3, 256, 256))
for row, vis_item in enumerate(vis_item_row):
print("[train] vis_item[{}][{}].shape={} :".
format(row, col, vis_item.shape))
board_add_images(board_train, 'train', visuals, step + 1)
#=====================================
# 検証用データの処理
#=====================================
if (step != 0 and (step % args.n_display_valid_step == 0)):
loss_G_total = 0
loss_entropy_total = 0
n_valid_loop = 0
for iter, inputs in enumerate(
tqdm(dloader_valid, desc="eval iters")):
model_G.eval()
# 一番最後のミニバッチループで、バッチサイズに満たない場合は無視する（後の計算で、shape の不一致をおこすため）
if inputs["image"].shape[0] != args.batch_size_valid:
break
# ミニバッチデータを GPU へ転送
image_name = inputs["image_name"]
image = inputs["image"].to(device)
mask = inputs["mask"].to(device)
mask = mask.unsqueeze(1)
mask_rgb = decode_labels_tsr(mask)
#====================================================
# 推論処理
#====================================================
class CustomModel():
def __init__(self, cfg, writer, logger):
# super(CustomModel, self).__init__()
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.objective_vectors = np.zeros([19, 256])
self.objective_vectors_num = np.zeros([19])
self.objective_vectors_dis = np.zeros([19, 19])
self.class_threshold = np.zeros(self.class_numbers)
self.class_threshold = np.full([19], 0.95)
self.metrics = CustomMetrics(self.class_numbers)
self.cls_feature_weight = cfg['training']['cls_feature_weight']
bn = cfg_model['bn']
if bn == 'sync_bn':
BatchNorm = SynchronizedBatchNorm2d
# elif bn == 'sync_abn':
# BatchNorm = InPlaceABNSync
elif bn == 'bn':
BatchNorm = nn.BatchNorm2d
# elif bn == 'abn':
# BatchNorm = InPlaceABN
elif bn == 'gn':
BatchNorm = nn.GroupNorm
else:
raise NotImplementedError(
'batch norm choice {} is not implemented'.format(bn))
self.PredNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=True,
).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.BaseNet = DeepLab(
num_classes=19,
backbone=cfg_model['basenet']['version'],
output_stride=16,
bn=cfg_model['bn'],
freeze_bn=False,
)
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]
self.optimizers = []
self.schedulers = []
# optimizer_cls = get_optimizer(cfg)
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.SGD
# optimizer_params_D = {k:v for k, v in cfg['training']['optimizer_D'].items()
# if k != 'name'}
self.BaseOpti = optimizer_cls(self.BaseNet.parameters(),
**optimizer_params)
self.optimizers.extend([self.BaseOpti])
self.BaseSchedule = get_scheduler(self.BaseOpti,
cfg['training']['lr_schedule'])
self.schedulers.extend([self.BaseSchedule])
self.setup(cfg, writer, logger)
self.adv_source_label = 0
self.adv_target_label = 1
self.bceloss = nn.BCEWithLogitsLoss(size_average=True)
self.loss_fn = get_loss_function(cfg)
self.mseloss = nn.MSELoss()
self.l1loss = nn.L1Loss()
self.smoothloss = nn.SmoothL1Loss()
self.triplet_loss = nn.TripletMarginLoss()
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,
).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 forward(self, input):
feat, feat_low, feat_cls, output = self.BaseNet_DP(input)
return feat, feat_low, feat_cls, output
def forward_Up(self, input):
feat, feat_low, feat_cls, output = self.forward(input)
output = F.interpolate(output,
size=input.size()[2:],
mode='bilinear',
align_corners=True)
return feat, feat_low, feat_cls, output
def PredNet_Forward(self, input):
with torch.no_grad():
_, _, feat_cls, output_result = self.PredNet_DP(input)
return _, _, 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, target_x, target_label):
_, _, source_feat_cls, source_output = self.forward(input=source_x)
source_outputUp = F.interpolate(source_output,
size=source_x.size()[2:],
mode='bilinear',
align_corners=True)
loss_GTA = self.loss_fn(input=source_outputUp, target=source_label)
self.PredNet.eval()
with torch.no_grad():
_, _, feat_cls, output = self.PredNet_Forward(target_x)
# calculate pseudo-labels
threshold_arg, cluster_arg = self.metrics.update(
feat_cls, output, target_label, self)
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.size()[2:],
mode='nearest') #or F.softmax(input=source_output, dim=1)
source_vectors, source_ids = self.calculate_mean_vector(
source_feat_cls, source_output, source_label_downsampled)
target_vectors, target_ids = self.calculate_mean_vector(
target_feat_cls, target_output, cluster_arg.float())
loss_L2_source_cls = self.class_vectors_alignment(
source_ids, source_vectors)
loss_L2_target_cls = self.class_vectors_alignment(
target_ids, target_vectors)
# target_vectors, target_ids = self.calculate_mean_vector(target_feat_cls, target_output, threshold_arg.float())
# loss_L2_target_cls += self.class_vectors_alignment(target_ids, target_vectors)
loss_L2_cls = self.cls_feature_weight * (loss_L2_source_cls +
loss_L2_target_cls)
loss = torch.Tensor([0]).cuda()
batch, _, w, h = cluster_arg.shape
# cluster_arg[cluster_arg != threshold_arg] = 250
loss_CTS = (self.loss_fn(input=target_output, target=cluster_arg.reshape([batch, w, h])) \
+ self.loss_fn(input=target_output, target=threshold_arg.reshape([batch, w, h]))) / 2 # CAG-based and probability-based PLA
# loss_CTS = self.loss_fn(input=target_output, target=cluster_arg.reshape([batch, w, h])) # CAG-based PLA
# loss_CTS = self.loss_fn(input=target_output, target=threshold_arg.reshape([batch, w, h])) # probability-based PLA
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)
if loss.item() != 0:
loss.backward()
self.BaseOpti.step()
self.BaseOpti.zero_grad()
return loss, loss_L2_cls.item(), loss_CTS.item()
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):
loss = torch.Tensor([0]).cuda(self.default_gpu)
for i in range(len(ids)):
if ids[i] not in self.valid_classes:
continue
new_loss = self.smoothloss(
vectors[i].squeeze().cuda(self.default_gpu),
torch.Tensor(self.objective_vectors[ids[i]]).cuda(
self.default_gpu))
while (new_loss.item() > 5):
new_loss = new_loss / 10
loss = loss + new_loss
loss = loss / len(ids) * 10
pass
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):
# for net in self.nets:
# self.schedulers[net.__class__.__name__].step()
for scheduler in self.schedulers:
scheduler.step()
def optimizer_zerograd(self):
# for net in self.nets:
# self.optimizers[net.__class__.__name__].zero_grad()
for optimizer in self.optimizers:
optimizer.zero_grad()
def optimizer_step(self):
# for net in self.nets:
# self.optimizers[net.__class__.__name__].step()
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 torch.cuda.is_available():
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 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()
# if logger!=None:
# logger.info("Successfully set the model train mode")
else:
net.train()
# if logger!= None:
# logger.info(print("Successfully set {} train mode".format(net.__class__.__name__)))
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
"""
# if issubclass(net, nn.Module) or issubclass(net, BaseModel):
for parameter in net.parameters():
parameter.requires_grad = requires_grad
# print("Successfully set {} requires_grad with {}".format(net.__class__.__name__, requires_grad))
# return
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
}
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
for net in self.nets:
name = net.__class__.__name__
_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 cfg['training']['optimizer_resume']:
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"]
self.best_iou = checkpoint['best_iou']
logger.info("Loaded checkpoint '{}' (iter {})".format(
cfg['training']['resume'], checkpoint["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
self.adaptive_load_nets(net, checkpoint[name]["model_state"])
iter = checkpoint["iter"]
best_iou = checkpoint['best_iou']
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'):
net.best_iou = best_iou
return best_iou
def set_optimizer(self, optimizer): #set optimizer to all nets
pass
def reset_objective_SingleVector(self, ):
self.objective_vectors = np.zeros([19, 256])
self.objective_vectors_num = np.zeros([19])
self.objective_vectors_dis = np.zeros([19, 19])
def update_objective_SingleVector(
self,
id,
vector,
name='moving_average',
):
if isinstance(vector, torch.Tensor):
vector = vector.squeeze().detach().cpu().numpy()
if np.sum(vector) == 0:
return
if self.objective_vectors_num[id] < 100:
name = 'mean'
if name == 'moving_average':
self.objective_vectors[id] = self.objective_vectors[
id] * 0.9999 + 0.0001 * 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))