def trainingNetwork(images_folder_train, labels_folder_train,
images_folder_val, labels_folder_val, dictionary,
target_classes, num_classes, save_network_as,
classifier_name, epochs, batch_sz, batch_mult,
learning_rate, L2_penalty, validation_frequency,
flagShuffle, experiment_name, progress):
##### DATA #####
# setup the training dataset
datasetTrain = CoralsDataset(images_folder_train, labels_folder_train,
dictionary, target_classes, num_classes)
print("Dataset setup..", end='')
datasetTrain.computeAverage()
datasetTrain.computeWeights()
target_classes = datasetTrain.dict_target
print("done.")
datasetTrain.enableAugumentation()
datasetVal = CoralsDataset(images_folder_val, labels_folder_val,
dictionary, target_classes, num_classes)
datasetVal.dataset_average = datasetTrain.dataset_average
datasetVal.weights = datasetTrain.weights
#AUGUMENTATION IS NOT APPLIED ON THE VALIDATION SET
datasetVal.disableAugumentation()
# setup the data loader
dataloaderTrain = DataLoader(datasetTrain,
batch_size=batch_sz,
shuffle=flagShuffle,
num_workers=0,
drop_last=True,
pin_memory=True)
validation_batch_size = 4
dataloaderVal = DataLoader(datasetVal,
batch_size=validation_batch_size,
shuffle=False,
num_workers=0,
drop_last=True,
pin_memory=True)
training_images_number = len(datasetTrain.images_names)
validation_images_number = len(datasetVal.images_names)
###### SETUP THE NETWORK #####
net = DeepLab(backbone='resnet',
output_stride=16,
num_classes=datasetTrain.num_classes)
models_dir = "models/"
network_name = os.path.join(models_dir, "deeplab-resnet.pth.tar")
state = torch.load(network_name)
# RE-INIZIALIZE THE CLASSIFICATION LAYER WITH THE RIGHT NUMBER OF CLASSES, DON'T LOAD WEIGHTS OF THE CLASSIFICATION LAYER
new_dictionary = state['state_dict']
del new_dictionary['decoder.last_conv.8.weight']
del new_dictionary['decoder.last_conv.8.bias']
net.load_state_dict(state['state_dict'], strict=False)
print("NETWORK USED: DEEPLAB V3+")
# LOSS
weights = datasetTrain.weights
class_weights = torch.FloatTensor(weights).cuda()
lossfn = nn.CrossEntropyLoss(weight=class_weights, ignore_index=-1)
# OPTIMIZER
# optimizer = optim.SGD(net.parameters(), lr=learning_rate, weight_decay=0.0002, momentum=0.9)
optimizer = optim.Adam(net.parameters(),
lr=learning_rate,
weight_decay=L2_penalty)
USE_CUDA = torch.cuda.is_available()
if USE_CUDA:
device = torch.device("cuda")
net.to(device)
##### TRAINING LOOP #####
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=2,
verbose=True)
best_accuracy = 0.0
best_jaccard_score = 0.0
print("Training Network")
for epoch in range(epochs): # loop over the dataset multiple times
txt = "Epoch " + str(epoch + 1) + "/" + str(epochs)
progress.setMessage(txt)
progress.setProgress((100.0 * epoch) / epochs)
QApplication.processEvents()
net.train()
optimizer.zero_grad()
running_loss = 0.0
for i, minibatch in enumerate(dataloaderTrain):
# get the inputs
images_batch = minibatch['image']
labels_batch = minibatch['labels']
if USE_CUDA:
images_batch = images_batch.to(device)
labels_batch = labels_batch.to(device)
# forward+loss+backward
outputs = net(images_batch)
loss = lossfn(outputs, labels_batch)
loss.backward()
# TO AVOID MEMORY TRUBLE UPDATE WEIGHTS EVERY BATCH SIZE X BATCH MULT
if (i + 1) % batch_mult == 0:
optimizer.step()
optimizer.zero_grad()
print(epoch, i, loss.item())
running_loss += loss.item()
print("Epoch: %d , Running loss = %f" % (epoch, running_loss))
### VALIDATION ###
if epoch > 0 and (epoch + 1) % validation_frequency == 0:
print("RUNNING VALIDATION.. ", end='')
# datasetVal.weights are the same of datasetTrain
metrics_val, mean_loss_val = evaluateNetwork(
dataloaderVal,
datasetVal.weights,
datasetVal.num_classes,
net,
flagTrainingDataset=False)
accuracy = metrics_val['Accuracy']
jaccard_score = metrics_val['JaccardScore']
scheduler.step(mean_loss_val)
metrics_train, mean_loss_train = evaluateNetwork(
dataloaderTrain,
datasetTrain.weights,
datasetTrain.num_classes,
net,
flagTrainingDataset=True)
accuracy_training = metrics_train['Accuracy']
jaccard_training = metrics_train['JaccardScore']
if jaccard_score > best_jaccard_score:
best_accuracy = accuracy
best_jaccard_score = jaccard_score
torch.save(net.state_dict(), save_network_as)
# performance of the best accuracy network on the validation dataset
metrics_filename = save_network_as[:len(save_network_as) -
4] + "-val-metrics.txt"
saveMetrics(metrics_val, metrics_filename)
metrics_filename = save_network_as[:len(save_network_as) -
4] + "-train-metrics.txt"
saveMetrics(metrics_train, metrics_filename)
print("-> CURRENT BEST ACCURACY ", best_accuracy)
print("***** TRAINING FINISHED *****")
return datasetTrain
def train_net(args):
torch.manual_seed(7)
np.random.seed(7)
checkpoint = args.checkpoint
start_epoch = 0
best_loss = float('inf')
writer = SummaryWriter()
epochs_since_improvement = 0
# Initialize / load checkpoint
if checkpoint is None:
model = DeepLab(backbone='mobilenet',
output_stride=16,
num_classes=num_classes)
model = nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
betas=(0.9, 0.99),
weight_decay=args.weight_decay)
else:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint['epoch'] + 1
epochs_since_improvement = checkpoint['epochs_since_improvement']
model = checkpoint['model']
optimizer = checkpoint['optimizer']
logger = get_logger()
# Move to GPU, if available
model = model.to(device)
# Custom dataloaders
train_dataset = MICDataset('train')
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=num_workers)
valid_dataset = MICDataset('val')
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=num_workers)
# Epochs
for epoch in range(start_epoch, args.end_epoch):
# One epoch's training
train_loss, train_acc = train(train_loader=train_loader,
model=model,
optimizer=optimizer,
epoch=epoch,
logger=logger)
lr = get_learning_rate(optimizer)
print('Current effective learning rate: {}\n'.format(lr))
writer.add_scalar('model/train_loss', train_loss, epoch)
writer.add_scalar('model/train_acc', train_acc, epoch)
# One epoch's validation
valid_loss, valid_acc = valid(valid_loader=valid_loader,
model=model,
logger=logger)
writer.add_scalar('model/valid_loss', valid_loss, epoch)
writer.add_scalar('model/valid_acc', valid_acc, epoch)
# Check if there was an improvement
is_best = valid_loss < best_loss
best_loss = min(valid_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
print("\nEpochs since last improvement: %d\n" %
(epochs_since_improvement, ))
else:
epochs_since_improvement = 0
# Save checkpoint
save_checkpoint(epoch, epochs_since_improvement, model, optimizer,
best_loss, is_best)
def train_net(args):
torch.manual_seed(7)
np.random.seed(7)
checkpoint = args.checkpoint
start_epoch = 0
best_loss = float('inf')
writer = SummaryWriter()
epochs_since_improvement = 0
# Initialize / load checkpoint
if checkpoint is None:
model = DeepLab(backbone='mobilenet', output_stride=16, num_classes=1)
model = nn.DataParallel(model)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
else:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint['epoch'] + 1
epochs_since_improvement = checkpoint['epochs_since_improvement']
model = checkpoint['model'].module
model = nn.DataParallel(model)
optimizer = checkpoint['optimizer']
logger = get_logger()
# Move to GPU, if available
model = model.to(device)
# Custom dataloaders
train_dataset = DIMDataset('train')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True,
num_workers=num_workers)
# valid_dataset = DIMDataset('valid')
# valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.batch_size, shuffle=False,
# num_workers=num_workers)
# scheduler = MultiStepLR(optimizer, milestones=[10, 20], gamma=0.1)
# Epochs
for epoch in range(start_epoch, args.end_epoch):
# scheduler.step(epoch)
# One epoch's training
train_loss = train(train_loader=train_loader,
model=model,
optimizer=optimizer,
epoch=epoch,
logger=logger)
effective_lr = get_learning_rate(optimizer)
print('Current effective learning rate: {}\n'.format(effective_lr))
writer.add_scalar('model/train_loss', train_loss, epoch)
# One epoch's validation
# valid_loss = valid(valid_loader=valid_loader,
# model=model,
# logger=logger)
#
# writer.add_scalar('Valid_Loss', valid_loss, epoch)
# One epoch's test
sad_loss, mse_loss = test(model)
writer.add_scalar('model/sad_loss', sad_loss, epoch)
writer.add_scalar('model/mse_loss', mse_loss, epoch)
# Print status
status = 'Test: SAD {:.4f} MSE {:.4f}\n'.format(sad_loss, mse_loss)
logger.info(status)
# Check if there was an improvement
is_best = mse_loss < best_loss
best_loss = min(mse_loss, best_loss)
if not is_best:
epochs_since_improvement += 1
print("\nEpochs since last improvement: %d\n" % (epochs_since_improvement,))
else:
epochs_since_improvement = 0
# Save checkpoint
save_checkpoint(epoch, epochs_since_improvement, model, optimizer, best_loss, is_best)
pretrained_backbone=True).to(device)
else:
NotImplementedError()
if (args.debug):
print("model_G :\n", model_G)
# モデルを読み込む
if not args.load_checkpoints_path_G == '' and os.path.exists(
args.load_checkpoints_path_G):
load_checkpoint(model_G, device, args.load_checkpoints_path_G)
#================================
# optimizer_G の設定
#================================
optimizer_G = optim.Adam(params=model_G.parameters(),
lr=args.lr,
betas=(args.beta1, args.beta2))
#================================
# loss_G 関数の設定
#================================
loss_entropy_fn = CrossEntropy2DLoss(device)
#================================
# モデルの学習
#================================
if (args.train_mode == "train"):
print("Starting Training Loop...")
n_print = 1
step = 0
def trainingNetwork(images_folder_train, labels_folder_train, images_folder_val, labels_folder_val,
dictionary, target_classes, output_classes, save_network_as, classifier_name,
epochs, batch_sz, batch_mult, learning_rate, L2_penalty, validation_frequency, loss_to_use,
epochs_switch, epochs_transition, tversky_alpha, tversky_gamma, optimiz,
flag_shuffle, flag_training_accuracy, progress):
##### DATA #####
# setup the training dataset
datasetTrain = CoralsDataset(images_folder_train, labels_folder_train, dictionary, target_classes)
print("Dataset setup..", end='')
datasetTrain.computeAverage()
datasetTrain.computeWeights()
print(datasetTrain.dict_target)
print(datasetTrain.weights)
freq = 1.0 / datasetTrain.weights
print(freq)
print("done.")
save_classifier_as = save_network_as.replace(".net", ".json")
datasetTrain.enableAugumentation()
datasetVal = CoralsDataset(images_folder_val, labels_folder_val, dictionary, target_classes)
datasetVal.dataset_average = datasetTrain.dataset_average
datasetVal.weights = datasetTrain.weights
#AUGUMENTATION IS NOT APPLIED ON THE VALIDATION SET
datasetVal.disableAugumentation()
# setup the data loader
dataloaderTrain = DataLoader(datasetTrain, batch_size=batch_sz, shuffle=flag_shuffle, num_workers=0, drop_last=True,
pin_memory=True)
validation_batch_size = 4
dataloaderVal = DataLoader(datasetVal, batch_size=validation_batch_size, shuffle=False, num_workers=0, drop_last=True,
pin_memory=True)
training_images_number = len(datasetTrain.images_names)
validation_images_number = len(datasetVal.images_names)
print("NETWORK USED: DEEPLAB V3+")
if os.path.exists(save_network_as):
net = DeepLab(backbone='resnet', output_stride=16, num_classes=output_classes)
net.load_state_dict(torch.load(save_network_as))
print("Checkpoint loaded.")
else:
###### SETUP THE NETWORK #####
net = DeepLab(backbone='resnet', output_stride=16, num_classes=output_classes)
state = torch.load("models/deeplab-resnet.pth.tar")
# RE-INIZIALIZE THE CLASSIFICATION LAYER WITH THE RIGHT NUMBER OF CLASSES, DON'T LOAD WEIGHTS OF THE CLASSIFICATION LAYER
new_dictionary = state['state_dict']
del new_dictionary['decoder.last_conv.8.weight']
del new_dictionary['decoder.last_conv.8.bias']
net.load_state_dict(state['state_dict'], strict=False)
# OPTIMIZER
if optimiz == "SGD":
optimizer = optim.SGD(net.parameters(), lr=learning_rate, weight_decay=L2_penalty, momentum=0.9)
elif optimiz == "ADAM":
optimizer = optim.Adam(net.parameters(), lr=learning_rate, weight_decay=L2_penalty)
USE_CUDA = torch.cuda.is_available()
if USE_CUDA:
device = torch.device("cuda")
net.to(device)
##### TRAINING LOOP #####
reduce_lr_patience = 2
if loss_to_use == "DICE+BOUNDARY":
reduce_lr_patience = 200
print("patience increased !")
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=reduce_lr_patience, verbose=True)
best_accuracy = 0.0
best_jaccard_score = 0.0
# Crossentropy loss
weights = datasetTrain.weights
class_weights = torch.FloatTensor(weights).cuda()
CEloss = nn.CrossEntropyLoss(weight=class_weights, ignore_index=-1)
# weights for GENERALIZED DICE LOSS (GDL)
freq = 1.0 / datasetTrain.weights[1:]
w = 1.0 / (freq * freq)
w = w / w.sum() + 0.00001
w_for_GDL = torch.from_numpy(w)
w_for_GDL = w_for_GDL.to(device)
# Focal Tversky loss
focal_tversky_gamma = torch.tensor(tversky_gamma)
focal_tversky_gamma = focal_tversky_gamma.to(device)
tversky_loss_alpha = torch.tensor(tversky_alpha)
tversky_loss_beta = torch.tensor(1.0 - tversky_alpha)
tversky_loss_alpha = tversky_loss_alpha.to(device)
tversky_loss_beta = tversky_loss_beta.to(device)
print("Training Network")
num_iter = 0
total_iter = epochs * int(len(datasetTrain) / dataloaderTrain.batch_size)
for epoch in range(epochs):
net.train()
optimizer.zero_grad()
loss_values = []
for i, minibatch in enumerate(dataloaderTrain):
txt = "Training - Iterations " + str(num_iter + 1) + "/" + str(total_iter)
progress.setMessage(txt)
progress.setProgress((100.0 * num_iter) / total_iter)
QApplication.processEvents()
num_iter += 1
# get the inputs
images_batch = minibatch['image']
labels_batch = minibatch['labels']
if USE_CUDA:
images_batch = images_batch.to(device)
labels_batch = labels_batch.to(device)
# forward+loss+backward
outputs = net(images_batch)
loss = computeLoss(loss_to_use, CEloss, w_for_GDL, tversky_loss_alpha, tversky_loss_beta, focal_tversky_gamma,
epoch, epochs_switch, epochs_transition, labels_batch, outputs)
loss.backward()
# TO AVOID MEMORY TROUBLE UPDATE WEIGHTS EVERY BATCH SIZE x BATCH MULT
if (i+1)% batch_mult == 0:
optimizer.step()
optimizer.zero_grad()
print(epoch, i, loss.item())
loss_values.append(loss.item())
mean_loss_train = sum(loss_values) / len(loss_values)
print("Epoch: %d , Mean loss = %f" % (epoch, mean_loss_train))
### VALIDATION ###
if epoch > 0 and (epoch+1) % validation_frequency == 0:
print("RUNNING VALIDATION.. ", end='')
metrics_val, mean_loss_val = evaluateNetwork(datasetVal, dataloaderVal, loss_to_use, CEloss, w_for_GDL,
tversky_loss_alpha, tversky_loss_beta, focal_tversky_gamma,
epoch, epochs_switch, epochs_transition,
output_classes, net, flag_compute_mIoU=False)
accuracy = metrics_val['Accuracy']
jaccard_score = metrics_val['JaccardScore']
scheduler.step(mean_loss_val)
accuracy_training = 0.0
jaccard_training = 0.0
if flag_training_accuracy is True:
metrics_train, mean_loss_train = evaluateNetwork(datasetTrain, dataloaderTrain, loss_to_use, CEloss, w_for_GDL,
tversky_loss_alpha, tversky_loss_beta, focal_tversky_gamma,
epoch, epochs_switch, epochs_transition,
output_classes, net, flag_compute_mIoU=False)
accuracy_training = metrics_train['Accuracy']
jaccard_training = metrics_train['JaccardScore']
#if jaccard_score > best_jaccard_score:
if accuracy > best_accuracy:
best_accuracy = accuracy
best_jaccard_score = jaccard_score
torch.save(net.state_dict(), save_network_as)
# performance of the best accuracy network on the validation dataset
metrics_filename = save_network_as[:len(save_network_as) - 4] + "-val-metrics.txt"
saveMetrics(metrics_val, metrics_filename)
print("-> CURRENT BEST ACCURACY ", best_accuracy)
# main loop ended
torch.cuda.empty_cache()
del net
net = None
print("***** TRAINING FINISHED *****")
print("BEST ACCURACY REACHED ON THE VALIDATION SET: %.3f " % best_accuracy)
return datasetTrain
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))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=0)
#valid_loader = torch.utils.data.DataLoader(validate_dataset, batch_size=1,shuffle=True, num_workers=0)
print("trainset lenght :: ", len(train_dataset))
m = nn.Upsample(scale_factor=0.0625)
#loss
criterion = nn.CrossEntropyLoss()
#criterion2 = FocalLoss(a, b, gamma=0, alpha=None)
seg_criterion = nn.NLLLoss2d(weight=None)
cls_criterion = nn.BCEWithLogitsLoss(weight=None)
#optim setting
optimizer = optim.RMSprop(model.parameters(),
lr=startlr,
weight_decay=5e-4,
momentum=0.9)
scheduler = optim.lr_scheduler.CyclicLR(optimizer,
base_lr=startlr,
max_lr=startlr * 3,
step_size_up=2000,
mode='triangular2',
gamma=0.9994,
cycle_momentum=False)
opt = SWA(optimizer, swa_start=10, swa_freq=5, swa_lr=0.05)
global_iter = 0
for epoch in range(num_epochs):
losses = list()
def main():
# define and parse arguments
parser = argparse.ArgumentParser()
# general
parser.add_argument('--experiment_name',
type=str,
default="experiment",
help="experiment name. will be used in the path names \
for log- and savefiles")
parser.add_argument('--seed',
type=int,
default=None,
help='fixes random seed and sets model to \
the potentially faster cuDNN deterministic mode \
(default: non-deterministic mode)')
parser.add_argument('--val_freq',
type=int,
default=1000,
help='validation will be run every val_freq \
batches/optimization steps during training')
parser.add_argument('--save_freq',
type=int,
default=1000,
help='training state will be saved every save_freq \
batches/optimization steps during training')
parser.add_argument('--log_freq',
type=int,
default=100,
help='tensorboard logs will be written every log_freq \
number of batches/optimization steps')
# input/output
parser.add_argument('--use_s2hr',
action='store_true',
default=False,
help='use sentinel-2 high-resolution (10 m) bands')
parser.add_argument('--use_s2mr',
action='store_true',
default=False,
help='use sentinel-2 medium-resolution (20 m) bands')
parser.add_argument('--use_s2lr',
action='store_true',
default=False,
help='use sentinel-2 low-resolution (60 m) bands')
parser.add_argument('--use_s1',
action='store_true',
default=False,
help='use sentinel-1 data')
parser.add_argument('--no_savanna',
action='store_true',
default=False,
help='ignore class savanna')
# training hyperparameters
parser.add_argument('--lr',
type=float,
default=0.01,
help='learning rate (default: 1e-2)')
parser.add_argument('--momentum',
type=float,
default=0.9,
help='momentum (default: 0.9), only used for deeplab')
parser.add_argument('--weight_decay',
type=float,
default=5e-4,
help='weight-decay (default: 5e-4)')
parser.add_argument('--batch_size',
type=int,
default=32,
help='batch size for training and validation \
(default: 32)')
parser.add_argument('--workers',
type=int,
default=4,
help='number of workers for dataloading (default: 4)')
parser.add_argument('--max_epochs',
type=int,
default=100,
help='number of training epochs (default: 100)')
# network
parser.add_argument('--model',
type=str,
choices=['deeplab', 'unet'],
default='deeplab',
help="network architecture (default: deeplab)")
# deeplab-specific
parser.add_argument('--pretrained_backbone',
action='store_true',
default=False,
help='initialize ResNet-101 backbone with ImageNet \
pre-trained weights')
parser.add_argument('--out_stride',
type=int,
choices=[8, 16],
default=16,
help='network output stride (default: 16)')
# data
parser.add_argument('--data_dir_train',
type=str,
default=None,
help='path to training dataset')
parser.add_argument(
'--dataset_val',
type=str,
default="sen12ms_holdout",
choices=['sen12ms_holdout', 'dfc2020_val', 'dfc2020_test'],
help='dataset to use for validation (default: \
sen12ms_holdout)')
parser.add_argument('--data_dir_val',
type=str,
default=None,
help='path to validation dataset')
parser.add_argument('--log_dir',
type=str,
default=None,
help='path to dir for tensorboard logs \
(default runs/CURRENT_DATETIME_HOSTNAME)')
args = parser.parse_args()
print("=" * 20, "CONFIG", "=" * 20)
for arg in vars(args):
print('{0:20} {1}'.format(arg, getattr(args, arg)))
print()
# fix seeds and set pytorch to deterministic mode
if args.seed is not None:
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# set flags for GPU processing if available
if torch.cuda.is_available():
args.use_gpu = True
if torch.cuda.device_count() > 1:
raise NotImplementedError("multi-gpu training not implemented! " +
"try to run script as: " +
"CUDA_VISIBLE_DEVICES=0 train.py")
else:
args.use_gpu = False
# load datasets
train_set = SEN12MS(args.data_dir_train,
subset="train",
no_savanna=args.no_savanna,
use_s2hr=args.use_s2hr,
use_s2mr=args.use_s2mr,
use_s2lr=args.use_s2lr,
use_s1=args.use_s1)
n_classes = train_set.n_classes
n_inputs = train_set.n_inputs
if args.dataset_val == "sen12ms_holdout":
val_set = SEN12MS(args.data_dir_train,
subset="holdout",
no_savanna=args.no_savanna,
use_s2hr=args.use_s2hr,
use_s2mr=args.use_s2mr,
use_s2lr=args.use_s2lr,
use_s1=args.use_s1)
else:
dfc2020_subset = args.dataset_val.split("_")[-1]
val_set = DFC2020(args.data_dir_val,
subset=dfc2020_subset,
no_savanna=args.no_savanna,
use_s2hr=args.use_s2hr,
use_s2mr=args.use_s2mr,
use_s2lr=args.use_s2lr,
use_s1=args.use_s1)
# set up dataloaders
train_loader = DataLoader(train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=False)
# set up network
if args.model == "deeplab":
model = DeepLab(num_classes=n_classes,
backbone='resnet',
pretrained_backbone=args.pretrained_backbone,
output_stride=args.out_stride,
sync_bn=False,
freeze_bn=False,
n_in=n_inputs)
else:
model = UNet(n_classes=n_classes, n_channels=n_inputs)
if args.use_gpu:
model = model.cuda()
# define loss function
loss_fn = nn.CrossEntropyLoss(ignore_index=255, reduction='mean')
# set up optimizer
if args.model == "deeplab":
train_params = [{
'params': model.get_1x_lr_params(),
'lr': args.lr
}, {
'params': model.get_10x_lr_params(),
'lr': args.lr * 10
}]
optimizer = torch.optim.SGD(train_params,
momentum=args.momentum,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.RMSprop(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
# set up tensorboard logging
if args.log_dir is None:
args.log_dir = "logs"
writer = SummaryWriter(
log_dir=os.path.join(args.log_dir, args.experiment_name))
# create checkpoint dir
args.checkpoint_dir = os.path.join(args.log_dir, args.experiment_name,
"checkpoints")
os.makedirs(args.checkpoint_dir, exist_ok=True)
# save config
pkl.dump(args, open(os.path.join(args.checkpoint_dir, "args.pkl"), "wb"))
# train network
step = 0
trainer = ModelTrainer(args)
for epoch in range(args.max_epochs):
print("=" * 20, "EPOCH", epoch + 1, "/", str(args.max_epochs),
"=" * 20)
# run training for one epoch
model, step = trainer.train(model,
train_loader,
val_loader,
loss_fn,
optimizer,
writer,
step=step)
# export final set of weights
trainer.export_model(model, args.checkpoint_dir, name="final")
