def augment_samples(images,
labels,
probs,
do_classmix,
batch_size,
ignore_label,
weak=False):
"""
Perform data augmentation
Args:
images: BxCxWxH images to augment
labels: BxWxH labels to augment
probs: BxWxH probability maps to augment
do_classmix: whether to apply classmix augmentation
batch_size: batch size
ignore_label: ignore class value
weak: whether to perform weak or strong augmentation
Returns:
augmented data, augmented labels, augmented probs
"""
if do_classmix:
# ClassMix: Get mask for image A
for image_i in range(batch_size): # for each image
classes = torch.unique(
labels[image_i]) # get unique classes in pseudolabel A
nclasses = classes.shape[0]
# remove ignore class
if ignore_label in classes and len(classes) > 1 and nclasses > 1:
classes = classes[classes != ignore_label]
nclasses = nclasses - 1
if dataset == 'pascal_voc': # if voc dataaset, remove class 0, background
if 0 in classes and len(classes) > 1 and nclasses > 1:
classes = classes[classes != 0]
nclasses = nclasses - 1
# pick half of the classes randomly
classes = (classes[torch.Tensor(
np.random.choice(nclasses,
int(((nclasses - nclasses % 2) / 2) + 1),
replace=False)).long()]).cuda()
# acumulate masks
if image_i == 0:
MixMask = transformmasks.generate_class_mask(
labels[image_i], classes).unsqueeze(0).cuda()
else:
MixMask = torch.cat((MixMask,
transformmasks.generate_class_mask(
labels[image_i],
classes).unsqueeze(0).cuda()))
params = {"Mix": MixMask}
else:
params = {}
if weak:
params["flip"] = random.random() < 0.5
params["ColorJitter"] = random.random() < 0.2
params["GaussianBlur"] = random.random() < 0.
params["Grayscale"] = random.random() < 0.0
params["Solarize"] = random.random() < 0.0
if random.random() < 0.5:
scale = random.uniform(0.75, 1.75)
else:
scale = 1
params["RandomScaleCrop"] = scale
# Apply strong augmentations to unlabeled images
image_aug, labels_aug, probs_aug = augmentationTransform(
params,
data=images,
target=labels,
probs=probs,
jitter_vale=0.125,
min_sigma=0.1,
max_sigma=1.5,
ignore_label=ignore_label)
else:
params["flip"] = random.random() < 0.5
params["ColorJitter"] = random.random() < 0.8
params["GaussianBlur"] = random.random() < 0.2
params["Grayscale"] = random.random() < 0.0
params["Solarize"] = random.random() < 0.0
if random.random() < 0.80:
scale = random.uniform(0.75, 1.75)
else:
scale = 1
params["RandomScaleCrop"] = scale
# Apply strong augmentations to unlabeled images
image_aug, labels_aug, probs_aug = augmentationTransform(
params,
data=images,
target=labels,
probs=probs,
jitter_vale=0.25,
min_sigma=0.1,
max_sigma=1.5,
ignore_label=ignore_label)
return image_aug, labels_aug, probs_aug, params
def main():
torch.cuda.empty_cache()
print(config)
best_mIoU = 0
if consistency_loss == 'CE':
if len(gpus) > 1:
unlabeled_loss = torch.nn.DataParallel(
CrossEntropyLoss2dPixelWiseWeighted(ignore_index=ignore_label),
device_ids=gpus).cuda()
else:
unlabeled_loss = CrossEntropyLoss2dPixelWiseWeighted().cuda()
elif consistency_loss == 'MSE':
if len(gpus) > 1:
unlabeled_loss = torch.nn.DataParallel(MSELoss2d(),
device_ids=gpus).cuda()
else:
unlabeled_loss = MSELoss2d().cuda()
cudnn.enabled = True
# create network
model = Res_Deeplab(num_classes=num_classes)
# load pretrained parameters
if restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(restore_from)
else:
saved_state_dict = torch.load(restore_from)
# Copy loaded parameters to model
new_params = model.state_dict().copy()
for name, param in new_params.items():
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
model.load_state_dict(new_params)
# Initiate ema-model
if train_unlabeled:
ema_model = create_ema_model(model)
ema_model.train()
ema_model = ema_model.cuda()
else:
ema_model = None
if len(gpus) > 1:
if use_sync_batchnorm:
model = convert_model(model)
model = DataParallelWithCallback(model, device_ids=gpus)
else:
model = torch.nn.DataParallel(model, device_ids=gpus)
model.train()
model.cuda()
cudnn.benchmark = True
if dataset == 'pascal_voc':
data_loader = get_loader(dataset)
data_path = get_data_path(dataset)
train_dataset = data_loader(data_path,
crop_size=input_size,
scale=random_scale,
mirror=random_flip)
elif dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
if random_crop:
data_aug = Compose([RandomCrop_city(input_size)])
else:
data_aug = None
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug,
img_size=input_size)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
partial_size = labeled_samples
print('Training on number of samples:', partial_size)
if split_id is not None:
train_ids = pickle.load(open(split_id, 'rb'))
print('loading train ids from {}'.format(split_id))
else:
np.random.seed(random_seed)
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True)
trainloader_iter = iter(trainloader)
if train_unlabeled:
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
trainloader_remain = data.DataLoader(train_dataset,
batch_size=batch_size,
sampler=train_remain_sampler,
num_workers=1,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
# Optimizer for segmentation network
learning_rate_object = Learning_Rate_Object(
config['training']['learning_rate'])
if optimizer_type == 'SGD':
if len(gpus) > 1:
optimizer = optim.SGD(
model.module.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
else:
optimizer = optim.SGD(
model.optim_parameters(
learning_rate_object), ## DOES THIS CAUSE THE USERWARNING?
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
optimizer.zero_grad()
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
start_iteration = 0
if args.resume:
start_iteration, model, optimizer, ema_model = _resume_checkpoint(
args.resume, model, optimizer, ema_model)
accumulated_loss_l = []
if train_unlabeled:
accumulated_loss_u = []
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
with open(checkpoint_dir + '/config.json', 'w') as handle:
json.dump(config, handle, indent=4, sort_keys=False)
pickle.dump(train_ids,
open(os.path.join(checkpoint_dir, 'train_split.pkl'), 'wb'))
epochs_since_start = 0
for i_iter in range(start_iteration, num_iterations):
model.train()
loss_l_value = 0
if train_unlabeled:
loss_u_value = 0
optimizer.zero_grad()
if lr_schedule:
adjust_learning_rate(optimizer, i_iter)
# Training loss for labeled data only
try:
batch = next(trainloader_iter)
if batch[0].shape[0] != batch_size:
batch = next(trainloader_iter)
except:
epochs_since_start = epochs_since_start + 1
print('Epochs since start: ', epochs_since_start)
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
weak_parameters = {"flip": 0}
images, labels, _, _, _ = batch
images = images.cuda()
labels = labels.cuda()
images, labels = weakTransform(weak_parameters,
data=images,
target=labels)
intermediary_var = model(images)
pred = interp(intermediary_var)
L_l = loss_calc(pred, labels)
if train_unlabeled:
try:
batch_remain = next(trainloader_remain_iter)
if batch_remain[0].shape[0] != batch_size:
batch_remain = next(trainloader_remain_iter)
except:
trainloader_remain_iter = iter(trainloader_remain)
batch_remain = next(trainloader_remain_iter)
images_remain, _, _, _, _ = batch_remain
images_remain = images_remain.cuda()
inputs_u_w, _ = weakTransform(weak_parameters, data=images_remain)
logits_u_w = interp(ema_model(inputs_u_w))
logits_u_w, _ = weakTransform(
getWeakInverseTransformParameters(weak_parameters),
data=logits_u_w.detach())
softmax_u_w = torch.softmax(logits_u_w.detach(), dim=1)
max_probs, argmax_u_w = torch.max(softmax_u_w, dim=1)
if mix_mask == "class":
for image_i in range(batch_size):
classes = torch.unique(argmax_u_w[image_i])
classes = classes[classes != ignore_label]
nclasses = classes.shape[0]
classes = (classes[torch.Tensor(
np.random.choice(nclasses,
int((nclasses - nclasses % 2) / 2),
replace=False)).long()]).cuda()
if image_i == 0:
MixMask = transformmasks.generate_class_mask(
argmax_u_w[image_i], classes).unsqueeze(0).cuda()
else:
MixMask = torch.cat(
(MixMask,
transformmasks.generate_class_mask(
argmax_u_w[image_i],
classes).unsqueeze(0).cuda()))
elif mix_mask == 'cut':
img_size = inputs_u_w.shape[2:4]
for image_i in range(batch_size):
if image_i == 0:
MixMask = torch.from_numpy(
transformmasks.generate_cutout_mask(
img_size)).unsqueeze(0).cuda().float()
else:
MixMask = torch.cat(
(MixMask,
torch.from_numpy(
transformmasks.generate_cutout_mask(
img_size)).unsqueeze(0).cuda().float()))
elif mix_mask == "cow":
img_size = inputs_u_w.shape[2:4]
sigma_min = 8
sigma_max = 32
p_min = 0.5
p_max = 0.5
for image_i in range(batch_size):
sigma = np.exp(
np.random.uniform(np.log(sigma_min),
np.log(sigma_max))) # Random sigma
p = np.random.uniform(p_min, p_max) # Random p
if image_i == 0:
MixMask = torch.from_numpy(
transformmasks.generate_cow_mask(
img_size, sigma, p,
seed=None)).unsqueeze(0).cuda().float()
else:
MixMask = torch.cat(
(MixMask,
torch.from_numpy(
transformmasks.generate_cow_mask(
img_size, sigma, p,
seed=None)).unsqueeze(0).cuda().float()))
elif mix_mask == None:
MixMask = torch.ones((inputs_u_w.shape)).cuda()
strong_parameters = {"Mix": MixMask}
if random_flip:
strong_parameters["flip"] = random.randint(0, 1)
else:
strong_parameters["flip"] = 0
if color_jitter:
strong_parameters["ColorJitter"] = random.uniform(0, 1)
else:
strong_parameters["ColorJitter"] = 0
if gaussian_blur:
strong_parameters["GaussianBlur"] = random.uniform(0, 1)
else:
strong_parameters["GaussianBlur"] = 0
inputs_u_s, _ = strongTransform(strong_parameters,
data=images_remain)
logits_u_s = interp(model(inputs_u_s))
softmax_u_w_mixed, _ = strongTransform(strong_parameters,
data=softmax_u_w)
max_probs, pseudo_label = torch.max(softmax_u_w_mixed, dim=1)
if pixel_weight == "threshold_uniform":
unlabeled_weight = torch.sum(
max_probs.ge(0.968).long() == 1).item() / np.size(
np.array(pseudo_label.cpu()))
pixelWiseWeight = unlabeled_weight * torch.ones(
max_probs.shape).cuda()
elif pixel_weight == "threshold":
pixelWiseWeight = max_probs.ge(0.968).long().cuda()
elif pixel_weight == 'sigmoid':
max_iter = 10000
pixelWiseWeight = sigmoid_ramp_up(
i_iter, max_iter) * torch.ones(max_probs.shape).cuda()
elif pixel_weight == False:
pixelWiseWeight = torch.ones(max_probs.shape).cuda()
if consistency_loss == 'CE':
L_u = consistency_weight * unlabeled_loss(
logits_u_s, pseudo_label, pixelWiseWeight)
elif consistency_loss == 'MSE':
unlabeled_weight = torch.sum(
max_probs.ge(0.968).long() == 1).item() / np.size(
np.array(pseudo_label.cpu()))
#softmax_u_w_mixed = torch.cat((softmax_u_w_mixed[1].unsqueeze(0),softmax_u_w_mixed[0].unsqueeze(0)))
L_u = consistency_weight * unlabeled_weight * unlabeled_loss(
logits_u_s, softmax_u_w_mixed)
loss = L_l + L_u
else:
loss = L_l
if len(gpus) > 1:
loss = loss.mean()
loss_l_value += L_l.mean().item()
if train_unlabeled:
loss_u_value += L_u.mean().item()
else:
loss_l_value += L_l.item()
if train_unlabeled:
loss_u_value += L_u.item()
loss.backward()
optimizer.step()
# update Mean teacher network
if ema_model is not None:
alpha_teacher = 0.99
ema_model = update_ema_variables(ema_model=ema_model,
model=model,
alpha_teacher=alpha_teacher,
iteration=i_iter)
if train_unlabeled:
print('iter = {0:6d}/{1:6d}, loss_l = {2:.3f}, loss_u = {3:.3f}'.
format(i_iter, num_iterations, loss_l_value, loss_u_value))
else:
print('iter = {0:6d}/{1:6d}, loss_l = {2:.3f}'.format(
i_iter, num_iterations, loss_l_value))
if i_iter % save_checkpoint_every == 0 and i_iter != 0:
_save_checkpoint(i_iter, model, optimizer, config, ema_model)
if use_tensorboard:
if 'tensorboard_writer' not in locals():
tensorboard_writer = tensorboard.SummaryWriter(log_dir,
flush_secs=30)
accumulated_loss_l.append(loss_l_value)
if train_unlabeled:
accumulated_loss_u.append(loss_u_value)
if i_iter % log_per_iter == 0 and i_iter != 0:
tensorboard_writer.add_scalar('Training/Supervised loss',
np.mean(accumulated_loss_l),
i_iter)
accumulated_loss_l = []
if train_unlabeled:
tensorboard_writer.add_scalar('Training/Unsupervised loss',
np.mean(accumulated_loss_u),
i_iter)
accumulated_loss_u = []
if i_iter % val_per_iter == 0 and i_iter != 0:
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
ignore_label=ignore_label,
input_size=(512, 1024),
save_dir=checkpoint_dir)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter,
model,
optimizer,
config,
ema_model,
save_best=True)
if use_tensorboard:
tensorboard_writer.add_scalar('Validation/mIoU', mIoU, i_iter)
tensorboard_writer.add_scalar('Validation/Loss', eval_loss,
i_iter)
if save_unlabeled_images and train_unlabeled and i_iter % save_checkpoint_every == 0:
# Saves two mixed images and the corresponding prediction
save_image(inputs_u_s[0].cpu(), i_iter, 'input1',
palette.CityScpates_palette)
save_image(inputs_u_s[1].cpu(), i_iter, 'input2',
palette.CityScpates_palette)
_, pred_u_s = torch.max(logits_u_s, dim=1)
save_image(pred_u_s[0].cpu(), i_iter, 'pred1',
palette.CityScpates_palette)
save_image(pred_u_s[1].cpu(), i_iter, 'pred2',
palette.CityScpates_palette)
_save_checkpoint(num_iterations, model, optimizer, config, ema_model)
model.eval()
mIoU, val_loss = evaluate(model,
dataset,
ignore_label=ignore_label,
input_size=(512, 1024),
save_dir=checkpoint_dir)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter,
model,
optimizer,
config,
ema_model,
save_best=True)
if use_tensorboard:
tensorboard_writer.add_scalar('Validation/mIoU', mIoU, i_iter)
tensorboard_writer.add_scalar('Validation/Loss', val_loss, i_iter)
end = timeit.default_timer()
print('Total time: ' + str(end - start) + ' seconds')
def main():
print(config)
best_mIoU = 0
if consistency_loss == 'MSE':
if len(gpus) > 1:
unlabeled_loss = torch.nn.DataParallel(MSELoss2d(),
device_ids=gpus).cuda()
else:
unlabeled_loss = MSELoss2d().cuda()
elif consistency_loss == 'CE':
if len(gpus) > 1:
unlabeled_loss = torch.nn.DataParallel(
CrossEntropyLoss2dPixelWiseWeighted(ignore_index=ignore_label),
device_ids=gpus).cuda()
else:
unlabeled_loss = CrossEntropyLoss2dPixelWiseWeighted(
ignore_index=ignore_label).cuda()
cudnn.enabled = True
# create network
model = Res_Deeplab(num_classes=num_classes)
# load pretrained parameters
#saved_state_dict = torch.load(args.restore_from)
# load pretrained parameters
if restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(restore_from)
else:
saved_state_dict = torch.load(restore_from)
# Copy loaded parameters to model
new_params = model.state_dict().copy()
for name, param in new_params.items():
if name in saved_state_dict and param.size(
) == saved_state_dict[name].size():
new_params[name].copy_(saved_state_dict[name])
model.load_state_dict(new_params)
# init ema-model
if train_unlabeled:
ema_model = create_ema_model(model)
ema_model.train()
ema_model = ema_model.cuda()
else:
ema_model = None
if len(gpus) > 1:
if use_sync_batchnorm:
model = convert_model(model)
model = DataParallelWithCallback(model, device_ids=gpus)
else:
model = torch.nn.DataParallel(model, device_ids=gpus)
model.train()
model.cuda()
cudnn.benchmark = True
data_loader = get_loader(config['dataset'])
# data_path = get_data_path(config['dataset'])
# if random_crop:
# data_aug = Compose([RandomCrop_city(input_size)])
# else:
# data_aug = None
data_aug = Compose([RandomHorizontallyFlip()])
if dataset == 'cityscapes':
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug,
img_size=input_size,
img_mean=IMG_MEAN)
elif dataset == 'multiview':
# adaption data
data_path = '/tmp/tcn_data/texture_multibot_push_left10050/videos/train_adaptation'
train_dataset = data_loader(data_path,
is_transform=True,
view_idx=0,
number_views=1,
load_seg_mask=False,
augmentations=data_aug,
img_size=input_size,
img_mean=IMG_MEAN)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
if labeled_samples is None:
trainloader = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
else:
partial_size = labeled_samples
print('Training on number of samples:', partial_size)
np.random.seed(random_seed)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
#New loader for Domain transfer
# if random_crop:
# data_aug = Compose([RandomCrop_gta(input_size)])
# else:
# data_aug = None
# SUPERVSIED DATA
data_path = '/tmp/tcn_data/texture_multibot_push_left10050/videos/train_adaptation'
data_aug = Compose([RandomHorizontallyFlip()])
if dataset == 'multiview':
train_dataset = data_loader(data_path,
is_transform=True,
view_idx=0,
number_views=1,
load_seg_mask=True,
augmentations=data_aug,
img_size=input_size,
img_mean=IMG_MEAN)
else:
data_loader = get_loader('gta')
data_path = get_data_path('gta')
train_dataset = data_loader(data_path,
list_path='./data/gta5_list/train.txt',
augmentations=data_aug,
img_size=(1280, 720),
mean=IMG_MEAN)
trainloader = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True)
# training loss for labeled data only
trainloader_iter = iter(trainloader)
print('gta size:', len(trainloader))
#Load new data for domain_transfer
# optimizer for segmentation network
learning_rate_object = Learning_Rate_Object(
config['training']['learning_rate'])
if optimizer_type == 'SGD':
if len(gpus) > 1:
optimizer = optim.SGD(
model.module.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
else:
optimizer = optim.SGD(model.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
elif optimizer_type == 'Adam':
if len(gpus) > 1:
optimizer = optim.Adam(
model.module.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
else:
optimizer = optim.Adam(
model.optim_parameters(learning_rate_object),
lr=learning_rate,
weight_decay=weight_decay)
optimizer.zero_grad()
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
start_iteration = 0
if args.resume:
start_iteration, model, optimizer, ema_model = _resume_checkpoint(
args.resume, model, optimizer, ema_model)
accumulated_loss_l = []
accumulated_loss_u = []
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
with open(checkpoint_dir + '/config.json', 'w') as handle:
json.dump(config, handle, indent=4, sort_keys=True)
epochs_since_start = 0
for i_iter in range(start_iteration, num_iterations):
model.train()
loss_u_value = 0
loss_l_value = 0
optimizer.zero_grad()
if lr_schedule:
adjust_learning_rate(optimizer, i_iter)
# training loss for labeled data only
try:
batch = next(trainloader_iter)
if batch[0].shape[0] != batch_size:
batch = next(trainloader_iter)
except:
epochs_since_start = epochs_since_start + 1
print('Epochs since start: ', epochs_since_start)
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
#if random_flip:
# weak_parameters={"flip":random.randint(0,1)}
#else:
weak_parameters = {"flip": 0}
images, labels, _, _ = batch
images = images.cuda()
labels = labels.cuda().long()
#images, labels = weakTransform(weak_parameters, data = images, target = labels)
pred = interp(model(images))
L_l = loss_calc(pred, labels) # Cross entropy loss for labeled data
#L_l = torch.Tensor([0.0]).cuda()
if train_unlabeled:
try:
batch_remain = next(trainloader_remain_iter)
if batch_remain[0].shape[0] != batch_size:
batch_remain = next(trainloader_remain_iter)
except:
trainloader_remain_iter = iter(trainloader_remain)
batch_remain = next(trainloader_remain_iter)
images_remain, *_ = batch_remain
images_remain = images_remain.cuda()
inputs_u_w, _ = weakTransform(weak_parameters, data=images_remain)
#inputs_u_w = inputs_u_w.clone()
logits_u_w = interp(ema_model(inputs_u_w))
logits_u_w, _ = weakTransform(
getWeakInverseTransformParameters(weak_parameters),
data=logits_u_w.detach())
pseudo_label = torch.softmax(logits_u_w.detach(), dim=1)
max_probs, targets_u_w = torch.max(pseudo_label, dim=1)
if mix_mask == "class":
for image_i in range(batch_size):
classes = torch.unique(labels[image_i])
#classes=classes[classes!=ignore_label]
nclasses = classes.shape[0]
#if nclasses > 0:
classes = (classes[torch.Tensor(
np.random.choice(nclasses,
int((nclasses + nclasses % 2) / 2),
replace=False)).long()]).cuda()
if image_i == 0:
MixMask0 = transformmasks.generate_class_mask(
labels[image_i], classes).unsqueeze(0).cuda()
else:
MixMask1 = transformmasks.generate_class_mask(
labels[image_i], classes).unsqueeze(0).cuda()
elif mix_mask == None:
MixMask = torch.ones((inputs_u_w.shape))
strong_parameters = {"Mix": MixMask0}
if random_flip:
strong_parameters["flip"] = random.randint(0, 1)
else:
strong_parameters["flip"] = 0
if color_jitter:
strong_parameters["ColorJitter"] = random.uniform(0, 1)
else:
strong_parameters["ColorJitter"] = 0
if gaussian_blur:
strong_parameters["GaussianBlur"] = random.uniform(0, 1)
else:
strong_parameters["GaussianBlur"] = 0
inputs_u_s0, _ = strongTransform(
strong_parameters,
data=torch.cat(
(images[0].unsqueeze(0), images_remain[0].unsqueeze(0))))
strong_parameters["Mix"] = MixMask1
inputs_u_s1, _ = strongTransform(
strong_parameters,
data=torch.cat(
(images[1].unsqueeze(0), images_remain[1].unsqueeze(0))))
inputs_u_s = torch.cat((inputs_u_s0, inputs_u_s1))
logits_u_s = interp(model(inputs_u_s))
strong_parameters["Mix"] = MixMask0
_, targets_u0 = strongTransform(strong_parameters,
target=torch.cat(
(labels[0].unsqueeze(0),
targets_u_w[0].unsqueeze(0))))
strong_parameters["Mix"] = MixMask1
_, targets_u1 = strongTransform(strong_parameters,
target=torch.cat(
(labels[1].unsqueeze(0),
targets_u_w[1].unsqueeze(0))))
targets_u = torch.cat((targets_u0, targets_u1)).long()
if pixel_weight == "threshold_uniform":
unlabeled_weight = torch.sum(
max_probs.ge(0.968).long() == 1).item() / np.size(
np.array(targets_u.cpu()))
pixelWiseWeight = unlabeled_weight * torch.ones(
max_probs.shape).cuda()
elif pixel_weight == "threshold":
pixelWiseWeight = max_probs.ge(0.968).float().cuda()
elif pixel_weight == False:
pixelWiseWeight = torch.ones(max_probs.shape).cuda()
onesWeights = torch.ones((pixelWiseWeight.shape)).cuda()
strong_parameters["Mix"] = MixMask0
_, pixelWiseWeight0 = strongTransform(
strong_parameters,
target=torch.cat((onesWeights[0].unsqueeze(0),
pixelWiseWeight[0].unsqueeze(0))))
strong_parameters["Mix"] = MixMask1
_, pixelWiseWeight1 = strongTransform(
strong_parameters,
target=torch.cat((onesWeights[1].unsqueeze(0),
pixelWiseWeight[1].unsqueeze(0))))
pixelWiseWeight = torch.cat(
(pixelWiseWeight0, pixelWiseWeight1)).cuda()
if consistency_loss == 'MSE':
unlabeled_weight = torch.sum(
max_probs.ge(0.968).long() == 1).item() / np.size(
np.array(targets_u.cpu()))
#pseudo_label = torch.cat((pseudo_label[1].unsqueeze(0),pseudo_label[0].unsqueeze(0)))
L_u = consistency_weight * unlabeled_weight * unlabeled_loss(
logits_u_s, pseudo_label)
elif consistency_loss == 'CE':
L_u = consistency_weight * unlabeled_loss(
logits_u_s, targets_u, pixelWiseWeight)
loss = L_l + L_u
else:
loss = L_l
if len(gpus) > 1:
#print('before mean = ',loss)
loss = loss.mean()
#print('after mean = ',loss)
loss_l_value += L_l.mean().item()
if train_unlabeled:
loss_u_value += L_u.mean().item()
else:
loss_l_value += L_l.item()
if train_unlabeled:
loss_u_value += L_u.item()
loss.backward()
optimizer.step()
# update Mean teacher network
if ema_model is not None:
alpha_teacher = 0.99
ema_model = update_ema_variables(ema_model=ema_model,
model=model,
alpha_teacher=alpha_teacher,
iteration=i_iter)
print(
'iter = {0:6d}/{1:6d}, loss_l = {2:.3f}, loss_u = {3:.3f}'.format(
i_iter, num_iterations, loss_l_value, loss_u_value))
if i_iter % save_checkpoint_every == 0 and i_iter != 0:
if epochs_since_start * len(trainloader) < save_checkpoint_every:
_save_checkpoint(i_iter,
model,
optimizer,
config,
ema_model,
overwrite=False)
else:
_save_checkpoint(i_iter, model, optimizer, config, ema_model)
if config['utils']['tensorboard']:
if 'tensorboard_writer' not in locals():
tensorboard_writer = tensorboard.SummaryWriter(log_dir,
flush_secs=30)
accumulated_loss_l.append(loss_l_value)
if train_unlabeled:
accumulated_loss_u.append(loss_u_value)
if i_iter % log_per_iter == 0 and i_iter != 0:
tensorboard_writer.add_scalar('Training/Supervised loss',
np.mean(accumulated_loss_l),
i_iter)
accumulated_loss_l = []
if train_unlabeled:
tensorboard_writer.add_scalar('Training/Unsupervised loss',
np.mean(accumulated_loss_u),
i_iter)
accumulated_loss_u = []
if i_iter % val_per_iter == 0 and i_iter != 0:
model.eval()
if dataset == 'cityscapes':
mIoU, eval_loss = evaluate(model,
dataset,
ignore_label=250,
input_size=(512, 1024),
save_dir=checkpoint_dir)
elif dataset == 'multiview':
mIoU, eval_loss = evaluate(model,
dataset,
ignore_label=255,
input_size=(300, 300),
save_dir=checkpoint_dir)
else:
print('erro dataset: {}'.format(dataset))
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter,
model,
optimizer,
config,
ema_model,
save_best=True)
if config['utils']['tensorboard']:
tensorboard_writer.add_scalar('Validation/mIoU', mIoU, i_iter)
tensorboard_writer.add_scalar('Validation/Loss', eval_loss,
i_iter)
print('iter {}, mIoU: {}'.format(mIoU, i_iter))
if save_unlabeled_images and train_unlabeled and i_iter % save_checkpoint_every == 0:
# Saves two mixed images and the corresponding prediction
save_image(inputs_u_s[0].cpu(), i_iter, 'input1',
palette.CityScpates_palette)
save_image(inputs_u_s[1].cpu(), i_iter, 'input2',
palette.CityScpates_palette)
_, pred_u_s = torch.max(logits_u_s, dim=1)
save_image(pred_u_s[0].cpu(), i_iter, 'pred1',
palette.CityScpates_palette)
save_image(pred_u_s[1].cpu(), i_iter, 'pred2',
palette.CityScpates_palette)
_save_checkpoint(num_iterations, model, optimizer, config, ema_model)
model.eval()
if dataset == 'cityscapes':
mIoU, val_loss = evaluate(model,
dataset,
ignore_label=250,
input_size=(512, 1024),
save_dir=checkpoint_dir)
elif dataset == 'multiview':
mIoU, val_loss = evaluate(model,
dataset,
ignore_label=255,
input_size=(300, 300),
save_dir=checkpoint_dir)
else:
print('erro dataset: {}'.format(dataset))
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter,
model,
optimizer,
config,
ema_model,
save_best=True)
if config['utils']['tensorboard']:
tensorboard_writer.add_scalar('Validation/mIoU', mIoU, i_iter)
tensorboard_writer.add_scalar('Validation/Loss', val_loss, i_iter)
end = timeit.default_timer()
print('Total time: ' + str(end - start) + 'seconds')