def main():
"""Create the model and start the evaluation process."""
gpu0 = args.gpu
if not os.path.exists(save_dir):
os.makedirs(save_dir)
#model = torch.nn.DataParallel(Res_Deeplab(num_classes=num_classes), device_ids=args.gpu)
model = Res_Deeplab(num_classes=num_classes)
checkpoint = torch.load(args.model_path)
try:
model.load_state_dict(checkpoint['model'])
except:
model = torch.nn.DataParallel(model, device_ids=args.gpu)
model.load_state_dict(checkpoint['model'])
model.cuda()
model.eval()
evaluate(model,
dataset,
ignore_label=ignore_label,
save_output_images=args.save_output_images,
save_dir=save_dir,
input_size=input_size)
def create_ema_model(model):
ema_model = Res_Deeplab(num_classes=num_classes)
for param in ema_model.parameters():
param.detach_()
mp = list(model.parameters())
mcp = list(ema_model.parameters())
n = len(mp)
for i in range(0, n):
mcp[i].data[:] = mp[i].data[:].clone()
if len(gpus) > 1:
if use_sync_batchnorm:
ema_model = convert_model(ema_model)
ema_model = DataParallelWithCallback(ema_model, device_ids=gpus)
else:
ema_model = torch.nn.DataParallel(ema_model, device_ids=gpus)
return ema_model
def main():
"""Create the model and start the evaluation process."""
deeplabv2 = "2" in config['version']
if deeplabv2:
if pretraining == 'COCO': # coco and iamgenet resnet architectures differ a little, just on how to do the stride
from model.deeplabv2 import Res_Deeplab
else: # imagenet pretrained (more modern modification)
from model.deeplabv2_imagenet import Res_Deeplab
else:
from model.deeplabv3 import Res_Deeplab
model = Res_Deeplab(num_classes=num_classes)
checkpoint = torch.load(args.model_path)
model.load_state_dict(checkpoint['model'])
model = model.cuda()
model.eval()
evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
pretraining=pretraining)
def create_ema_model(model):
#ema_model = getattr(models, config['arch']['type'])(self.train_loader.dataset.num_classes, **config['arch']['args']).to(self.device)
ema_model = Res_Deeplab(num_classes=num_classes)
for param in ema_model.parameters():
param.detach_()
mp = list(model.parameters())
mcp = list(ema_model.parameters())
n = len(mp)
for i in range(0, n):
mcp[i].data[:] = mp[i].data[:].clone()
#_, availble_gpus = self._get_available_devices(self.config['n_gpu'])
#ema_model = torch.nn.DataParallel(ema_model, device_ids=availble_gpus)
if len(gpus) > 1:
#return torch.nn.DataParallel(ema_model, device_ids=gpus)
if use_sync_batchnorm:
ema_model = convert_model(ema_model)
ema_model = DataParallelWithCallback(ema_model, device_ids=gpus)
else:
ema_model = torch.nn.DataParallel(ema_model, device_ids=gpus)
return ema_model
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)
cudnn.enabled = True
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
torch.backends.cudnn.deterministic = True
if pretraining == 'COCO': # depending the pretraining, normalize with bgr or rgb
from utils.transformsgpu import normalize_bgr as normalize
else:
from utils.transformsgpu import normalize_rgb as normalize
batch_size_unlabeled = int(
batch_size /
2) # because of augmentation anchoring, 2 augmentations per sample
batch_size_labeled = int(batch_size * 1)
assert batch_size_unlabeled >= 2, "batch size should be higher than 2"
assert batch_size_labeled >= 2, "batch size should be higher than 2"
RAMP_UP_ITERS = 2000 # iterations until contrastive and self-training are taken into account
# DATASETS / LOADERS
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=False,
mirror=False,
pretraining=pretraining)
elif dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
if deeplabv2:
data_aug = Compose([RandomCrop_city(input_size)])
else: # for deeplabv3 original resolution
data_aug = Compose([RandomCrop_city_highres(input_size)])
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug,
img_size=input_size,
pretraining=pretraining)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
partial_size = labeled_samples
print('Training on number of samples:', partial_size)
# class weighting taken unlabeled data into acount in an incremental fashion.
class_weights_curr = ClassBalancing(
labeled_iters=int(labeled_samples / batch_size_labeled),
unlabeled_iters=int(
(train_dataset_size - labeled_samples) / batch_size_unlabeled),
n_classes=num_classes)
# Memory Bank
feature_memory = FeatureMemory(num_samples=labeled_samples,
dataset=dataset,
memory_per_class=256,
feature_size=256,
n_classes=num_classes)
# select the partition
if split_id is not None:
train_ids = pickle.load(open(split_id, 'rb'))
print('loading train ids from {}'.format(split_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
# Samplers for labeled data
train_sampler = data.sampler.SubsetRandomSampler(train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=batch_size_labeled,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True)
trainloader_iter = iter(trainloader)
# Samplers for unlabeled data
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
trainloader_remain = data.DataLoader(train_dataset,
batch_size=batch_size_unlabeled,
sampler=train_remain_sampler,
num_workers=num_workers,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
# supervised loss
supervised_loss = CrossEntropy2d(ignore_label=ignore_label).cuda()
''' Deeplab model '''
# Define network
if deeplabv2:
if pretraining == 'COCO': # coco and imagenet resnet architectures differ a little, just on how to do the stride
from model.deeplabv2 import Res_Deeplab
else: # imagenet pretrained (more modern modification)
from model.deeplabv2_imagenet import Res_Deeplab
# load pretrained parameters
if pretraining == 'COCO':
saved_state_dict = model_zoo.load_url(
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/resnet101COCO-41f33a49.pth'
) # COCO pretraining
else:
saved_state_dict = model_zoo.load_url(
'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'
) # iamgenet pretrainning
else:
from model.deeplabv3 import Res_Deeplab50 as Res_Deeplab
saved_state_dict = model_zoo.load_url(
'https://download.pytorch.org/models/resnet50-19c8e357.pth'
) # iamgenet pretrainning
# create network
model = Res_Deeplab(num_classes=num_classes)
# 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)
# Optimizer for segmentation network
learning_rate_object = Learning_Rate_Object(
config['training']['learning_rate'])
optimizer = torch.optim.SGD(model.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
ema_model = create_ema_model(model, Res_Deeplab)
ema_model.train()
ema_model = ema_model.cuda()
model.train()
model = model.cuda()
cudnn.benchmark = True
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'))
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
epochs_since_start = 0
start_iteration = 0
best_mIoU = 0 # best metric while training
iters_without_improve = 0
# TRAINING
for i_iter in range(start_iteration, num_iterations):
model.train() # set mode to training
optimizer.zero_grad()
loss_l_value = 0.
adjust_learning_rate(optimizer, i_iter)
labeled_turn = i_iter % 2 == 0
if labeled_turn: # labeled data optimization
''' LABELED SAMPLES '''
# Get batch
try:
batch = next(trainloader_iter)
if batch[0].shape[0] != batch_size_labeled:
batch = next(trainloader_iter)
except: # finish epoch, rebuild the iterator
epochs_since_start = epochs_since_start + 1
# print('Epochs since start: ',epochs_since_start)
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
images, labels, _, _, _ = batch
images = images.cuda()
labels = labels.cuda()
model.train()
if dataset == 'cityscapes':
class_weights_curr.add_frequencies_labeled(
labels.cpu().numpy())
images_aug, labels_aug, _, _ = augment_samples(
images,
labels,
None,
random.random() < 0.2,
batch_size_labeled,
ignore_label,
weak=True)
# labeled data
labeled_pred, labeled_features = model(normalize(
images_aug, dataset),
return_features=True)
labeled_pred = interp(labeled_pred)
# apply class balance for cityspcaes dataset
class_weights = torch.from_numpy(np.ones((num_classes))).cuda()
if i_iter > RAMP_UP_ITERS and dataset == 'cityscapes':
class_weights = torch.from_numpy(
class_weights_curr.get_weights(num_iterations,
only_labeled=False)).cuda()
loss = 0
# SUPERVISED SEGMENTATION
labeled_loss = supervised_loss(labeled_pred,
labels_aug,
weight=class_weights.float())
loss = loss + labeled_loss
# CONTRASTIVE LEARNING
if i_iter > RAMP_UP_ITERS - 1000:
# Build Memory Bank 1000 iters before starting to do contrsative
with torch.no_grad():
# Get feature vectors from labeled images with EMA model
if use_teacher:
labeled_pred_ema, labeled_features_ema = ema_model(
normalize(images_aug, dataset),
return_features=True)
else:
model.eval()
labeled_pred_ema, labeled_features_ema = model(
normalize(images_aug, dataset),
return_features=True)
model.train()
labeled_pred_ema = interp(labeled_pred_ema)
probability_prediction_ema, label_prediction_ema = torch.max(
torch.softmax(labeled_pred_ema,
dim=1), dim=1) # Get pseudolabels
# Resize labels, predictions and probabilities, to feature map resolution
labels_down = nn.functional.interpolate(
labels_aug.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
label_prediction_down = nn.functional.interpolate(
label_prediction_ema.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
probability_prediction_down = nn.functional.interpolate(
probability_prediction_ema.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
# get mask where the labeled predictions are correct and have a confidence higher than 0.95
mask_prediction_correctly = (
(label_prediction_down == labels_down).float() *
(probability_prediction_down > 0.95).float()).bool()
# Apply the filter mask to the features and its labels
labeled_features_correct = labeled_features_ema.permute(
0, 2, 3, 1)
labels_down_correct = labels_down[mask_prediction_correctly]
labeled_features_correct = labeled_features_correct[
mask_prediction_correctly, ...]
# get projected features
with torch.no_grad():
if use_teacher:
proj_labeled_features_correct = ema_model.projection_head(
labeled_features_correct)
else:
model.eval()
proj_labeled_features_correct = model.projection_head(
labeled_features_correct)
model.train()
# updated memory bank
feature_memory.add_features_from_sample_learned(
ema_model, proj_labeled_features_correct,
labels_down_correct, batch_size_labeled)
if i_iter > RAMP_UP_ITERS:
'''
CONTRASTIVE LEARNING ON LABELED DATA. Force features from labeled samples, to be similar to other features from the same class (which also leads to good predictions
'''
# mask features that do not have ignore label in the labels (zero-padding because of data augmentation like resize/crop)
mask_prediction_correctly = (labels_down != ignore_label)
labeled_features_all = labeled_features.permute(0, 2, 3, 1)
labels_down_all = labels_down[mask_prediction_correctly]
labeled_features_all = labeled_features_all[
mask_prediction_correctly, ...]
# get predicted features
proj_labeled_features_all = model.projection_head(
labeled_features_all)
pred_labeled_features_all = model.prediction_head(
proj_labeled_features_all)
# Apply contrastive learning loss
loss_contr_labeled = contrastive_class_to_class_learned_memory(
model, pred_labeled_features_all, labels_down_all,
num_classes, feature_memory.memory)
loss = loss + loss_contr_labeled * 0.1
else: # unlabeled data optimization
''' UNLABELED SAMPLES '''
try:
batch_remain = next(trainloader_remain_iter)
if batch_remain[0].shape[0] != batch_size_unlabeled:
batch_remain = next(trainloader_remain_iter)
except:
trainloader_remain_iter = iter(trainloader_remain)
batch_remain = next(trainloader_remain_iter)
# Unlabeled
unlabeled_images, _, _, _, _ = batch_remain
unlabeled_images = unlabeled_images.cuda()
# Create pseudolabels
with torch.no_grad():
if use_teacher:
logits_u_w, features_weak_unlabeled = ema_model(
normalize(unlabeled_images, dataset),
return_features=True)
else:
model.eval()
logits_u_w, features_weak_unlabeled = model(
normalize(unlabeled_images, dataset),
return_features=True)
logits_u_w = interp(
logits_u_w).detach() # prediction unlabeled
softmax_u_w = torch.softmax(logits_u_w, dim=1)
max_probs, pseudo_label = torch.max(softmax_u_w,
dim=1) # Get pseudolabels
model.train()
if dataset == 'cityscapes':
class_weights_curr.add_frequencies_unlabeled(
pseudo_label.cpu().numpy())
'''
UNLABELED DATA
'''
unlabeled_images_aug1, pseudo_label1, max_probs1, unlabeled_aug1_params = augment_samples(
unlabeled_images, pseudo_label, max_probs,
i_iter > RAMP_UP_ITERS and random.random() < 0.75,
batch_size_unlabeled, ignore_label)
unlabeled_images_aug2, pseudo_label2, max_probs2, unlabeled_aug2_params = augment_samples(
unlabeled_images, pseudo_label, max_probs,
i_iter > RAMP_UP_ITERS and random.random() < 0.75,
batch_size_unlabeled, ignore_label)
# concatenate two augmentations of unlabeled data
joined_unlabeled = torch.cat(
(unlabeled_images_aug1, unlabeled_images_aug2), dim=0)
joined_pseudolabels = torch.cat((pseudo_label1, pseudo_label2),
dim=0)
joined_maxprobs = torch.cat((max_probs1, max_probs2), dim=0)
pred_joined_unlabeled, features_joined_unlabeled = model(
normalize(joined_unlabeled, dataset), return_features=True)
pred_joined_unlabeled = interp(pred_joined_unlabeled)
# apply clas balance for cityspcaes dataset
if dataset == 'cityscapes':
class_weights = torch.from_numpy(
class_weights_curr.get_weights(num_iterations,
only_labeled=False)).cuda()
else:
class_weights = torch.from_numpy(np.ones((num_classes))).cuda()
loss = 0
# SELF-SUPERVISED SEGMENTATION
unlabeled_loss = CrossEntropyLoss2dPixelWiseWeighted(
ignore_index=ignore_label,
weight=class_weights.float()).cuda() #
# Pseudo-label weighting
pixelWiseWeight = sigmoid_ramp_up(
i_iter, RAMP_UP_ITERS) * torch.ones(
joined_maxprobs.shape).cuda()
pixelWiseWeight = pixelWiseWeight * torch.pow(
joined_maxprobs.detach(), 6)
# Pseudo-label loss
loss_ce_unlabeled = unlabeled_loss(pred_joined_unlabeled,
joined_pseudolabels,
pixelWiseWeight)
loss = loss + loss_ce_unlabeled
# entropy loss
valid_mask = (joined_pseudolabels != ignore_label).unsqueeze(1)
loss = loss + entropy_loss(
torch.nn.functional.softmax(pred_joined_unlabeled, dim=1),
valid_mask) * 0.01
if i_iter > RAMP_UP_ITERS:
'''
CONTRASTIVE LEARNING ON UNLABELED DATA. align unlabeled features to labeled features
'''
joined_pseudolabels_down = nn.functional.interpolate(
joined_pseudolabels.float().unsqueeze(1),
size=(features_joined_unlabeled.shape[2],
features_joined_unlabeled.shape[3]),
mode='nearest').squeeze(1)
# mask features that do not have ignore label in the labels (zero-padding because of data augmentation like resize/crop)
mask = (joined_pseudolabels_down != ignore_label)
features_joined_unlabeled = features_joined_unlabeled.permute(
0, 2, 3, 1)
features_joined_unlabeled = features_joined_unlabeled[mask,
...]
joined_pseudolabels_down = joined_pseudolabels_down[mask]
# get predicted features
proj_feat_unlabeled = model.projection_head(
features_joined_unlabeled)
pred_feat_unlabeled = model.prediction_head(
proj_feat_unlabeled)
# Apply contrastive learning loss
loss_contr_unlabeled = contrastive_class_to_class_learned_memory(
model, pred_feat_unlabeled, joined_pseudolabels_down,
num_classes, feature_memory.memory)
loss = loss + loss_contr_unlabeled * 0.1
# common code
loss_l_value += loss.item()
# optimize
loss.backward()
optimizer.step()
m = 1 - (1 - 0.995) * (math.cos(math.pi * i_iter / num_iterations) +
1) / 2
ema_model = update_ema_variables(ema_model=ema_model,
model=model,
alpha_teacher=m,
iteration=i_iter)
if i_iter % save_checkpoint_every == 0 and i_iter != 0:
_save_checkpoint(i_iter, model, optimizer, config)
if i_iter % val_per_iter == 0 and i_iter != 0:
print('iter = {0:6d}/{1:6d}'.format(i_iter, num_iterations))
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU:
best_mIoU = mIoU
if save_teacher:
_save_checkpoint(i_iter,
ema_model,
optimizer,
config,
save_best=True)
else:
_save_checkpoint(i_iter,
model,
optimizer,
config,
save_best=True)
iters_without_improve = 0
else:
iters_without_improve += val_per_iter
'''
if the performance has not improve in N iterations, try to reload best model to optimize again with a lower LR
Simulating an iterative training'''
if iters_without_improve > num_iterations / 5.:
print('Re-loading a previous best model')
checkpoint = torch.load(
os.path.join(checkpoint_dir, f'best_model.pth'))
model.load_state_dict(checkpoint['model'])
ema_model = create_ema_model(model, Res_Deeplab)
ema_model.train()
ema_model = ema_model.cuda()
model.train()
model = model.cuda()
iters_without_improve = 0 # reset timer
_save_checkpoint(num_iterations, model, optimizer, config)
# FINISH TRAINING, evaluate again
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter, model, optimizer, config, save_best=True)
# TRY IMPROVING BEST MODEL WITH EMA MODEL OR UPDATING BN STATS
# Load best model
checkpoint = torch.load(os.path.join(checkpoint_dir, f'best_model.pth'))
model.load_state_dict(checkpoint['model'])
model = model.cuda()
model = update_BN_weak_unlabeled_data(model, normalize,
batch_size_unlabeled,
trainloader_remain)
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter, model, optimizer, config, save_best=True)
print('BEST MIOU')
print(max(best_mIoU_improved, best_mIoU))
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')
def main():
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
model.cuda()
# load pretrained parameters
saved_state_dict = torch.load(args.restore_from)
# only copy the params that exist in current model (caffe-like)
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)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if args.dataset == 'pascal_voc':
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
elif args.dataset == 'CMR':
train_dataset = CMRDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
elif args.dataset == 'pascal_context':
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
data_kwargs = {
'transform': input_transform,
'base_size': 505,
'crop_size': 321
}
#train_dataset = get_segmentation_dataset('pcontext', split='train', mode='train', **data_kwargs)
data_loader = get_loader('pascal_context')
data_path = get_data_path('pascal_context')
train_dataset = data_loader(data_path,
split='train',
mode='train',
**data_kwargs)
elif args.dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
data_aug = Compose(
[RandomCrop_city((256, 512)),
RandomHorizontallyFlip()])
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
if args.labeled_ratio is None:
trainloader = data.DataLoader(train_dataset,
shuffle=True,
batch_size=args.batch_size,
num_workers=4,
pin_memory=True)
else:
partial_size = int(args.labeled_ratio * train_dataset_size)
if args.split_id is not None:
train_ids = pickle.load(open(args.split_id, 'rb'))
print('loading train ids from {}'.format(args.split_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(train_ids,
open(os.path.join(args.checkpoint_dir, 'split.pkl'), 'wb'))
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=4,
pin_memory=True)
trainloader_iter = iter(trainloader)
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# loss/ bilinear upsampling
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
for i_iter in range(args.num_steps):
loss_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
try:
batch_lab = next(trainloader_iter)
except:
trainloader_iter = iter(trainloader)
batch_lab = next(trainloader_iter)
images, labels, _, _, index = batch_lab
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
loss = loss_calc(pred, labels, args.gpu)
loss.backward()
loss_value += loss.item()
optimizer.step()
print('iter = {0:8d}/{1:8d}, loss_seg = {2:.3f}'.format(
i_iter, args.num_steps, loss_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
osp.join(args.checkpoint_dir,
'VOC_' + str(args.num_steps) + '.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('saving checkpoint ...')
torch.save(
model.state_dict(),
osp.join(args.checkpoint_dir, 'VOC_' + str(i_iter) + '.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
print(args)
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
# load pretrained parameters
saved_state_dict = torch.load(args.restore_from)
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)
model.train()
model.cuda(args.gpu)
cudnn.benchmark = True
# init D
model_D = s4GAN_discriminator(num_classes=args.num_classes,
dataset=args.dataset)
if args.restore_from_D is not None:
model_D.load_state_dict(torch.load(args.restore_from_D))
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
if args.dataset == 'pascal_voc':
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
#train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size,
#scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
elif args.dataset == 'pascal_context':
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.406, .456, .485], [.229, .224, .225])
])
data_kwargs = {
'transform': input_transform,
'base_size': 505,
'crop_size': 321
}
#train_dataset = get_segmentation_dataset('pcontext', split='train', mode='train', **data_kwargs)
data_loader = get_loader('pascal_context')
data_path = get_data_path('pascal_context')
train_dataset = data_loader(data_path,
split='train',
mode='train',
**data_kwargs)
#train_gt_dataset = data_loader(data_path, split='train', mode='train', **data_kwargs)
elif args.dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
data_aug = Compose(
[RandomCrop_city((256, 512)),
RandomHorizontallyFlip()])
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug)
#train_gt_dataset = data_loader( data_path, is_transform=True, augmentations=data_aug)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
if args.labeled_ratio is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_gt = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
else:
partial_size = int(args.labeled_ratio * train_dataset_size)
if args.split_id is not None:
train_ids = pickle.load(open(args.split_id, 'rb'))
print('loading train ids from {}'.format(args.split_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(
train_ids,
open(os.path.join(args.checkpoint_dir, 'train_voc_split.pkl'),
'wb'))
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=4,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=4,
pin_memory=True)
trainloader_gt = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=4,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
trainloader_iter = iter(trainloader)
trainloader_gt_iter = iter(trainloader_gt)
# optimizer for segmentation network
optimizer = optim.SGD(model.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
# labels for adversarial training
pred_label = 0
gt_label = 1
y_real_, y_fake_ = Variable(torch.ones(args.batch_size,
1).cuda()), Variable(
torch.zeros(args.batch_size,
1).cuda())
for i_iter in range(args.num_steps):
loss_ce_value = 0
loss_D_value = 0
loss_fm_value = 0
loss_S_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
# train Segmentation Network
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# training loss for labeled data only
try:
batch = next(trainloader_iter)
except:
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
images, labels, _, _, _ = batch
images = Variable(images).cuda(args.gpu)
pred = interp(model(images))
loss_ce = loss_calc(pred, labels,
args.gpu) # Cross entropy loss for labeled data
#training loss for remaining unlabeled data
try:
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 = Variable(images_remain).cuda(args.gpu)
pred_remain = interp(model(images_remain))
# concatenate the prediction with the input images
images_remain = (images_remain - torch.min(images_remain)) / (
torch.max(images_remain) - torch.min(images_remain))
#print (pred_remain.size(), images_remain.size())
pred_cat = torch.cat((F.softmax(pred_remain, dim=1), images_remain),
dim=1)
D_out_z, D_out_y_pred = model_D(
pred_cat) # predicts the D ouput 0-1 and feature map for FM-loss
# find predicted segmentation maps above threshold
pred_sel, labels_sel, count = find_good_maps(D_out_z, pred_remain)
# training loss on above threshold segmentation predictions (Cross Entropy Loss)
if count > 0 and i_iter > 0:
loss_st = loss_calc(pred_sel, labels_sel, args.gpu)
else:
loss_st = 0.0
# Concatenates the input images and ground-truth maps for the Districrimator 'Real' input
try:
batch_gt = next(trainloader_gt_iter)
except:
trainloader_gt_iter = iter(trainloader_gt)
batch_gt = next(trainloader_gt_iter)
images_gt, labels_gt, _, _, _ = batch_gt
# Converts grounth truth segmentation into 'num_classes' segmentation maps.
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
images_gt = images_gt.cuda()
images_gt = (images_gt - torch.min(images_gt)) / (torch.max(images) -
torch.min(images))
D_gt_v_cat = torch.cat((D_gt_v, images_gt), dim=1)
D_out_z_gt, D_out_y_gt = model_D(D_gt_v_cat)
# L1 loss for Feature Matching Loss
loss_fm = torch.mean(
torch.abs(torch.mean(D_out_y_gt, 0) - torch.mean(D_out_y_pred, 0)))
if count > 0 and i_iter > 0: # if any good predictions found for self-training loss
loss_S = loss_ce + args.lambda_fm * loss_fm + args.lambda_st * loss_st
else:
loss_S = loss_ce + args.lambda_fm * loss_fm
loss_S.backward()
loss_fm_value += args.lambda_fm * loss_fm
loss_ce_value += loss_ce.item()
loss_S_value += loss_S.item()
# train D
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred_cat = pred_cat.detach(
) # detach does not allow the graddients to back propagate.
D_out_z, _ = model_D(pred_cat)
y_fake_ = Variable(torch.zeros(D_out_z.size(0), 1).cuda())
loss_D_fake = criterion(D_out_z, y_fake_)
# train with gt
D_out_z_gt, _ = model_D(D_gt_v_cat)
y_real_ = Variable(torch.ones(D_out_z_gt.size(0), 1).cuda())
loss_D_real = criterion(D_out_z_gt, y_real_)
loss_D = (loss_D_fake + loss_D_real) / 2.0
loss_D.backward()
loss_D_value += loss_D.item()
optimizer.step()
optimizer_D.step()
print(
'iter = {0:8d}/{1:8d}, loss_ce = {2:.3f}, loss_fm = {3:.3f}, loss_S = {4:.3f}, loss_D = {5:.3f}'
.format(i_iter, args.num_steps, loss_ce_value, loss_fm_value,
loss_S_value, loss_D_value))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(
model.state_dict(),
os.path.join(args.checkpoint_dir,
'VOC_' + str(args.num_steps) + '.pth'))
torch.save(
model_D.state_dict(),
os.path.join(args.checkpoint_dir,
'VOC_' + str(args.num_steps) + '_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('saving checkpoint ...')
torch.save(
model.state_dict(),
os.path.join(args.checkpoint_dir,
'VOC_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
os.path.join(args.checkpoint_dir,
'VOC_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
print(args)
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
cudnn.enabled = True
gpu = args.gpu
# create network
model = Res_Deeplab(num_classes=args.num_classes)
model.train()
model.cuda(args.gpu)
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
summary(model, (110, 64, 64))
# load pretrained parameters
saved_state_dict = torch.load(args.restore_from)
#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(saved_state_dict)
# init D
model_D = s4GAN_discriminator(num_classes=args.num_classes,
dataset=args.dataset)
fig, axarr = plt.subplots(6, sharex=False, figsize=(20, 20))
#p_class = [0.0355738, 0.00141609, 0.48528844, 0.07337901, 0.0388637, 0.1026877,
#0.00271774, 0.18383373, 0.0359457, 0., 0., 0.02593297,
#0.01436112, 0., 0.] # small
p_class = [
7.25703402e-02, 1.57180553e-01, 1.81395714e-01, 2.15331438e-01,
8.59744781e-02, 6.45834114e-02, 2.08535688e-03, 2.95754679e-02,
2.30909954e-02, 1.18364523e-03, 5.40670110e-04, 4.34120229e-02,
1.03664125e-01, 2.07385748e-03, 1.73379244e-02
]
#p_class = [0.0688357, 0.16084504, 0.17536666, 0.19976606, 0.12290404, 0.06232464,
# 0.00192151, 0.02954287, 0.02216445, 0.0013851, 0.00159172, 0.03166692,
# 0.1009182, 0.00169153, 0.01907556] # 50%
weights_t = (1 / torch.log(1.02 + torch.tensor(p_class))).cuda()
model_D = torch.nn.DataParallel(model_D).cuda()
#if args.restore_from_D is not None:
#model_D.load_state_dict(torch.load(args.restore_from_D))
cudnn.benchmark = True
model_D.train()
model_D.cuda(args.gpu)
if not os.path.exists(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
#os.makedirs(os.path.join(args.checkpoint_dir,'rend'))
if args.dataset == 'pascal_voc':
train_dataset = VOCDataSet(args.data_dir,
args.data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=IMG_MEAN)
#train_gt_dataset = VOCGTDataSet(args.data_dir, args.data_list, crop_size=input_size,
#scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN)
elif args.dataset == 'pascal_context':
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.406, .456, .485], [.229, .224, .225])
])
data_kwargs = {
'transform': input_transform,
'base_size': 505,
'crop_size': 321
}
#train_dataset = get_segmentation_dataset('pcontext', split='train', mode='train', **data_kwargs)
data_loader = get_loader('pascal_context')
data_path = get_data_path('pascal_context')
train_dataset = data_loader(data_path,
split='train',
mode='train',
**data_kwargs)
#train_gt_dataset = data_loader(data_path, split='train', mode='train', **data_kwargs)
elif args.dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
data_aug = Compose(
[RandomCrop_city((256, 512)),
RandomHorizontallyFlip()])
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug)
#train_gt_dataset = data_loader( data_path, is_transform=True, augmentations=data_aug)
elif args.dataset == 'sen2':
data_loader = get_loader('sen2')
data_path = get_data_path('sen2')
train_dataset = get_dataloader(data_loader, data_path, input_size,
["labelled_train"], "train_sup")
test_dataset = get_dataloader(data_loader, data_path, input_size,
["labelled_test"], "test")
train_dataset_size = len(train_dataset)
test_dataset_size = len(test_dataset)
print('train dataset size: ', train_dataset_size)
print('test dataset size: ', test_dataset_size)
num_batches_train = int(train_dataset_size / args.batch_size) + 1
last_batch_sz = train_dataset_size % args.batch_size
num_batches_test = int(test_dataset_size / args.batch_size) + 1
print('num batches train : ', num_batches_train)
print('last batch size : ', last_batch_sz)
if args.labeled_ratio is None:
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_gt = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
testloader = data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
pin_memory=True)
else:
partial_size = int(args.labeled_ratio * train_dataset_size)
if args.split_id is not None:
train_ids = pickle.load(open(args.split_id, 'rb'))
print('loading train ids from {}'.format(args.split_id))
else:
train_ids = np.arange(train_dataset_size)
np.random.shuffle(train_ids)
pickle.dump(
train_ids,
open(os.path.join(args.checkpoint_dir, 'train_voc_split.pkl'),
'wb'))
train_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
train_gt_sampler = data.sampler.SubsetRandomSampler(
train_ids[:partial_size])
trainloader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_sampler,
num_workers=4,
pin_memory=True)
trainloader_remain = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_remain_sampler,
num_workers=4,
pin_memory=True)
trainloader_gt = data.DataLoader(train_dataset,
batch_size=args.batch_size,
sampler=train_gt_sampler,
num_workers=4,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
trainloader_iter = iter(trainloader)
trainloader_gt_iter = iter(trainloader_gt)
# optimizer for segmentation network
optimizer = optim.SGD(model.module.optim_parameters(args),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
optimizer.zero_grad()
# optimizer for discriminator network
optimizer_D = optim.Adam(model_D.parameters(),
lr=args.learning_rate_D,
betas=(0.9, 0.99))
optimizer_D.zero_grad()
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
# labels for adversarial training
pred_label = 0
gt_label = 1
y_real_, y_fake_ = Variable(torch.ones(args.batch_size,
1).cuda()), Variable(
torch.zeros(args.batch_size,
1).cuda())
train_accs = []
test_accs = []
train_kscores = []
test_kscores = []
losses_ce = []
losses_fm = []
losses_S = []
losses_D = []
e_i = 0
loss_ce_value = 0
loss_D_value = 0
loss_fm_value = 0
loss_S_value = 0
for i_iter in range(args.num_steps):
#loss_ce_value = 0
#loss_D_value = 0
#loss_fm_value = 0
#loss_S_value = 0
optimizer.zero_grad()
adjust_learning_rate(optimizer, i_iter)
optimizer_D.zero_grad()
adjust_learning_rate_D(optimizer_D, i_iter)
# train Segmentation Network
# don't accumulate grads in D
for param in model_D.parameters():
param.requires_grad = False
# training loss for labeled data only
try:
batch = next(trainloader_iter)
except:
print("end epoch %s" % e_i)
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
images, labels = batch
images = Variable(images).cuda(args.gpu)
pred = F.interpolate(model(images),
size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
loss_ce = loss_calc(pred, labels, weights_t,
args.gpu) # Cross entropy loss for labeled data
#training loss for remaining unlabeled data
try:
batch_remain = next(trainloader_remain_iter)
except:
trainloader_remain_iter = iter(trainloader_remain)
batch_remain = next(trainloader_remain_iter)
images_remain, labels_remain = batch_remain
images_remain = Variable(images_remain).cuda(args.gpu)
pred_remain = F.interpolate(model(images_remain),
size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
# concatenate the prediction with the input images
images_remain = (images_remain - torch.min(images_remain)) / (
torch.max(images_remain) - torch.min(images_remain))
#print (pred_remain.size(), images_remain.size())
n, c, w, h = pred_remain.size()
mask = (labels_remain != args.ignore_label)
pred_remain[mask.view(n, 1, w, h).repeat(1, c, 1, 1)] = 0
pred_cat = torch.cat((F.softmax(pred_remain, dim=1), images_remain),
dim=1)
D_out_z, D_out_y_pred = model_D(
pred_cat
) # predicts the D ouput 0-1 and feature map for FM-loss D_out_y_pred
# find predicted segmentation maps above threshold
pred_sel, labels_sel, count = find_good_maps(D_out_z, pred_remain)
# training loss on above threshold segmentation predictions (Cross Entropy Loss)
if count > 0 and i_iter > 0:
loss_st = loss_calc(pred_sel, labels_sel, weights_t, args.gpu)
else:
loss_st = 0.0
# Concatenates the input images and ground-truth maps for the Districrimator 'Real' input
try:
batch_gt = next(trainloader_gt_iter)
except:
trainloader_gt_iter = iter(trainloader_gt)
batch_gt = next(trainloader_gt_iter)
images_gt, labels_gt = batch_gt
# Converts grounth truth segmentation into 'num_classes' segmentation maps.
D_gt_v = Variable(one_hot(labels_gt)).cuda(args.gpu)
images_gt = images_gt.cuda()
images_gt = (images_gt - torch.min(images_gt)) / (torch.max(images) -
torch.min(images))
D_gt_v_cat = torch.cat((D_gt_v, images_gt), dim=1)
D_out_z_gt, D_out_y_gt = model_D(D_gt_v_cat) # D_out_y_gt
# L1 loss for Feature Matching Loss
loss_fm = torch.mean(
torch.abs(torch.mean(D_out_y_gt, 0) - torch.mean(D_out_y_pred, 0)))
if count > 0 and i_iter > 0: # if any good predictions found for self-training loss
loss_S = loss_ce + args.lambda_st * loss_st + args.lambda_fm * loss_fm
else:
loss_S = loss_ce + args.lambda_fm * loss_fm
loss_S.backward()
loss_fm_value += args.lambda_fm * loss_fm
loss_ce_value += loss_ce.item()
loss_S_value += loss_S.item()
# train D
for param in model_D.parameters():
param.requires_grad = True
# train with pred
pred_cat = pred_cat.detach(
) # detach does not allow the graddients to back propagate.
D_out_z, _ = model_D(pred_cat)
y_fake_ = Variable(torch.zeros(D_out_z.size(0), 1).cuda())
loss_D_fake = criterion(D_out_z, y_fake_)
# train with gt
D_out_z_gt, _ = model_D(D_gt_v_cat)
y_real_ = Variable(torch.ones(D_out_z_gt.size(0), 1).cuda())
loss_D_real = criterion(D_out_z_gt, y_real_)
loss_D = (loss_D_fake + loss_D_real) / 2.0
loss_D.backward()
loss_D_value += loss_D.item()
optimizer.step()
optimizer_D.step()
print(
'iter = {0:8d}/{1:8d}, loss_ce = {2:.3f}, loss_fm = {3:.3f}, loss_S = {4:.3f}, loss_D = {5:.3f}'
.format(i_iter, args.num_steps, loss_ce_value, loss_fm_value,
loss_S_value, loss_D_value))
# EVALUATION
# -------------------------------------------------------------------------------
if (i_iter % (num_batches_train - 1) == 0) & (i_iter > 0):
e_i += 1
train_acc, train_kappa_score, train_cm = _eval(model,
trainloader,
args.batch_size,
input_size,
num_batches_train,
args.gpu,
render_preds=False)
test_acc, test_kappa_score, test_cm = _eval(model,
testloader,
args.batch_size,
input_size,
num_batches_test,
args.gpu,
render_preds=False)
print('train_acc = ', train_acc)
print('test_acc = ', test_acc)
cm_name_train = "confusion_matrix_%d.png" % e_i
plot_confusion_matrix(cm=train_cm,
classes=(range(args.num_classes)),
normalize=True)
plt.savefig(os.path.join(args.checkpoint_dir, cm_name_train))
avg_loss_ce = loss_ce_value / num_batches_train
avg_loss_fm = loss_fm_value / num_batches_train
avg_loss_S = loss_S_value / num_batches_train
avg_loss_D = loss_D_value / num_batches_train
loss_ce_value = 0
loss_D_value = 0
loss_fm_value = 0
loss_S_value = 0
train_accs.append(train_acc)
test_accs.append(test_acc)
train_kscores.append(train_kappa_score)
test_kscores.append(test_kappa_score)
losses_ce.append(avg_loss_ce)
losses_fm.append(avg_loss_fm)
losses_S.append(avg_loss_S)
losses_D.append(avg_loss_D)
axarr[0].clear()
axarr[0].plot(train_accs, 'g')
axarr[0].plot(test_accs, 'r')
axarr[0].set_title("acc (best), top train : %f" % max(train_accs))
axarr[1].clear()
axarr[1].plot(train_kscores, 'g')
axarr[1].plot(test_kscores, 'r')
axarr[1].set_title("Cohen's kappa score (best) : %f" %
max(train_kscores))
axarr[2].clear()
axarr[2].plot(losses_D)
axarr[2].set_title("Loss D")
axarr[3].clear()
axarr[3].plot(losses_S)
axarr[3].set_title("Loss S")
axarr[4].clear()
axarr[4].plot(losses_ce)
axarr[4].set_title("Loss ce")
axarr[5].clear()
axarr[5].plot(losses_fm)
axarr[5].set_title("Loss fm")
fig.canvas.draw_idle()
fig.savefig(os.path.join(args.checkpoint_dir, "plots.png"))
if train_acc >= max(train_accs):
print('save model ...')
torch.save(model.state_dict(),
os.path.join(args.checkpoint_dir, 'best.pth'))
torch.save(model_D.state_dict(),
os.path.join(args.checkpoint_dir, 'best_D.pth'))
if i_iter >= args.num_steps - 1:
print('save model ...')
torch.save(model.state_dict(),
os.path.join(args.checkpoint_dir, 'latest.pth'))
torch.save(model_D.state_dict(),
os.path.join(args.checkpoint_dir, 'latest_D.pth'))
break
if i_iter % args.save_pred_every == 0 and i_iter != 0:
print('saving checkpoint ...')
torch.save(
model.state_dict(),
os.path.join(args.checkpoint_dir,
'sen2_' + str(i_iter) + '.pth'))
torch.save(
model_D.state_dict(),
os.path.join(args.checkpoint_dir,
'sen2_' + str(i_iter) + '_D.pth'))
end = timeit.default_timer()
print(end - start, 'seconds')
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model = Res_Deeplab(num_classes=args.num_classes)
model.cuda()
if args.restore_from[:4] == 'http':
saved_state_dict = model_zoo.load_url(args.restore_from)
else:
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
if args.dataset == 'pascal_voc':
testloader = data.DataLoader(VOCDataSet(args.data_dir,
args.data_list,
crop_size=(505, 505),
mean=IMG_MEAN,
scale=False,
mirror=False),
batch_size=1,
shuffle=False,
pin_memory=True)
interp = nn.Upsample(size=(505, 505),
mode='bilinear',
align_corners=True)
elif args.dataset == 'pascal_context':
input_transform = transform.Compose([
transform.ToTensor(),
transform.Normalize([.485, .456, .406], [.229, .224, .225])
])
data_kwargs = {
'transform': input_transform,
'base_size': 512,
'crop_size': 512
}
data_loader = get_loader('pascal_context')
data_path = get_data_path('pascal_context')
test_dataset = data_loader(data_path,
split='val',
mode='val',
**data_kwargs)
testloader = data.DataLoader(test_dataset,
batch_size=1,
drop_last=False,
shuffle=False,
num_workers=1,
pin_memory=True)
interp = nn.Upsample(size=(512, 512),
mode='bilinear',
align_corners=True)
elif args.dataset == 'cityscapes':
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
test_dataset = data_loader(data_path,
img_size=(512, 1024),
is_transform=True,
split='val')
testloader = data.DataLoader(test_dataset,
batch_size=1,
shuffle=False,
pin_memory=True)
interp = nn.Upsample(size=(512, 1024),
mode='bilinear',
align_corners=True)
data_list = []
colorize = VOCColorize()
if args.with_mlmt:
mlmt_preds = np.loadtxt('mlmt_output/output_ema_p_1_0_voc_5.txt',
dtype=float) # best mt 0.05
mlmt_preds[mlmt_preds >= 0.2] = 1
mlmt_preds[mlmt_preds < 0.2] = 0
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % (index))
image, label, size, name, _ = batch
size = size[0]
output = model(Variable(image, volatile=True).cuda(gpu0))
output = interp(output).cpu().data[0].numpy()
if args.dataset == 'pascal_voc':
output = output[:, :size[0], :size[1]]
gt = np.asarray(label[0].numpy()[:size[0], :size[1]], dtype=np.int)
elif args.dataset == 'pascal_context':
gt = np.asarray(label[0].numpy(), dtype=np.int)
elif args.dataset == 'cityscapes':
gt = np.asarray(label[0].numpy(), dtype=np.int)
if args.with_mlmt:
for i in range(args.num_classes):
output[i] = output[i] * mlmt_preds[index][i]
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.int)
if args.save_output_images:
if args.dataset == 'pascal_voc':
filename = os.path.join(args.save_dir,
'{}.png'.format(name[0]))
color_file = Image.fromarray(
colorize(output).transpose(1, 2, 0), 'RGB')
color_file.save(filename)
elif args.dataset == 'pascal_context':
filename = os.path.join(args.save_dir, filename[0])
scipy.misc.imsave(filename, gt)
data_list.append([gt.flatten(), output.flatten()])
filename = os.path.join(args.save_dir, 'result.txt')
get_iou(args, data_list, args.num_classes, filename)
def main():
print(config)
cudnn.enabled = True
torch.manual_seed(random_seed)
torch.cuda.manual_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
torch.backends.cudnn.deterministic = True
if pretraining == 'COCO':
from utils.transformsgpu import normalize_bgr as normalize
else:
from utils.transformsgpu import normalize_rgb as normalize
batch_size_unlabeled = int(batch_size / 2)
batch_size_labeled = int(batch_size * 1)
RAMP_UP_ITERS = 2000
data_loader = get_loader('cityscapes')
data_path = get_data_path('cityscapes')
data_aug = Compose([
RandomCrop_city(input_size)
]) # from 1024x2048 to resize 512x1024 to crop input_size (512x512)
train_dataset = data_loader(data_path,
is_transform=True,
augmentations=data_aug,
img_size=input_size,
pretraining=pretraining)
from data.gta5_loader import gtaLoader
data_loader_gta = gtaLoader
data_path_gta = get_data_path('gta5')
data_aug_gta = Compose([
RandomCrop_city(input_size)
]) # from 1024x2048 to resize 512x1024 to crop input_size (512x512)
train_dataset_gta = data_loader_gta(data_path_gta,
is_transform=True,
augmentations=data_aug_gta,
img_size=input_size,
pretraining=pretraining)
train_dataset_size = len(train_dataset)
print('dataset size: ', train_dataset_size)
partial_size = labeled_samples
print('Training on number of samples:', partial_size)
class_weights_curr = ClassBalancing(
labeled_iters=int(labeled_samples / batch_size_labeled),
unlabeled_iters=int(
(train_dataset_size - labeled_samples) / batch_size_unlabeled),
n_classes=num_classes)
feature_memory = FeatureMemory(num_samples=labeled_samples,
dataset=dataset,
memory_per_class=256,
feature_size=256,
n_classes=num_classes)
# select the partition
if split_id is not None:
train_ids = pickle.load(open(split_id, 'rb'))
print('loading train ids from {}'.format(split_id))
else:
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_labeled,
sampler=train_sampler,
num_workers=num_workers,
pin_memory=True)
trainloader_iter = iter(trainloader)
# GTA5
train_ids_gta = np.arange(len(train_dataset_gta))
np.random.shuffle(train_ids_gta)
train_sampler_gta = data.sampler.SubsetRandomSampler(train_ids_gta)
trainloader_gta = data.DataLoader(train_dataset_gta,
batch_size=batch_size_labeled,
sampler=train_sampler_gta,
num_workers=num_workers,
pin_memory=True)
trainloader_iter_gta = iter(trainloader_gta)
train_remain_sampler = data.sampler.SubsetRandomSampler(
train_ids[partial_size:])
trainloader_remain = data.DataLoader(train_dataset,
batch_size=batch_size_unlabeled,
sampler=train_remain_sampler,
num_workers=num_workers,
pin_memory=True)
trainloader_remain_iter = iter(trainloader_remain)
# LOSSES
unlabeled_loss = CrossEntropyLoss2dPixelWiseWeighted().cuda()
supervised_loss = CrossEntropy2d(ignore_label=ignore_label).cuda()
''' Deeplab model '''
# Define network
if deeplabv2:
if pretraining == 'COCO': # coco and iamgenet resnet architectures differ a little, just on how to do the stride
from model.deeplabv2 import Res_Deeplab
else: # imagenet pretrained (more modern modification)
from model.deeplabv2_imagenet import Res_Deeplab
else:
from model.deeplabv3 import Res_Deeplab
# create network
model = Res_Deeplab(num_classes=num_classes)
# load pretrained parameters
if pretraining == 'COCO':
saved_state_dict = model_zoo.load_url(
'http://vllab1.ucmerced.edu/~whung/adv-semi-seg/resnet101COCO-41f33a49.pth'
) # COCO pretraining
else:
saved_state_dict = model_zoo.load_url(
'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth'
) # iamgenet pretrainning
# 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)
# Optimizer for segmentation network
learning_rate_object = Learning_Rate_Object(
config['training']['learning_rate'])
optimizer = torch.optim.SGD(model.optim_parameters(learning_rate_object),
lr=learning_rate,
momentum=momentum,
weight_decay=weight_decay)
ema_model = create_ema_model(model, Res_Deeplab)
ema_model.train()
ema_model = ema_model.cuda()
model.train()
model = model.cuda()
cudnn.benchmark = True
# checkpoint = torch.load('/home/snowflake/Escritorio/Semi-Sup/saved/Deep_cont/best_model.pth')
# model.load_state_dict(checkpoint['model'])
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'))
interp = nn.Upsample(size=(input_size[0], input_size[1]),
mode='bilinear',
align_corners=True)
epochs_since_start = 0
start_iteration = 0
best_mIoU = 0 # best metric while training
iters_without_improve = 0
# TRAINING
for i_iter in range(start_iteration, num_iterations):
model.train() # set mode to training
optimizer.zero_grad()
a = time.time()
loss_l_value = 0.
adjust_learning_rate(optimizer, i_iter)
''' LABELED SAMPLES '''
# Get batch
is_cityscapes = i_iter % 2 == 0
is_gta = not is_cityscapes
if num_iterations - i_iter > 100:
# Last 100 itereations only citysacpes data
is_cityscapes = True
if is_cityscapes:
try:
batch = next(trainloader_iter)
if batch[0].shape[0] != batch_size_labeled:
batch = next(trainloader_iter)
except: # finish epoch, rebuild the iterator
epochs_since_start = epochs_since_start + 1
# print('Epochs since start: ',epochs_since_start)
trainloader_iter = iter(trainloader)
batch = next(trainloader_iter)
else:
try:
batch = next(trainloader_iter_gta)
if batch[0].shape[0] != batch_size_labeled:
train_ids_gta = np.arange(len(train_dataset_gta))
np.random.shuffle(train_ids_gta)
train_sampler_gta = data.sampler.SubsetRandomSampler(
train_ids_gta)
trainloader_gta = data.DataLoader(
train_dataset_gta,
batch_size=batch_size_labeled,
sampler=train_sampler_gta,
num_workers=num_workers,
pin_memory=True)
trainloader_iter_gta = iter(trainloader_gta)
batch = next(trainloader_iter_gta)
except: # finish epoch, rebuild the iterator
# print('Epochs since start: ',epochs_since_start)
trainloader_iter_gta = iter(trainloader_gta)
batch = next(trainloader_iter_gta)
images, labels, _, _, _ = batch
images = images.cuda()
labels = labels.cuda()
''' UNLABELED SAMPLES '''
try:
batch_remain = next(trainloader_remain_iter)
if batch_remain[0].shape[0] != batch_size_unlabeled:
batch_remain = next(trainloader_remain_iter)
except:
trainloader_remain_iter = iter(trainloader_remain)
batch_remain = next(trainloader_remain_iter)
# Unlabeled
unlabeled_images, _, _, _, _ = batch_remain
unlabeled_images = unlabeled_images.cuda()
# Create pseudolabels
with torch.no_grad():
if use_teacher:
logits_u_w, features_weak_unlabeled = ema_model(
normalize(unlabeled_images, dataset), return_features=True)
else:
model.eval()
logits_u_w, features_weak_unlabeled = model(
normalize(unlabeled_images, dataset), return_features=True)
logits_u_w = interp(logits_u_w).detach() # prediction unlabeled
softmax_u_w = torch.softmax(logits_u_w, dim=1)
max_probs, pseudo_label = torch.max(softmax_u_w,
dim=1) # Get pseudolabels
model.train()
if is_cityscapes:
class_weights_curr.add_frequencies(labels.cpu().numpy(),
pseudo_label.cpu().numpy())
images2, labels2, _, _ = augment_samples(images,
labels,
None,
random.random() < 0.25,
batch_size_labeled,
ignore_label,
weak=True)
'''
UNLABELED DATA
'''
'''
CROSS ENTROPY FOR UNLABELED USING PSEUDOLABELS
Once you have the speudolabel, perform strong augmetnation to force the netowrk to yield lower confidence scores for pushing them up
'''
do_classmix = i_iter > RAMP_UP_ITERS and random.random(
) < 0.75 # only after rampup perfrom classmix
unlabeled_images_aug1, pseudo_label1, max_probs1, unlabeled_aug1_params = augment_samples(
unlabeled_images, pseudo_label, max_probs, do_classmix,
batch_size_unlabeled, ignore_label)
do_classmix = i_iter > RAMP_UP_ITERS and random.random(
) < 0.75 # only after rampup perfrom classmix
unlabeled_images_aug2, pseudo_label2, max_probs2, unlabeled_aug2_params = augment_samples(
unlabeled_images, pseudo_label, max_probs, do_classmix,
batch_size_unlabeled, ignore_label)
joined_unlabeled = torch.cat(
(unlabeled_images_aug1, unlabeled_images_aug2), dim=0)
joined_pseudolabels = torch.cat((pseudo_label1, pseudo_label2), dim=0)
joined_maxprobs = torch.cat((max_probs1, max_probs2), dim=0)
pred_joined_unlabeled, features_joined_unlabeled = model(
normalize(joined_unlabeled, dataset), return_features=True)
pred_joined_unlabeled = interp(pred_joined_unlabeled)
joined_labeled = images2
joined_labels = labels2
labeled_pred, labeled_features = model(normalize(
joined_labeled, dataset),
return_features=True)
labeled_pred = interp(labeled_pred)
class_weights = torch.from_numpy(np.ones((num_classes))).cuda()
if i_iter > RAMP_UP_ITERS:
class_weights = torch.from_numpy(
class_weights_curr.get_weights(num_iterations,
only_labeled=False)).cuda()
loss = 0
# SUPERVISED SEGMENTATION
labeled_loss = supervised_loss(labeled_pred,
joined_labels,
weight=class_weights.float()) #
loss = loss + labeled_loss
# SELF-SUPERVISED SEGMENTATION
'''
Cross entropy loss using pseudolabels.
'''
unlabeled_loss = CrossEntropyLoss2dPixelWiseWeighted(
ignore_index=ignore_label, weight=class_weights.float()).cuda() #
# Pseudo-label weighting
pixelWiseWeight = sigmoid_ramp_up(i_iter, RAMP_UP_ITERS) * torch.ones(
joined_maxprobs.shape).cuda()
pixelWiseWeight = pixelWiseWeight * torch.pow(joined_maxprobs.detach(),
6)
# Pseudo-label loss
loss_ce_unlabeled = unlabeled_loss(pred_joined_unlabeled,
joined_pseudolabels,
pixelWiseWeight)
loss = loss + loss_ce_unlabeled
# entropy loss
valid_mask = (joined_pseudolabels != ignore_label).unsqueeze(1)
loss = loss + entropy_loss(
torch.nn.functional.softmax(pred_joined_unlabeled, dim=1),
valid_mask) * 0.01
# CONTRASTIVE LEARNING
if is_cityscapes:
if i_iter > RAMP_UP_ITERS - 1000:
# Build Memory Bank 1000 iters before starting to do contrsative
with torch.no_grad():
if use_teacher:
labeled_pred_ema, labeled_features_ema = ema_model(
normalize(joined_labeled, dataset),
return_features=True)
else:
model.eval()
labeled_pred_ema, labeled_features_ema = model(
normalize(joined_labeled, dataset),
return_features=True)
model.train()
labeled_pred_ema = interp(labeled_pred_ema)
probability_prediction_ema, label_prediction_ema = torch.max(
torch.softmax(labeled_pred_ema,
dim=1), dim=1) # Get pseudolabels
labels_down = nn.functional.interpolate(
joined_labels.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
label_prediction_down = nn.functional.interpolate(
label_prediction_ema.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
probability_prediction_down = nn.functional.interpolate(
probability_prediction_ema.float().unsqueeze(1),
size=(labeled_features_ema.shape[2],
labeled_features_ema.shape[3]),
mode='nearest').squeeze(1)
# get mask where the labeled predictions are correct
mask_prediction_correctly = (
(label_prediction_down == labels_down).float() *
(probability_prediction_down > 0.95).float()).bool()
labeled_features_correct = labeled_features_ema.permute(
0, 2, 3, 1)
labels_down_correct = labels_down[mask_prediction_correctly]
labeled_features_correct = labeled_features_correct[
mask_prediction_correctly, ...]
# get projected features
with torch.no_grad():
if use_teacher:
proj_labeled_features_correct = ema_model.projection_head(
labeled_features_correct)
else:
model.eval()
proj_labeled_features_correct = model.projection_head(
labeled_features_correct)
model.train()
# updated memory bank
feature_memory.add_features_from_sample_learned(
ema_model, proj_labeled_features_correct,
labels_down_correct, batch_size_labeled)
if i_iter > RAMP_UP_ITERS:
'''
LABELED TO LABELED. Force features from laeled samples, to be similar to other features from the same class (which also leads to good predictions)
'''
# now we can take all. as they are not the prototypes, here we are gonan force these features to be similar as the correct ones
mask_prediction_correctly = (labels_down != ignore_label)
labeled_features_all = labeled_features.permute(0, 2, 3, 1)
labels_down_all = labels_down[mask_prediction_correctly]
labeled_features_all = labeled_features_all[
mask_prediction_correctly, ...]
# get prediction features
proj_labeled_features_all = model.projection_head(
labeled_features_all)
pred_labeled_features_all = model.prediction_head(
proj_labeled_features_all)
loss_contr_labeled = contrastive_class_to_class_learned_memory(
model, pred_labeled_features_all, labels_down_all, num_classes,
feature_memory.memory)
loss = loss + loss_contr_labeled * 0.2
'''
CONTRASTIVE LEARNING ON UNLABELED DATA. align unlabeled features to labeled features
'''
joined_pseudolabels_down = nn.functional.interpolate(
joined_pseudolabels.float().unsqueeze(1),
size=(features_joined_unlabeled.shape[2],
features_joined_unlabeled.shape[3]),
mode='nearest').squeeze(1)
# take out the features from black pixels from zooms out and augmetnations (ignore labels on pseduoalebl)
mask = (joined_pseudolabels_down != ignore_label)
features_joined_unlabeled = features_joined_unlabeled.permute(
0, 2, 3, 1)
features_joined_unlabeled = features_joined_unlabeled[mask, ...]
joined_pseudolabels_down = joined_pseudolabels_down[mask]
# get projected features
proj_feat_unlabeled = model.projection_head(
features_joined_unlabeled)
pred_feat_unlabeled = model.prediction_head(proj_feat_unlabeled)
loss_contr_unlabeled = contrastive_class_to_class_learned_memory(
model, pred_feat_unlabeled, joined_pseudolabels_down,
num_classes, feature_memory.memory)
loss = loss + loss_contr_unlabeled * 0.2
loss_l_value += loss.item()
# optimize
loss.backward()
optimizer.step()
m = 1 - (1 - 0.995) * (math.cos(math.pi * i_iter / num_iterations) +
1) / 2
ema_model = update_ema_variables(ema_model=ema_model,
model=model,
alpha_teacher=m,
iteration=i_iter)
# 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)
if i_iter % val_per_iter == 0 and i_iter != 0:
print('iter = {0:6d}/{1:6d}'.format(i_iter, num_iterations))
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU:
best_mIoU = mIoU
if save_teacher:
_save_checkpoint(i_iter,
ema_model,
optimizer,
config,
save_best=True)
else:
_save_checkpoint(i_iter,
model,
optimizer,
config,
save_best=True)
iters_without_improve = 0
else:
iters_without_improve += val_per_iter
'''
if the performance has not improve in N iterations, try to reload best model to optimize again with a lower LR
Simulating an iterative training'''
if iters_without_improve > num_iterations / 5.:
print('Re-loading a previous best model')
checkpoint = torch.load(
os.path.join(checkpoint_dir, f'best_model.pth'))
model.load_state_dict(checkpoint['model'])
ema_model = create_ema_model(model, Res_Deeplab)
ema_model.train()
ema_model = ema_model.cuda()
model.train()
model = model.cuda()
iters_without_improve = 0 # reset timer
_save_checkpoint(num_iterations, model, optimizer, config)
# FINISH TRAINING, evaluate again
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter, model, optimizer, config, save_best=True)
# TRY IMPROVING BEST MODEL WITH EMA MODEL OR UPDATING BN STATS
# Load best model
checkpoint = torch.load(os.path.join(checkpoint_dir, f'best_model.pth'))
model.load_state_dict(checkpoint['model'])
model = model.cuda()
model = update_BN_weak_unlabeled_data(model, normalize,
batch_size_unlabeled,
trainloader_remain)
model.eval()
mIoU, eval_loss = evaluate(model,
dataset,
deeplabv2=deeplabv2,
ignore_label=ignore_label,
save_dir=checkpoint_dir,
pretraining=pretraining)
model.train()
if mIoU > best_mIoU and save_best_model:
best_mIoU = mIoU
_save_checkpoint(i_iter, model, optimizer, config, save_best=True)
print('BEST MIOU')
print(max(best_mIoU_improved, best_mIoU))
end = timeit.default_timer()
print('Total time: ' + str(end - start) + ' seconds')