def validation(args):
### For selecting the number of channels
if args.dataset == 'voc2012':
n_channels = 21
elif args.dataset == 'cityscapes':
n_channels = 20
elif args.dataset == 'acdc':
n_channels = 4
transform = get_transformation((args.crop_height, args.crop_width), resize=True, dataset=args.dataset)
## let the choice of dataset configurable
if args.dataset == 'voc2012':
val_set = VOCDataset(root_path=root, name='val', ratio=0.5, transformation=transform, augmentation=None)
elif args.dataset == 'cityscapes':
val_set = CityscapesDataset(root_path=root_cityscapes, name='val', ratio=0.5, transformation=transform, augmentation=None)
elif args.dataset == 'acdc':
val_set = ACDCDataset(root_path=root_acdc, name='val', ratio=0.5, transformation=transform, augmentation=None)
val_loader = DataLoader(val_set, batch_size=args.batch_size, shuffle=False)
Gsi = define_Gen(input_nc=3, output_nc=n_channels, ngf=args.ngf, netG='deeplab',
norm=args.norm, use_dropout= not args.no_dropout, gpu_ids=args.gpu_ids)
Gis = define_Gen(input_nc=n_channels, output_nc=3, ngf=args.ngf, netG='deeplab',
norm=args.norm, use_dropout=not args.no_dropout, gpu_ids=args.gpu_ids)
### best_iou
best_iou = 0
### Interpolation
interp = nn.Upsample(size = (args.crop_height, args.crop_width), mode='bilinear', align_corners=True)
### Softmax activation
activation_softmax = nn.Softmax2d()
activation_tanh = nn.Tanh()
if(args.model == 'supervised_model'):
### loading the checkpoint
try:
ckpt = utils.load_checkpoint('%s/latest_supervised_model.ckpt' % (args.checkpoint_dir))
Gsi.load_state_dict(ckpt['Gsi'])
best_iou = ckpt['best_iou']
except:
print(' [*] No checkpoint!')
### run
Gsi.eval()
for i, (image_test, real_segmentation, image_name) in enumerate(val_loader):
image_test = utils.cuda(image_test, args.gpu_ids)
seg_map = Gsi(image_test)
seg_map = interp(seg_map)
seg_map = activation_softmax(seg_map)
prediction = seg_map.data.max(1)[1].squeeze_(1).cpu().numpy() ### To convert from 22 --> 1 channel
for j in range(prediction.shape[0]):
new_img = prediction[j] ### Taking a particular image from the batch
new_img = utils.colorize_mask(new_img, args.dataset) ### So as to convert it back to a paletted image
### Now the new_img is PIL.Image
new_img.save(os.path.join(args.validation_dir+'/supervised/'+image_name[j]+'.png'))
print('Epoch-', str(i+1), ' Done!')
print('The iou of the resulting segment maps: ', str(best_iou))
elif(args.model == 'semisupervised_cycleGAN'):
### loading the checkpoint
try:
ckpt = utils.load_checkpoint('%s/latest_semisuper_cycleGAN.ckpt' % (args.checkpoint_dir))
Gsi.load_state_dict(ckpt['Gsi'])
Gis.load_state_dict(ckpt['Gis'])
best_iou = ckpt['best_iou']
except:
print(' [*] No checkpoint!')
### run
Gsi.eval()
for i, (image_test, real_segmentation, image_name) in enumerate(val_loader):
image_test, real_segmentation = utils.cuda([image_test, real_segmentation], args.gpu_ids)
seg_map = Gsi(image_test)
seg_map = interp(seg_map)
seg_map = activation_softmax(seg_map)
fake_img = Gis(seg_map).detach()
fake_img = interp(fake_img)
fake_img = activation_tanh(fake_img)
fake_img_from_labels = Gis(make_one_hot(real_segmentation, args.dataset, args.gpu_ids).float()).detach()
fake_img_from_labels = interp(fake_img_from_labels)
fake_img_from_labels = activation_tanh(fake_img_from_labels)
fake_label_regenerated = Gsi(fake_img_from_labels).detach()
fake_label_regenerated = interp(fake_label_regenerated)
fake_label_regenerated = activation_softmax(fake_label_regenerated)
prediction = seg_map.data.max(1)[1].squeeze_(1).cpu().numpy() ### To convert from 22 --> 1 channel
fake_regenerated_label = fake_label_regenerated.data.max(1)[1].squeeze_(1).cpu().numpy()
fake_img = fake_img.cpu()
fake_img_from_labels = fake_img_from_labels.cpu()
### Now i am going to revert back the transformation on these images
if args.dataset == 'voc2012' or args.dataset == 'cityscapes':
trans_mean = [0.5, 0.5, 0.5]
trans_std = [0.5, 0.5, 0.5]
for k in range(3):
fake_img[:, k, :, :] = ((fake_img[:, k, :, :] * trans_std[k]) + trans_mean[k])
fake_img_from_labels[:, k, :, :] = ((fake_img_from_labels[:, k, :, :] * trans_std[k]) + trans_mean[k])
elif args.dataset == 'acdc':
trans_mean = [0.5]
trans_std = [0.5]
for k in range(1):
fake_img[:, k, :, :] = ((fake_img[:, k, :, :] * trans_std[k]) + trans_mean[k])
fake_img_from_labels[:, k, :, :] = ((fake_img_from_labels[:, k, :, :] * trans_std[k]) + trans_mean[k])
for j in range(prediction.shape[0]):
new_img = prediction[j] ### Taking a particular image from the batch
new_img = utils.colorize_mask(new_img, args.dataset) ### So as to convert it back to a paletted image
regen_label = fake_regenerated_label[j]
regen_label = utils.colorize_mask(regen_label, args.dataset)
### Now the new_img is PIL.Image
new_img.save(os.path.join(args.validation_dir+'/unsupervised/generated_labels/'+image_name[j]+'.png'))
regen_label.save(os.path.join(args.validation_dir+'/unsupervised/regenerated_labels/'+image_name[j]+'.png'))
torchvision.utils.save_image(fake_img[j], os.path.join(args.validation_dir+'/unsupervised/regenerated_image/'+image_name[j]+'.jpg'))
torchvision.utils.save_image(fake_img_from_labels[j], os.path.join(args.validation_dir+'/unsupervised/image_from_labels/'+image_name[j]+'.jpg'))
print('Epoch-', str(i+1), ' Done!')
print('The iou of the resulting segment maps: ', str(best_iou))
def train(self, args):
transform = get_transformation(
(self.args.crop_height, self.args.crop_width),
resize=True,
dataset=args.dataset)
val_transform = get_transformation((512, 512),
resize=True,
dataset=args.dataset)
# let the choice of dataset configurable
if self.args.dataset == 'voc2012':
labeled_set = VOCDataset(root_path=root,
name='label',
ratio=1.0,
transformation=transform,
augmentation=None)
val_set = VOCDataset(root_path=root,
name='val',
ratio=0.5,
transformation=val_transform,
augmentation=None)
labeled_loader = DataLoader(labeled_set,
batch_size=self.args.batch_size,
shuffle=True,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=self.args.batch_size,
shuffle=True)
elif self.args.dataset == 'cityscapes':
labeled_set = CityscapesDataset(root_path=root_cityscapes,
name='label',
ratio=0.5,
transformation=transform,
augmentation=None)
val_set = CityscapesDataset(root_path=root_cityscapes,
name='val',
ratio=0.5,
transformation=transform,
augmentation=None)
labeled_loader = DataLoader(labeled_set,
batch_size=self.args.batch_size,
shuffle=True,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=self.args.batch_size,
shuffle=True,
drop_last=True)
elif self.args.dataset == 'acdc':
labeled_set = ACDCDataset(root_path=root_acdc,
name='label',
ratio=0.5,
transformation=transform,
augmentation=None)
val_set = ACDCDataset(root_path=root_acdc,
name='val',
ratio=0.5,
transformation=transform,
augmentation=None)
labeled_loader = DataLoader(labeled_set,
batch_size=self.args.batch_size,
shuffle=True,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=self.args.batch_size,
shuffle=True,
drop_last=True)
img_fake_sample = utils.Sample_from_Pool()
gt_fake_sample = utils.Sample_from_Pool()
for epoch in range(self.start_epoch, self.args.epochs):
self.Gsi.train()
for i, (l_img, l_gt, img_name) in enumerate(labeled_loader):
# step
step = epoch * len(labeled_loader) + i + 1
self.gsi_optimizer.zero_grad()
l_img, l_gt = utils.cuda([l_img, l_gt], args.gpu_ids)
lab_gt = self.Gsi(l_img)
lab_gt = self.interp(
lab_gt) ### To get the output of model same as labels
# CE losses
fullsupervisedloss = self.CE(lab_gt, l_gt.squeeze(1))
fullsupervisedloss.backward()
self.gsi_optimizer.step()
print("Epoch: (%3d) (%5d/%5d) | Crossentropy Loss:%.2e" %
(epoch, i + 1, len(labeled_loader),
fullsupervisedloss.item()))
self.writer_supervised.add_scalars(
'Supervised Loss', {'CE Loss ': fullsupervisedloss},
len(labeled_loader) * epoch + i)
### For getting the IoU for the image
self.Gsi.eval()
with torch.no_grad():
for i, (val_img, val_gt, _) in enumerate(val_loader):
val_img, val_gt = utils.cuda([val_img, val_gt],
args.gpu_ids)
outputs = self.Gsi(val_img)
outputs = self.interp_val(outputs)
outputs = self.activation_softmax(outputs)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = val_gt.squeeze().data.cpu().numpy()
self.running_metrics_val.update(gt, pred)
score, class_iou = self.running_metrics_val.get_scores()
self.running_metrics_val.reset()
### For displaying the images generated by generator on tensorboard
val_img, val_gt, _ = iter(val_loader).next()
val_img, val_gt = utils.cuda([val_img, val_gt], args.gpu_ids)
with torch.no_grad():
fake = self.Gsi(val_img).detach()
fake = self.interp_val(fake)
fake = self.activation_softmax(fake)
fake_prediction = fake.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy()
val_gt = val_gt.cpu()
### display_tensor is the final tensor that will be displayed on tensorboard
display_tensor = torch.zeros(
[fake.shape[0], 3, fake.shape[2], fake.shape[3]])
display_tensor_gt = torch.zeros(
[val_gt.shape[0], 3, val_gt.shape[2], val_gt.shape[3]])
for i in range(fake_prediction.shape[0]):
new_img = fake_prediction[i]
new_img = utils.colorize_mask(
new_img, self.args.dataset
) ### So this is the generated image in PIL.Image format
img_tensor = utils.PIL_to_tensor(new_img, self.args.dataset)
display_tensor[i, :, :, :] = img_tensor
display_tensor_gt[i, :, :, :] = val_gt[i]
self.writer_supervised.add_image(
'Generated segmented image',
torchvision.utils.make_grid(display_tensor,
nrow=2,
normalize=True), epoch)
self.writer_supervised.add_image(
'Ground truth for the image',
torchvision.utils.make_grid(display_tensor_gt,
nrow=2,
normalize=True), epoch)
if score["Mean IoU : \t"] >= self.best_iou:
self.best_iou = score["Mean IoU : \t"]
# Override the latest checkpoint
utils.save_checkpoint(
{
'epoch': epoch + 1,
'Gsi': self.Gsi.state_dict(),
'gsi_optimizer': self.gsi_optimizer.state_dict(),
'best_iou': self.best_iou,
'class_iou': class_iou
}, '%s/latest_supervised_model.ckpt' %
(self.args.checkpoint_dir))
self.writer_supervised.close()
def train(self, args):
transform = get_transformation((args.crop_height, args.crop_width),
resize=True,
dataset=args.dataset)
# let the choice of dataset configurable
if self.args.dataset == 'voc2012':
labeled_set = VOCDataset(root_path=root,
name='label',
ratio=0.2,
transformation=transform,
augmentation=None)
unlabeled_set = VOCDataset(root_path=root,
name='unlabel',
ratio=0.2,
transformation=transform,
augmentation=None)
val_set = VOCDataset(root_path=root,
name='val',
ratio=0.5,
transformation=transform,
augmentation=None)
elif self.args.dataset == 'cityscapes':
labeled_set = CityscapesDataset(root_path=root_cityscapes,
name='label',
ratio=0.5,
transformation=transform,
augmentation=None)
unlabeled_set = CityscapesDataset(root_path=root_cityscapes,
name='unlabel',
ratio=0.5,
transformation=transform,
augmentation=None)
val_set = CityscapesDataset(root_path=root_cityscapes,
name='val',
ratio=0.5,
transformation=transform,
augmentation=None)
elif self.args.dataset == 'acdc':
labeled_set = ACDCDataset(root_path=root_acdc,
name='label',
ratio=0.5,
transformation=transform,
augmentation=None)
unlabeled_set = ACDCDataset(root_path=root_acdc,
name='unlabel',
ratio=0.5,
transformation=transform,
augmentation=None)
val_set = ACDCDataset(root_path=root_acdc,
name='val',
ratio=0.5,
transformation=transform,
augmentation=None)
'''
https://discuss.pytorch.org/t/about-the-relation-between-batch-size-and-length-of-data-loader/10510
^^ The reason for using drop_last=True so as to obtain an even size of all the batches and
deleting the last batch with less images
'''
labeled_loader = DataLoader(labeled_set,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
unlabeled_loader = DataLoader(unlabeled_set,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
val_loader = DataLoader(val_set,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
new_img_fake_sample = utils.Sample_from_Pool()
img_fake_sample = utils.Sample_from_Pool()
gt_fake_sample = utils.Sample_from_Pool()
img_dis_loss, gt_dis_loss, unsupervisedloss, fullsupervisedloss = 0, 0, 0, 0
### Variable to regulate the frequency of update between Discriminators and Generators
counter = 0
for epoch in range(self.start_epoch, args.epochs):
lr = self.g_optimizer.param_groups[0]['lr']
print('learning rate = %.7f' % lr)
self.Gsi.train()
self.Gis.train()
# if (epoch+1)%10 == 0:
# args.lamda_img = args.lamda_img + 0.08
# args.lamda_gt = args.lamda_gt + 0.04
for i, ((l_img, l_gt, _),
(unl_img, _,
_)) in enumerate(zip(labeled_loader, unlabeled_loader)):
# step
step = epoch * min(len(labeled_loader),
len(unlabeled_loader)) + i + 1
l_img, unl_img, l_gt = utils.cuda([l_img, unl_img, l_gt],
args.gpu_ids)
# Generator Computations
##################################################
set_grad([self.Di, self.Ds, self.old_Di], False)
set_grad([self.old_Gsi, self.old_Gis], False)
self.g_optimizer.zero_grad()
# Forward pass through generators
##################################################
fake_img = self.Gis(
make_one_hot(l_gt, args.dataset, args.gpu_ids).float())
fake_gt = self.Gsi(unl_img.float()) ### having 21 channels
lab_gt = self.Gsi(l_img) ### having 21 channels
### Getting the outputs of the model to correct dimensions
fake_img = self.interp(fake_img)
fake_gt = self.interp(fake_gt)
lab_gt = self.interp(lab_gt)
# fake_gt = fake_gt.data.max(1)[1].squeeze_(1).squeeze_(0) ### will get into no channels
# fake_gt = fake_gt.unsqueeze(1) ### will get into 1 channel only
# fake_gt = make_one_hot(fake_gt, args.dataset, args.gpu_ids)
lab_loss_CE = self.CE(lab_gt, l_gt.squeeze(1))
### Again applying activations
lab_gt = self.activation_softmax(lab_gt)
fake_gt = self.activation_softmax(fake_gt)
# fake_gt = fake_gt.data.max(1)[1].squeeze_(1).squeeze_(0)
# fake_gt = fake_gt.unsqueeze(1)
# fake_gt = make_one_hot(fake_gt, args.dataset, args.gpu_ids)
# fake_img = self.activation_tanh(fake_img)
recon_img = self.Gis(fake_gt.float())
recon_lab_img = self.Gis(lab_gt.float())
recon_gt = self.Gsi(fake_img.float())
### Getting the outputs of the model to correct dimensions
recon_img = self.interp(recon_img)
recon_lab_img = self.interp(recon_lab_img)
recon_gt = self.interp(recon_gt)
### This is for the case of the new loss between the recon_img from resnet and deeplab network
resnet_fake_gt = self.old_Gsi(unl_img.float())
resnet_lab_gt = self.old_Gsi(l_img)
resnet_lab_gt = self.activation_softmax(resnet_lab_gt)
resnet_fake_gt = self.activation_softmax(resnet_fake_gt)
resnet_recon_img = self.old_Gis(resnet_fake_gt.float())
resnet_recon_lab_img = self.old_Gis(resnet_lab_gt.float())
## Applying the tanh activations
# recon_img = self.activation_tanh(recon_img)
# recon_lab_img = self.activation_tanh(recon_lab_img)
# Adversarial losses
###################################################
fake_img_dis = self.Di(fake_img)
resnet_fake_img_dis = self.old_Di(recon_img)
### For passing different type of input to Ds
fake_gt_discriminator = fake_gt.data.max(1)[1].squeeze_(
1).squeeze_(0)
fake_gt_discriminator = fake_gt_discriminator.unsqueeze(1)
fake_gt_discriminator = make_one_hot(fake_gt_discriminator,
args.dataset,
args.gpu_ids)
fake_gt_dis = self.Ds(fake_gt_discriminator.float())
# lab_gt_dis = self.Ds(lab_gt)
real_label_gt = utils.cuda(
Variable(torch.ones(fake_gt_dis.size())), args.gpu_ids)
real_label_img = utils.cuda(
Variable(torch.ones(fake_img_dis.size())), args.gpu_ids)
# here is much better to have a cross entropy loss for classification.
img_gen_loss = self.MSE(fake_img_dis, real_label_img)
gt_gen_loss = self.MSE(fake_gt_dis, real_label_gt)
# gt_label_gen_loss = self.MSE(lab_gt_dis, real_label)
# Cycle consistency losses
###################################################
resnet_img_cycle_loss = self.MSE(resnet_fake_img_dis,
real_label_img)
# img_cycle_loss = self.L1(recon_img, unl_img)
# img_cycle_loss_perceptual = perceptual_loss(recon_img, unl_img, args.gpu_ids)
gt_cycle_loss = self.CE(recon_gt, l_gt.squeeze(1))
# lab_img_cycle_loss = self.L1(recon_lab_img, l_img) * args.lamda
# Total generators losses
###################################################
# lab_loss_CE = self.CE(lab_gt, l_gt.squeeze(1))
lab_loss_MSE = self.L1(fake_img, l_img)
# lab_loss_perceptual = perceptual_loss(fake_img, l_img, args.gpu_ids)
fullsupervisedloss = args.lab_CE_weight * lab_loss_CE + args.lab_MSE_weight * lab_loss_MSE
unsupervisedloss = args.adversarial_weight * (
img_gen_loss + gt_gen_loss
) + resnet_img_cycle_loss + gt_cycle_loss * args.lamda_gt
gen_loss = fullsupervisedloss + unsupervisedloss
# Update generators
###################################################
gen_loss.backward()
self.g_optimizer.step()
if counter % 1 == 0:
# Discriminator Computations
#################################################
set_grad([self.Di, self.Ds, self.old_Di], True)
self.d_optimizer.zero_grad()
# Sample from history of generated images
#################################################
if torch.rand(1) < 0.0:
fake_img = self.gauss_noise(fake_img.cpu())
fake_gt = self.gauss_noise(fake_gt.cpu())
recon_img = Variable(
torch.Tensor(
new_img_fake_sample([recon_img.cpu().data.numpy()
])[0]))
fake_img = Variable(
torch.Tensor(
img_fake_sample([fake_img.cpu().data.numpy()])[0]))
# lab_gt = Variable(torch.Tensor(gt_fake_sample([lab_gt.cpu().data.numpy()])[0]))
fake_gt = Variable(
torch.Tensor(
gt_fake_sample([fake_gt.cpu().data.numpy()])[0]))
recon_img, fake_img, fake_gt = utils.cuda(
[recon_img, fake_img, fake_gt], args.gpu_ids)
# Forward pass through discriminators
#################################################
unl_img_dis = self.Di(unl_img)
fake_img_dis = self.Di(fake_img)
resnet_recon_img_dis = self.old_Di(resnet_recon_img)
resnet_fake_img_dis = self.old_Di(recon_img)
# lab_gt_dis = self.Ds(lab_gt)
l_gt = make_one_hot(l_gt, args.dataset, args.gpu_ids)
real_gt_dis = self.Ds(l_gt.float())
fake_gt_discriminator = fake_gt.data.max(1)[1].squeeze_(
1).squeeze_(0)
fake_gt_discriminator = fake_gt_discriminator.unsqueeze(1)
fake_gt_discriminator = make_one_hot(
fake_gt_discriminator, args.dataset, args.gpu_ids)
fake_gt_dis = self.Ds(fake_gt_discriminator.float())
real_label_img = utils.cuda(
Variable(torch.ones(unl_img_dis.size())), args.gpu_ids)
fake_label_img = utils.cuda(
Variable(torch.zeros(fake_img_dis.size())),
args.gpu_ids)
real_label_gt = utils.cuda(
Variable(torch.ones(real_gt_dis.size())), args.gpu_ids)
fake_label_gt = utils.cuda(
Variable(torch.zeros(fake_gt_dis.size())),
args.gpu_ids)
# Discriminator losses
##################################################
img_dis_real_loss = self.MSE(unl_img_dis, real_label_img)
img_dis_fake_loss = self.MSE(fake_img_dis, fake_label_img)
gt_dis_real_loss = self.MSE(real_gt_dis, real_label_gt)
gt_dis_fake_loss = self.MSE(fake_gt_dis, fake_label_gt)
# lab_gt_dis_fake_loss = self.MSE(lab_gt_dis, fake_label)
cycle_img_dis_real_loss = self.MSE(resnet_recon_img_dis,
real_label_img)
cycle_img_dis_fake_loss = self.MSE(resnet_fake_img_dis,
fake_label_img)
# Total discriminators losses
img_dis_loss = (img_dis_real_loss +
img_dis_fake_loss) * 0.5
gt_dis_loss = (gt_dis_real_loss + gt_dis_fake_loss) * 0.5
# lab_gt_dis_loss = (gt_dis_real_loss + lab_gt_dis_fake_loss)*0.33
cycle_img_dis_loss = cycle_img_dis_real_loss + cycle_img_dis_fake_loss
# Update discriminators
##################################################
discriminator_loss = args.discriminator_weight * (
img_dis_loss + gt_dis_loss) + cycle_img_dis_loss
discriminator_loss.backward()
# lab_gt_dis_loss.backward()
self.d_optimizer.step()
print(
"Epoch: (%3d) (%5d/%5d) | Dis Loss:%.2e | Unlab Gen Loss:%.2e | Lab Gen loss:%.2e"
% (epoch, i + 1,
min(len(labeled_loader),
len(unlabeled_loader)), img_dis_loss + gt_dis_loss,
unsupervisedloss, fullsupervisedloss))
self.writer_semisuper.add_scalars(
'Dis Loss', {
'img_dis_loss': img_dis_loss,
'gt_dis_loss': gt_dis_loss,
'cycle_img_dis_loss': cycle_img_dis_loss
},
len(labeled_loader) * epoch + i)
self.writer_semisuper.add_scalars(
'Unlabelled Loss', {
'img_gen_loss': img_gen_loss,
'gt_gen_loss': gt_gen_loss,
'img_cycle_loss': resnet_img_cycle_loss,
'gt_cycle_loss': gt_cycle_loss
},
len(labeled_loader) * epoch + i)
self.writer_semisuper.add_scalars(
'Labelled Loss', {
'lab_loss_CE': lab_loss_CE,
'lab_loss_MSE': lab_loss_MSE
},
len(labeled_loader) * epoch + i)
counter += 1
### For getting the mean IoU
self.Gsi.eval()
self.Gis.eval()
with torch.no_grad():
for i, (val_img, val_gt, _) in enumerate(val_loader):
val_img, val_gt = utils.cuda([val_img, val_gt],
args.gpu_ids)
outputs = self.Gsi(val_img)
outputs = self.interp(outputs)
outputs = self.activation_softmax(outputs)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = val_gt.squeeze().data.cpu().numpy()
self.running_metrics_val.update(gt, pred)
score, class_iou = self.running_metrics_val.get_scores()
self.running_metrics_val.reset()
print('The mIoU for the epoch is: ', score["Mean IoU : \t"])
### For displaying the images generated by generator on tensorboard using validation images
val_image, val_gt, _ = iter(val_loader).next()
val_image, val_gt = utils.cuda([val_image, val_gt], args.gpu_ids)
with torch.no_grad():
fake_label = self.Gsi(val_image).detach()
fake_label = self.interp(fake_label)
fake_label = self.activation_softmax(fake_label)
fake_label = fake_label.data.max(1)[1].squeeze_(1).squeeze_(0)
fake_label = fake_label.unsqueeze(1)
fake_label = make_one_hot(fake_label, args.dataset,
args.gpu_ids)
fake_img = self.Gis(fake_label).detach()
fake_img = self.interp(fake_img)
# fake_img = self.activation_tanh(fake_img)
fake_img_from_labels = self.Gis(
make_one_hot(val_gt, args.dataset,
args.gpu_ids).float()).detach()
fake_img_from_labels = self.interp(fake_img_from_labels)
# fake_img_from_labels = self.activation_tanh(fake_img_from_labels)
fake_label_regenerated = self.Gsi(
fake_img_from_labels).detach()
fake_label_regenerated = self.interp(fake_label_regenerated)
fake_label_regenerated = self.activation_softmax(
fake_label_regenerated)
fake_prediction_label = fake_label.data.max(1)[1].squeeze_(
1).cpu().numpy()
fake_regenerated_label = fake_label_regenerated.data.max(
1)[1].squeeze_(1).cpu().numpy()
val_gt = val_gt.cpu()
fake_img = fake_img.cpu()
fake_img_from_labels = fake_img_from_labels.cpu()
### Now i am going to revert back the transformation on these images
if self.args.dataset == 'voc2012' or self.args.dataset == 'cityscapes':
trans_mean = [0.5, 0.5, 0.5]
trans_std = [0.5, 0.5, 0.5]
for i in range(3):
fake_img[:, i, :, :] = (
(fake_img[:, i, :, :] * trans_std[i]) + trans_mean[i])
fake_img_from_labels[:, i, :, :] = (
(fake_img_from_labels[:, i, :, :] * trans_std[i]) +
trans_mean[i])
elif self.args.dataset == 'acdc':
trans_mean = [0.5]
trans_std = [0.5]
for i in range(1):
fake_img[:, i, :, :] = (
(fake_img[:, i, :, :] * trans_std[i]) + trans_mean[i])
fake_img_from_labels[:, i, :, :] = (
(fake_img_from_labels[:, i, :, :] * trans_std[i]) +
trans_mean[i])
### display_tensor is the final tensor that will be displayed on tensorboard
display_tensor_label = torch.zeros([
fake_label.shape[0], 3, fake_label.shape[2],
fake_label.shape[3]
])
display_tensor_gt = torch.zeros(
[val_gt.shape[0], 3, val_gt.shape[2], val_gt.shape[3]])
display_tensor_regen_label = torch.zeros([
fake_label_regenerated.shape[0], 3,
fake_label_regenerated.shape[2],
fake_label_regenerated.shape[3]
])
for i in range(fake_prediction_label.shape[0]):
new_img_label = fake_prediction_label[i]
new_img_label = utils.colorize_mask(
new_img_label, self.args.dataset
) ### So this is the generated image in PIL.Image format
img_tensor_label = utils.PIL_to_tensor(new_img_label,
self.args.dataset)
display_tensor_label[i, :, :, :] = img_tensor_label
display_tensor_gt[i, :, :, :] = val_gt[i]
regen_label = fake_regenerated_label[i]
regen_label = utils.colorize_mask(regen_label,
self.args.dataset)
regen_tensor_label = utils.PIL_to_tensor(
regen_label, self.args.dataset)
display_tensor_regen_label[i, :, :, :] = regen_tensor_label
self.writer_semisuper.add_image(
'Generated segmented image: ',
torchvision.utils.make_grid(display_tensor_label,
nrow=2,
normalize=True), epoch)
self.writer_semisuper.add_image(
'Generated image back from segmentation: ',
torchvision.utils.make_grid(fake_img, nrow=2, normalize=True),
epoch)
self.writer_semisuper.add_image(
'Ground truth for the image: ',
torchvision.utils.make_grid(display_tensor_gt,
nrow=2,
normalize=True), epoch)
self.writer_semisuper.add_image(
'Image generated from val labels: ',
torchvision.utils.make_grid(fake_img_from_labels,
nrow=2,
normalize=True), epoch)
self.writer_semisuper.add_image(
'Labels generated back from the cycle: ',
torchvision.utils.make_grid(display_tensor_regen_label,
nrow=2,
normalize=True), epoch)
if score["Mean IoU : \t"] >= self.best_iou:
self.best_iou = score["Mean IoU : \t"]
# Override the latest checkpoint
#######################################################
utils.save_checkpoint(
{
'epoch': epoch + 1,
'Di': self.Di.state_dict(),
'Ds': self.Ds.state_dict(),
'Gis': self.Gis.state_dict(),
'Gsi': self.Gsi.state_dict(),
'd_optimizer': self.d_optimizer.state_dict(),
'g_optimizer': self.g_optimizer.state_dict(),
'best_iou': self.best_iou,
'class_iou': class_iou
}, '%s/latest_semisuper_cycleGAN.ckpt' %
(args.checkpoint_dir))
# Update learning rates
########################
self.g_lr_scheduler.step()
self.d_lr_scheduler.step()
self.writer_semisuper.close()
def test(args):
### For selecting the number of channels
if args.dataset == 'voc2012':
n_channels = 21
elif args.dataset == 'cityscapes':
n_channels = 20
elif args.dataset == 'acdc':
n_channels = 4
transform = get_transformation((args.crop_height, args.crop_width),
resize=True,
dataset=args.dataset)
## let the choice of dataset configurable
if args.dataset == 'voc2012':
test_set = VOCDataset(root_path=root,
name='test',
ratio=0.5,
transformation=transform,
augmentation=None)
elif args.dataset == 'cityscapes':
test_set = CityscapesDataset(root_path=root_cityscapes,
name='test',
ratio=0.5,
transformation=transform,
augmentation=None)
elif args.dataset == 'acdc':
test_set = ACDCDataset(root_path=root_acdc,
name='test',
ratio=0.5,
transformation=transform,
augmentation=None)
test_loader = DataLoader(test_set,
batch_size=args.batch_size,
shuffle=False)
Gsi = define_Gen(input_nc=3,
output_nc=n_channels,
ngf=args.ngf,
netG='resnet_9blocks_softmax',
norm=args.norm,
use_dropout=not args.no_dropout,
gpu_ids=args.gpu_ids)
### activation_softmax
activation_softmax = nn.Softmax2d()
if (args.model == 'supervised_model'):
### loading the checkpoint
try:
ckpt = utils.load_checkpoint('%s/latest_supervised_model.ckpt' %
(args.checkpoint_dir))
Gsi.load_state_dict(ckpt['Gsi'])
except:
print(' [*] No checkpoint!')
### run
Gsi.eval()
for i, (image_test, image_name) in enumerate(test_loader):
image_test = utils.cuda(image_test, args.gpu_ids)
seg_map = Gsi(image_test)
seg_map = activation_softmax(seg_map)
prediction = seg_map.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy() ### To convert from 22 --> 1 channel
for j in range(prediction.shape[0]):
new_img = prediction[
j] ### Taking a particular image from the batch
new_img = utils.colorize_mask(
new_img, args.dataset
) ### So as to convert it back to a paletted image
### Now the new_img is PIL.Image
new_img.save(
os.path.join(args.results_dir + '/supervised/' +
image_name[j] + '.png'))
print('Epoch-', str(i + 1), ' Done!')
elif (args.model == 'semisupervised_cycleGAN'):
### loading the checkpoint
try:
ckpt = utils.load_checkpoint('%s/latest_semisuper_cycleGAN.ckpt' %
(args.checkpoint_dir))
Gsi.load_state_dict(ckpt['Gsi'])
except:
print(' [*] No checkpoint!')
### run
Gsi.eval()
for i, (image_test, image_name) in enumerate(test_loader):
image_test = utils.cuda(image_test, args.gpu_ids)
seg_map = Gsi(image_test)
seg_map = activation_softmax(seg_map)
prediction = seg_map.data.max(1)[1].squeeze_(1).squeeze_(
0).cpu().numpy() ### To convert from 22 --> 1 channel
for j in range(prediction.shape[0]):
new_img = prediction[
j] ### Taking a particular image from the batch
new_img = utils.colorize_mask(
new_img, args.dataset
) ### So as to convert it back to a paletted image
### Now the new_img is PIL.Image
new_img.save(
os.path.join(args.results_dir + '/unsupervised/' +
image_name[j] + '.png'))
print('Epoch-', str(i + 1), ' Done!')