def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(
image_size=101,
translation=lambda rs: (rs.randint(-10, 10), rs.randint(-10, 10)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
rotation=lambda rs: rs.randint(-5, 5),
**utils.transformations_options
),
transformations.Padding(((13, 14), (13, 14), (0, 0)))
])
transforms_image = generator.TransformationsGenerator([
random.RandomColorPerturbation(std=1)
])
dataset = datasets.ImageDataset(samples, settings.train, transforms, transforms_image=transforms_image)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16,
shuffle=True
)
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False) as pbar, torch.enable_grad():
net.train()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions, aux_pam, aux_cam = net(images)
loss_pam = criterion(F.interpolate(aux_pam, size=128, mode='bilinear'), masks_targets)
loss_cam = criterion(F.interpolate(aux_cam, size=128, mode='bilinear'), masks_targets)
loss_segmentation = criterion(masks_predictions, masks_targets)
loss = loss_segmentation + loss_pam + loss_cam
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(
torch.sigmoid(masks_predictions),
masks_targets,
pbar,
average_meter_train,
'Training epoch {}'.format(e)
)
train_stats = {'train_' + k: v for k, v in average_meter_train.get_all().items()}
return train_stats
def test(self, samples_test, dir_test=settings.test, predict=None):
if predict is None:
predict = self.predict
net = DataParallel(self.net).cuda()
transforms = generator.TransformationsGenerator([])
test_dataset = datasets.ImageDataset(samples_test,
dir_test,
transforms,
test=True)
test_dataloader = DataLoader(test_dataset,
num_workers=10,
batch_size=32)
with tqdm(total=len(test_dataloader), leave=True,
ascii=True) as pbar, torch.no_grad():
net.eval()
for images, ids in test_dataloader:
masks_predictions = predict(net, images)
pbar.set_description('Creating test predictions...')
pbar.update()
masks_predictions = masks_predictions.cpu().squeeze().numpy()
for p, id in zip(masks_predictions, ids):
yield p, id
def validate(self, net, samples, e):
transforms = generator.TransformationsGenerator([])
dataset = datasets.ImageDataset(samples, settings.train, transforms)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16
)
average_meter_val = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=True, ascii=True) as pbar, torch.no_grad():
net.eval()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = self.predict(net, images)
self.update_pbar(
masks_predictions,
masks_targets,
pbar,
average_meter_val,
'Validation epoch {}'.format(e)
)
val_stats = {'val_' + k: v for k, v in average_meter_val.get_all().items()}
return val_stats
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((100 - e) / 100))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(image_size=101,
translation=lambda rs:
(rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options),
transformations.Padding(((13, 14), (13, 14), (0, 0)))
])
pseudo_dataset = datasets.SemiSupervisedImageDataset(
samples_test,
settings.test,
transforms,
size=len(samples_test),
test_predictions=self.test_predictions,
momentum=0.0)
dataset = datasets.ImageDataset(samples, settings.train, transforms)
weights = [len(pseudo_dataset) / len(dataset) * 2
] * len(dataset) + [1] * len(pseudo_dataset)
dataloader = DataLoader(ConcatDataset([dataset, pseudo_dataset]),
num_workers=10,
batch_size=16,
sampler=WeightedRandomSampler(
weights=weights, num_samples=3200))
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader),
leave=False) as pbar, torch.enable_grad():
net.train()
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = net(images)
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(torch.sigmoid(masks_predictions),
masks_targets, pbar, average_meter_train,
'Training epoch {}'.format(e))
train_stats = {
'train_' + k: v
for k, v in average_meter_train.get_all().items()
}
return train_stats
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(
image_size=101,
translation=lambda rs: (rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options
)
])
dataset = datasets.ImageDataset(samples, settings.train, transforms)
dataloader = DataLoader(
dataset,
num_workers=10,
batch_size=16,
shuffle=True
)
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False, ascii=True) as pbar, torch.enable_grad():
net.train()
padding = tta.Pad((13, 14, 13, 14))
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = padding.transform_backward(net(padding.transform_forward(images))).contiguous()
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(
torch.sigmoid(masks_predictions),
masks_targets,
pbar,
average_meter_train,
'Training epoch {}'.format(e)
)
train_stats = {'train_' + k: v for k, v in average_meter_train.get_all().items()}
return train_stats
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(image_size=101,
translation=lambda rs:
(rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options)
])
samples_aux = list(
set(samples).intersection(set(utils.get_aux_samples())))
dataset_aux = datasets.BoundaryImageDataset(samples_aux,
settings.train, transforms)
dataset_pseudo = datasets.BoundarySemiSupervisedImageDataset(
samples_test,
settings.test,
transforms,
size=len(samples_test),
test_predictions=self.test_predictions,
momentum=0.0)
dataset = datasets.BoundaryImageDataset(samples, settings.train,
transforms)
weight_train = len(dataset_pseudo) / len(dataset) * 2
weight_aux = weight_train / 2
weights = [weight_train] * len(dataset) + [weight_aux] * len(
dataset_aux) + [1] * len(dataset_pseudo)
dataloader = DataLoader(
ConcatDataset([dataset, dataset_aux, dataset_pseudo]),
num_workers=10,
batch_size=16,
sampler=WeightedRandomSampler(weights=weights, num_samples=3200))
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False,
ascii=True) as pbar, torch.enable_grad():
net.train()
padding = tta.Pad((13, 14, 13, 14))
criterion_boundary = losses.BCEWithLogitsLoss()
for images, masks_targets, boundary_targets in dataloader:
masks_targets = masks_targets.to(gpu)
boundary_targets = boundary_targets.to(gpu)
masks_predictions, boundary_predictions = net(
padding.transform_forward(images))
masks_predictions = padding.transform_backward(
masks_predictions).contiguous()
boundary_predictions = padding.transform_backward(
boundary_predictions).contiguous()
loss = 0.1 * criterion(
masks_predictions, masks_targets) + criterion_boundary(
boundary_predictions, boundary_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(torch.sigmoid(masks_predictions),
masks_targets, pbar, average_meter_train,
'Training epoch {}'.format(e))
train_stats = {
'train_' + k: v
for k, v in average_meter_train.get_all().items()
}
return train_stats
'nopoolrefinenet_seresnet152_dual_hypercolumn_aux_data_poly_lr_pseudo_labels',
'nopoolrefinenet_seresnet152_dual_hypercolumn_aux_data_poly_lr_pseudo_labels_ensemble',
'nopoolrefinenet_dpn92_dual_hypercolumn_poly_lr_aux_data_pseudo_labels',
]
output = 'ensemble-top-12-val'
test_predictions_experiment = []
for name in experiments:
test_predictions = utils.TestPredictions('{}'.format(name), mode='val')
test_predictions_experiment.append(test_predictions.load_raw())
train_samples = utils.get_train_samples()
transforms = generator.TransformationsGenerator([])
dataset = datasets.AnalysisDataset(train_samples, settings.train, transforms, utils.TestPredictions('{}'.format(name), mode='val').load())
split_map = []
val = utils.get_train_samples()
predictions = []
masks = []
with tqdm(total=len(val), leave=False) as pbar:
for id in val:
_, mask, _ = dataset.get_by_id(id)
test_prediction = np.concatenate([predictions[id] for predictions in test_predictions_experiment], axis=0)
prediction = torch.FloatTensor(test_prediction)
mask = torch.FloatTensor(mask)
predictions.append(prediction)