def test_epoch(self,
loader_src,
loader_tar_ul,
loader_tar_l,
epoch,
writer=None,
write_images=False,
device=0):
"""
Trains the network for one epoch
:param loader_src: source dataloader (labeled)
:param loader_tar_ul: target dataloader (unlabeled)
:param loader_tar_l: target dataloader (labeled)
:param epoch: current epoch
:param writer: summary writer
:param write_images: frequency of writing images
:param device: GPU device where the computations should occur
:return: average training loss over the epoch
"""
# perform training on GPU/CPU
module_to_device(self, device)
self.eval()
# keep track of the average loss during the epoch
loss_seg_src_cum = 0.0
loss_seg_tar_cum = 0.0
total_loss_cum = 0.0
cnt = 0
# zip dataloaders
if loader_tar_l is None:
dl = zip(loader_src)
else:
dl = zip(loader_src, loader_tar_l)
# start epoch
y_preds = []
ys = []
time_start = datetime.datetime.now()
for i, data in enumerate(dl):
# transfer to suitable device
x_src, y_src = tensor_to_device(data[0], device)
x_tar_l, y_tar_l = tensor_to_device(data[1], device)
x_src = x_src.float()
x_tar_l = x_tar_l.float()
y_src = y_src.long()
y_tar_l = y_tar_l.long()
# forward prop and compute loss
y_src_pred = self(x_src)
y_tar_l_pred = self(x_tar_l)
loss_seg_src = self.seg_loss(y_src_pred, y_src[:, 0, ...])
loss_seg_tar = self.seg_loss(y_tar_l_pred, y_tar_l[:, 0, ...])
total_loss = loss_seg_src + loss_seg_tar
loss_seg_src_cum += loss_seg_src.data.cpu().numpy()
loss_seg_tar_cum += loss_seg_tar.data.cpu().numpy()
total_loss_cum += total_loss.data.cpu().numpy()
cnt += 1
for b in range(y_tar_l_pred.size(0)):
y_preds.append(
F.softmax(y_tar_l_pred,
dim=1)[b, ...].view(y_tar_l_pred.size(1),
-1).data.cpu().numpy())
ys.append(y_tar_l[b, 0, ...].flatten().cpu().numpy())
# keep track of time
runtime = datetime.datetime.now() - time_start
seconds = runtime.total_seconds()
hours = seconds // 3600
minutes = (seconds - hours * 3600) // 60
seconds = seconds - hours * 3600 - minutes * 60
print_frm(
'Epoch %5d - Runtime for testing: %d hours, %d minutes, %f seconds'
% (epoch, hours, minutes, seconds))
# prep for metric computation
y_preds = np.concatenate(y_preds, axis=1)
ys = np.concatenate(ys)
js = np.asarray([
jaccard((ys == i).astype(int), y_preds[i, :])
for i in range(len(self.coi))
])
ams = np.asarray([
accuracy_metrics((ys == i).astype(int), y_preds[i, :])
for i in range(len(self.coi))
])
# don't forget to compute the average and print it
loss_seg_src_avg = loss_seg_src_cum / cnt
loss_seg_tar_avg = loss_seg_tar_cum / cnt
total_loss_avg = total_loss_cum / cnt
print(
'[%s] Testing Epoch %4d - Loss seg src: %.6f - Loss seg tar: %.6f - Loss: %.6f'
% (datetime.datetime.now(), epoch, loss_seg_src_avg,
loss_seg_tar_avg, total_loss_avg))
# log everything
if writer is not None:
# always log scalars
log_scalars([
loss_seg_src_avg, loss_seg_tar_avg, total_loss_avg,
np.mean(js, axis=0), *(np.mean(ams, axis=0))
], [
'test/' + s for s in [
'loss-seg-src', 'loss-seg-tar', 'total-loss', 'jaccard',
'accuracy', 'balanced-accuracy', 'precision', 'recall',
'f-score'
]
],
writer,
epoch=epoch)
# log images if necessary
if write_images:
y_src_pred = F.softmax(y_src_pred, dim=1)[:, 1:2, :, :].data
y_tar_l_pred = F.softmax(y_tar_l_pred, dim=1)[:,
1:2, :, :].data
log_images_2d([
x_src.data, y_src.data, y_src_pred, x_tar_l.data, y_tar_l,
y_tar_l_pred
], [
'test/' + s for s in [
'src/x', 'src/y', 'src/y-pred', 'tar/x-l', 'tar/y-l',
'tar/y-l-pred'
]
],
writer,
epoch=epoch)
return total_loss_avg
def test_epoch(self,
loader_src,
loader_tar_ul,
loader_tar_l,
epoch,
writer=None,
write_images=False,
device=0):
"""
Trains the network for one epoch
:param loader_src: source dataloader (labeled)
:param loader_tar_ul: target dataloader (unlabeled)
:param loader_tar_l: target dataloader (labeled)
:param epoch: current epoch
:param writer: summary writer
:param write_images: frequency of writing images
:param device: GPU device where the computations should occur
:return: average training loss over the epoch
"""
# perform training on GPU/CPU
module_to_device(self, device)
self.eval()
# keep track of the average loss during the epoch
loss_seg_src_cum = 0.0
loss_seg_tar_cum = 0.0
loss_rec_src_cum = 0.0
loss_rec_tar_cum = 0.0
loss_dc_x_cum = 0.0
loss_dc_y_cum = 0.0
total_loss_cum = 0.0
cnt = 0
# zip dataloaders
dl = zip(loader_src, loader_tar_ul, loader_tar_l)
# start epoch
y_preds = []
ys = []
time_start = datetime.datetime.now()
for i, data in enumerate(dl):
# transfer to suitable device
x_src, y_src = tensor_to_device(data[0], device)
x_tar_ul = tensor_to_device(data[1], device)
x_tar_l, y_tar_l = tensor_to_device(data[2], device)
x_src = x_src.float()
x_tar_ul = x_tar_ul.float()
x_tar_l = x_tar_l.float()
y_src = y_src.long()
y_tar_l = y_tar_l.long()
# get domain labels for domain confusion
dom_labels_x = tensor_to_device(
torch.zeros((x_src.size(0) + x_tar_ul.size(0))),
device).long()
dom_labels_x[x_src.size(0):] = 1
dom_labels_y = tensor_to_device(
torch.zeros((x_src.size(0) + x_tar_ul.size(0))),
device).long()
dom_labels_y[x_src.size(0):] = 1
# check train mode and compute loss
loss_seg_src = torch.Tensor([0])
loss_seg_tar = torch.Tensor([0])
loss_rec_src = torch.Tensor([0])
loss_rec_tar = torch.Tensor([0])
loss_dc_x = torch.Tensor([0])
loss_dc_y = torch.Tensor([0])
if self.train_mode == RECONSTRUCTION:
x_src_rec, x_src_rec_dom = self.forward_rec(x_src)
x_tar_ul_rec, x_tar_ul_rec_dom = self.forward_rec(x_tar_ul)
loss_rec_src = self.rec_loss(x_src_rec, x_src)
loss_rec_tar = self.rec_loss(x_tar_ul, x_tar_ul_rec)
loss_dc_x = self.dc_loss(
torch.cat((x_src_rec_dom, x_tar_ul_rec_dom), dim=0),
dom_labels_x)
total_loss = loss_rec_src + loss_rec_tar + self.lambda_dc * loss_dc_x
elif self.train_mode == SEGMENTATION:
# switch between reconstructed and original inputs
if np.random.rand() < self.p:
y_src_pred, y_src_pred_dom = self.forward_seg(x_src)
else:
x_src_rec, _ = self.forward_rec(x_src)
y_src_pred, y_src_pred_dom = self.forward_seg(x_src_rec)
dom_labels_y[:x_src.size(0)] = 1
if np.random.rand() < self.p:
y_tar_ul_pred, y_tar_ul_pred_dom = self.forward_seg(
x_tar_ul)
else:
x_tar_ul_rec, _ = self.forward_rec(x_tar_ul)
y_tar_ul_pred, y_tar_ul_pred_dom = self.forward_seg(
x_tar_ul_rec)
dom_labels_y[x_src.size(0):] = 1
loss_seg_src = self.seg_loss(y_src_pred, y_src[:, 0, ...])
loss_dc_y = self.dc_loss(
torch.cat((y_src_pred_dom, y_tar_ul_pred_dom), dim=0),
dom_labels_y)
total_loss = loss_seg_src + self.lambda_dc * loss_dc_y
y_tar_l_pred, _ = self.forward_seg(x_tar_l)
loss_seg_tar = self.seg_loss(y_tar_l_pred, y_tar_l[:, 0, ...])
total_loss = total_loss + loss_seg_tar
else:
x_src_rec, x_src_rec_dom = self.forward_rec(x_src)
if np.random.rand() < self.p:
y_src_pred, y_src_pred_dom = self.forward_seg(x_src)
else:
y_src_pred, y_src_pred_dom = self.forward_seg(x_src_rec)
dom_labels_y[:x_src.size(0)] = 1
x_tar_ul_rec, x_tar_ul_rec_dom = self.forward_rec(x_tar_ul)
if np.random.rand() < self.p:
y_tar_ul_pred, y_tar_ul_pred_dom = self.forward_seg(
x_tar_ul)
else:
y_tar_ul_pred, y_tar_ul_pred_dom = self.forward_seg(
x_tar_ul_rec)
dom_labels_y[x_src.size(0):] = 1
loss_rec_src = self.rec_loss(x_src_rec, x_src)
loss_rec_tar = self.rec_loss(x_tar_ul, x_tar_ul_rec)
loss_seg_src = self.seg_loss(y_src_pred, y_src[:, 0, ...])
loss_dc_x = self.dc_loss(
torch.cat((x_src_rec_dom, x_tar_ul_rec_dom), dim=0),
dom_labels_x)
loss_dc_y = self.dc_loss(
torch.cat((y_src_pred_dom, y_tar_ul_pred_dom), dim=0),
dom_labels_y)
total_loss = loss_seg_src + self.lambda_rec * (loss_rec_src + loss_rec_tar) + \
self.lambda_dc * (loss_dc_x + loss_dc_y)
_, y_tar_l_pred, _, y_tar_l_pred_dom = self(x_tar_l)
loss_seg_tar = self.seg_loss(y_tar_l_pred, y_tar_l[:, 0, ...])
total_loss = total_loss + loss_seg_tar
loss_seg_src_cum += loss_seg_src.data.cpu().numpy()
loss_seg_tar_cum += loss_seg_tar.data.cpu().numpy()
loss_rec_src_cum += loss_rec_src.data.cpu().numpy()
loss_rec_tar_cum += loss_rec_tar.data.cpu().numpy()
loss_dc_x_cum += loss_dc_x.data.cpu().numpy()
loss_dc_y_cum += loss_dc_y.data.cpu().numpy()
total_loss_cum += total_loss.data.cpu().numpy()
cnt += 1
if self.train_mode == SEGMENTATION or self.train_mode == JOINT:
for b in range(y_tar_l_pred.size(0)):
y_preds.append(
F.softmax(y_tar_l_pred,
dim=1)[b, ...].view(y_tar_l_pred.size(1),
-1).data.cpu().numpy())
ys.append(y_tar_l[b, 0, ...].flatten().cpu().numpy())
# keep track of time
runtime = datetime.datetime.now() - time_start
seconds = runtime.total_seconds()
hours = seconds // 3600
minutes = (seconds - hours * 3600) // 60
seconds = seconds - hours * 3600 - minutes * 60
print_frm(
'Epoch %5d - Runtime for testing: %d hours, %d minutes, %f seconds'
% (epoch, hours, minutes, seconds))
# prep for metric computation
if self.train_mode == SEGMENTATION or self.train_mode == JOINT:
y_preds = np.concatenate(y_preds, axis=1)
ys = np.concatenate(ys)
js = np.asarray([
jaccard((ys == i).astype(int), y_preds[i, :])
for i in range(len(self.coi))
])
ams = np.asarray([
accuracy_metrics((ys == i).astype(int), y_preds[i, :])
for i in range(len(self.coi))
])
# don't forget to compute the average and print it
loss_seg_src_avg = loss_seg_src_cum / cnt
loss_seg_tar_avg = loss_seg_tar_cum / cnt
loss_rec_src_avg = loss_rec_src_cum / cnt
loss_rec_tar_avg = loss_rec_tar_cum / cnt
loss_dc_x_avg = loss_dc_x_cum / cnt
loss_dc_y_avg = loss_dc_y_cum / cnt
total_loss_avg = total_loss_cum / cnt
print(
'[%s] Testing Epoch %5d - Loss seg src: %.6f - Loss seg tar: %.6f - Loss rec src: %.6f - Loss rec tar: %.6f - Loss DCX: %.6f - Loss DCY: %.6f - Loss: %.6f'
% (datetime.datetime.now(), epoch, loss_seg_src_avg,
loss_seg_tar_avg, loss_rec_src_avg, loss_rec_tar_avg,
loss_dc_x_avg, loss_dc_y_avg, total_loss_avg))
# log everything
if writer is not None:
# always log scalars
if self.train_mode == RECONSTRUCTION:
log_scalars(
[loss_rec_src_avg, loss_rec_tar_avg, loss_dc_x_avg], [
'test/' + s
for s in ['loss-rec-src', 'loss-rec-tar', 'loss-dc-x']
],
writer,
epoch=epoch)
elif self.train_mode == SEGMENTATION:
log_scalars([
loss_seg_src_avg, loss_seg_tar_avg, loss_dc_y_avg,
np.mean(js, axis=0), *(np.mean(ams, axis=0))
], [
'test/' + s for s in [
'loss-seg-src', 'loss-seg-tar', 'loss-dc-y', 'jaccard',
'accuracy', 'balanced-accuracy', 'precision', 'recall',
'f-score'
]
],
writer,
epoch=epoch)
else:
log_scalars([
loss_seg_src_avg, loss_seg_tar_avg, loss_rec_src_avg,
loss_rec_tar_avg, loss_dc_x_avg, loss_dc_y_avg,
np.mean(js, axis=0), *(np.mean(ams, axis=0))
], [
'test/' + s for s in [
'loss-seg-src', 'loss-seg-tar', 'loss-rec-src',
'loss-rec-tar', 'loss-dc-x', 'loss-dc-y', 'jaccard',
'accuracy', 'balanced-accuracy', 'precision', 'recall',
'f-score'
]
],
writer,
epoch=epoch)
log_scalars([total_loss_avg],
['test/' + s for s in ['total-loss']],
writer,
epoch=epoch)
# log images if necessary
if write_images:
log_images_2d([x_src.data], ['test/' + s for s in ['src/x']],
writer,
epoch=epoch)
if self.train_mode == RECONSTRUCTION:
log_images_2d(
[x_src_rec.data, x_tar_ul.data, x_tar_ul_rec.data], [
'test/' + s
for s in ['src/x-rec', 'tar/x-ul', 'tar/x-ul-rec']
],
writer,
epoch=epoch)
elif self.train_mode == SEGMENTATION:
y_src_pred = F.softmax(y_src_pred, dim=1)[:,
1:2, :, :].data
log_images_2d(
[y_src.data, y_src_pred],
['test/' + s for s in ['src/y', 'src/y-pred']],
writer,
epoch=epoch)
if loader_tar_l is not None:
y_tar_l_pred = F.softmax(y_tar_l_pred,
dim=1)[:, 1:2, :, :].data
log_images_2d([x_tar_l.data, y_tar_l, y_tar_l_pred], [
'test/' + s
for s in ['tar/x-l', 'tar/y-l', 'tar/y-l-pred']
],
writer,
epoch=epoch)
else:
y_src_pred = F.softmax(y_src_pred, dim=1)[:,
1:2, :, :].data
log_images_2d([
x_src_rec.data, y_src.data, y_src_pred, x_tar_ul.data,
x_tar_ul_rec.data
], [
'test/' + s for s in [
'src/x-rec', 'src/y', 'src/y-pred', 'tar/x-ul',
'tar/x-ul-rec'
]
],
writer,
epoch=epoch)
if loader_tar_l is not None:
y_tar_l_pred = F.softmax(y_tar_l_pred,
dim=1)[:, 1:2, :, :].data
log_images_2d([x_tar_l.data, y_tar_l, y_tar_l_pred], [
'test/' + s
for s in ['tar/x-l', 'tar/y-l', 'tar/y-l-pred']
],
writer,
epoch=epoch)
return total_loss_avg
def test_epoch(self,
loader_src,
loader_tar,
loss_seg_fn,
loss_rec_fn,
epoch,
writer=None,
write_images=False,
device=0):
"""
Tests the network for one epoch
:param loader_src: source dataloader (should be labeled)
:param loader_tar: target dataloader (should be labeled)
:param loss_seg_fn: segmentation loss function
:param loss_rec_fn: reconstruction loss function
:param epoch: current epoch
:param writer: summary writer
:param write_images: frequency of writing images
:param device: GPU device where the computations should occur
:return: average training loss over the epoch
"""
# perform training on GPU/CPU
module_to_device(self, device)
self.eval()
# keep track of the average loss and metrics during the epoch
loss_seg_cum = 0.0
loss_rec_cum = 0.0
total_loss_cum = 0.0
cnt = 0
# start epoch
y_src_preds = []
ys_src = []
y_tar_preds = []
ys_tar = []
for i, data in enumerate(zip(loader_src, loader_tar)):
# get inputs and transfer to suitable device
x_src, y_src = tensor_to_device(data[0], device)
x_tar, y_tar = tensor_to_device(data[1], device)
y_src = get_labels(y_src, coi=self.coi, dtype=int)
y_tar = get_labels(y_tar, coi=self.coi, dtype=int)
x_src = x_src.float()
x_tar = x_tar.float()
# zero the gradient buffers
self.zero_grad()
# forward prop
y_src_pred = self(x_src)
x_src_pred = self.reconstruction_outputs
y_tar_pred = self(x_tar)
x_tar_pred = self.reconstruction_outputs
# compute loss
loss_seg = loss_seg_fn(y_src_pred, y_src)
loss_rec = 0.5 * (loss_rec_fn(x_src_pred, x_src) +
loss_rec_fn(x_tar_pred, x_tar))
total_loss = loss_seg + self.lambda_rec * loss_rec
loss_seg_cum += loss_seg.data.cpu().numpy()
loss_rec_cum += loss_rec.data.cpu().numpy()
total_loss_cum += total_loss.data.cpu().numpy()
cnt += 1
for b in range(y_src_pred.size(0)):
y_src_preds.append(
F.softmax(y_src_pred, dim=1).data.cpu().numpy()[b, 1, ...])
y_tar_preds.append(
F.softmax(y_tar_pred, dim=1).data.cpu().numpy()[b, 1, ...])
ys_src.append(y_src[b, 0, ...].cpu().numpy())
ys_tar.append(y_tar[b, 0, ...].cpu().numpy())
# compute interesting metrics
y_src_preds = np.asarray(y_src_preds)
y_tar_preds = np.asarray(y_tar_preds)
ys_src = np.asarray(ys_src)
ys_tar = np.asarray(ys_tar)
j_src = jaccard(ys_src, y_src_preds)
j_tar = jaccard(ys_src, y_tar_preds)
a_src, ba_src, p_src, r_src, f_src = accuracy_metrics(
ys_src, y_src_preds)
a_tar, ba_tar, p_tar, r_tar, f_tar = accuracy_metrics(
ys_tar, y_tar_preds)
# don't forget to compute the average and print it
loss_seg_avg = loss_seg_cum / cnt
loss_rec_avg = loss_rec_cum / cnt
total_loss_avg = total_loss_cum / cnt
print('[%s] Epoch %5d - Loss seg: %.6f - Loss rec: %.6f - Loss: %.6f' %
(datetime.datetime.now(), epoch, loss_seg_avg, loss_rec_avg,
total_loss_avg))
# log everything
if writer is not None:
# always log scalars
log_scalars([
loss_seg_avg, loss_rec_avg, total_loss_avg, j_src, a_src,
ba_src, p_src, r_src, f_src, j_tar, a_tar, ba_tar, p_tar,
r_tar, f_tar
], [
'test/' + s for s in [
'loss-rec', 'loss-seg', 'total-loss', 'src/jaccard',
'src/accuracy', 'src/balanced-accuracy', 'src/precision',
'src/recall', 'src/f-score', 'tar/jaccard', 'tar/accuracy',
'tar/balanced-accuracy', 'tar/precision', 'tar/recall',
'tar/f-score'
]
],
writer,
epoch=epoch)
# log images if necessary
if write_images:
y_src_pred = F.softmax(y_src_pred, dim=1)[:, 1:2, ...].data
y_tar_pred = F.softmax(y_tar_pred, dim=1)[:, 1:2, ...].data
log_images_3d([
x_src, x_src_pred.data, y_src, y_src_pred, x_tar,
x_tar_pred.data, y_tar, y_tar_pred
], [
'test/' + s for s in [
'src/x', 'src/x-pred', 'src/y', 'src/y-pred', 'tar/x',
'tar/x-pred', 'tar/y', 'tar/y-pred'
]
],
writer,
epoch=epoch)
return total_loss_avg
def test_epoch(self, loader, loss_fn, epoch, writer=None, write_images=False, device=0):
"""
Tests the network for one epoch
:param loader: dataloader
:param loss_fn: loss function
:param epoch: current epoch
:param writer: summary writer
:param write_images: frequency of writing images
:param device: GPU device where the computations should occur
:return: average testing loss over the epoch
"""
# perform training on GPU/CPU
module_to_device(self, device)
self.eval()
# keep track of the average loss and metrics during the epoch
loss_cum = 0.0
cnt = 0
# test loss
y_preds = []
ys = []
ys_ = []
time_start = datetime.datetime.now()
for i, data in enumerate(loader):
# get the inputs and transfer to suitable device
x, y = tensor_to_device(data, device)
y_ = get_unlabeled(y)
x = x.float()
y = get_labels(y, coi=self.coi, dtype=int)
y_ = get_labels(y_, coi=[0, 255], dtype=bool)
# forward prop
y_pred = self(x)
# compute loss
loss = loss_fn(y_pred, y[:, 0, ...], mask=~y_)
loss_cum += loss.data.cpu().numpy()
cnt += 1
for b in range(y_pred.size(0)):
y_preds.append(F.softmax(y_pred, dim=1)[b, ...].view(y_pred.size(1), -1).data.cpu().numpy())
ys.append(y[b, 0, ...].flatten().cpu().numpy())
ys_.append(y_[b, 0, ...].flatten().cpu().numpy())
# keep track of time
runtime = datetime.datetime.now() - time_start
seconds = runtime.total_seconds()
hours = seconds // 3600
minutes = (seconds - hours * 3600) // 60
seconds = seconds - hours * 3600 - minutes * 60
print_frm(
'Epoch %5d - Runtime for testing: %d hours, %d minutes, %f seconds' % (epoch, hours, minutes, seconds))
# prep for metric computation
y_preds = np.concatenate(y_preds, axis=1)
ys = np.concatenate(ys)
ys_ = np.concatenate(ys_)
w = (1 - ys_).astype(bool)
js = np.asarray([jaccard((ys == i).astype(int), y_preds[i, :], w=w) for i in range(len(self.coi))])
ams = np.asarray([accuracy_metrics((ys == i).astype(int), y_preds[i, :], w=w) for i in range(len(self.coi))])
# don't forget to compute the average and print it
loss_avg = loss_cum / cnt
print_frm('Epoch %5d - Average test loss: %.6f' % (epoch, loss_avg))
# log everything
if writer is not None:
# always log scalars
log_scalars([loss_avg, np.mean(js, axis=0), *(np.mean(ams, axis=0))], ['test/' + s for s in
['loss-seg', 'jaccard', 'accuracy',
'balanced-accuracy', 'precision',
'recall', 'f-score']], writer,
epoch=epoch)
# log images if necessary
if write_images:
log_images_3d([x], ['test/' + s for s in ['x']], writer, epoch=epoch)
y_pred = F.softmax(y_pred, dim=1)
for i, c in enumerate(self.coi):
if not i == 0: # skip background class
y_p = y_pred[:, i:i + 1, ...].data
y_t = (y == i).long()
log_images_3d([y_t, y_p],
['test/' + s for s in ['y_class_%d)' % (c), 'y_pred_class_%d)' % (c)]], writer,
epoch=epoch)
return np.mean(js)
def validate(net,
data,
labels,
input_size,
in_channels=1,
classes_of_interest=(0, 1),
batch_size=1,
write_dir=None,
val_file=None,
track_progress=False,
device=0,
orientations=(0, ),
normalization='unit'):
"""
Validate a network on a dataset and its labels
:param net: image-to-image segmentation network
:param data: 3D array (Z, Y, X) representing the 3D image
:param labels: 3D array (Z, Y, X) representing the 3D labels
:param input_size: size of the inputs (either 2 or 3-tuple) for processing
:param in_channels: Amount of subsequent slices that serve as input for the network (should be odd)
:param classes_of_interest: index of the label of interest
:param batch_size: batch size for processing
:param write_dir: optionally, specify a directory to write the output
:param val_file: optionally, specify a file to write the validation results
:param track_progress: optionally, for tracking progress with progress bar
:param device: GPU device where the computations should occur
:param orientations: list of orientations to perform segmentation: 0-Z, 1-Y, 2-X (only for 2D based segmentation)
:param normalization: type of data normalization (unit, z or minmax)
:return: validation results, i.e. accuracy, precision, recall, f-score, jaccard and dice score
"""
print_frm('Validating the trained network...')
# compute segmentation for each orientation and average results
segmentation = np.zeros((net.out_channels, *data.shape))
for orientation in orientations:
segmentation += segment(data,
net,
input_size,
in_channels=in_channels,
batch_size=batch_size,
track_progress=track_progress,
device=device,
orientation=orientation,
normalization=normalization)
segmentation = segmentation / len(orientations)
# compute metrics
w = labels != 255
comp_hausdorff = np.sum(labels == 255) == 0
js = np.asarray([
jaccard(segmentation[i], (labels == c).astype('float'), w=w)
for i, c in enumerate(classes_of_interest)
])
ams = np.asarray([
accuracy_metrics(segmentation[i], (labels == c).astype('float'), w=w)
for i, c in enumerate(classes_of_interest)
])
for i, c in enumerate(classes_of_interest):
if comp_hausdorff:
h = hausdorff_distance(segmentation[i], labels)[0]
else:
h = -1
# report results
print_frm('Validation performance for class %d: ' % c)
print_frm(' - Accuracy: %f' % ams[i, 0])
print_frm(' - Balanced accuracy: %f' % ams[i, 1])
print_frm(' - Precision: %f' % ams[i, 2])
print_frm(' - Recall: %f' % ams[i, 3])
print_frm(' - F1 score: %f' % ams[i, 4])
print_frm(' - IoU: %f' % js[i])
print_frm(' - Hausdorff distance: %f' % h)
# report results
print_frm('Validation performance mean: ')
print_frm(' - Accuracy: %f' % np.mean(ams[:, 0]))
print_frm(' - Balanced accuracy: %f' % np.mean(ams[:, 1]))
print_frm(' - Precision: %f' % np.mean(ams[:, 2]))
print_frm(' - Recall: %f' % np.mean(ams[:, 3]))
print_frm(' - F1 score: %f' % np.mean(ams[:, 4]))
print_frm(' - mIoU: %f' % np.mean(js))
# write stuff if necessary
if write_dir is not None:
print_frm('Writing out the segmentation...')
mkdir(write_dir)
segmentation_volume = np.zeros(segmentation.shape[1:])
for i, c in enumerate(classes_of_interest):
segmentation_volume[segmentation[i] > 0.5] = c
write_volume(segmentation_volume, write_dir, type='pngseq')
if val_file is not None:
np.save(val_file, np.concatenate((js[:, np.newaxis], ams), axis=1))
return js, ams