def __call__(self, input, target):
"""
Compute ARand Error for each input, target pair in the batch and return the mean value.
Args:
input (torch.tensor): 5D (NCDHW) output from the network
target (torch.tensor): 4D (NDHW) ground truth segmentation
Returns:
average ARand Error across the batch
"""
def _arand_err(gt, seg):
n_seg = len(np.unique(seg))
if n_seg == 1:
return 0.
return adapted_rand_error(gt, seg)[0]
# converts input and target to numpy arrays
input, target = convert_to_numpy(input, target)
if self.use_last_target:
target = target[:, -1, ...] # 4D
else:
# use 1st target channel
target = target[:, 0, ...] # 4D
# ensure target is of integer type
target = target.astype(np.int)
per_batch_arand = []
for _input, _target in zip(input, target):
n_clusters = len(np.unique(_target))
# skip ARand eval if there is only one label in the patch due to the zero-division error in Arand impl
# xxx/skimage/metrics/_adapted_rand_error.py:70: RuntimeWarning: invalid value encountered in double_scalars
# precision = sum_p_ij2 / sum_a2
logger.info(f'Number of ground truth clusters: {n_clusters}')
if n_clusters == 1:
logger.info('Skipping ARandError computation: only 1 label present in the ground truth')
per_batch_arand.append(0.)
continue
# convert _input to segmentation CDHW
segm = self.input_to_segm(_input)
assert segm.ndim == 4
if self.save_plots:
# save predicted and ground truth segmentation
plot_segm(segm, _target, self.plots_dir)
# compute per channel arand and return the minimum value
per_channel_arand = [_arand_err(_target, channel_segm) for channel_segm in segm]
logger.info(f'Min ARand for channel: {np.argmin(per_channel_arand)}')
per_batch_arand.append(np.min(per_channel_arand))
# return mean arand error
mean_arand = torch.mean(torch.tensor(per_batch_arand))
logger.info(f'ARand: {mean_arand.item()}')
return mean_arand
def __call__(self, input, target):
"""
Compute ARand Error for each input, target pair in the batch and return the mean value.
Args:
input (torch.tensor): 5D (NCDHW) output from the network
target (torch.tensor): 4D (NDHW) ground truth segmentation
Returns:
average ARand Error across the batch
"""
# converts input and target to numpy arrays
input, target = self._convert_to_numpy(input, target)
# ensure target is of integer type
target = target.astype(np.int)
per_batch_arand = []
_batch_inst = 0
for _input, _target in zip(input, target):
LOGGER.info(
f'Number of ground truth clusters: {len(np.unique(_target))}')
# convert _input to segmentation
segm = self.input_to_segm(_input)
# run connected components if necessary
if self.run_target_cc:
_target = measure.label(_target, connectivity=1)
if self.save_plots:
# save predicted and ground truth segmentation
plot_segm(segm, _target, self.plots_dir)
assert segm.ndim == 4
# compute per channel arand and return the minimum value
per_channel_arand = []
for channel_segm in segm:
per_channel_arand.append(adapted_rand(channel_segm, _target))
# get the min arand across channels
min_arand, c_index = np.min(per_channel_arand), np.argmin(
per_channel_arand)
LOGGER.info(
f'Batch: {_batch_inst}. Min AdaptedRand error: {min_arand}, channel: {c_index}'
)
per_batch_arand.append(min_arand)
_batch_inst += 1
# return mean arand error
return torch.mean(torch.tensor(per_batch_arand))