def _multi_view_predict_on(image_pair, image_pair_loader, model,
views, hparams, results, per_view_results,
out_dir, args):
from mpunet.utils.fusion import predict_volume, map_real_space_pred
from mpunet.interpolation.sample_grid import get_voxel_grid_real_space
# Set image_pair_loader object with only the given file
image_pair_loader.images = [image_pair]
n_classes = hparams["build"]["n_classes"]
# Load views
kwargs = hparams["fit"]
kwargs.update(hparams["build"])
seq = image_pair_loader.get_sequencer(views=views, **kwargs)
# Get voxel grid in real space
voxel_grid_real_space = get_voxel_grid_real_space(image_pair)
# Prepare tensor to store combined prediction
d = image_pair.image.shape[:-1]
combined = np.empty(
shape=(len(views), d[0], d[1], d[2], n_classes),
dtype=np.float32
)
print("Predicting on brain hyper-volume of shape:", combined.shape)
# Predict for each view
for n_view, view in enumerate(views):
print("\n[*] (%i/%i) View: %s" % (n_view + 1, len(views), view))
# for each view, predict on all voxels and map the predictions
# back into the original coordinate system
# Sample planes from the image at grid_real_space grid
# in real space (scanner RAS) coordinates.
X, y, grid, inv_basis = seq.get_view_from(image_pair.id, view,
n_planes="same+20")
# Predict on volume using model
pred = predict_volume(model, X, axis=2, batch_size=seq.batch_size)
# Map the real space coordiante predictions to nearest
# real space coordinates defined on voxel grid
mapped_pred = map_real_space_pred(pred, grid, inv_basis,
voxel_grid_real_space,
method="nearest")
combined[n_view] = mapped_pred
if not args.no_eval:
_per_view_evaluation(image_id=image_pair.id,
pred=pred,
true=y,
mapped_pred=mapped_pred,
mapped_true=image_pair.labels,
view=view,
n_classes=n_classes,
results=results,
per_view_results=per_view_results,
out_dir=out_dir,
args=args)
return combined
def predict_and_map(model,
seq,
image,
view,
batch_size=None,
voxel_grid_real_space=None,
targets=None,
eval_prob=1.0,
n_planes='same+20'):
"""
Args:
model:
seq:
image:
view:
batch_size:
voxel_grid_real_space:
targets:
n_planes:
Returns:
"""
# Sample planes from the image at grid_real_space grid
# in real space (scanner RAS) coordinates.
X, y, grid, inv_basis = seq.get_view_from(image.id,
view,
n_planes=n_planes)
# Predict on volume using model
bs = seq.batch_size if batch_size is None else batch_size
from mpunet.utils.fusion import predict_volume
pred = predict_volume(model, X, axis=2, batch_size=bs)
# Map the real space coordiante predictions to nearest
# real space coordinates defined on voxel grid
if voxel_grid_real_space is None:
from mpunet.interpolation.sample_grid import get_voxel_grid_real_space
voxel_grid_real_space = get_voxel_grid_real_space(image)
# Map the predicted volume to real space
mapped = map_real_space_pred(pred, grid, inv_basis, voxel_grid_real_space)
# Print dice scores
if targets is not None and np.random.rand(1)[0] <= eval_prob:
print("Computing evaluations...")
print("View dice scores: ",
dice_all(y, pred.argmax(-1), ignore_zero=False))
print(
"Mapped dice scores: ",
dice_all(targets,
mapped.argmax(-1).reshape(-1, 1),
ignore_zero=False))
else:
print("-- Skipping evaluation")
return mapped