def get_example(load_example, eval_tracker, model, get_offsets):
"""Generates individual training examples.
Args:
load_example: callable returning a tuple of image and label ndarrays
as well as the seed coordinate and volume name of the example
eval_tracker: EvalTracker object
model: FFNModel object
get_offsets: iterable of (x, y, z) offsets to investigate within the
training patch
Yields:
tuple of:
seed array, shape [1, z, y, x, 1]
image array, shape [1, z, y, x, 1]
label array, shape [1, z, y, x, 1]
"""
seed_shape = train_canvas_size(model).tolist()[::-1]
while True:
full_patches, full_labels, loss_weights, coord, volname = load_example(
)
# Write a random fraction of paired examples to images and make sure they have
# matching and correct orientations.
if FLAGS.debug:
if random.uniform(0, 1) > 0.999:
write_patch_and_label_to_img(
patch=full_patches[0, 0, :, :, 0] * FLAGS.image_stddev +
FLAGS.image_mean,
label=full_labels[0, 0, :, :, 0] * 255,
unique_id='_'.join(coord[0].astype(str).tolist()),
dirname="./debug")
# Always start with a clean seed.
seed = logit(mask.make_seed(seed_shape, 1, pad=FLAGS.seed_pad))
for off in get_offsets(model, seed):
predicted = mask.crop_and_pad(seed, off,
model.input_seed_size[::-1])
patches = mask.crop_and_pad(full_patches, off,
model.input_image_size[::-1])
labels = mask.crop_and_pad(full_labels, off,
model.pred_mask_size[::-1])
weights = mask.crop_and_pad(loss_weights, off,
model.pred_mask_size[::-1])
# Necessary, since the caller is going to update the array and these
# changes need to be visible in the following iterations.
assert predicted.base is seed
yield predicted, patches, labels, weights
# TODO(jpgard): track volname in eval_tracker. Currently nothing is done with
# volname, but it should be monitored to ensure coverage of all training
# volumes. Similar for coord; would be good to check coordinates covered,
# or at least a sample of them.
eval_tracker.add_patch(full_labels, seed, loss_weights, coord, volname,
full_patches)
def get_example(load_example, eval_tracker, model, get_offsets):
"""Generates individual training examples.
Args:
load_example: callable returning a tuple of image and label ndarrays
as well as the seed coordinate and volume name of the example
eval_tracker: EvalTracker object
model: FFNModel object
get_offsets: iterable of (x, y, z) offsets to investigate within the
training patch
Yields:
tuple of:
seed array, shape [1, z, y, x, 1]
image array, shape [1, z, y, x, 1]
label array, shape [1, z, y, x, 1]
"""
seed_shape = train_canvas_size(model).tolist()[::-1]
while True:
full_patches, full_labels, loss_weights, coord, volname = load_example(
)
# Always start with a clean seed.
seed = logit(mask.make_seed(seed_shape, 1, pad=FLAGS.seed_pad))
for off in get_offsets(model, seed):
predicted = mask.crop_and_pad(seed, off,
model.input_seed_size[::-1])
patches = mask.crop_and_pad(full_patches, off,
model.input_image_size[::-1])
labels = mask.crop_and_pad(full_labels, off,
model.pred_mask_size[::-1])
weights = mask.crop_and_pad(loss_weights, off,
model.pred_mask_size[::-1])
# Necessary, since the caller is going to update the array and these
# changes need to be visible in the following iterations.
assert predicted.base is seed
yield predicted, patches, labels, weights
# eval_tracker.add_patch(
# full_labels, seed, loss_weights, coord, volname, full_patches)
eval_tracker.add_patch_v2(full_labels, seed, loss_weights, coord,
volname, full_patches)
