def _run_fusion_training(sets, logger, hparams, min_val_images, is_validation,
views, n_classes, unet, fusion_model, early_stopping,
fm_batch_size, epochs, eval_prob,
fusion_weights_path):
for _round, _set in enumerate(sets):
s = "Set %i/%i:\n%s" % (_round + 1, len(sets), _set)
logger("\n%s" % highlighted(s))
# Reload data
images = ImagePairLoader(**hparams["val_data"])
if len(images) < min_val_images:
images.add_images(ImagePairLoader(**hparams["train_data"]))
# Get list of ImagePair objects to run on
image_set_dict = {m.id: m for m in images if m.id in _set}
# Fetch points from the set images
points_collection = []
targets_collection = []
N_im = len(image_set_dict)
for num_im, image_id in enumerate(list(image_set_dict.keys())):
logger("")
logger(
highlighted("(%i/%i) Running on %s (%s)" %
(num_im + 1, N_im, image_id,
"val" if is_validation[image_id] else "train")))
# Set the current ImagePair
image = image_set_dict[image_id]
images.images = [image]
# Load views
kwargs = hparams["fit"]
kwargs.update(hparams["build"])
seq = images.get_sequencer(views=views, **kwargs)
# Get voxel grid in real space
voxel_grid_real_space = get_voxel_grid_real_space(image)
# Get array to store predictions across all views
targets = image.labels.reshape(-1, 1)
points = np.empty(shape=(len(targets), len(views), n_classes),
dtype=np.float32)
points.fill(np.nan)
# Predict on all views
for k, v in enumerate(views):
print("\n%s" % "View: %s" % v)
points[:, k, :] = predict_and_map(
model=unet,
seq=seq,
image=image,
view=v,
voxel_grid_real_space=voxel_grid_real_space,
n_planes='same+20',
targets=targets,
eval_prob=eval_prob).reshape(-1, n_classes)
# Clean up a bit
del image_set_dict[image_id]
del image # Should be GC at this point anyway
# add to collections
points_collection.append(points)
targets_collection.append(targets)
# Stack points into one matrix
logger("Stacking points...")
X, y = stack_collections(points_collection, targets_collection)
# Shuffle train
print("Shuffling points...")
X, y = shuffle(X, y)
print("Getting validation set...")
val_ind = int(0.20 * X.shape[0])
X_val, y_val = X[:val_ind], y[:val_ind]
X, y = X[val_ind:], y[val_ind:]
# Prepare dice score callback for validation data
val_cb = ValDiceScores((X_val, y_val), n_classes, 50000, logger)
# Callbacks
cbs = [
val_cb,
CSVLogger(filename="logs/fusion_training.csv",
separator=",",
append=True),
PrintLayerWeights(fusion_model.layers[-1],
every=1,
first=1000,
per_epoch=True,
logger=logger)
]
es = EarlyStopping(monitor='val_dice',
min_delta=0.0,
patience=early_stopping,
verbose=1,
mode='max')
cbs.append(es)
# Start training
try:
fusion_model.fit(X,
y,
batch_size=fm_batch_size,
epochs=epochs,
callbacks=cbs,
verbose=1)
except KeyboardInterrupt:
pass
fusion_model.save_weights(fusion_weights_path)
def predict_single(image, model, hparams, verbose=1):
"""
A generic prediction function that sets up a ImagePairLoader object for the
given image, prepares the image and predicts.
Note that this function should only be used for convinience in scripts that
work on single images at a time anyway, as batch-preparing the entire
ImagePairLoader object prior to prediction is faster.
NOTE: Only works with iso_live intrp modes at this time
"""
mode = hparams["fit"]["intrp_style"].lower()
assert mode in ("iso_live", "iso_live_3d")
# Prepare image for prediction
kwargs = hparams["fit"]
kwargs.update(hparams["build"])
# Set verbose memory
verb_mem = kwargs["verbose"]
kwargs["verbose"] = verbose
# Create a ImagePairLoader with only the given file
from mpunet.image import ImagePairLoader
image_pair_loader = ImagePairLoader(predict_mode=True,
initialize_empty=True,
no_log=bool(verbose))
image_pair_loader.add_image(image)
# Get N classes
n_classes = kwargs["n_classes"]
if mode == "iso_live":
# Add views if SMMV model
kwargs["views"] = np.load(hparams.project_path + "/views.npz")["arr_0"]
# Get sequence object
sequence = image_pair_loader.get_sequencer(**kwargs)
# Get voxel grid in real space
voxel_grid_real_space = get_voxel_grid_real_space(image)
# Prepare tensor to store combined prediction
d = image.image.shape
predicted = np.empty(shape=(len(kwargs["views"]), d[0], d[1], d[2],
n_classes),
dtype=np.float32)
print("Predicting on brain hyper-volume of shape:", predicted.shape)
for n_view, v in enumerate(kwargs["views"]):
print("\nView %i/%i: %s" % (n_view + 1, len(kwargs["views"]), v))
# Sample the volume along the view
X, y, grid, inv_basis = sequence.get_view_from(image.id,
v,
n_planes="same+20")
# Predict on volume using model
pred = predict_volume(model, X, axis=2)
# Map the real space coordiante predictions to nearest
# real space coordinates defined on voxel grid
predicted[n_view] = map_real_space_pred(pred,
grid,
inv_basis,
voxel_grid_real_space,
method="nearest")
else:
predicted = pred_3D_iso(
model=model,
sequence=image_pair_loader.get_sequencer(**kwargs),
image=image,
extra_boxes="3x",
min_coverage=None)
# Revert verbose mem
kwargs["verbose"] = verb_mem
return predicted
def entry_func(args=None):
# Get command line arguments
args = vars(get_argparser().parse_args(args))
base_dir = os.path.abspath(args["project_dir"])
_file = args["f"]
label = args["l"]
N_extra = args["extra"]
try:
N_extra = int(N_extra)
except ValueError:
pass
# Get settings from YAML file
from mpunet.hyperparameters import YAMLHParams
hparams = YAMLHParams(os.path.join(base_dir, "train_hparams.yaml"))
# Set strides
hparams["fit"]["strides"] = args["strides"]
if not _file:
try:
# Data specified from command line?
data_dir = os.path.abspath(args["data_dir"])
# Set with default sub dirs
hparams["test_data"] = {
"base_dir": data_dir,
"img_subdir": "images",
"label_subdir": "labels"
}
except (AttributeError, TypeError):
data_dir = hparams["test_data"]["base_dir"]
else:
data_dir = False
out_dir = os.path.abspath(args["out_dir"])
overwrite = args["overwrite"]
predict_mode = args["no_eval"]
save_only_pred = args["save_only_pred"]
# Check if valid dir structures
validate_folders(base_dir, data_dir, out_dir, overwrite)
# Import all needed modules (folder is valid at this point)
import numpy as np
from mpunet.image import ImagePairLoader, ImagePair
from mpunet.utils import get_best_model, create_folders, \
pred_to_class, await_and_set_free_gpu, set_gpu
from mpunet.utils.fusion import predict_3D_patches, predict_3D_patches_binary, pred_3D_iso
from mpunet.logging import init_result_dict_3D, save_all_3D
from mpunet.evaluate import dice_all
from mpunet.bin.predict import save_nii_files
# Fetch GPU(s)
num_GPUs = args["num_GPUs"]
force_gpu = args["force_GPU"]
# Wait for free GPU
if force_gpu == -1:
await_and_set_free_gpu(N=num_GPUs, sleep_seconds=240)
else:
set_gpu(force_gpu)
# Read settings from the project hyperparameter file
dim = hparams["build"]["dim"]
n_classes = hparams["build"]["n_classes"]
mode = hparams["fit"]["intrp_style"]
# Set ImagePairLoader object
if not _file:
image_pair_loader = ImagePairLoader(predict_mode=predict_mode,
**hparams["test_data"])
else:
predict_mode = not bool(label)
image_pair_loader = ImagePairLoader(predict_mode=predict_mode,
initialize_empty=True)
image_pair_loader.add_image(ImagePair(_file, label))
# Put them into a dict and remove from image_pair_loader to gain more control with
# garbage collection
all_images = {image.id: image for image in image_pair_loader.images}
image_pair_loader.images = None
""" Define UNet model """
from mpunet.models import model_initializer
hparams["build"]["batch_size"] = 1
unet = model_initializer(hparams, False, base_dir)
model_path = get_best_model(base_dir + "/model")
unet.load_weights(model_path)
# Evaluate?
if not predict_mode:
# Prepare dictionary to store results in pd df
results, detailed_res = init_result_dict_3D(all_images, n_classes)
# Save to check correct format
save_all_3D(results, detailed_res, out_dir)
# Define result paths
nii_res_dir = os.path.join(out_dir, "nii_files")
create_folders(nii_res_dir)
image_ids = sorted(all_images)
for n_image, image_id in enumerate(image_ids):
print("\n[*] Running on: %s" % image_id)
# Set image_pair_loader object with only the given file
image = all_images[image_id]
image_pair_loader.images = [image]
seq = image_pair_loader.get_sequencer(n_classes=n_classes,
no_log=True,
dim=dim,
**hparams["fit"])
if mode.lower() == "iso_live_3d":
pred = pred_3D_iso(model=unet,
sequence=seq,
image=image,
extra_boxes=N_extra,
min_coverage=None)
else:
# Predict on volume using model
if n_classes > 1:
pred = predict_3D_patches(model=unet,
patches=seq,
image=image,
N_extra=N_extra)
else:
pred = predict_3D_patches_binary(model=unet,
patches=seq,
image_id=image_id,
N_extra=N_extra)
if not predict_mode:
# Get patches for the current image
y = image.labels
# Calculate dice score
print("Mean dice: ", end="", flush=True)
p = pred_to_class(pred, img_dims=3, has_batch_dim=False)
dices = dice_all(y, p, n_classes=n_classes, ignore_zero=True)
mean_dice = dices[~np.isnan(dices)].mean()
print("Dices: ", dices)
print("%s (n=%i)" % (mean_dice, len(dices)))
# Add to results
results[image_id] = [mean_dice]
detailed_res[image_id] = dices
# Overwrite with so-far results
save_all_3D(results, detailed_res, out_dir)
# Save results
save_nii_files(p, image, nii_res_dir, save_only_pred)
# Remove image from dictionary and image_pair_loader to free memory
del all_images[image_id]
image_pair_loader.images.remove(image)
if not predict_mode:
# Write final results
save_all_3D(results, detailed_res, out_dir)