def entry_func(args=None):
parser = get_parser()
args = vars(parser.parse_args(args))
# Get arguments
project_folder = os.path.abspath(args["project_folder"])
copy_weights = args["copy_weights"]
weights = os.path.abspath(args["weights_file"]
or get_best_model(project_folder + "/model"))
out_dir = os.path.join(project_folder, args["out_dir"])
# Get main hyperparamter file and check if correct modelt ype
hparams = YAMLHParams(project_folder + "/train_hparams.yaml", no_log=True)
tasks = hparams.get("tasks", False)
if not tasks:
print("[ERROR] Project must be of type 'MultiTask'.")
sys.exit(0)
# Branch out each sub-task
create_folders(out_dir)
for name, hparams_file in zip(tasks["task_names"], tasks["hparam_files"]):
print("\n[*] Branching task %s" % name)
# Reload the hparams in each iteration as we overwrite fields each time
hparams = YAMLHParams(project_folder + "/train_hparams.yaml",
no_log=False)
# Get task specific parameters
task_hparams = YAMLHParams(project_folder + "/%s" % hparams_file)
branch(task_name=name,
out_dir=os.path.join(out_dir, name),
task_hparams=task_hparams,
task_hparams_file=hparams_file,
shared_hparams=hparams,
weights=weights,
copy_weights=copy_weights,
views_file=os.path.join(project_folder, "views.npz"))
def init_and_load_best_model(hparams, model_dir, logger=None, by_name=True):
"""
Initializes a model according to hparams. Then finds the best model in
model_dir and loads it (see MultiPlanarUNet.utils.get_best_model).
Args:
hparams: A YAMLHparams object of hyperparameters
model_dir: A path to the directory that stores model param files
logger: Optional Logger object
by_name: Load parameters by layer names instead of order (default).
Returns:
A tf.keras Model instance
The file name of the parameter file that was loaded
"""
from MultiPlanarUNet.utils import get_best_model
model = init_model(hparams["build"], logger)
model_path = get_best_model(model_dir)
load_from_file(model, model_path, logger, by_name=by_name)
model_file_name = os.path.split(model_path)[-1]
return model, model_file_name
def entry_func(args=None):
# Project base path
args = vars(get_argparser().parse_args(args))
basedir = os.path.abspath(args["project_dir"])
overwrite = args["overwrite"]
continue_training = args["continue_training"]
eval_prob = args["eval_prob"]
await_PID = args["wait_for"]
dice_weight = args["dice_weight"]
print("Fitting fusion model for project-folder: %s" % basedir)
# Minimum images in validation set before also using training images
min_val_images = 15
# Fusion model training params
epochs = args['epochs']
fm_batch_size = args["batch_size"]
# Early stopping params
early_stopping = args["early_stopping"]
# Wait for PID?
if await_PID:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(await_PID)
# Fetch GPU(s)
num_GPUs = args["num_GPUs"]
force_gpu = args["force_GPU"]
# Wait for free GPU
if not force_gpu:
await_and_set_free_gpu(N=num_GPUs, sleep_seconds=120)
num_GPUs = 1
else:
set_gpu(force_gpu)
num_GPUs = len(force_gpu.split(","))
# Get logger
logger = Logger(base_path=basedir,
active_file="train_fusion",
overwrite_existing=overwrite)
# Get YAML hyperparameters
hparams = YAMLHParams(os.path.join(basedir, "train_hparams.yaml"))
# Get some key settings
n_classes = hparams["build"]["n_classes"]
if hparams["build"]["out_activation"] == "linear":
# Trained with logit targets?
hparams["build"][
"out_activation"] = "softmax" if n_classes > 1 else "sigmoid"
# Get views
views = np.load("%s/views.npz" % basedir)["arr_0"]
del hparams["fit"]["views"]
# Get weights and set fusion (output) path
weights = get_best_model("%s/model" % basedir)
weights_name = os.path.splitext(os.path.split(weights)[-1])[0]
fusion_weights = "%s/model/fusion_weights/" \
"%s_fusion_weights.h5" % (basedir, weights_name)
create_folders(os.path.split(fusion_weights)[0])
# Log a few things
log(logger, hparams, views, weights, fusion_weights)
# Check if exists already...
if not overwrite and os.path.exists(fusion_weights):
from sys import exit
print("\n[*] A fusion weights file already exists at '%s'."
"\n Use the --overwrite flag to overwrite." % fusion_weights)
exit(0)
# Load validation data
images = ImagePairLoader(**hparams["val_data"], logger=logger)
is_validation = {m.id: True for m in images}
# Define random sets of images to train on simul. (cant be all due
# to memory constraints)
image_IDs = [m.id for m in images]
if len(images) < min_val_images:
# Pick N random training images
diff = min_val_images - len(images)
logger("Adding %i training images to set" % diff)
# Load the training data and pick diff images
train = ImagePairLoader(**hparams["train_data"], logger=logger)
indx = np.random.choice(np.arange(len(train)),
diff,
replace=diff > len(train))
# Add the images to the image set set
train_add = [train[i] for i in indx]
for m in train_add:
is_validation[m.id] = False
image_IDs.append(m.id)
images.add_images(train_add)
# Append to length % sub_size == 0
sub_size = args["images_per_round"]
rest = int(sub_size * np.ceil(len(image_IDs) / sub_size)) - len(image_IDs)
if rest:
image_IDs += list(np.random.choice(image_IDs, rest, replace=False))
# Shuffle and split
random.shuffle(image_IDs)
sets = [
set(s) for s in np.array_split(image_IDs,
len(image_IDs) / sub_size)
]
assert (contains_all_images(sets, image_IDs))
# Define fusion model (named 'org' to store reference to orgiginal model if
# multi gpu model is created below)
fusion_model_org = FusionModel(n_inputs=len(views),
n_classes=n_classes,
weight=dice_weight,
logger=logger,
verbose=False)
if continue_training:
fusion_model_org.load_weights(fusion_weights)
print("\n[OBS] CONTINUED TRAINING FROM:\n", fusion_weights)
# Define model
unet = init_model(hparams["build"], logger)
print("\n[*] Loading weights: %s\n" % weights)
unet.load_weights(weights, by_name=True)
if num_GPUs > 1:
from tensorflow.keras.utils import multi_gpu_model
# Set for predictor model
n_classes = n_classes
unet = multi_gpu_model(unet, gpus=num_GPUs)
unet.n_classes = n_classes
# Set for fusion model
fusion_model = multi_gpu_model(fusion_model_org, gpus=num_GPUs)
else:
fusion_model = fusion_model_org
# Compile the model
logger("Compiling...")
metrics = [
"sparse_categorical_accuracy", sparse_fg_precision, sparse_fg_recall
]
fusion_model.compile(optimizer=Adam(lr=1e-3),
loss=fusion_model_org.loss,
metrics=metrics)
fusion_model_org._log()
try:
_run_fusion_training(sets, logger, hparams, min_val_images,
is_validation, views, n_classes, unet,
fusion_model_org, fusion_model, early_stopping,
fm_batch_size, epochs, eval_prob, fusion_weights)
except KeyboardInterrupt:
pass
finally:
if not os.path.exists(os.path.split(fusion_weights)[0]):
os.mkdir(os.path.split(fusion_weights)[0])
# Save fusion model weights
# OBS: Must be original model if multi-gpu is performed!
fusion_model_org.save_weights(fusion_weights)
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 MultiPlanarUNet.train.hparams 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 MultiPlanarUNet.image import ImagePairLoader, ImagePair
from MultiPlanarUNet.utils import get_best_model, create_folders, \
pred_to_class, await_and_set_free_gpu, set_gpu
from MultiPlanarUNet.utils.fusion import predict_3D_patches, predict_3D_patches_binary, pred_3D_iso
from MultiPlanarUNet.logging import init_result_dict_3D, save_all_3D
from MultiPlanarUNet.evaluate import dice_all
from MultiPlanarUNet.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 MultiPlanarUNet.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,
**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)
def entry_func(args=None):
# Get command line arguments
args = vars(get_argparser().parse_args(args))
base_dir = os.path.abspath(args["project_dir"])
analytical = args["analytical"]
majority = args["majority"]
_file = args["f"]
label = args["l"]
await_PID = args["wait_for"]
eval_prob = args["eval_prob"]
_continue = args["continue"]
if analytical and majority:
raise ValueError("Cannot specify both --analytical and --majority.")
# Get settings from YAML file
from MultiPlanarUNet.train.hparams import YAMLHParams
hparams = YAMLHParams(os.path.join(base_dir, "train_hparams.yaml"))
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_input_files = args["save_input_files"]
no_argmax = args["no_argmax"]
on_val = args["on_val"]
# Check if valid dir structures
validate_folders(base_dir, out_dir, overwrite, _continue)
# Import all needed modules (folder is valid at this point)
import numpy as np
from MultiPlanarUNet.image import ImagePairLoader, ImagePair
from MultiPlanarUNet.models import FusionModel
from MultiPlanarUNet.models.model_init import init_model
from MultiPlanarUNet.utils import await_and_set_free_gpu, get_best_model, \
create_folders, pred_to_class, set_gpu
from MultiPlanarUNet.logging import init_result_dicts, save_all, load_result_dicts
from MultiPlanarUNet.evaluate import dice_all
from MultiPlanarUNet.utils.fusion import predict_volume, map_real_space_pred
from MultiPlanarUNet.interpolation.sample_grid import get_voxel_grid_real_space
# Wait for PID?
if await_PID:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(await_PID)
# Set GPU device
# Fetch GPU(s)
num_GPUs = args["num_GPUs"]
force_gpu = args["force_GPU"]
# Wait for free GPU
if not force_gpu:
await_and_set_free_gpu(N=num_GPUs, sleep_seconds=120)
num_GPUs = 1
else:
set_gpu(force_gpu)
num_GPUs = len(force_gpu.split(","))
# Read settings from the project hyperparameter file
n_classes = hparams["build"]["n_classes"]
# Get views
views = np.load("%s/views.npz" % base_dir)["arr_0"]
# Force settings
hparams["fit"]["max_background"] = 1
hparams["fit"]["test_mode"] = True
hparams["fit"]["mix_planes"] = False
hparams["fit"]["live_intrp"] = False
if "use_bounds" in hparams["fit"]:
del hparams["fit"]["use_bounds"]
del hparams["fit"]["views"]
if hparams["build"]["out_activation"] == "linear":
# Trained with logit targets?
hparams["build"][
"out_activation"] = "softmax" if n_classes > 1 else "sigmoid"
# Set ImagePairLoader object
if not _file:
data = "test_data" if not on_val else "val_data"
image_pair_loader = ImagePairLoader(predict_mode=predict_mode,
**hparams[data])
else:
predict_mode = not bool(label)
image_pair_loader = ImagePairLoader(predict_mode=predict_mode,
single_file_mode=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
if _continue:
all_images = remove_already_predicted(all_images, out_dir)
# Evaluate?
if not predict_mode:
if _continue:
csv_dir = os.path.join(out_dir, "csv")
results, detailed_res = load_result_dicts(csv_dir=csv_dir,
views=views)
else:
# Prepare dictionary to store results in pd df
results, detailed_res = init_result_dicts(views, all_images,
n_classes)
# Save to check correct format
save_all(results, detailed_res, out_dir)
# Define result paths
nii_res_dir = os.path.join(out_dir, "nii_files")
create_folders(nii_res_dir)
""" Define UNet model """
model_path = get_best_model(base_dir + "/model")
unet = init_model(hparams["build"])
unet.load_weights(model_path, by_name=True)
if num_GPUs > 1:
from tensorflow.keras.utils import multi_gpu_model
n_classes = unet.n_classes
unet = multi_gpu_model(unet, gpus=num_GPUs)
unet.n_classes = n_classes
weights_name = os.path.splitext(os.path.split(model_path)[1])[0]
if not analytical and not majority:
# Get Fusion model
fm = FusionModel(n_inputs=len(views), n_classes=n_classes)
weights = base_dir + "/model/fusion_weights/%s_fusion_weights.h5" % weights_name
print("\n[*] Loading weights:\n", weights)
# Load fusion weights
fm.load_weights(weights)
print("\nLoaded weights:\n\n%s\n%s\n---" %
tuple(fm.layers[-1].get_weights()))
# Multi-gpu?
if num_GPUs > 1:
print("Using multi-GPU model (%i GPUs)" % num_GPUs)
fm = multi_gpu_model(fm, gpus=num_GPUs)
"""
Finally predict on the images
"""
image_ids = sorted(all_images)
N_images = len(image_ids)
for n_image, image_id in enumerate(image_ids):
print("\n[*] (%i/%s) Running on: %s" %
(n_image + 1, N_images, image_id))
# Set image_pair_loader object with only the given file
image = all_images[image_id]
image_pair_loader.images = [image]
# 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)
# Prepare tensor to store combined prediction
d = image.image.shape[:-1]
if not majority:
combined = np.empty(shape=(len(views), d[0], d[1], d[2],
n_classes),
dtype=np.float32)
else:
combined = np.empty(shape=(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, v in enumerate(views):
print("\n[*] (%i/%i) View: %s" % (n_view + 1, len(views), v))
# 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.id,
v,
n_planes="same+20")
# Predict on volume using model
pred = predict_volume(unet, 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")
if not majority:
combined[n_view] = mapped_pred
else:
combined += mapped_pred
if n_classes == 1:
# Set to background if outside pred domain
combined[n_view][np.isnan(combined[n_view])] = 0.
if not predict_mode and np.random.rand() <= eval_prob:
view_dices = dice_all(y,
pred_to_class(pred,
img_dims=3,
has_batch_dim=False),
ignore_zero=False,
n_classes=n_classes,
skip_if_no_y=False)
mapped_dices = dice_all(image.labels,
pred_to_class(mapped_pred,
img_dims=3,
has_batch_dim=False),
ignore_zero=False,
n_classes=n_classes,
skip_if_no_y=False)
mean_dice = mapped_dices[~np.isnan(mapped_dices)][1:].mean()
# Print dice scores
print("View dice scores: ", view_dices)
print("Mapped dice scores: ", mapped_dices)
print("Mean dice (n=%i): " % (len(mapped_dices) - 1),
mean_dice)
# Add to results
results.loc[image_id, str(v)] = mean_dice
detailed_res[str(v)][image_id] = mapped_dices[1:]
# Overwrite with so-far results
save_all(results, detailed_res, out_dir)
else:
print("Skipping evaluation for this view... "
"(eval_prob=%.3f, predict_mode=%s)" %
(eval_prob, predict_mode))
if not analytical and not majority:
# Combine predictions across views using Fusion model
print("\nFusing views...")
combined = np.moveaxis(combined, 0, -2).reshape(
(-1, len(views), n_classes))
combined = fm.predict(combined, batch_size=10**4,
verbose=1).reshape(
(d[0], d[1], d[2], n_classes))
elif analytical:
print("\nFusing views (analytical)...")
combined = np.sum(combined, axis=0)
if not no_argmax:
print("\nComputing majority vote...")
combined = pred_to_class(combined.squeeze(),
img_dims=3).astype(np.uint8)
if not predict_mode:
if no_argmax:
# MAP only for dice calculation
c_temp = pred_to_class(combined, img_dims=3).astype(np.uint8)
else:
c_temp = combined
# Calculate combined prediction dice
dices = dice_all(image.labels,
c_temp,
n_classes=n_classes,
ignore_zero=True,
skip_if_no_y=False)
mean_dice = dices[~np.isnan(dices)].mean()
detailed_res["MJ"][image_id] = dices
print("Combined dices: ", dices)
print("Combined mean dice: ", mean_dice)
results.loc[image_id, "MJ"] = mean_dice
# Overwrite with so-far results
save_all(results, detailed_res, out_dir)
# Save combined prediction volume as .nii file
print("Saving .nii files...")
save_nii_files(combined, image, nii_res_dir, save_input_files)
# 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(results, detailed_res, out_dir)