def run(project_dir, gpu_mon, logger, args):
"""
Runs training of a model in a MultiPlanarUNet project directory.
Args:
project_dir: A path to a MultiPlanarUNet project
gpu_mon: An initialized GPUMonitor object
logger: A MultiPlanarUNet logging object
args: argparse arguments
"""
# Read in hyperparameters from YAML file
from MultiPlanarUNet.train import Trainer, YAMLHParams
hparams = YAMLHParams(project_dir + "/train_hparams.yaml", logger=logger)
validate_hparams(hparams)
# Wait for PID to terminate before continuing?
if args.wait_for:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(args.wait_for)
# Prepare sequence generators and potential model specific hparam changes
train, val, hparams = get_data_sequences(project_dir=project_dir,
hparams=hparams,
logger=logger,
args=args)
# Set GPU visibility
num_GPUs = set_gpu(gpu_mon, args)
# Get model (potentially multi-gpu, 'org_model' refers to
# non-distributed model, weights should be saved/loaded into 'org_model')
model, org_model = get_model(project_dir=project_dir,
train_seq=train,
hparams=hparams,
num_GPUs=num_GPUs,
logger=logger,
args=args)
# Get trainer and compile model
trainer = Trainer(model, org_model=org_model, logger=logger)
trainer.compile_model(n_classes=hparams["build"].get("n_classes"),
**hparams["fit"])
# Debug mode?
if args.debug:
from tensorflow.python import debug as tfdbg
from tensorflow.keras import backend as K
K.set_session(tfdbg.LocalCLIDebugWrapperSession(K.get_session()))
# Fit the model
_ = trainer.fit(train=train,
val=val,
train_im_per_epoch=args.train_images_per_epoch,
val_im_per_epoch=args.val_images_per_epoch,
hparams=hparams,
no_im=args.no_images,
**hparams["fit"])
save_final_weights(org_model, project_dir, logger)
def entry_func(args=None):
# Get parser
parser = vars(get_parser().parse_args(args))
# Get parser arguments
cv_dir = os.path.abspath(parser["CV_dir"])
out_dir = os.path.abspath(parser["out_dir"])
create_folders(out_dir)
await_PID = parser["wait_for"]
run_split = parser["run_on_split"]
start_from = parser["start_from"] or 0
num_jobs = parser["num_jobs"] or 1
# GPU settings
num_GPUs = parser["num_GPUs"]
force_GPU = parser["force_GPU"]
ignore_GPU = parser["ignore_GPU"]
monitor_GPUs_every = parser["monitor_GPUs_every"]
# User input assertions
_assert_force_and_ignore_gpus(force_GPU, ignore_GPU)
if run_split:
_assert_run_split(start_from, monitor_GPUs_every, num_jobs)
# Wait for PID?
if await_PID:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(await_PID)
# Get file paths
script = os.path.abspath(parser["script_prototype"])
hparams = os.path.abspath(parser["hparams_prototype"])
no_hparams = parser["no_hparams"]
# Get list of folders of CV data to run on
cv_folders = get_CV_folders(cv_dir)
if run_split is not None:
if run_split < 0 or run_split >= len(cv_folders):
raise ValueError("--run_on_split should be in range [0-{}], "
"got {}".format(len(cv_folders) - 1, run_split))
cv_folders = [cv_folders[run_split]]
log_appendix = "_split{}".format(run_split)
else:
log_appendix = ""
# Get a logger object
logger = Logger(base_path="./",
active_file="output" + log_appendix,
print_calling_method=False,
overwrite_existing=True)
if force_GPU:
# Only these GPUs fill be chosen from
from MultiPlanarUNet.utils import set_gpu
set_gpu(force_GPU)
if num_GPUs:
# Get GPU sets (up to the number of splits)
gpu_sets = get_free_GPU_sets(num_GPUs, ignore_GPU)[:len(cv_folders)]
elif not num_jobs or num_jobs < 0:
raise ValueError("Should specify a number of jobs to run in parallel "
"with the --num_jobs flag when using 0 GPUs pr. "
"process (--num_GPUs=0 was set).")
else:
gpu_sets = ["''"] * parser["num_jobs"]
# Get process pool, lock and GPU queue objects
lock = Lock()
gpu_queue = Queue()
for gpu in gpu_sets:
gpu_queue.put(gpu)
procs = []
if monitor_GPUs_every is not None and monitor_GPUs_every:
logger("\nOBS: Monitoring GPU pool every %i seconds\n" %
monitor_GPUs_every)
# Start a process monitoring new GPU availability over time
stop_event = Event()
t = Process(target=monitor_GPUs,
args=(monitor_GPUs_every, gpu_queue, num_GPUs, ignore_GPU,
gpu_sets, stop_event))
t.start()
procs.append(t)
else:
stop_event = None
try:
for cv_folder in cv_folders[start_from:]:
gpus = gpu_queue.get()
t = Process(target=run_sub_experiment,
args=(cv_folder, out_dir, script, hparams, no_hparams,
gpus, gpu_queue, lock, logger))
t.start()
procs.append(t)
for t in procs:
if not t.is_alive():
t.join()
except KeyboardInterrupt:
for t in procs:
t.terminate()
if stop_event is not None:
stop_event.set()
for t in procs:
t.join()
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 parser
parser = vars(get_parser().parse_args(args))
# Get parser arguments
cv_dir = os.path.abspath(parser["CV_dir"])
out_dir = os.path.abspath(parser["out_dir"])
create_folders(out_dir)
start_from = parser["start_from"]
await_PID = parser["wait_for"]
monitor_GPUs_every = parser["monitor_GPUs_every"]
# Get a logger object
logger = Logger(base_path="./", active_file="output",
print_calling_method=False, overwrite_existing=True)
# Wait for PID?
if await_PID:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(await_PID)
# Get number of GPUs per process
num_GPUs = parser["num_GPUs"]
# Get file paths
script = os.path.abspath(parser["script_prototype"])
hparams = os.path.abspath(parser["hparams_prototype"])
# Get list of folders of CV data to run on
cv_folders = get_CV_folders(cv_dir)
# Get GPU sets
gpu_sets = get_free_GPU_sets(num_GPUs)
# Get process pool, lock and GPU queue objects
lock = Lock()
gpu_queue = Queue()
for gpu in gpu_sets:
gpu_queue.put(gpu)
procs = []
if monitor_GPUs_every is not None and monitor_GPUs_every:
logger("\nOBS: Monitoring GPU pool every %i seconds\n" % monitor_GPUs_every)
# Start a process monitoring new GPU availability over time
stop_event = Event()
t = Process(target=monitor_GPUs, args=(monitor_GPUs_every, gpu_queue,
num_GPUs, gpu_sets, stop_event))
t.start()
procs.append(t)
else:
stop_event = None
try:
for cv_folder in cv_folders[start_from:]:
gpus = gpu_queue.get()
t = Process(target=run_sub_experiment,
args=(cv_folder, out_dir, script, hparams,
gpus, gpu_queue, lock, logger))
t.start()
procs.append(t)
for t in procs:
if not t.is_alive():
t.join()
except KeyboardInterrupt:
for t in procs:
t.terminate()
if stop_event is not None:
stop_event.set()
for t in procs:
t.join()
def run(base_path, gpu_mon, num_GPUs, continue_training, force_GPU, just_one,
no_val, no_images, debug, wait_for, logger, train_images_per_epoch,
val_images_per_epoch, **kwargs):
from MultiPlanarUNet.train import Trainer, YAMLHParams
from MultiPlanarUNet.models import model_initializer
from MultiPlanarUNet.preprocessing import get_preprocessing_func
# Read in hyperparameters from YAML file
hparams = YAMLHParams(base_path + "/train_hparams.yaml", logger=logger)
validate_hparams(hparams)
# Wait for PID?
if wait_for:
from MultiPlanarUNet.utils import await_PIDs
await_PIDs(wait_for)
# Prepare Sequence generators and potential model specific hparam changes
f = get_preprocessing_func(hparams["build"].get("model_class_name"))
train, val, hparams = f(hparams,
logger=logger,
just_one=just_one,
no_val=no_val,
continue_training=continue_training,
base_path=base_path)
if gpu_mon:
# Wait for free GPU
if not force_GPU:
gpu_mon.await_and_set_free_GPU(N=num_GPUs, sleep_seconds=120)
else:
gpu_mon.set_GPUs = force_GPU
num_GPUs = len(force_GPU.split(","))
gpu_mon.stop()
# Build new model (or continue training an existing one)
org_model = model_initializer(hparams=hparams,
continue_training=continue_training,
project_dir=base_path,
logger=logger)
# Initialize weights in final layer?
if not continue_training and hparams["build"].get("biased_output_layer"):
from MultiPlanarUNet.utils.utils import set_bias_weights_on_all_outputs
set_bias_weights_on_all_outputs(org_model, train, hparams, logger)
# Multi-GPU?
if num_GPUs > 1:
from tensorflow.keras.utils import multi_gpu_model
model = multi_gpu_model(org_model,
gpus=num_GPUs,
cpu_merge=False,
cpu_relocation=False)
logger("Creating multi-GPU model: N=%i" % num_GPUs)
else:
model = org_model
# Init trainer
trainer = Trainer(model, logger=logger)
trainer.org_model = org_model
# Compile model
trainer.compile_model(n_classes=hparams["build"].get("n_classes"),
**hparams["fit"])
# Debug mode?
if debug:
from tensorflow.python import debug as tfdbg
from tensorflow.keras import backend as k
k.set_session(tfdbg.LocalCLIDebugWrapperSession(k.get_session()))
# Fit the model
_ = trainer.fit(train=train,
val=val,
train_im_per_epoch=train_images_per_epoch,
val_im_per_epoch=val_images_per_epoch,
hparams=hparams,
no_im=no_images,
**hparams["fit"])
# Save final model weights (usually not used, but maybe....?)
if not os.path.exists("%s/model" % base_path):
os.mkdir("%s/model" % base_path)
model_path = "%s/model/model_weights.h5" % base_path
logger("Saving current model to: %s" % model_path)
org_model.save_weights(model_path)
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)