def save_multilabel_img_as_multiple_files_endings(HP, img, affine, path):
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
img_seg = nib.Nifti1Image(img[:, :, :, idx], affine)
ExpUtils.make_dir(join(path, "endings_segmentations"))
nib.save(img_seg,
join(path, "endings_segmentations", bundle + ".nii.gz"))
def load_model(path):
ExpUtils.print_verbose(self.HP,
"Loading weights ... ({})".format(path))
with np.load(
path
) as f: #if both pathes are absolute and beginning of pathes are the same, join will merge the beginning
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
L.layers.set_all_param_values(output_layer_for_loss, param_values)
def save_fusion_nifti_as_npy():
#Can leave this always the same (for 270g and 32g)
class HP:
DATASET = "HCP"
RESOLUTION = "1.25mm"
FEATURES_FILENAME = "270g_125mm_peaks"
LABELS_TYPE = np.int16
LABELS_FILENAME = "bundle_masks"
DATASET_FOLDER = "HCP"
#change this for 270g and 32g
DIFFUSION_FOLDER = "32g_25mm"
subjects = get_all_subjects()
# fold0 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']
# fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044']
# fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766']
# fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056']
# fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152']
# subjects = fold2 + fold3 + fold4
# subjects = ['654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']
print("\n\nProcessing Data...")
for s in subjects:
print("processing data subject {}".format(s))
start_time = time.time()
data = nib.load(
join(C.NETWORK_DRIVE, "HCP_fusion_" + DIFFUSION_FOLDER,
s + "_probmap.nii.gz")).get_data()
print("Done Loading")
data = np.nan_to_num(data)
data = DatasetUtils.scale_input_to_unet_shape(
data, HP.DATASET, HP.RESOLUTION)
data = data[:-1, :, :
-1, :] # cut one pixel at the end, because in scale_input_to_world_shape we ouputted 146 -> one too much at the end
ExpUtils.make_dir(
join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s))
np.save(
join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s,
DIFFUSION_FOLDER + "_xyz.npy"), data)
print("Took {}s".format(time.time() - start_time))
print("processing seg subject {}".format(s))
start_time = time.time()
# seg = ImgUtils.create_multilabel_mask(HP, s, labels_type=HP.LABELS_TYPE)
seg = nib.load(
join(C.NETWORK_DRIVE, "HCP_for_training_COPY", s,
HP.LABELS_FILENAME + ".nii.gz")).get_data()
if HP.RESOLUTION == "2.5mm":
seg = ImgUtils.resize_first_three_dims(seg, order=0, zoom=0.5)
seg = DatasetUtils.scale_input_to_unet_shape(
seg, HP.DATASET, HP.RESOLUTION)
np.save(
join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s,
"bundle_masks.npy"), seg)
print("Took {}s".format(time.time() - start_time))
def precompute_batches(custom_type=None):
'''
9000 slices per epoch -> 200 batches (batchsize=44) per epoch
=> 200-1000 batches needed
270g_125mm_bundle_peaks_Y: no DAug, no Norm, only Y
All_sizes_DAug_XYZ: 12g, 90g, 270g, DAug (no rotation, no elastic deform), Norm, XYZ
270g_125mm_bundle_peaks_XYZ: no DAug, Norm, XYZ
'''
class HP:
NORMALIZE_DATA = True
DATA_AUGMENTATION = False
CV_FOLD = 0
INPUT_DIM = (144, 144)
BATCH_SIZE = 44
DATASET_FOLDER = "HCP"
TYPE = "single_direction"
EXP_PATH = "~"
LABELS_FILENAME = "bundle_peaks"
FEATURES_FILENAME = "270g_125mm_peaks"
DATASET = "HCP"
RESOLUTION = "1.25mm"
LABELS_TYPE = np.float32
HP.TRAIN_SUBJECTS, HP.VALIDATE_SUBJECTS, HP.TEST_SUBJECTS = ExpUtils.get_cv_fold(HP.CV_FOLD)
num_batches_base = 5000
num_batches = {
"train": num_batches_base,
"validate": int(num_batches_base / 3.),
"test": int(num_batches_base / 3.),
}
if custom_type is None:
types = ["train", "validate", "test"]
else:
types = [custom_type]
for type in types:
dataManager = DataManagerTrainingNiftiImgs(HP)
batch_gen = dataManager.get_batches(batch_size=HP.BATCH_SIZE, type=type,
subjects=getattr(HP, type.upper() + "_SUBJECTS"), num_batches=num_batches[type])
for idx, batch in enumerate(batch_gen):
print("Processing: {}".format(idx))
# DATASET_DIR = "HCP_batches/270g_125mm_bundle_peaks_Y"
# DATASET_DIR = "HCP_batches/All_sizes_DAug_XYZ"
DATASET_DIR = "HCP_batches/270g_125mm_bundle_peaks_XYZ"
ExpUtils.make_dir(join(C.HOME, DATASET_DIR, type))
data = nib.Nifti1Image(batch["data"], ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(data, join(C.HOME, DATASET_DIR, type, "batch_" + str(idx) + "_data.nii.gz"))
seg = nib.Nifti1Image(batch["seg"], ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(seg, join(C.HOME, DATASET_DIR, type, "batch_" + str(idx) + "_seg.nii.gz"))
def save_multilabel_img_as_multiple_files_peaks(HP, img, affine, path):
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
data = img[:, :, :, (idx * 3):(idx * 3) + 3]
if HP.FLIP_OUTPUT_PEAKS:
data[:, :, :, 2] *= -1 # flip z Axis for correct view in MITK
filename = bundle + "_f.nii.gz"
else:
filename = bundle + ".nii.gz"
img_seg = nib.Nifti1Image(data, affine)
ExpUtils.make_dir(join(path, "TOM"))
nib.save(img_seg, join(path, "TOM", filename))
def save_multilabel_img_as_multiple_files_peaks(HP, img, affine, path):
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
data = img[:, :, :, (idx*3):(idx*3)+3]
if HP.FLIP_OUTPUT_PEAKS:
data[:, :, :, 2] *= -1 # flip z Axis for correct view in MITK
filename = bundle + "_f.nii.gz"
else:
filename = bundle + ".nii.gz"
img_seg = nib.Nifti1Image(data, affine)
ExpUtils.make_dir(join(path, "TOM"))
nib.save(img_seg, join(path, "TOM", filename))
def calc_peak_dice_onlySeg(HP, y_pred, y_true):
'''
Create binary mask of peaks by simple thresholding. Then calculate Dice.
:param y_pred:
:param y_true:
:return:
'''
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
# 0.1 -> keep some outliers, but also some holes already; 0.2 also ok (still looks like e.g. CST)
# Resulting dice for 0.1 and 0.2 very similar
y_pred_binary = np.abs(y_pred_bund).sum(axis=-1) > 0.2
y_true_binary = np.abs(y_true_bund).sum(axis=-1) > 1e-3
f1 = f1_score(y_true_binary.flatten(),
y_pred_binary.flatten(),
average="binary")
score_per_bundle[bundle] = f1
return score_per_bundle
def create_multilabel_mask(HP,
subject,
labels_type=np.int16,
dataset_folder="HCP",
labels_folder="bundle_masks"):
'''
One-hot encoding of all bundles in one big image
:param subject:
:return: image of shape (x, y, z, nr_of_bundles + 1)
'''
bundles = ExpUtils.get_bundle_names(HP.CLASSES)
#Masks sind immer HCP_highRes (später erst downsample)
mask_ml = np.zeros((145, 174, 145, len(bundles)))
background = np.ones(
(145, 174, 145)) # everything that contains no bundle
for idx, bundle in enumerate(
bundles[1:]
): #first bundle is background -> already considered by setting np.ones in the beginning
mask = nib.load(
join(C.HOME, dataset_folder, subject, labels_folder,
bundle + ".nii.gz"))
mask_data = mask.get_data() # dtype: uint8
mask_ml[:, :, :, idx + 1] = mask_data
background[mask_data ==
1] = 0 # remove this bundle from background
mask_ml[:, :, :, 0] = background
return mask_ml.astype(labels_type)
def save_fusion_nifti_as_npy():
#Can leave this always the same (for 270g and 32g)
class HP:
DATASET = "HCP"
RESOLUTION = "1.25mm"
FEATURES_FILENAME = "270g_125mm_peaks"
LABELS_TYPE = np.int16
LABELS_FILENAME = "bundle_masks"
DATASET_FOLDER = "HCP"
#change this for 270g and 32g
DIFFUSION_FOLDER = "32g_25mm"
subjects = get_all_subjects()
# fold0 = ['687163', '685058', '683256', '680957', '679568', '677968', '673455', '672756', '665254', '654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']
# fold1 = ['992774', '991267', '987983', '984472', '983773', '979984', '978578', '965771', '965367', '959574', '958976', '957974', '951457', '932554', '930449', '922854', '917255', '912447', '910241', '907656', '904044']
# fold2 = ['901442', '901139', '901038', '899885', '898176', '896879', '896778', '894673', '889579', '887373', '877269', '877168', '872764', '872158', '871964', '871762', '865363', '861456', '859671', '857263', '856766']
# fold3 = ['849971', '845458', '837964', '837560', '833249', '833148', '826454', '826353', '816653', '814649', '802844', '792766', '792564', '789373', '786569', '784565', '782561', '779370', '771354', '770352', '765056']
# fold4 = ['761957', '759869', '756055', '753251', '751348', '749361', '748662', '748258', '742549', '734045', '732243', '729557', '729254', '715647', '715041', '709551', '705341', '704238', '702133', '695768', '690152']
# subjects = fold2 + fold3 + fold4
# subjects = ['654754', '645551', '644044', '638049', '627549', '623844', '622236', '620434', '613538', '601127', '599671', '599469']
print("\n\nProcessing Data...")
for s in subjects:
print("processing data subject {}".format(s))
start_time = time.time()
data = nib.load(join(C.NETWORK_DRIVE, "HCP_fusion_" + DIFFUSION_FOLDER, s + "_probmap.nii.gz")).get_data()
print("Done Loading")
data = np.nan_to_num(data)
data = DatasetUtils.scale_input_to_unet_shape(data, HP.DATASET, HP.RESOLUTION)
data = data[:-1, :, :-1, :] # cut one pixel at the end, because in scale_input_to_world_shape we ouputted 146 -> one too much at the end
ExpUtils.make_dir(join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s))
np.save(join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s, DIFFUSION_FOLDER + "_xyz.npy"), data)
print("Took {}s".format(time.time() - start_time))
print("processing seg subject {}".format(s))
start_time = time.time()
# seg = ImgUtils.create_multilabel_mask(HP, s, labels_type=HP.LABELS_TYPE)
seg = nib.load(join(C.NETWORK_DRIVE, "HCP_for_training_COPY", s, HP.LABELS_FILENAME + ".nii.gz")).get_data()
if HP.RESOLUTION == "2.5mm":
seg = ImgUtils.resize_first_three_dims(seg, order=0, zoom=0.5)
seg = DatasetUtils.scale_input_to_unet_shape(seg, HP.DATASET, HP.RESOLUTION)
np.save(join(C.NETWORK_DRIVE, "HCP_fusion_npy_" + DIFFUSION_FOLDER, s, "bundle_masks.npy"), seg)
print("Took {}s".format(time.time() - start_time))
def copy_training_files_to_ssd(HP, data_path):
def id_generator(size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
target_data_path = join("/ssd/", "tmp_" + id_generator(), HP.DATASET_FOLDER)
ExpUtils.make_dir(join(target_data_path))
#get all folders in data_path directory
subjects = [os.path.basename(os.path.normpath(d)) for d in glob(data_path + "/*/")]
for subject in subjects:
src = join(data_path, subject, HP.FEATURES_FILENAME)
target = join(target_data_path, subject, HP.FEATURES_FILENAME)
print("cp: {} -> {}".format(src, target))
# shutil.copyfile(src, target)
return target_data_path
def soft_dice_paul(HP, idxs, marker, preds, ys):
n_classes = len(ExpUtils.get_bundle_names(HP.CLASSES))
dice = T.constant(0)
for cl in range(n_classes):
pred = preds[marker, cl, :, :]
y = ys[marker, cl, :, :]
intersect = T.sum(pred * y)
denominator = T.sum(pred) + T.sum(y)
dice += T.constant(2) * intersect / (denominator + T.constant(1e-6))
return 1 - (dice / n_classes)
def soft_dice_paul(HP, idxs, marker, preds, ys):
n_classes = len(ExpUtils.get_bundle_names(HP.CLASSES))
dice = T.constant(0)
for cl in range(n_classes):
pred = preds[marker, cl, :, :]
y = ys[marker, cl, :, :]
intersect = T.sum(pred * y)
denominator = T.sum(pred) + T.sum(y)
dice += T.constant(2) * intersect / (denominator + T.constant(1e-6))
return 1 - (dice / n_classes)
def calc_peak_length_dice_pytorch(HP, y_pred, y_true, max_angle_error=[0.9], max_length_error=0.1):
'''
Ca
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
Can be list with several values -> calculate for several thresholds
:return:
'''
import torch
from tractseg.libs.PytorchEinsum import einsum
from tractseg.libs.PytorchUtils import PytorchUtils
y_true = y_true.permute(0, 2, 3, 1)
y_pred = y_pred.permute(0, 2, 3, 1)
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
return torch.abs(einsum('abcd,abcd->abc', a, b) / (torch.norm(a, 2., -1) * torch.norm(b, 2, -1) + 1e-7))
#Single threshold
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
lenghts_pred = torch.norm(y_pred_bund, 2., -1)
lengths_true = torch.norm(y_true_bund, 2, -1)
lengths_binary = torch.abs(lenghts_pred-lengths_true) < (max_length_error * lengths_true)
lengths_binary = lengths_binary.view(-1)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.view(-1)
combined = lengths_binary * angles_binary
f1 = PytorchUtils.f1_score_binary(gt_binary, combined)
score_per_bundle[bundle] = f1
return score_per_bundle
def copy_training_files_to_ssd(HP, data_path):
def id_generator(size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
target_data_path = join("/ssd/", "tmp_" + id_generator(),
HP.DATASET_FOLDER)
ExpUtils.make_dir(join(target_data_path))
#get all folders in data_path directory
subjects = [
os.path.basename(os.path.normpath(d))
for d in glob(data_path + "/*/")
]
for subject in subjects:
src = join(data_path, subject, HP.FEATURES_FILENAME)
target = join(target_data_path, subject, HP.FEATURES_FILENAME)
print("cp: {} -> {}".format(src, target))
# shutil.copyfile(src, target)
return target_data_path
def calc_peak_length_dice(HP,
y_pred,
y_true,
max_angle_error=[0.9],
max_length_error=0.1):
'''
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
:return:
'''
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
# print(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1))
return abs(
np.einsum('...i,...i', a, b) /
(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1) +
1e-7))
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
lenghts_pred = np.linalg.norm(y_pred_bund, axis=-1)
lengths_true = np.linalg.norm(y_true_bund, axis=-1)
lengths_binary = abs(lenghts_pred - lengths_true) < (
max_length_error * lengths_true)
lengths_binary = lengths_binary.flatten()
gt_binary = y_true_bund.sum(axis=-1) > 0
gt_binary = gt_binary.flatten() # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.flatten()
combined = lengths_binary * angles_binary
f1 = MetricUtils.my_f1_score(gt_binary, combined)
score_per_bundle[bundle] = f1
return score_per_bundle
def save_multilabel_img_as_multiple_files_endings_OLD(HP, img, affine, path, multilabel=True):
'''
multilabel True: save as 1 and 2 without fourth dimension
multilabel False: save with beginnings and endings combined
'''
# bundles = ExpUtils.get_bundle_names("20")[1:]
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
data = img[:, :, :, (idx * 2):(idx * 2) + 2] > 0
multilabel_img = np.zeros(data.shape[:3])
if multilabel:
multilabel_img[data[:, :, :, 0]] = 1
multilabel_img[data[:, :, :, 1]] = 2
else:
multilabel_img[data[:, :, :, 0]] = 1
multilabel_img[data[:, :, :, 1]] = 1
img_seg = nib.Nifti1Image(multilabel_img, affine)
ExpUtils.make_dir(join(path, "endings"))
nib.save(img_seg, join(path, "endings", bundle + ".nii.gz"))
def save_multilabel_img_as_multiple_files_endings_OLD(HP, img, affine, path, multilabel=True):
'''
multilabel True: save as 1 and 2 without fourth dimension
multilabel False: save with beginnings and endings combined
'''
# bundles = ExpUtils.get_bundle_names("20")[1:]
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
data = img[:, :, :, (idx * 2):(idx * 2) + 2] > 0
multilabel_img = np.zeros(data.shape[:3])
if multilabel:
multilabel_img[data[:, :, :, 0]] = 1
multilabel_img[data[:, :, :, 1]] = 2
else:
multilabel_img[data[:, :, :, 0]] = 1
multilabel_img[data[:, :, :, 1]] = 1
img_seg = nib.Nifti1Image(multilabel_img, affine)
ExpUtils.make_dir(join(path, "endings"))
nib.save(img_seg, join(path, "endings", bundle + ".nii.gz"))
def theano_f1_score_OLD(HP, idxs, marker, preds, ys):
'''
Von Paul
'''
n_classes = len(ExpUtils.get_bundle_names(HP.CLASSES))
dice = T.constant(0)
for cl in range(n_classes):
pred = preds[marker, cl, :, :]
y = ys[marker, cl, :, :]
pred = T.gt(pred, T.constant(0.5))
intersect = T.sum(pred * y)
denominator = T.sum(pred) + T.sum(y)
dice += T.constant(2) * intersect / (denominator + T.constant(1e-6))
return dice / n_classes
def theano_f1_score_OLD(HP, idxs, marker, preds, ys):
'''
Von Paul
'''
n_classes = len(ExpUtils.get_bundle_names(HP.CLASSES))
dice = T.constant(0)
for cl in range(n_classes):
pred = preds[marker, cl, :, :]
y = ys[marker, cl, :, :]
pred = T.gt(pred, T.constant(0.5))
intersect = T.sum(pred * y)
denominator = T.sum(pred) + T.sum(y)
dice += T.constant(2) * intersect / (denominator + T.constant(1e-6))
return dice / n_classes
def calc_peak_dice(HP, y_pred, y_true, max_angle_error=[0.9]):
'''
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
:return:
'''
def angle(a, b):
'''
Calculate the angle between two 1d-arrays (2 vectors) along the last dimension
without anything further: 1->0°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
'''
return abs(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
# print(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1))
return abs(
np.einsum('...i,...i', a, b) /
(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1) +
1e-7))
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
angles_binary = angles > max_angle_error[0]
gt_binary = y_true_bund.sum(axis=-1) > 0
f1 = f1_score(gt_binary.flatten(),
angles_binary.flatten(),
average="binary")
score_per_bundle[bundle] = f1
return score_per_bundle
def calc_peak_dice(HP, y_pred, y_true, max_angle_error=[0.9]):
'''
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
:return:
'''
def angle(a, b):
'''
Calculate the angle between two 1d-arrays (2 vectors) along the last dimension
without anything further: 1->0°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
'''
return abs(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
# print(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1))
return abs(np.einsum('...i,...i', a, b) / (np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1) + 1e-7))
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
angles_binary = angles > max_angle_error[0]
gt_binary = y_true_bund.sum(axis=-1) > 0
f1 = f1_score(gt_binary.flatten(), angles_binary.flatten(), average="binary")
score_per_bundle[bundle] = f1
return score_per_bundle
def calc_peak_length_dice(HP, y_pred, y_true, max_angle_error=[0.9], max_length_error=0.1):
'''
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
:return:
'''
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
# print(np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1))
return abs(np.einsum('...i,...i', a, b) / (np.linalg.norm(a, axis=-1) * np.linalg.norm(b, axis=-1) + 1e-7))
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
lenghts_pred = np.linalg.norm(y_pred_bund, axis=-1)
lengths_true = np.linalg.norm(y_true_bund, axis=-1)
lengths_binary = abs(lenghts_pred - lengths_true) < (max_length_error * lengths_true)
lengths_binary = lengths_binary.flatten()
gt_binary = y_true_bund.sum(axis=-1) > 0
gt_binary = gt_binary.flatten() # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.flatten()
combined = lengths_binary * angles_binary
f1 = MetricUtils.my_f1_score(gt_binary, combined)
score_per_bundle[bundle] = f1
return score_per_bundle
def create_multilabel_mask(HP, subject, labels_type=np.int16, dataset_folder="HCP", labels_folder="bundle_masks"):
'''
One-hot encoding of all bundles in one big image
:param subject:
:return: image of shape (x, y, z, nr_of_bundles + 1)
'''
bundles = ExpUtils.get_bundle_names(HP.CLASSES)
#Masks sind immer HCP_highRes (später erst downsample)
mask_ml = np.zeros((145, 174, 145, len(bundles)))
background = np.ones((145, 174, 145)) # everything that contains no bundle
for idx, bundle in enumerate(bundles[1:]): #first bundle is background -> already considered by setting np.ones in the beginning
mask = nib.load(join(C.HOME, dataset_folder, subject, labels_folder, bundle + ".nii.gz"))
mask_data = mask.get_data() # dtype: uint8
mask_ml[:, :, :, idx+1] = mask_data
background[mask_data == 1] = 0 # remove this bundle from background
mask_ml[:, :, :, 0] = background
return mask_ml.astype(labels_type)
def calc_peak_dice_onlySeg(HP, y_pred, y_true):
'''
Create binary mask of peaks by simple thresholding. Then calculate Dice.
:param y_pred:
:param y_true:
:return:
'''
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3]
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3] # [x,y,z,3]
# 0.1 -> keep some outliers, but also some holes already; 0.2 also ok (still looks like e.g. CST)
# Resulting dice for 0.1 and 0.2 very similar
y_pred_binary = np.abs(y_pred_bund).sum(axis=-1) > 0.2
y_true_binary = np.abs(y_true_bund).sum(axis=-1) > 1e-3
f1 = f1_score(y_true_binary.flatten(), y_pred_binary.flatten(), average="binary")
score_per_bundle[bundle] = f1
return score_per_bundle
def precompute_batches(custom_type=None):
'''
9000 slices per epoch -> 200 batches (batchsize=44) per epoch
=> 200-1000 batches needed
270g_125mm_bundle_peaks_Y: no DAug, no Norm, only Y
All_sizes_DAug_XYZ: 12g, 90g, 270g, DAug (no rotation, no elastic deform), Norm, XYZ
270g_125mm_bundle_peaks_XYZ: no DAug, Norm, XYZ
'''
class HP:
NORMALIZE_DATA = True
DATA_AUGMENTATION = False
CV_FOLD = 0
INPUT_DIM = (144, 144)
BATCH_SIZE = 44
DATASET_FOLDER = "HCP"
TYPE = "single_direction"
EXP_PATH = "~"
LABELS_FILENAME = "bundle_peaks"
FEATURES_FILENAME = "270g_125mm_peaks"
DATASET = "HCP"
RESOLUTION = "1.25mm"
LABELS_TYPE = np.float32
HP.TRAIN_SUBJECTS, HP.VALIDATE_SUBJECTS, HP.TEST_SUBJECTS = ExpUtils.get_cv_fold(
HP.CV_FOLD)
num_batches_base = 5000
num_batches = {
"train": num_batches_base,
"validate": int(num_batches_base / 3.),
"test": int(num_batches_base / 3.),
}
if custom_type is None:
types = ["train", "validate", "test"]
else:
types = [custom_type]
for type in types:
dataManager = DataManagerTrainingNiftiImgs(HP)
batch_gen = dataManager.get_batches(
batch_size=HP.BATCH_SIZE,
type=type,
subjects=getattr(HP,
type.upper() + "_SUBJECTS"),
num_batches=num_batches[type])
for idx, batch in enumerate(batch_gen):
print("Processing: {}".format(idx))
# DATASET_DIR = "HCP_batches/270g_125mm_bundle_peaks_Y"
# DATASET_DIR = "HCP_batches/All_sizes_DAug_XYZ"
DATASET_DIR = "HCP_batches/270g_125mm_bundle_peaks_XYZ"
ExpUtils.make_dir(join(C.HOME, DATASET_DIR, type))
data = nib.Nifti1Image(
batch["data"],
ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(
data,
join(C.HOME, DATASET_DIR, type,
"batch_" + str(idx) + "_data.nii.gz"))
seg = nib.Nifti1Image(
batch["seg"],
ImgUtils.get_dwi_affine(HP.DATASET, HP.RESOLUTION))
nib.save(
seg,
join(C.HOME, DATASET_DIR, type,
"batch_" + str(idx) + "_seg.nii.gz"))
def calc_peak_dice_pytorch(HP, y_pred, y_true, max_angle_error=[0.9]):
'''
Calculate angle between groundtruth and prediction and keep the voxels where
angle is smaller than MAX_ANGLE_ERROR.
From groundtruth generate a binary mask by selecting all voxels with len > 0.
Calculate Dice from these 2 masks.
-> Penalty on peaks outside of tract or if predicted peak=0
-> no penalty on very very small with right direction -> bad
=> Peak_dice can be high even if peaks inside of tract almost missing (almost 0)
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
Can be list with several values -> calculate for several thresholds
:return:
'''
import torch
from tractseg.libs.PytorchEinsum import einsum
from tractseg.libs.PytorchUtils import PytorchUtils
y_true = y_true.permute(0, 2, 3, 1)
y_pred = y_pred.permute(0, 2, 3, 1)
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
return torch.abs(einsum('abcd,abcd->abc', a, b) / (torch.norm(a, 2., -1) * torch.norm(b, 2, -1) + 1e-7))
#Single threshold
if len(max_angle_error) == 1:
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.view(-1)
f1 = PytorchUtils.f1_score_binary(gt_binary, angles_binary)
score_per_bundle[bundle] = f1
return score_per_bundle
#multiple thresholds
else:
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) + 3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
score_per_bundle[bundle] = []
for threshold in max_angle_error:
angles_binary = angles > threshold
angles_binary = angles_binary.view(-1)
f1 = PytorchUtils.f1_score_binary(gt_binary, angles_binary)
score_per_bundle[bundle].append(f1)
return score_per_bundle
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(random.uniform(0, len(subjects))) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if self.HP.FEATURES_FILENAME == "12g90g270g":
# if np.random.random() < 0.5:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
rnd_choice = np.random.random()
if rnd_choice < 0.33:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
elif self.HP.FEATURES_FILENAME == "T1_Peaks270g":
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
elif self.HP.FEATURES_FILENAME == "T1_Peaks12g90g270g":
rnd_choice = np.random.random()
if rnd_choice < 0.33:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
else:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.FEATURES_FILENAME + ".nii.gz")).get_data()
seg = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(self.HP, "\n\nWARNING: Could not load file. Trying again in 20s (Try number: " + str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.LABELS_FILENAME not in ["bundle_peaks_11_808080", "bundle_peaks_20_808080", "bundle_peaks_808080",
"bundle_masks_20_808080", "bundle_masks_72_808080"]:
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False, None)
# Randomly sample slice orientation
if self.HP.TRAINING_SLICE_DIRECTION == "xyz":
slice_direction = int(round(random.uniform(0,2)))
else:
slice_direction = 1 #always use Y
if slice_direction == 0:
x = data[slice_idxs, :, :].astype(np.float32) # (batch_size, y, z, channels)
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(0, 3, 1, 2) # depth-channel has to be before width and height for Unet (but after batches)
y = np.array(y).transpose(0, 3, 1, 2) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
x = data[:, slice_idxs, :].astype(np.float32) # (x, batch_size, z, channels)
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(1, 3, 0, 2)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
x = data[:, :, slice_idxs].astype(np.float32) # (x, y, batch_size, channels)
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(2, 3, 0, 1)
y = np.array(y).transpose(2, 3, 0, 1)
data_dict = {"data": x, # (batch_size, channels, x, y, [z])
"seg": y} # (batch_size, channels, x, y, [z])
return data_dict
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(
random.uniform(0, len(subjects))
) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if self.HP.FEATURES_FILENAME == "12g90g270g":
# if np.random.random() < 0.5:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
rnd_choice = np.random.random()
if rnd_choice < 0.33:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"12g_125mm_peaks.nii.gz")).get_data()
elif self.HP.FEATURES_FILENAME == "T1_Peaks270g":
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
elif self.HP.FEATURES_FILENAME == "T1_Peaks12g90g270g":
rnd_choice = np.random.random()
if rnd_choice < 0.33:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
else:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"12g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], self.HP.FEATURES_FILENAME +
".nii.gz")).get_data()
seg = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(
self.HP,
"\n\nWARNING: Could not load file. Trying again in 20s (Try number: "
+ str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(
data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.LABELS_FILENAME not in [
"bundle_peaks_11_808080", "bundle_peaks_20_808080",
"bundle_peaks_808080", "bundle_masks_20_808080",
"bundle_masks_72_808080"
]:
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(
seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(
seg, self.HP.DATASET,
self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False,
None)
# Randomly sample slice orientation
if self.HP.TRAINING_SLICE_DIRECTION == "xyz":
slice_direction = int(round(random.uniform(0, 2)))
else:
slice_direction = 1 #always use Y
if slice_direction == 0:
x = data[slice_idxs, :, :].astype(
np.float32) # (batch_size, y, z, channels)
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(
0, 3, 1, 2
) # depth-channel has to be before width and height for Unet (but after batches)
y = np.array(y).transpose(
0, 3, 1, 2
) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
x = data[:, slice_idxs, :].astype(
np.float32) # (x, batch_size, z, channels)
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(1, 3, 0, 2)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
x = data[:, :, slice_idxs].astype(
np.float32) # (x, y, batch_size, channels)
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(2, 3, 0, 1)
y = np.array(y).transpose(2, 3, 0, 1)
data_dict = {
"data": x, # (batch_size, channels, x, y, [z])
"seg": y
} # (batch_size, channels, x, y, [z])
return data_dict
def save_multilabel_img_as_multiple_files_endings(HP, img, affine, path):
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
img_seg = nib.Nifti1Image(img[:,:,:,idx], affine)
ExpUtils.make_dir(join(path, "endings_segmentations"))
nib.save(img_seg, join(path, "endings_segmentations", bundle + ".nii.gz"))
def create_network(self):
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda()) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(), volatile=True), Variable(y.cuda(), volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "best_weights_ep" + str(epoch_nr) + ".npz"), unet=net)
except IOError:
print("\nERROR: Could not save weights because of IO Error\n")
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3*self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(self.HP, "Loading weights ... ({})".format(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y, weight_factor=10):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
else:
probs = None #faster
return loss.data[0], probs, f1
def test(X, y, weight_factor=10):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = self.HP.NR_OF_GRADIENTS
# NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
# NR_OF_GRADIENTS = 9 * 9
# NR_OF_GRADIENTS = 33
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
self.HP.NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
self.HP.NR_OF_GRADIENTS = 33
if self.HP.LOSS_FUNCTION == "soft_sample_dice":
criterion = PytorchUtils.soft_sample_dice
final_activation = "sigmoid"
elif self.HP.LOSS_FUNCTION == "soft_batch_dice":
criterion = PytorchUtils.soft_batch_dice
final_activation = "sigmoid"
else:
# weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
# weights[:, 5, :, :] *= 10 #CA
# weights[:, 21, :, :] *= 10 #FX_left
# weights[:, 22, :, :] *= 10 #FX_right
# criterion = nn.BCEWithLogitsLoss(weight=weights)
criterion = nn.BCEWithLogitsLoss()
final_activation = None
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT,
batchnorm=self.HP.BATCH_NORM,
final_activation=final_activation)
if torch.cuda.is_available():
net = net.cuda()
# else:
# net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT,
# batchnorm=self.HP.BATCH_NORM)
# net = nn.DataParallel(net, device_ids=[0,1])
# if self.HP.TRAIN:
# ExpUtils.print_and_save(self.HP, str(net), only_log=True)
if self.HP.OPTIMIZER == "Adamax":
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
elif self.HP.OPTIMIZER == "Adam":
#todo important: change
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE)
optimizer = Adam(net.parameters(),
lr=self.HP.LEARNING_RATE,
weight_decay=self.HP.WEIGHT_DECAY)
else:
raise ValueError("Optimizer not defined")
# scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
def plot_tracts(HP,
bundle_segmentations,
affine,
out_dir,
brain_mask=None):
'''
By default this does not work on a remote server connection (ssh -X) because -X does not support OpenGL.
On the remote Server you can do 'export DISPLAY=":0"' (you should set the value you get if you do 'echo $DISPLAY' if you
login locally on the remote server). Then all graphics will get rendered locally and not via -X.
(important: graphical session needs to be running on remote server (e.g. via login locally))
(important: login needed, not just stay at login screen)
'''
from dipy.viz import window
from tractseg.libs.VtkUtils import VtkUtils
SMOOTHING = 10
WINDOW_SIZE = (800, 800)
bundles = ["CST_right", "CA", "IFO_right"]
renderer = window.Renderer()
renderer.projection('parallel')
rows = len(bundles)
X, Y, Z = bundle_segmentations.shape[:3]
for j, bundle in enumerate(bundles):
i = 0 #only one method
bundle_idx = ExpUtils.get_bundle_names(
HP.CLASSES)[1:].index(bundle)
mask_data = bundle_segmentations[:, :, :, bundle_idx]
if bundle == "CST_right":
orientation = "axial"
elif bundle == "CA":
orientation = "axial"
elif bundle == "IFO_right":
orientation = "sagittal"
else:
orientation = "axial"
#bigger: more border
if orientation == "axial":
border_y = -100 #-60
else:
border_y = -100
x_current = X * i # column (width)
y_current = rows * (Y * 2 + border_y) - (
Y * 2 + border_y) * j # row (height) (starts from bottom?)
PlotUtils.plot_mask(renderer,
mask_data,
affine,
x_current,
y_current,
orientation=orientation,
smoothing=SMOOTHING,
brain_mask=brain_mask)
#Bundle label
text_offset_top = -50 # 60
text_offset_side = -100 # -30
position = (0 - int(X) + text_offset_side,
y_current + text_offset_top, 50)
text_actor = VtkUtils.label(text=bundle,
pos=position,
scale=(6, 6, 6),
color=(1, 1, 1))
renderer.add(text_actor)
renderer.reset_camera()
window.record(renderer,
out_path=join(out_dir, "preview.png"),
size=(WINDOW_SIZE[0], WINDOW_SIZE[1]),
reset_camera=False,
magnification=2)
def run_tractseg(data, output_type="tract_segmentation", input_type="peaks",
single_orientation=False, verbose=False, dropout_sampling=False, threshold=0.5,
bundle_specific_threshold=False, get_probs=False, peak_threshold=0.1):
'''
Run TractSeg
:param data: input peaks (4D numpy array with shape [x,y,z,9])
:param output_type: "tract_segmentation" | "endings_segmentation" | "TOM" | "dm_regression"
:param input_type: "peaks"
:param verbose: show debugging infos
:param dropout_sampling: create uncertainty map by monte carlo dropout (https://arxiv.org/abs/1506.02142)
:param threshold: Threshold for converting probability map to binary map
:param bundle_specific_threshold: Threshold is lower for some bundles which need more sensitivity (CA, CST, FX)
:param get_probs: Output raw probability map instead of binary map
:param peak_threshold: all peaks shorter than peak_threshold will be set to zero
:return: 4D numpy array with the output of tractseg
for tract_segmentation: [x,y,z,nr_of_bundles]
for endings_segmentation: [x,y,z,2*nr_of_bundles]
for TOM: [x,y,z,3*nr_of_bundles]
'''
start_time = time.time()
config = get_config_name(input_type, output_type)
HP = getattr(importlib.import_module("tractseg.config.PretrainedModels." + config), "HP")()
HP.VERBOSE = verbose
HP.TRAIN = False
HP.TEST = False
HP.SEGMENT = False
HP.GET_PROBS = get_probs
HP.LOAD_WEIGHTS = True
HP.DROPOUT_SAMPLING = dropout_sampling
HP.THRESHOLD = threshold
if bundle_specific_threshold:
HP.GET_PROBS = True
if input_type == "peaks":
if HP.EXPERIMENT_TYPE == "tract_segmentation" and HP.DROPOUT_SAMPLING:
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_dropout_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg_12g90g270g_125mm_DAugAll_Dropout", "best_weights_ep114.npz")
elif HP.EXPERIMENT_TYPE == "tract_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "x_Pretrained_TractSeg_Models/TractSeg_T1_12g90g270g_125mm_DAugAll", "best_weights_ep392.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg72_888", "best_weights_ep247.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg72_888_SchizoFineT_lr001", "best_weights_ep186.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg_12g90g270g_125mm_DS_DAugAll_RotMir", "best_weights_ep200.npz")
elif HP.EXPERIMENT_TYPE == "endings_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_endings_segmentation_v3.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "EndingsSeg_12g90g270g_125mm_DS_DAugAll", "best_weights_ep234.npz")
elif HP.EXPERIMENT_TYPE == "peak_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_peak_regression_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "x_Pretrained_TractSeg_Models/Peaks20_12g90g270g_125mm_DAugSimp_constW5", "best_weights_ep441.npz") #more oversegmentation with DAug
elif HP.EXPERIMENT_TYPE == "dm_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_dm_regression_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "DmReg_12g90g270g_125mm_DAugAll_Ubuntu", "best_weights_ep80.npz")
elif input_type == "T1":
if HP.EXPERIMENT_TYPE == "tract_segmentation":
# HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_v1.npz")
HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes/x_Pretrained_TractSeg_Models", "TractSeg_T1_125mm_DAugAll", "best_weights_ep142.npz")
elif HP.EXPERIMENT_TYPE == "endings_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_endings_segmentation_v1.npz")
elif HP.EXPERIMENT_TYPE == "peak_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_peak_regression_v1.npz")
print("Loading weights from: {}".format(HP.WEIGHTS_PATH))
if HP.EXPERIMENT_TYPE == "peak_regression":
HP.NR_OF_CLASSES = 3*len(ExpUtils.get_bundle_names(HP.CLASSES)[1:])
else:
HP.NR_OF_CLASSES = len(ExpUtils.get_bundle_names(HP.CLASSES)[1:])
if HP.VERBOSE:
print("Hyperparameters:")
ExpUtils.print_HPs(HP)
Utils.download_pretrained_weights(experiment_type=HP.EXPERIMENT_TYPE, dropout_sampling=HP.DROPOUT_SAMPLING)
data = np.nan_to_num(data)
# brain_mask = ImgUtils.simple_brain_mask(data)
# if HP.VERBOSE:
# nib.save(nib.Nifti1Image(brain_mask, np.eye(4)), "otsu_brain_mask_DEBUG.nii.gz")
if input_type == "T1":
data = np.reshape(data, (data.shape[0], data.shape[1], data.shape[2], 1))
data, seg_None, bbox, original_shape = DatasetUtils.crop_to_nonzero(data)
data, transformation = DatasetUtils.pad_and_scale_img_to_square_img(data, target_size=HP.INPUT_DIM[0])
model = BaseModel(HP)
if HP.EXPERIMENT_TYPE == "tract_segmentation" or HP.EXPERIMENT_TYPE == "endings_segmentation" or HP.EXPERIMENT_TYPE == "dm_regression":
if single_orientation: # mainly needed for testing because of less RAM requirements
dataManagerSingle = DataManagerSingleSubjectByFile(HP, data=data)
trainerSingle = Trainer(model, dataManagerSingle)
if HP.DROPOUT_SAMPLING or HP.EXPERIMENT_TYPE == "dm_regression" or HP.GET_PROBS:
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=True, scale_to_world_shape=False, only_prediction=True)
else:
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=False, scale_to_world_shape=False, only_prediction=True)
else:
seg_xyz, gt = DirectionMerger.get_seg_single_img_3_directions(HP, model, data=data, scale_to_world_shape=False, only_prediction=True)
if HP.DROPOUT_SAMPLING or HP.EXPERIMENT_TYPE == "dm_regression" or HP.GET_PROBS:
seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=True)
else:
seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=False)
elif HP.EXPERIMENT_TYPE == "peak_regression":
dataManagerSingle = DataManagerSingleSubjectByFile(HP, data=data)
trainerSingle = Trainer(model, dataManagerSingle)
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=True, scale_to_world_shape=False, only_prediction=True)
if bundle_specific_threshold:
seg = ImgUtils.remove_small_peaks_bundle_specific(seg, ExpUtils.get_bundle_names(HP.CLASSES)[1:], len_thr=0.3)
else:
seg = ImgUtils.remove_small_peaks(seg, len_thr=peak_threshold)
#3 dir for Peaks -> not working (?)
# seg_xyz, gt = DirectionMerger.get_seg_single_img_3_directions(HP, model, data=data, scale_to_world_shape=False, only_prediction=True)
# seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=True)
if bundle_specific_threshold and HP.EXPERIMENT_TYPE == "tract_segmentation":
seg = ImgUtils.probs_to_binary_bundle_specific(seg, ExpUtils.get_bundle_names(HP.CLASSES)[1:])
#remove following two lines to keep super resolution
seg = DatasetUtils.cut_and_scale_img_back_to_original_img(seg, transformation)
seg = DatasetUtils.add_original_zero_padding_again(seg, bbox, original_shape, HP.NR_OF_CLASSES)
ExpUtils.print_verbose(HP, "Took {}s".format(round(time.time() - start_time, 2)))
return seg
def create_network(self):
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT).cuda()
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT)
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
def test_bundle_names(self):
bundles = ExpUtils.get_bundle_names("CST_right")
self.assertListEqual(bundles, ["BG", "CST_right"], "Error in list of bundle names")
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y, weight_factor=10):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
weights = torch.ones(
(self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES,
self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
bundle_mask = y > 0
weights[bundle_mask.data] *= weight_factor #10
loss = criterion(outputs, y, Variable(weights))
# loss = criterion1(outputs, y, Variable(weights)) + criterion2(outputs, y, Variable(weights))
loss.backward() # backward
optimizer.step() # optimise
if self.HP.CALC_F1:
# f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# f1_a = MetricUtils.calc_peak_dice_pytorch(self.HP, outputs.data, y.data, max_angle_error=self.HP.PEAK_DICE_THR)
f1 = MetricUtils.calc_peak_length_dice_pytorch(
self.HP,
outputs.data,
y.data,
max_angle_error=self.HP.PEAK_DICE_THR,
max_length_error=self.HP.PEAK_DICE_LEN_THR)
# f1 = (f1_a, f1_b)
else:
f1 = np.ones(outputs.shape[3])
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
else:
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None #faster
return loss.data[0], probs, f1
def test(X, y, weight_factor=10):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
weights = torch.ones(
(self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES,
self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
bundle_mask = y > 0
weights[bundle_mask.data] *= weight_factor #10
loss = criterion(outputs, y, Variable(weights))
# loss = criterion1(outputs, y, Variable(weights)) + criterion2(outputs, y, Variable(weights))
if self.HP.CALC_F1:
# f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# f1_a = MetricUtils.calc_peak_dice_pytorch(self.HP, outputs.data, y.data, max_angle_error=self.HP.PEAK_DICE_THR)
f1 = MetricUtils.calc_peak_length_dice_pytorch(
self.HP,
outputs.data,
y.data,
max_angle_error=self.HP.PEAK_DICE_THR,
max_length_error=self.HP.PEAK_DICE_LEN_THR)
# f1 = (f1_a, f1_b)
else:
f1 = np.ones(outputs.shape[3])
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
#Saving Last Epoch:
# print(" Saving weights last epoch...")
# for fl in glob.glob(join(self.HP.EXP_PATH, "weights_ep*")): # remove weights from previous epochs
# os.remove(fl)
# try:
# # Actually is a pkl not a npz
# PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "weights_ep" + str(epoch_nr) + ".npz"), unet=net)
# except IOError:
# print("\nERROR: Could not save weights because of IO Error\n")
# self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = self.HP.NR_OF_GRADIENTS
# NR_OF_GRADIENTS = 9 * 5
# NR_OF_GRADIENTS = 9 * 9
# NR_OF_GRADIENTS = 33
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT)
# if self.HP.TRAIN:
# ExpUtils.print_and_save(self.HP, str(net), only_log=True)
# criterion1 = PytorchUtils.MSE_weighted
# criterion2 = PytorchUtils.angle_loss
# criterion = PytorchUtils.MSE_weighted
# criterion = PytorchUtils.angle_loss
criterion = PytorchUtils.angle_length_loss
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
def __init__(self, HP, data):
self.data = data
self.HP = HP
ExpUtils.print_verbose(self.HP, "Loading data from PREDICT_IMG input file")
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y, weight_factor=10):
X = torch.tensor(X, dtype=torch.float32).to(device) # X: (bs, features, x, y) y: (bs, classes, x, y)
y = torch.tensor(y, dtype=torch.float32).to(device)
optimizer.zero_grad()
net.train()
outputs, outputs_sigmoid = net(X) # forward # outputs: (bs, classes, x, y)
if weight_factor > 1:
# weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, y.shape[2], y.shape[3])).cuda()
bundle_mask = y > 0
weights[bundle_mask.data] *= weight_factor # 10
if self.HP.EXPERIMENT_TYPE == "peak_regression":
loss = criterion(outputs, y, weights)
else:
loss = nn.BCEWithLogitsLoss(weight=weights)(outputs, y)
else:
if self.HP.LOSS_FUNCTION == "soft_sample_dice" or self.HP.LOSS_FUNCTION == "soft_batch_dice":
loss = criterion(outputs_sigmoid, y)
# loss = criterion(outputs_sigmoid, y) + nn.BCEWithLogitsLoss()(outputs, y)
else:
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
if self.HP.EXPERIMENT_TYPE == "peak_regression":
# f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# f1_a = MetricUtils.calc_peak_dice_pytorch(self.HP, outputs.data, y.data, max_angle_error=self.HP.PEAK_DICE_THR)
f1 = MetricUtils.calc_peak_length_dice_pytorch(self.HP, outputs.detach(), y.detach(),
max_angle_error=self.HP.PEAK_DICE_THR, max_length_error=self.HP.PEAK_DICE_LEN_THR)
# f1 = (f1_a, f1_b)
elif self.HP.EXPERIMENT_TYPE == "dm_regression": #density map regression
f1 = PytorchUtils.f1_score_macro(y.detach()>0.5, outputs.detach(), per_class=True)
else:
f1 = PytorchUtils.f1_score_macro(y.detach(), outputs_sigmoid.detach(), per_class=True, threshold=self.HP.THRESHOLD)
if self.HP.USE_VISLOGGER:
# probs = outputs_sigmoid.detach().cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = outputs_sigmoid
else:
probs = None #faster
return loss.item(), probs, f1
def test(X, y, weight_factor=10):
with torch.no_grad():
X = torch.tensor(X, dtype=torch.float32).to(device)
y = torch.tensor(y, dtype=torch.float32).to(device)
if self.HP.DROPOUT_SAMPLING:
net.train()
else:
net.train(False)
outputs, outputs_sigmoid = net(X) # forward
if weight_factor > 1:
# weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, y.shape[2], y.shape[3])).cuda()
bundle_mask = y > 0
weights[bundle_mask.data] *= weight_factor # 10
if self.HP.EXPERIMENT_TYPE == "peak_regression":
loss = criterion(outputs, y, weights)
else:
loss = nn.BCEWithLogitsLoss(weight=weights)(outputs, y)
else:
if self.HP.LOSS_FUNCTION == "soft_sample_dice" or self.HP.LOSS_FUNCTION == "soft_batch_dice":
loss = criterion(outputs_sigmoid, y)
# loss = criterion(outputs_sigmoid, y) + nn.BCEWithLogitsLoss()(outputs, y)
else:
loss = criterion(outputs, y)
if self.HP.EXPERIMENT_TYPE == "peak_regression":
# f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# f1_a = MetricUtils.calc_peak_dice_pytorch(self.HP, outputs.data, y.data, max_angle_error=self.HP.PEAK_DICE_THR)
f1 = MetricUtils.calc_peak_length_dice_pytorch(self.HP, outputs.detach(), y.detach(),
max_angle_error=self.HP.PEAK_DICE_THR, max_length_error=self.HP.PEAK_DICE_LEN_THR)
# f1 = (f1_a, f1_b)
elif self.HP.EXPERIMENT_TYPE == "dm_regression": #density map regression
f1 = PytorchUtils.f1_score_macro(y.detach()>0.5, outputs.detach(), per_class=True)
else:
f1 = PytorchUtils.f1_score_macro(y.detach(), outputs_sigmoid.detach(), per_class=True, threshold=self.HP.THRESHOLD)
if self.HP.USE_VISLOGGER:
# probs = outputs_sigmoid.detach().cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = outputs_sigmoid
else:
probs = None # faster
return loss.item(), probs, f1
def predict(X):
with torch.no_grad():
X = torch.tensor(X, dtype=torch.float32).to(device)
if self.HP.DROPOUT_SAMPLING:
net.train()
else:
net.train(False)
outputs, outputs_sigmoid = net(X) # forward
if self.HP.EXPERIMENT_TYPE == "peak_regression" or self.HP.EXPERIMENT_TYPE == "dm_regression":
probs = outputs.detach().cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
else:
probs = outputs_sigmoid.detach().cpu().numpy().transpose(0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "best_weights_ep" + str(epoch_nr) + ".npz"), unet=net)
except IOError:
print("\nERROR: Could not save weights because of IO Error\n")
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(self.HP, "current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = self.HP.NR_OF_GRADIENTS
# NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
# NR_OF_GRADIENTS = 9 * 9
# NR_OF_GRADIENTS = 33
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
self.HP.NR_OF_GRADIENTS = 3*self.HP.NR_OF_CLASSES
else:
self.HP.NR_OF_GRADIENTS = 33
if self.HP.LOSS_FUNCTION == "soft_sample_dice":
criterion = PytorchUtils.soft_sample_dice
elif self.HP.LOSS_FUNCTION == "soft_batch_dice":
criterion = PytorchUtils.soft_batch_dice
elif self.HP.EXPERIMENT_TYPE == "peak_regression":
criterion = PytorchUtils.angle_length_loss
else:
# weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
# weights[:, 5, :, :] *= 10 #CA
# weights[:, 21, :, :] *= 10 #FX_left
# weights[:, 22, :, :] *= 10 #FX_right
# criterion = nn.BCEWithLogitsLoss(weight=weights)
criterion = nn.BCEWithLogitsLoss()
NetworkClass = getattr(importlib.import_module("tractseg.models." + self.HP.MODEL), self.HP.MODEL)
net = NetworkClass(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT,
batchnorm=self.HP.BATCH_NORM, dropout=self.HP.USE_DROPOUT)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net = net.to(device)
# if self.HP.TRAIN:
# ExpUtils.print_and_save(self.HP, str(net), only_log=True)
if self.HP.OPTIMIZER == "Adamax":
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
elif self.HP.OPTIMIZER == "Adam":
optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE)
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE, weight_decay=self.HP.WEIGHT_DECAY)
else:
raise ValueError("Optimizer not defined")
if self.HP.LR_SCHEDULE:
scheduler = lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
self.scheduler = scheduler
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(self.HP, "Loading weights ... ({})".format(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
if self.HP.RESET_LAST_LAYER:
# net.conv_5 = conv2d(self.HP.UNET_NR_FILT, self.HP.NR_OF_CLASSES, kernel_size=1, stride=1, padding=0, bias=True).to(device)
net.conv_5 = nn.Conv2d(self.HP.UNET_NR_FILT, self.HP.NR_OF_CLASSES, kernel_size=1, stride=1, padding=0, bias=True).to(device)
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(random.uniform(0, len(subjects))) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if np.random.random() < 0.5:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# rnd_choice = np.random.random()
# if rnd_choice < 0.33:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# elif rnd_choice < 0.66:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
seg = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(self.HP, "\n\nWARNING: Could not load file. Trying again in 20s (Try number: " + str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False, None)
# Randomly sample slice orientation
slice_direction = int(round(random.uniform(0,2)))
if slice_direction == 0:
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(0, 3, 1, 2) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(2, 3, 0, 1)
sw = 5 #slice_window (only odd numbers allowed)
pad = int((sw-1) / 2)
data_pad = np.zeros((data.shape[0]+sw-1, data.shape[1]+sw-1, data.shape[2]+sw-1, data.shape[3])).astype(data.dtype)
data_pad[pad:-pad, pad:-pad, pad:-pad, :] = data #padded with two slices of zeros on all sides
batch=[]
for s_idx in slice_idxs:
if slice_direction == 0:
#(s_idx+2)-2:(s_idx+2)+3 = s_idx:s_idx+5
x = data_pad[s_idx:s_idx+sw:, pad:-pad, pad:-pad, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(0, 3, 1, 2) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 1:
x = data_pad[pad:-pad, s_idx:s_idx+sw, pad:-pad, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(1, 3, 0, 2) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 2:
x = data_pad[pad:-pad, pad:-pad, s_idx:s_idx+sw, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(2, 3, 0, 1) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
data_dict = {"data": np.array(batch), # (batch_size, channels, x, y, [z])
"seg": y} # (batch_size, channels, x, y, [z])
return data_dict
class HP:
EXP_MULTI_NAME = "" #CV Parent Dir name # leave empty for Single Bundle Experiment
EXP_NAME = "HCP_TEST" # HCP_TEST
MODEL = "UNet_Pytorch" # UNet_Lasagne / UNet_Pytorch
EXPERIMENT_TYPE = "tract_segmentation" # tract_segmentation / endings_segmentation / dm_regression / peak_regression
NUM_EPOCHS = 250
DATA_AUGMENTATION = False
DAUG_SCALE = True
DAUG_NOISE = True
DAUG_ELASTIC_DEFORM = True
DAUG_RESAMPLE = True
DAUG_ROTATE = False
DAUG_MIRROR = False
DAUG_FLIP_PEAKS = False
DAUG_INFO = "Elastic(90,120)(9,11) - Scale(0.9, 1.5) - CenterDist60 - DownsampScipy(0.5,1) - Gaussian(0,0.05) - Rotate(-0.8,0.8)"
DATASET = "HCP" # HCP / HCP_32g / Schizo
RESOLUTION = "1.25mm" # 1.25mm (/ 2.5mm)
FEATURES_FILENAME = "12g90g270g" # 12g90g270g / 270g_125mm_xyz / 270g_125mm_peaks / 90g_125mm_peaks / 32g_25mm_peaks / 32g_25mm_xyz
LABELS_FILENAME = "" # autofilled #"bundle_peaks/CA" #IMPORTANT: Adapt BatchGen if 808080 # bundle_masks / bundle_masks_72 / bundle_masks_dm / bundle_peaks #Only used when using DataManagerNifti
LOSS_FUNCTION = "default" # default / soft_batch_dice
OPTIMIZER = "Adamax"
CLASSES = "All" # All / 11 / 20 / CST_right
NR_OF_GRADIENTS = 9
NR_OF_CLASSES = len(ExpUtils.get_bundle_names(CLASSES)[1:])
# NR_OF_CLASSES = 3 * len(ExpUtils.get_bundle_names(CLASSES)[1:])
INPUT_DIM = (144, 144) # (80, 80) / (144, 144)
LOSS_WEIGHT = 1 # 1: no weighting
LOSS_WEIGHT_LEN = -1 # -1: constant over all epochs
SLICE_DIRECTION = "y" # x, y, z (combined needs z)
TRAINING_SLICE_DIRECTION = "xyz" # y / xyz
INFO = "-" # Dropout, Deconv, 11bundles, LeakyRelu, PeakDiceThres=0.9
BATCH_NORM = False
WEIGHT_DECAY = 0
USE_DROPOUT = False
DROPOUT_SAMPLING = False
# DATASET_FOLDER = "HCP_batches/270g_125mm_bundle_peaks_Y_subset" # HCP / HCP_batches/XXX / TRACED / HCP_fusion_npy_270g_125mm / HCP_fusion_npy_32g_25mm
# DATASET_FOLDER = "HCP_batches/270g_125mm_bundle_peaks_XYZ"
DATASET_FOLDER = "HCP" # HCP / Schizo
LABELS_FOLDER = "bundle_masks" # bundle_masks / bundle_masks_dm
MULTI_PARENT_PATH = join(C.EXP_PATH, EXP_MULTI_NAME)
EXP_PATH = join(C.EXP_PATH, EXP_MULTI_NAME, EXP_NAME) # default path
BATCH_SIZE = 47 #30/44 #max: #Peak Prediction: 44 #Pytorch: 50 #Lasagne: 56 #Lasagne combined: 42 #Pytorch UpSample: 56 #Pytorch_SE_r16: 45 #Pytorch_SE_r64: 45
LEARNING_RATE = 0.001 # 0.002 #LR find: 0.000143 ? # 0.001
LR_SCHEDULE = False
UNET_NR_FILT = 64
LOAD_WEIGHTS = False
# WEIGHTS_PATH = join(C.EXP_PATH, "HCP100_45B_UNet_x_DM_lr002_slope2_dec992_ep800/best_weights_ep64.npz") # Can be absolute path or relative like "exp_folder/weights.npz"
WEIGHTS_PATH = "" # if empty string: autoloading the best_weights in get_best_weights_path()
TYPE = "single_direction" # single_direction / combined
CV_FOLD = 0
VALIDATE_SUBJECTS = []
TRAIN_SUBJECTS = []
TEST_SUBJECTS = []
TRAIN = True
TEST = True
SEGMENT = False
GET_PROBS = False
OUTPUT_MULTIPLE_FILES = False
RESET_LAST_LAYER = False
# Peak_regression specific
PEAK_DICE_THR = [0.95]
PEAK_DICE_LEN_THR = 0.05
FLIP_OUTPUT_PEAKS = True # flip peaks along z axis to make them compatible with MITK
# For TractSeg.py application
PREDICT_IMG = False
PREDICT_IMG_OUTPUT = None
TRACTSEG_DIR = "tractseg_output"
KEEP_INTERMEDIATE_FILES = False
CSD_RESOLUTION = "LOW" # HIGH / LOW
#Unimportant / rarly changed:
LABELS_TYPE = np.int16 # Binary: np.int16, Regression: np.float32
THRESHOLD = 0.5 # Binary: 0.5, Regression: 0.01 ?
TEST_TIME_DAUG = False
USE_VISLOGGER = False #only works with Python 3
SAVE_WEIGHTS = True
SEG_INPUT = "Peaks" # Gradients/ Peaks
NR_SLICES = 1 # adapt manually: NR_OF_GRADIENTS in UNet.py and get_batch... in train() and in get_seg_prediction()
PRINT_FREQ = 20 #20
NORMALIZE_DATA = True
NORMALIZE_PER_CHANNEL = False
BEST_EPOCH = 0
VERBOSE = True
CALC_F1 = True
def __init__(self, HP, data):
self.data = data
self.HP = HP
ExpUtils.print_verbose(self.HP,
"Loading data from PREDICT_IMG input file")
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda()) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs, intermediate = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
else:
probs = None #faster
return loss.data[0], probs, f1, intermediate
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(), volatile=True), Variable(y.cuda(), volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "best_weights_ep" + str(epoch_nr) + ".npz"), unet=net)
except IOError:
print("\nERROR: Could not save weights because of IO Error\n")
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(self.HP, "current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3*self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
# net = nn.DataParallel(net, device_ids=[0,1])
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE) #very slow (half speed of Adamax) -> strange
# scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(self.HP, "Loading weights ... ({})".format(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
#plot feature weights
# weights = list(list(net.children())[0].children())[0].weight.cpu().data.numpy() # sequential -> conv2d # (64, 9, 3, 3)
# weights = weights[:, 0:1, :, :] # select one input channel to plot # (64, 1, 3, 3)
# weights = (weights*100).astype(np.uint8) # can not plot negative values (and if float only 0-1 allowed) -> not good: we remove negatives
# plot_kernels(weights)
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
# self.scheduler = scheduler
def train(self, HP):
if HP.USE_VISLOGGER:
nvl = Nvl(name="Training")
ExpUtils.print_and_save(HP, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
metrics_new = {
"loss_" + type: [0],
"f1_macro_" + type: [0],
}
metrics = dict(list(metrics.items()) + list(metrics_new.items()))
for epoch_nr in range(HP.NUM_EPOCHS):
start_time = time.time()
# current_lr = HP.LEARNING_RATE * (HP.LR_DECAY ** epoch_nr)
# current_lr = HP.LEARNING_RATE
batch_gen_time = 0
data_preparation_time = 0
network_time = 0
metrics_time = 0
saving_time = 0
plotting_time = 0
batch_nr = {"train": 0, "test": 0, "validate": 0}
if HP.LOSS_WEIGHT_LEN == -1:
weight_factor = float(HP.LOSS_WEIGHT)
else:
if epoch_nr < HP.LOSS_WEIGHT_LEN:
# weight_factor = -(9./100.) * epoch_nr + 10. #ep0: 10 -> linear decrease -> ep100: 1
weight_factor = -((HP.LOSS_WEIGHT - 1) / float(
HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
# weight_factor = -((HP.LOSS_WEIGHT-5)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
else:
weight_factor = 1.
# weight_factor = 5.
for type in ["train", "test", "validate"]:
print_loss = []
start_time_batch_gen = time.time()
batch_generator = self.dataManager.get_batches(
batch_size=HP.BATCH_SIZE,
type=type,
subjects=getattr(HP,
type.upper() + "_SUBJECTS"))
batch_gen_time = time.time() - start_time_batch_gen
# print("batch_gen_time: {}s".format(batch_gen_time))
print("Start looping batches...")
start_time_batch_part = time.time()
for batch in batch_generator: #getting next batch takes around 0.14s -> second largest Time part after UNet!
start_time_data_preparation = time.time()
batch_nr[type] += 1
x = batch["data"] # (bs, nr_of_channels, x, y)
y = batch["seg"] # (bs, nr_of_classes, x, y)
# since using new BatchGenerator y is not int anymore but float -> would be good for Pytorch but not Lasagne
# y = y.astype(HP.LABELS_TYPE) #for bundle_peaks regression: is already float -> saves 0.2s/batch if left out
data_preparation_time += time.time(
) - start_time_data_preparation
# self.model.learning_rate.set_value(np.float32(current_lr))
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
loss, probs, f1 = self.model.train(
x, y, weight_factor=weight_factor
) # probs: # (bs, x, y, nrClasses)
# loss, probs, f1, intermediate = self.model.train(x, y)
elif type == "validate":
loss, probs, f1 = self.model.predict(
x, y, weight_factor=weight_factor)
elif type == "test":
loss, probs, f1 = self.model.predict(
x, y, weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if HP.CALC_F1:
if HP.LABELS_TYPE == np.int16:
metrics = MetricUtils.calculate_metrics(
metrics,
None,
None,
loss,
f1=np.mean(f1),
type=type,
threshold=HP.THRESHOLD)
else: #Regression
#Following two lines increase metrics_time by 30s (without < 1s); time per batch increases by 1.5s by these lines
# y_flat = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# y_flat = np.reshape(y_flat, (-1, y_flat.shape[-1])) # (bs*x*y, nr_of_classes)
# metrics = MetricUtils.calculate_metrics(metrics, y_flat, probs, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"CA": f1[5], "FX_left": f1[23], "FX_right": f1[24]})
#Numpy
# y_right_order = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# peak_f1 = MetricUtils.calc_peak_dice(HP, probs, y_right_order)
# peak_f1_mean = np.array([s for s in peak_f1.values()]).mean()
#Pytorch
peak_f1_mean = np.array([
s for s in list(f1.values())
]).mean() #if f1 for multiple bundles
metrics = MetricUtils.calculate_metrics(
metrics,
None,
None,
loss,
f1=peak_f1_mean,
type=type,
threshold=HP.THRESHOLD)
#Pytorch 2 F1
# peak_f1_mean_a = np.array([s for s in f1[0].values()]).mean()
# peak_f1_mean_b = np.array([s for s in f1[1].values()]).mean()
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean_a, type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"LenF1": peak_f1_mean_b})
#Single Bundle
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"][0], type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"Thr1": f1["CST_right"][1], "Thr2": f1["CST_right"][2]})
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"], type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics_onlyLoss(
metrics, loss, type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(loss)
if batch_nr[type] % HP.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
ExpUtils.print_and_save(
HP,
"{} Ep {}, Sp {}, loss {}, t print {}s, t batch {}s"
.format(type, epoch_nr,
batch_nr[type] * HP.BATCH_SIZE,
round(np.array(print_loss).mean(), 6),
round(time_batch_part, 3),
round(time_batch_part / HP.PRINT_FREQ, 3)))
print_loss = []
if HP.USE_VISLOGGER:
x_norm = (x - x.min()) / (x.max() - x.min())
nvl.show_images(
x_norm[0:1, :, :, :].transpose((1, 0, 2, 3)),
name="input batch",
title="Input batch") #all channels of one batch
probs_shaped = probs[:, :, :, 15:16].transpose(
(0, 3, 1, 2)) # (bs, 1, x, y)
probs_shaped_bin = (probs_shaped > 0.5).astype(
np.int16)
nvl.show_images(probs_shaped,
name="predictions",
title="Predictions Probmap")
# nvl.show_images(probs_shaped_bin, name="predictions_binary", title="Predictions Binary")
# Show GT and Prediction in one image (bundle: CST)
# GREEN: GT; RED: prediction (FP); YELLOW: prediction (TP)
combined = np.zeros(
(y.shape[0], 3, y.shape[2], y.shape[3]))
combined[:, 0:1, :, :] = probs_shaped_bin #Red
combined[:, 1:2, :, :] = y[:, 15:16, :, :] #Green
nvl.show_images(combined,
name="predictions_combined",
title="Combined")
#Show feature activations
contr_1_2 = intermediate[2].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
contr_1_2 = contr_1_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
contr_1_2 = (contr_1_2 - contr_1_2.min()) / (
contr_1_2.max() - contr_1_2.min())
nvl.show_images(contr_1_2,
name="contr_1_2",
title="contr_1_2")
# Show feature activations
contr_3_2 = intermediate[1].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
contr_3_2 = contr_3_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
contr_3_2 = (contr_3_2 - contr_3_2.min()) / (
contr_3_2.max() - contr_3_2.min())
nvl.show_images(contr_3_2,
name="contr_3_2",
title="contr_3_2")
# Show feature activations
deconv_2 = intermediate[0].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
deconv_2 = deconv_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
deconv_2 = (deconv_2 - deconv_2.min()) / (
deconv_2.max() - deconv_2.min())
nvl.show_images(deconv_2,
name="deconv_2",
title="deconv_2")
nvl.show_value(float(loss), name="loss")
nvl.show_value(float(np.mean(f1)), name="f1")
###################################
# Post Training tasks (each epoch)
###################################
#Adapt LR
# self.model.scheduler.step()
# self.model.scheduler.step(np.mean(f1))
# self.model.print_current_lr()
# Average loss per batch over entire epoch
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["train"],
type="train")
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["validate"],
type="validate")
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["test"],
type="test")
print(" Epoch {}, Average Epoch loss = {}".format(
epoch_nr, metrics["loss_train"][-1]))
print(" Epoch {}, nr_of_updates {}".format(
epoch_nr, nr_of_updates))
# Save Weights
start_time_saving = time.time()
if HP.SAVE_WEIGHTS:
self.model.save_model(metrics, epoch_nr)
saving_time += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(
metrics, open(join(HP.EXP_PATH, "metrics.pkl"), "wb")
) # wb -> write (override) and binary (binary only needed on windows, on unix also works without) # for loading: pickle.load(open("metrics.pkl", "rb"))
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME)
ExpUtils.create_exp_plot(metrics,
HP.EXP_PATH,
HP.EXP_NAME,
without_first_epochs=True)
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
ExpUtils.print_and_save(
HP, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
ExpUtils.print_and_save(
HP,
" Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
ExpUtils.print_and_save(
HP,
" Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
ExpUtils.print_and_save(
HP, " Epoch {}, time saving files: {}s".format(
epoch_nr, saving_time))
ExpUtils.print_and_save(HP, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < HP.NUM_EPOCHS - 1:
metrics = MetricUtils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(HP.EXP_PATH, "Hyperparameters.txt"),
"a") as f: # a for append
f.write("\n\n")
f.write("Average Epoch time: {}s".format(
sum(epoch_times) / float(len(epoch_times))))
return metrics
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(self.HP, "current learning rate: {}".format(param_group['lr']))
def calc_peak_length_dice_pytorch(HP,
y_pred,
y_true,
max_angle_error=[0.9],
max_length_error=0.1):
'''
Ca
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
Can be list with several values -> calculate for several thresholds
:return:
'''
import torch
from tractseg.libs.PytorchEinsum import einsum
from tractseg.libs.PytorchUtils import PytorchUtils
y_true = y_true.permute(0, 2, 3, 1)
y_pred = y_pred.permute(0, 2, 3, 1)
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
return torch.abs(
einsum('abcd,abcd->abc', a, b) /
(torch.norm(a, 2., -1) * torch.norm(b, 2, -1) + 1e-7))
#Single threshold
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :, (idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) +
3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
lenghts_pred = torch.norm(y_pred_bund, 2., -1)
lengths_true = torch.norm(y_true_bund, 2, -1)
lengths_binary = torch.abs(lenghts_pred - lengths_true) < (
max_length_error * lengths_true)
lengths_binary = lengths_binary.view(-1)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.view(-1)
combined = lengths_binary * angles_binary
f1 = PytorchUtils.f1_score_binary(gt_binary, combined)
score_per_bundle[bundle] = f1
return score_per_bundle
def run_tractseg(data, output_type="tract_segmentation", input_type="peaks",
single_orientation=False, verbose=False, dropout_sampling=False, threshold=0.5,
bundle_specific_threshold=False, get_probs=False):
'''
Run TractSeg
:param data: input peaks (4D numpy array with shape [x,y,z,9])
:param output_type: "tract_segmentation" | "endings_segmentation" | "TOM" | "dm_regression"
:param input_type: "peaks"
:param verbose: show debugging infos
:param dropout_sampling: create uncertainty map by monte carlo dropout (https://arxiv.org/abs/1506.02142)
:param threshold: Threshold for converting probability map to binary map
:param bundle_specific_threshold: Threshold is lower for some bundles which need more sensitivity (CA, CST, FX)
:param get_probs: Output raw probability map instead of binary map
:return: 4D numpy array with the output of tractseg
for tract_segmentation: [x,y,z,nr_of_bundles]
for endings_segmentation: [x,y,z,2*nr_of_bundles]
for TOM: [x,y,z,3*nr_of_bundles]
'''
start_time = time.time()
config = get_config_name(input_type, output_type)
HP = getattr(importlib.import_module("tractseg.config.PretrainedModels." + config), "HP")()
HP.VERBOSE = verbose
HP.TRAIN = False
HP.TEST = False
HP.SEGMENT = False
HP.GET_PROBS = get_probs
HP.LOAD_WEIGHTS = True
HP.DROPOUT_SAMPLING = dropout_sampling
HP.THRESHOLD = threshold
if bundle_specific_threshold:
HP.GET_PROBS = True
if input_type == "peaks":
if HP.EXPERIMENT_TYPE == "tract_segmentation" and HP.DROPOUT_SAMPLING:
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_dropout_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg_12g90g270g_125mm_DAugAll_Dropout", "best_weights_ep114.npz")
elif HP.EXPERIMENT_TYPE == "tract_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg_T1_12g90g270g_125mm_DAugAll", "best_weights_ep126.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg72_888", "best_weights_ep247.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg72_888_SchizoFineT_lr001", "best_weights_ep186.npz")
elif HP.EXPERIMENT_TYPE == "endings_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_endings_segmentation_v2.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "EndingsSeg_12g90g270g_125mm_DAugAll", "best_weights_ep16.npz")
elif HP.EXPERIMENT_TYPE == "peak_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_peak_regression_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "x_Pretrained_TractSeg_Models/Peaks20_12g90g270g_125mm_DAugSimp_constW5", "best_weights_ep441.npz") #more oversegmentation with DAug
elif HP.EXPERIMENT_TYPE == "dm_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_dm_regression_v1.npz")
# HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "DmReg_12g90g270g_125mm_DAugAll_Ubuntu", "best_weights_ep80.npz")
elif input_type == "T1":
if HP.EXPERIMENT_TYPE == "tract_segmentation":
# HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_tract_segmentation_v1.npz")
HP.WEIGHTS_PATH = join(C.NETWORK_DRIVE, "hcp_exp_nodes", "TractSeg_T1_125mm_DAugAll", "best_weights_ep142.npz")
elif HP.EXPERIMENT_TYPE == "endings_segmentation":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_endings_segmentation_v1.npz")
elif HP.EXPERIMENT_TYPE == "peak_regression":
HP.WEIGHTS_PATH = join(C.TRACT_SEG_HOME, "pretrained_weights_peak_regression_v1.npz")
print("Loading weights from: {}".format(HP.WEIGHTS_PATH))
if HP.EXPERIMENT_TYPE == "peak_regression":
HP.NR_OF_CLASSES = 3*len(ExpUtils.get_bundle_names(HP.CLASSES)[1:])
else:
HP.NR_OF_CLASSES = len(ExpUtils.get_bundle_names(HP.CLASSES)[1:])
if HP.VERBOSE:
print("Hyperparameters:")
ExpUtils.print_HPs(HP)
Utils.download_pretrained_weights(experiment_type=HP.EXPERIMENT_TYPE, dropout_sampling=HP.DROPOUT_SAMPLING)
data = np.nan_to_num(data)
# brain_mask = ImgUtils.simple_brain_mask(data)
# if HP.VERBOSE:
# nib.save(nib.Nifti1Image(brain_mask, np.eye(4)), "otsu_brain_mask_DEBUG.nii.gz")
if input_type == "T1":
data = np.reshape(data, (data.shape[0], data.shape[1], data.shape[2], 1))
data, seg_None, bbox, original_shape = DatasetUtils.crop_to_nonzero(data)
data, transformation = DatasetUtils.pad_and_scale_img_to_square_img(data, target_size=HP.INPUT_DIM[0])
model = BaseModel(HP)
if HP.EXPERIMENT_TYPE == "tract_segmentation" or HP.EXPERIMENT_TYPE == "endings_segmentation" or HP.EXPERIMENT_TYPE == "dm_regression":
if single_orientation: # mainly needed for testing because of less RAM requirements
dataManagerSingle = DataManagerSingleSubjectByFile(HP, data=data)
trainerSingle = Trainer(model, dataManagerSingle)
if HP.DROPOUT_SAMPLING or HP.EXPERIMENT_TYPE == "dm_regression" or HP.GET_PROBS:
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=True, scale_to_world_shape=False, only_prediction=True)
else:
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=False, scale_to_world_shape=False, only_prediction=True)
else:
seg_xyz, gt = DirectionMerger.get_seg_single_img_3_directions(HP, model, data=data, scale_to_world_shape=False, only_prediction=True)
if HP.DROPOUT_SAMPLING or HP.EXPERIMENT_TYPE == "dm_regression" or HP.GET_PROBS:
seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=True)
else:
seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=False)
elif HP.EXPERIMENT_TYPE == "peak_regression":
dataManagerSingle = DataManagerSingleSubjectByFile(HP, data=data)
trainerSingle = Trainer(model, dataManagerSingle)
seg, img_y = trainerSingle.get_seg_single_img(HP, probs=True, scale_to_world_shape=False, only_prediction=True)
if bundle_specific_threshold:
seg = ImgUtils.remove_small_peaks_bundle_specific(seg, ExpUtils.get_bundle_names(HP.CLASSES)[1:], len_thr=0.3)
else:
seg = ImgUtils.remove_small_peaks(seg, len_thr=0.3) # set lower for more sensitivity
#3 dir for Peaks -> not working (?)
# seg_xyz, gt = DirectionMerger.get_seg_single_img_3_directions(HP, model, data=data, scale_to_world_shape=False, only_prediction=True)
# seg = DirectionMerger.mean_fusion(HP.THRESHOLD, seg_xyz, probs=True)
if bundle_specific_threshold and HP.EXPERIMENT_TYPE == "tract_segmentation":
seg = ImgUtils.probs_to_binary_bundle_specific(seg, ExpUtils.get_bundle_names(HP.CLASSES)[1:])
#remove following two lines to keep super resolution
seg = DatasetUtils.cut_and_scale_img_back_to_original_img(seg, transformation)
seg = DatasetUtils.add_original_zero_padding_again(seg, bbox, original_shape, HP.NR_OF_CLASSES)
ExpUtils.print_verbose(HP, "Took {}s".format(round(time.time() - start_time, 2)))
return seg
def train(self, HP):
if HP.USE_VISLOGGER:
try:
from trixi.logger.visdom import PytorchVisdomLogger
except ImportError:
pass
trixi = PytorchVisdomLogger(port=8080, auto_start=True)
ExpUtils.print_and_save(HP, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
metrics_new = {
"loss_" + type: [0],
"f1_macro_" + type: [0],
}
metrics = dict(list(metrics.items()) + list(metrics_new.items()))
for epoch_nr in range(HP.NUM_EPOCHS):
start_time = time.time()
# current_lr = HP.LEARNING_RATE * (HP.LR_DECAY ** epoch_nr)
# current_lr = HP.LEARNING_RATE
batch_gen_time = 0
data_preparation_time = 0
network_time = 0
metrics_time = 0
saving_time = 0
plotting_time = 0
batch_nr = {
"train": 0,
"test": 0,
"validate": 0
}
if HP.LOSS_WEIGHT_LEN == -1:
weight_factor = float(HP.LOSS_WEIGHT)
else:
if epoch_nr < HP.LOSS_WEIGHT_LEN:
# weight_factor = -(9./100.) * epoch_nr + 10. #ep0: 10 -> linear decrease -> ep100: 1
weight_factor = -((HP.LOSS_WEIGHT-1)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
# weight_factor = -((HP.LOSS_WEIGHT-5)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
else:
weight_factor = 1.
# weight_factor = 5.
for type in ["train", "test", "validate"]:
print_loss = []
start_time_batch_gen = time.time()
batch_generator = self.dataManager.get_batches(batch_size=HP.BATCH_SIZE,
type=type, subjects=getattr(HP, type.upper() + "_SUBJECTS"))
batch_gen_time = time.time() - start_time_batch_gen
# print("batch_gen_time: {}s".format(batch_gen_time))
print("Start looping batches...")
start_time_batch_part = time.time()
for batch in batch_generator: #getting next batch takes around 0.14s -> second largest Time part after mode!
start_time_data_preparation = time.time()
batch_nr[type] += 1
x = batch["data"] # (bs, nr_of_channels, x, y)
y = batch["seg"] # (bs, nr_of_classes, x, y)
# since using new BatchGenerator y is not int anymore but float -> would be good for Pytorch but not Lasagne
# y = y.astype(HP.LABELS_TYPE) #for bundle_peaks regression: is already float -> saves 0.2s/batch if left out
data_preparation_time += time.time() - start_time_data_preparation
# self.model.learning_rate.set_value(np.float32(current_lr))
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
loss, probs, f1 = self.model.train(x, y, weight_factor=weight_factor) # probs: # (bs, x, y, nrClasses)
# loss, probs, f1, intermediate = self.model.train(x, y)
elif type == "validate":
loss, probs, f1 = self.model.predict(x, y, weight_factor=weight_factor)
elif type == "test":
loss, probs, f1 = self.model.predict(x, y, weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if HP.CALC_F1:
if HP.EXPERIMENT_TYPE == "peak_regression":
#Following two lines increase metrics_time by 30s (without < 1s); time per batch increases by 1.5s by these lines
# y_flat = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# y_flat = np.reshape(y_flat, (-1, y_flat.shape[-1])) # (bs*x*y, nr_of_classes)
# metrics = MetricUtils.calculate_metrics(metrics, y_flat, probs, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"CA": f1[5], "FX_left": f1[23], "FX_right": f1[24]})
#Numpy
# y_right_order = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# peak_f1 = MetricUtils.calc_peak_dice(HP, probs, y_right_order)
# peak_f1_mean = np.array([s for s in peak_f1.values()]).mean()
#Pytorch
peak_f1_mean = np.array([s for s in list(f1.values())]).mean() #if f1 for multiple bundles
metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean, type=type, threshold=HP.THRESHOLD)
#Pytorch 2 F1
# peak_f1_mean_a = np.array([s for s in f1[0].values()]).mean()
# peak_f1_mean_b = np.array([s for s in f1[1].values()]).mean()
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean_a, type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"LenF1": peak_f1_mean_b})
#Single Bundle
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"][0], type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"Thr1": f1["CST_right"][1], "Thr2": f1["CST_right"][2]})
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"], type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics_onlyLoss(metrics, loss, type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(loss)
if batch_nr[type] % HP.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
ExpUtils.print_and_save(HP, "{} Ep {}, Sp {}, loss {}, t print {}s, t batch {}s".format(type, epoch_nr,
batch_nr[type] * HP.BATCH_SIZE,
round(np.array(print_loss).mean(), 6), round(time_batch_part, 3),
round(time_batch_part / HP.PRINT_FREQ, 3)))
print_loss = []
if HP.USE_VISLOGGER:
ExpUtils.plot_result_trixi(trixi, x, y, probs, loss, f1, epoch_nr)
###################################
# Post Training tasks (each epoch)
###################################
#Adapt LR
if HP.LR_SCHEDULE:
self.model.scheduler.step()
# self.model.scheduler.step(np.mean(f1))
self.model.print_current_lr()
# Average loss per batch over entire epoch
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["train"], type="train")
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["validate"], type="validate")
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["test"], type="test")
print(" Epoch {}, Average Epoch loss = {}".format(epoch_nr, metrics["loss_train"][-1]))
print(" Epoch {}, nr_of_updates {}".format(epoch_nr, nr_of_updates))
# Save Weights
start_time_saving = time.time()
if HP.SAVE_WEIGHTS:
self.model.save_model(metrics, epoch_nr)
saving_time += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(metrics, open(join(HP.EXP_PATH, "metrics.pkl"), "wb")) # wb -> write (override) and binary (binary only needed on windows, on unix also works without) # for loading: pickle.load(open("metrics.pkl", "rb"))
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME)
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME, without_first_epochs=True)
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
ExpUtils.print_and_save(HP, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
ExpUtils.print_and_save(HP, " Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
ExpUtils.print_and_save(HP, " Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
ExpUtils.print_and_save(HP, " Epoch {}, time saving files: {}s".format(epoch_nr, saving_time))
ExpUtils.print_and_save(HP, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < HP.NUM_EPOCHS-1:
metrics = MetricUtils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(HP.EXP_PATH, "Hyperparameters.txt"), "a") as f: # a for append
f.write("\n\n")
f.write("Average Epoch time: {}s".format(sum(epoch_times) / float(len(epoch_times))))
return metrics
def calc_peak_dice_pytorch(HP, y_pred, y_true, max_angle_error=[0.9]):
'''
Calculate angle between groundtruth and prediction and keep the voxels where
angle is smaller than MAX_ANGLE_ERROR.
From groundtruth generate a binary mask by selecting all voxels with len > 0.
Calculate Dice from these 2 masks.
-> Penalty on peaks outside of tract or if predicted peak=0
-> no penalty on very very small with right direction -> bad
=> Peak_dice can be high even if peaks inside of tract almost missing (almost 0)
:param y_pred:
:param y_true:
:param max_angle_error: 0.7 -> angle error of 45° or less; 0.9 -> angle error of 23° or less
Can be list with several values -> calculate for several thresholds
:return:
'''
import torch
from tractseg.libs.PytorchEinsum import einsum
from tractseg.libs.PytorchUtils import PytorchUtils
y_true = y_true.permute(0, 2, 3, 1)
y_pred = y_pred.permute(0, 2, 3, 1)
def angle_last_dim(a, b):
'''
Calculate the angle between two nd-arrays (array of vectors) along the last dimension
without anything further: 1->0°, 0.9->23°, 0.7->45°, 0->90°
np.arccos -> returns degree in pi (90°: 0.5*pi)
return: one dimension less then input
'''
return torch.abs(
einsum('abcd,abcd->abc', a, b) /
(torch.norm(a, 2., -1) * torch.norm(b, 2, -1) + 1e-7))
#Single threshold
if len(max_angle_error) == 1:
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :,
(idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) +
3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
angles_binary = angles > max_angle_error[0]
angles_binary = angles_binary.view(-1)
f1 = PytorchUtils.f1_score_binary(gt_binary, angles_binary)
score_per_bundle[bundle] = f1
return score_per_bundle
#multiple thresholds
else:
score_per_bundle = {}
bundles = ExpUtils.get_bundle_names(HP.CLASSES)[1:]
for idx, bundle in enumerate(bundles):
# if bundle == "CST_right":
y_pred_bund = y_pred[:, :, :,
(idx * 3):(idx * 3) + 3].contiguous()
y_true_bund = y_true[:, :, :, (idx * 3):(idx * 3) +
3].contiguous() # [x,y,z,3]
angles = angle_last_dim(y_pred_bund, y_true_bund)
gt_binary = y_true_bund.sum(dim=-1) > 0
gt_binary = gt_binary.view(-1) # [bs*x*y]
score_per_bundle[bundle] = []
for threshold in max_angle_error:
angles_binary = angles > threshold
angles_binary = angles_binary.view(-1)
f1 = PytorchUtils.f1_score_binary(gt_binary, angles_binary)
score_per_bundle[bundle].append(f1)
return score_per_bundle
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(
random.uniform(0, len(subjects))
) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if np.random.random() < 0.5:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
# rnd_choice = np.random.random()
# if rnd_choice < 0.33:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# elif rnd_choice < 0.66:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
seg = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(
self.HP,
"\n\nWARNING: Could not load file. Trying again in 20s (Try number: "
+ str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(
data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(
seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(
seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False,
None)
# Randomly sample slice orientation
slice_direction = int(round(random.uniform(0, 2)))
if slice_direction == 0:
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(
0, 3, 1, 2
) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(2, 3, 0, 1)
sw = 5 #slice_window (only odd numbers allowed)
pad = int((sw - 1) / 2)
data_pad = np.zeros(
(data.shape[0] + sw - 1, data.shape[1] + sw - 1,
data.shape[2] + sw - 1, data.shape[3])).astype(data.dtype)
data_pad[
pad:-pad, pad:-pad,
pad:-pad, :] = data #padded with two slices of zeros on all sides
batch = []
for s_idx in slice_idxs:
if slice_direction == 0:
#(s_idx+2)-2:(s_idx+2)+3 = s_idx:s_idx+5
x = data_pad[s_idx:s_idx + sw:, pad:-pad, pad:-pad, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
0, 3, 1, 2
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 1:
x = data_pad[pad:-pad, s_idx:s_idx + sw, pad:-pad, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
1, 3, 0, 2
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 2:
x = data_pad[pad:-pad, pad:-pad, s_idx:s_idx + sw, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
2, 3, 0, 1
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
data_dict = {
"data": np.array(batch), # (batch_size, channels, x, y, [z])
"seg": y
} # (batch_size, channels, x, y, [z])
return data_dict
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
else:
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
# NR_OF_GRADIENTS = 9 * 9
# NR_OF_GRADIENTS = 33
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT)
#Initialisation from U-Net Paper
def weights_init(m):
classname = m.__class__.__name__
# Do not use with batchnorm -> has to be adapted for batchnorm
if classname.find('Conv') != -1:
N = m.in_channels * m.kernel_size[0] * m.kernel_size[0]
std = math.sqrt(2. / N)
m.weight.data.normal_(0.0, std)
net.apply(weights_init)
# net = nn.DataParallel(net, device_ids=[0,1])
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE) #very slow (half speed of Adamax) -> strange
# scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
