def load_data(datadir, img_size=416, crop_pct=0.875):
# Data loading code
print("Loading data")
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
scale_size = int(math.floor(img_size / crop_pct))
print("Loading training data")
st = time.time()
dataset = VOCDetection(datadir, image_set='train', download=True,
transforms=Compose([VOCTargetTransform(classes),
RandomResizedCrop((img_size, img_size), scale=(0.3, 1.0)),
RandomHorizontalFlip(),
convert_to_relative,
ImageTransform(transforms.ColorJitter(brightness=0.3, contrast=0.3,
saturation=0.1, hue=0.02)),
ImageTransform(transforms.ToTensor()), ImageTransform(normalize)]))
print("Took", time.time() - st)
print("Loading validation data")
st = time.time()
dataset_test = VOCDetection(datadir, image_set='val', download=True,
transforms=Compose([VOCTargetTransform(classes),
Resize(scale_size), CenterCrop(img_size),
convert_to_relative,
ImageTransform(transforms.ToTensor()), ImageTransform(normalize)]))
print("Took", time.time() - st)
print("Creating data loaders")
train_sampler = torch.utils.data.RandomSampler(dataset)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
return dataset, dataset_test, train_sampler, test_sampler
def main():
'''
Runs if the module is called as a script (eg: python3 dataset.py <dataset_name> <frametime>)
Executes self tests
'''
dataset_name = argv[1] if len(argv) > 1 else "clarissa"
wait = argv[2] if len(argv) > 2 else 10
print(
"Dataset module running as script, executing dataset unit test in {}".
format(dataset_name))
if dataset_name == "adni_slices":
unit_test(image_dataset=False,
adni=True,
hiponly=True,
plt_show=True,
nworkers=4,
e2d=True)
elif dataset_name == "clarissa_slices":
unit_test(image_dataset=False,
adni=False,
hiponly=True,
plt_show=True,
nworkers=4,
e2d=True)
elif dataset_name == "concat":
from transforms import ReturnPatch, Intensity, RandomFlip, Noisify, ToTensor, CenterCrop, RandomAffine
train_transforms = Compose([
ReturnPatch(patch_size=(32, 32)),
RandomAffine(),
Intensity(),
RandomFlip(modes=['horflip']),
Noisify(),
ToTensor()
]) #default is 32 32 patch
data_transforms = {
'train': train_transforms,
'validation': train_transforms,
'test': Compose([CenterCrop(160, 160),
ToTensor()])
}
mode = "train"
data, dsizes = get_data(data_transforms=data_transforms,
db="concat",
e2d=True,
batch_size=50 + 150 * (mode != "test"))
print("Dataset sizes: {}".format(dsizes))
for o in orientations:
batch = next(iter(data[o][mode]))
display_batch(batch, o + " concat " + mode + " data")
plt.show()
else:
view_volumes(dataset_name, wait=10)
print("All tests completed!")
def load_data(datadir, img_size=416, crop_pct=0.875):
# Data loading code
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
scale_size = int(math.floor(img_size / crop_pct))
print("Loading training data")
st = time.time()
train_set = VOCDetection(datadir,
image_set='train',
download=True,
transforms=Compose([
VOCTargetTransform(VOC_CLASSES),
RandomResizedCrop((img_size, img_size),
scale=(0.3, 1.0)),
RandomHorizontalFlip(), convert_to_relative,
ImageTransform(
transforms.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.1,
hue=0.02)),
ImageTransform(transforms.ToTensor()),
ImageTransform(normalize)
]))
print("Took", time.time() - st)
print("Loading validation data")
st = time.time()
val_set = VOCDetection(datadir,
image_set='val',
download=True,
transforms=Compose([
VOCTargetTransform(VOC_CLASSES),
Resize(scale_size),
CenterCrop(img_size), convert_to_relative,
ImageTransform(transforms.ToTensor()),
ImageTransform(normalize)
]))
print("Took", time.time() - st)
return train_set, val_set
def main():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--jaccard-weight', default=0.3, type=float)
arg('--device-ids',
type=str,
default='0',
help='For example 0,1 to run on two GPUs')
arg('--fold', type=int, help='fold', default=0)
arg('--root', default='runs/debug', help='checkpoint root')
arg('--batch-size', type=int, default=1)
arg('--limit', type=int, default=10000, help='number of images in epoch')
arg('--n-epochs', type=int, default=100)
arg('--lr', type=float, default=0.0001)
arg('--workers', type=int, default=12)
arg('--model',
type=str,
default='UNet',
choices=['UNet', 'UNet11', 'UNet16', 'AlbuNet34'])
args = parser.parse_args()
root = Path(args.root)
root.mkdir(exist_ok=True, parents=True)
num_classes = 1
if args.model == 'UNet':
model = UNet(num_classes=num_classes)
elif args.model == 'UNet11':
model = UNet11(num_classes=num_classes, pretrained=True)
elif args.model == 'UNet16':
model = UNet16(num_classes=num_classes, pretrained=True)
elif args.model == 'LinkNet34':
model = LinkNet34(num_classes=num_classes, pretrained=True)
elif args.model == 'AlbuNet':
model = AlbuNet34(num_classes=num_classes, pretrained=True)
else:
model = UNet(num_classes=num_classes, input_channels=3)
if torch.cuda.is_available():
if args.device_ids:
device_ids = list(map(int, args.device_ids.split(',')))
else:
device_ids = None
model = nn.DataParallel(model, device_ids=device_ids).cuda()
loss = LossBinary(jaccard_weight=args.jaccard_weight)
cudnn.benchmark = True
def make_loader(file_names, shuffle=False, transform=None, limit=None):
return DataLoader(dataset=AngyodysplasiaDataset(file_names,
transform=transform,
limit=limit),
shuffle=shuffle,
num_workers=args.workers,
batch_size=args.batch_size,
pin_memory=torch.cuda.is_available())
train_file_names, val_file_names = get_split(args.fold)
print('num train = {}, num_val = {}'.format(len(train_file_names),
len(val_file_names)))
train_transform = DualCompose([
SquarePaddingTraining(),
CenterCrop([574, 574]),
HorizontalFlip(),
VerticalFlip(),
Rotate(),
ImageOnly(RandomHueSaturationValue()),
ImageOnly(Normalize())
])
val_transform = DualCompose([
SquarePaddingTraining(),
CenterCrop([574, 574]),
ImageOnly(Normalize())
])
train_loader = make_loader(train_file_names,
shuffle=True,
transform=train_transform,
limit=args.limit)
valid_loader = make_loader(val_file_names, transform=val_transform)
root.joinpath('params.json').write_text(
json.dumps(vars(args), indent=True, sort_keys=True))
utils.train(init_optimizer=lambda lr: Adam(model.parameters(), lr=lr),
args=args,
model=model,
criterion=loss,
train_loader=train_loader,
valid_loader=valid_loader,
validation=validation_binary,
fold=args.fold)
def main(args):
print(args)
torch.backends.cudnn.benchmark = True
# Data loading
normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
crop_pct = 0.875
scale_size = int(math.floor(args.img_size / crop_pct))
train_loader, val_loader = None, None
if not args.test_only:
st = time.time()
train_set = VOCDetection(datadir,
image_set='train',
download=True,
transforms=Compose([
VOCTargetTransform(VOC_CLASSES),
RandomResizedCrop(
(args.img_size, args.img_size),
scale=(0.3, 1.0)),
RandomHorizontalFlip(),
convert_to_relative,
ImageTransform(
T.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.1,
hue=0.02)),
ImageTransform(T.ToTensor()),
ImageTransform(normalize)
]))
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=args.batch_size,
drop_last=True,
collate_fn=collate_fn,
sampler=RandomSampler(train_set),
num_workers=args.workers,
pin_memory=True,
worker_init_fn=worker_init_fn)
print(f"Training set loaded in {time.time() - st:.2f}s "
f"({len(train_set)} samples in {len(train_loader)} batches)")
if args.show_samples:
x, target = next(iter(train_loader))
plot_samples(x, target)
return
if not (args.lr_finder or args.check_setup):
st = time.time()
val_set = VOCDetection(datadir,
image_set='val',
download=True,
transforms=Compose([
VOCTargetTransform(VOC_CLASSES),
Resize(scale_size),
CenterCrop(args.img_size),
convert_to_relative,
ImageTransform(T.ToTensor()),
ImageTransform(normalize)
]))
val_loader = torch.utils.data.DataLoader(
val_set,
batch_size=args.batch_size,
drop_last=False,
collate_fn=collate_fn,
sampler=SequentialSampler(val_set),
num_workers=args.workers,
pin_memory=True,
worker_init_fn=worker_init_fn)
print(
f"Validation set loaded in {time.time() - st:.2f}s ({len(val_set)} samples in {len(val_loader)} batches)"
)
model = detection.__dict__[args.model](args.pretrained,
num_classes=len(VOC_CLASSES),
pretrained_backbone=True)
model_params = [p for p in model.parameters() if p.requires_grad]
if args.opt == 'sgd':
optimizer = torch.optim.SGD(model_params,
args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
elif args.opt == 'adam':
optimizer = torch.optim.Adam(model_params,
args.lr,
betas=(0.95, 0.99),
eps=1e-6,
weight_decay=args.weight_decay)
elif args.opt == 'radam':
optimizer = holocron.optim.RAdam(model_params,
args.lr,
betas=(0.95, 0.99),
eps=1e-6,
weight_decay=args.weight_decay)
elif args.opt == 'ranger':
optimizer = Lookahead(
holocron.optim.RAdam(model_params,
args.lr,
betas=(0.95, 0.99),
eps=1e-6,
weight_decay=args.weight_decay))
elif args.opt == 'tadam':
optimizer = holocron.optim.TAdam(model_params,
args.lr,
betas=(0.95, 0.99),
eps=1e-6,
weight_decay=args.weight_decay)
trainer = DetectionTrainer(model, train_loader, val_loader, None,
optimizer, args.device, args.output_file)
if args.resume:
print(f"Resuming {args.resume}")
checkpoint = torch.load(args.resume, map_location='cpu')
trainer.load(checkpoint)
if args.test_only:
print("Running evaluation")
eval_metrics = trainer.evaluate()
print(
f"Loc error: {eval_metrics['loc_err']:.2%} | Clf error: {eval_metrics['clf_err']:.2%} | "
f"Det error: {eval_metrics['det_err']:.2%}")
return
if args.lr_finder:
print("Looking for optimal LR")
trainer.lr_find(args.freeze_until, num_it=min(len(train_loader), 100))
trainer.plot_recorder()
return
if args.check_setup:
print("Checking batch overfitting")
is_ok = trainer.check_setup(args.freeze_until,
args.lr,
num_it=min(len(train_loader), 100))
print(is_ok)
return
print("Start training")
start_time = time.time()
trainer.fit_n_epochs(args.epochs, args.lr, args.freeze_until, args.sched)
total_time_str = str(
datetime.timedelta(seconds=int(time.time() - start_time)))
print(f"Training time {total_time_str}")
from prepare_train_val import get_split
from dataset import Polyp
import cv2
from models import UNet, UNet11, UNet16, AlbuNet34, MDeNet, EncDec, hourglass, MDeNetplus
import torch
from pathlib import Path
from tqdm import tqdm
import numpy as np
import utils
# import prepare_data
from torch.utils.data import DataLoader
from torch.nn import functional as F
from transforms import (ImageOnly, Normalize, CenterCrop, DualCompose)
img_transform = DualCompose([CenterCrop(512), ImageOnly(Normalize())])
def get_model(model_path, model_type):
"""
:param model_path:
:param model_type: 'UNet', 'UNet11', 'UNet16', 'AlbuNet34'
:return:
"""
num_classes = 1
if model_type == 'UNet11':
model = UNet11(num_classes=num_classes)
elif model_type == 'UNet16':
from pathlib import Path
from tqdm import tqdm
import numpy as np
import utils
# import prepare_data
from torch.utils.data import DataLoader
from torch.nn import functional as F
import torch
from transforms import (ImageOnly,
Normalize,
CenterCrop,
DualCompose)
img_transform = DualCompose([
CenterCrop(512),
ImageOnly(Normalize())
])
def get_model(model_path, model_type):
"""
:param model_path:
:param model_type: 'UNet', 'UNet11', 'UNet16', 'AlbuNet34'
:return:
"""
num_classes = 1
if model_type == 'UNet11':
import logging
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if __name__ == '__main__':
tbwiter = SummaryWriter(log_dir= config.LOG_DIR_PATH)
print("Initializing AlexNet model")
alexnet = alexnetmodel.AlexNet(num_classes = config.PARAMETERS['NUM_CLASSES'])
alexnet = torch.nn.parallel.DataParallel(alexnet, device_ids=config.DEVICE_IDS)
print("Initilaizing transfrmations to apply on the image")
transformations = tf.Compose([
CenterCrop(227),
# HorizontalFlip(),
ToTensor(),
Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
]
)
print("Generating train images and labels")
train_X, train_Y, enc = separate_dataset_and_classes()
print("Generating Validation images and labels")
val_X, val_Y = separate_classes_val_test(enc = enc)
print("Initilazing data loader for Alexnet")
train_dataset = alexnetdataloader.AlexNetDataLoader(train_X, train_Y, transform = transformations)
val_dataset = alexnetdataloader.AlexNetDataLoader(val_X, val_Y, None)
from train_results import TrainResults
from transforms import CenterCrop, ToTensor, Compose, CenterCrop, Resize, ToNumpy, ReturnPatch, RandomFlip, Intensity, Noisify, RandomAffine
import multiprocessing as mp
from utils import check_name, parse_argv, plots
plt.rcParams.update({'font.size': 16})
display_volume, train, volume, db_name, notest, study_ths, wait, finetune = parse_argv(argv)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
loss = DICELoss(apply_sigmoid=False)
metric = DICEMetric(apply_sigmoid=False)
#train_transforms = Compose([ReturnPatch(patch_size=(32, 32)), Intensity(), RandomFlip(modes=['horflip']), Noisify(), ToTensor()]) #default is 32 32 patch, arg* in sheet
train_transforms = Compose([ReturnPatch(patch_size=(32, 32)), RandomAffine(), Intensity(), RandomFlip(modes=['horflip']), Noisify(), ToTensor()])
data_transforms = {'train': train_transforms, 'validation': train_transforms, 'test': Compose([CenterCrop(160, 160), ToTensor()])}
print("Train transforms: {}".format(train_transforms))
bnames = []
'''
basename = "FLOAT0.01-DICE-120"
bnames.append(basename)
basename = "full-newdice-test" # trained in full data
bnames.append(basename)
basename = "tPATCH320.001-VOLDICE-400" # patch approach, 32, flip only, only positive patch
bnames.append(basename)
basename = "INTFLIP-PATCH320.001-400"
bnames.append(basename)
basename = "RES-INTFLIP-PATCH320.001-400"
bnames.append(basename)
def test(FLG):
device = torch.device('cuda:{}'.format(FLG.devices[0]))
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(FLG.devices[0])
report = [ScoreReport() for _ in range(FLG.fold)]
overall_report = ScoreReport()
target_dict = np.load(pjoin(FLG.data_root, 'target_dict.pkl'))
with open(FLG.model + '_stat.pkl', 'rb') as f:
stat = pickle.load(f)
summary = Summary(port=10001, env=str(FLG.model) + 'CAM')
class Feature(object):
def __init__(self):
self.blob = None
def capture(self, blob):
self.blob = blob
if 'plane' in FLG.model:
model = Plane(len(FLG.labels), name=FLG.model)
elif 'resnet11' in FLG.model:
model = resnet11(len(FLG.labels), FLG.model)
elif 'resnet19' in FLG.model:
model = resnet19(len(FLG.labels), FLG.model)
elif 'resnet35' in FLG.model:
model = resnet35(len(FLG.labels), FLG.model)
elif 'resnet51' in FLG.model:
model = resnet51(len(FLG.labels), FLG.model)
else:
raise NotImplementedError(FLG.model)
model.to(device)
ad_h = []
nl_h = []
adcams = np.zeros((4, 3, 112, 144, 112), dtype="f8")
nlcams = np.zeros((4, 3, 112, 144, 112), dtype="f8")
sb = [9.996e-01, 6.3e-01, 1.001e-01]
for running_fold in range(FLG.fold):
_, validblock, _ = fold_split(
FLG.fold, running_fold, FLG.labels,
np.load(pjoin(FLG.data_root, 'subject_indices.npy')), target_dict)
validset = ADNIDataset(FLG.labels,
pjoin(FLG.data_root, FLG.modal),
validblock,
target_dict,
transform=transform_presets(FLG.augmentation))
validloader = DataLoader(validset, pin_memory=True)
epoch, _ = load_checkpoint(model, FLG.checkpoint_root, running_fold,
FLG.model, None, True)
model.eval()
feature = Feature()
def hook(mod, inp, oup):
return feature.capture(oup.data.cpu().numpy())
_ = model.layer4.register_forward_hook(hook)
fc_weights = model.fc.weight.data.cpu().numpy()
transformer = Compose([CenterCrop((112, 144, 112)), ToFloatTensor()])
im, _ = original_load(validblock, target_dict, transformer, device)
for image, target in validloader:
true = target
npatches = 1
if len(image.shape) == 6:
_, npatches, c, x, y, z = image.shape
image = image.view(-1, c, x, y, z)
target = torch.stack([target
for _ in range(npatches)]).squeeze()
image = image.cuda(device, non_blocking=True)
target = target.cuda(device, non_blocking=True)
output = model(image)
if npatches == 1:
score = F.softmax(output, dim=1)
else:
score = torch.mean(F.softmax(output, dim=1),
dim=0,
keepdim=True)
report[running_fold].update_true(true)
report[running_fold].update_score(score)
overall_report.update_true(true)
overall_report.update_score(score)
print(target)
if FLG.cam:
s = 0
cams = []
if target[0] == 0:
s = score[0][0]
#s = s.cpu().numpy()[()]
cams = adcams
else:
sn = score[0][1]
#s = s.cpu().numpy()[()]
cams = nlcams
if s > sb[0]:
cams[0] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
cams[0],
s,
num_images=5)
elif s > sb[1]:
cams[1] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
cams[1],
s,
num_images=5)
elif s > sb[2]:
cams[2] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
cams[2],
s,
num_images=5)
else:
cams[3] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
cams[3],
s,
num_images=5)
#ad_h += [s]
#nl_h += [sn]
print('At {}'.format(epoch))
print(
metrics.classification_report(report[running_fold].y_true,
report[running_fold].y_pred,
target_names=FLG.labels,
digits=4))
print('accuracy {}'.format(report[running_fold].accuracy))
#print(np.histogram(ad_h))
#print(np.histogram(nl_h))
print('over all')
print(
metrics.classification_report(overall_report.y_true,
overall_report.y_pred,
target_names=FLG.labels,
digits=4))
print('accuracy {}'.format(overall_report.accuracy))
with open(FLG.model + '_stat.pkl', 'wb') as f:
pickle.dump(report, f, pickle.HIGHEST_PROTOCOL)
def test_cam():
with open(FLG.model + '_stat.pkl', 'rb') as f:
stat = pickle.load(f)
summary = Summary(port=10001, env=str(FLG.model) + 'CAM')
class Feature(object):
def __init__(self):
self.blob = None
def capture(self, blob):
self.blob = blob
# TODO: create model
device = torch.device('cuda:{}'.format(FLG.devices[0]))
torch.set_grad_enabled(False)
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(FLG.devices[0])
report = [ScoreReport() for _ in range(FLG.fold)]
target_dict = np.load(pjoin(FLG.data_root, 'target_dict.pkl'))
model = Plane(len(FLG.labels), name=FLG.model)
model.to(device)
transformer = Compose([CenterCrop((112, 144, 112)), ToFloatTensor()])
def original_load(validblock):
originalset = ADNIDataset(FLG.labels,
pjoin(FLG.data_root, 'spm_normalized'),
validblock,
target_dict,
transform=transformer)
originloader = DataLoader(originalset, pin_memory=True)
for image, target in originloader:
if len(image.shape) == 6:
_, npatches, c, x, y, z = image.shape
image = image.view(-1, c, x, y, z)
target = torch.stack([target
for _ in range(npatches)]).squeeze()
image = image.cuda(device, non_blocking=True)
target = target.cuda(device, non_blocking=True)
break
return image, target
hadcams = np.zeros((3, 112, 144, 112), dtype="f8")
madcams = np.zeros((3, 112, 144, 112), dtype="f8")
sadcams = np.zeros((3, 112, 144, 112), dtype="f8")
zadcams = np.zeros((3, 112, 144, 112), dtype="f8")
nlcams = np.zeros((4, 3, 112, 144, 112), dtype="f8")
sb = [4.34444371e-16, 1.67179015e-18, 4.08813312e-23]
#im, _ = original_load(validblock)
for running_fold in range(FLG.fold):
# validset
_, validblock, _ = fold_split(
FLG.fold, running_fold, FLG.labels,
np.load(pjoin(FLG.data_root, 'subject_indices.npy')), target_dict)
validset = ADNIDataset(FLG.labels,
pjoin(FLG.data_root, FLG.modal),
validblock,
target_dict,
transform=transformer)
validloader = DataLoader(validset, pin_memory=True)
load_checkpoint(model,
FLG.checkpoint_root,
running_fold,
FLG.model,
epoch=None,
is_best=True)
model.eval()
feature = Feature()
def hook(mod, inp, oup):
return feature.capture(oup.data.cpu().numpy())
_ = model.layer4.register_forward_hook(hook)
fc_weights = model.fc.weight.data.cpu().numpy()
im, _ = original_load(validblock)
ad_s = []
for image, target in validloader:
true = target
npatches = 1
if len(image.shape) == 6:
_, npatches, c, x, y, z = image.shape
image = image.view(-1, c, x, y, z)
target = torch.stack([target
for _ in range(npatches)]).squeeze()
image = image.cuda(device, non_blocking=True)
target = target.cuda(device, non_blocking=True)
#_ = model(image.view(*image.shape))
output = model(image)
if npatches == 1:
score = F.softmax(output, dim=1)
else:
score = torch.mean(F.softmax(output, dim=1),
dim=0,
keepdim=True)
sa = score[0][1]
#name = 'k'+str(running_fold)
sa = sa.cpu().numpy()[()]
print(score, score.shape)
if true == torch.tensor([0]):
if sa > sb[0]:
hadcams = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
hadcams,
sa,
num_images=5)
elif sa > sb[1]:
madcams = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
madcams,
sa,
num_images=5)
elif sa > sb[2]:
sadcams = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
sadcams,
sa,
num_images=5)
else:
zadcams = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
zadcams,
sa,
num_images=5)
else:
if s > sb[0]:
nlcams[0] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
nlcams[0],
sr,
num_images=5)
elif sr > sb[1]:
nlcams[1] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
nlcams[1],
sr,
num_images=5)
elif sr > sb[2]:
nlcams[2] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
nlcams[2],
sr,
num_images=5)
else:
nlcams[3] = summary.cam3d(FLG.labels[target],
im,
feature.blob,
fc_weights,
target,
nlcams[3],
sr,
num_images=5)
ad_s += [sr]
print('histogram', np.histogram(ad_s))
def run_once(volpath, models):
'''
Runs our best model in a provided volume and saves mask,
In a self contained matter
'''
print(
"\nALPHA VERSION: For this version of this code, the provided volume should return slices on the following way for optimal performance:"
)
print("volume[0, :, :] sagital, eyes facing down")
print("volume[:, 0, :] coronal")
print("volume[:, :, 0] axial, with eyes facing right\n")
begin = time.time()
save_path = volpath + "_e2dhipmask.nii.gz"
device = get_device()
orientations = ["sagital", "coronal", "axial"]
CROP_SHAPE = 160
slice_transform = Compose([CenterCrop(CROP_SHAPE, CROP_SHAPE), ToTensor()])
sample_v = normalizeMri(nib.load(volpath).get_fdata().astype(np.float32))
shape = sample_v.shape
sum_vol_total = torch.zeros(shape)
for o, model in models.items():
model.eval()
model.to(device)
print("Performing segmentation...")
for i, o in enumerate(orientations):
try:
slice_shape = myrotate(get_slice(sample_v, 0, o), 90).shape
for j in range(shape[i]):
# E2D
ts = np.zeros((3, slice_shape[0], slice_shape[1]),
dtype=np.float32)
for ii, jj in enumerate(range(j - 1, j + 2)):
if jj < 0:
jj = 0
elif jj == shape[i]:
jj = shape[i] - 1
if i == 0:
ts[ii] = myrotate(sample_v[jj, :, :], 90)
elif i == 1:
ts[ii] = myrotate(sample_v[:, jj, :], 90)
elif i == 2:
ts[ii] = myrotate(sample_v[:, :, jj], 90)
s, _ = slice_transform(ts, ts[1]) # work around, no mask
s = s.to(device)
probs = models[o](s.unsqueeze(0))
cpup = probs.squeeze().detach().cpu()
finalp = torch.from_numpy(myrotate(
cpup.numpy(), -90)).float() # back to volume orientation
# Add to final consensus volume, uses original orientation/shape
if i == 0:
toppad = shape[1] // 2 - CROP_SHAPE // 2
sidepad = shape[2] // 2 - CROP_SHAPE // 2
tf = 1 if shape[1] % 2 == 1 else 0
sf = 1 if shape[2] % 2 == 1 else 0
pad = F.pad(
finalp,
(sidepad + sf, sidepad, toppad, toppad + tf)) / 3
sum_vol_total[j, :, :] += pad
elif i == 1:
toppad = shape[0] // 2 - CROP_SHAPE // 2
sidepad = shape[2] // 2 - CROP_SHAPE // 2
tf = 1 if shape[0] % 2 == 1 else 0
sf = 1 if shape[2] % 2 == 1 else 0
pad = F.pad(
finalp,
(sidepad + sf, sidepad, toppad, toppad + tf)) / 3
sum_vol_total[:, j, :] += pad
elif i == 2:
toppad = shape[0] // 2 - CROP_SHAPE // 2
sidepad = shape[1] // 2 - CROP_SHAPE // 2
tf = 1 if shape[0] % 2 == 1 else 0
sf = 1 if shape[1] % 2 == 1 else 0
pad = F.pad(
finalp,
(sidepad + sf, sidepad, toppad, toppad + tf)) / 3
sum_vol_total[:, :, j] += pad
except Exception as e:
print(
"Error: {}, make sure your data is ok, please contact author https://github.com/dscarmo"
.format(e))
traceback.print_exc()
quit()
final_nppred = get_largest_components(sum_vol_total.numpy(), mask_ths=0.5)
print("Processing took {}s".format(time.time() - begin))
print("Saving to {}".format(save_path))
nib.save(nib.nifti1.Nifti1Image(final_nppred, None), save_path)
return sample_v, final_nppred
import network, dataset, criterion, transform
from dataset import VOC12
from network import PSPNet, FCN8, SegNet, FCN8s
from criterion import CrossEntropyLoss2d
from transform import Relabel, ToLabel, Colorize
import deeplab_resnet
import torch.nn.functional as F
from accuracy_metrics import pixel_accuracy, mean_accuracy, mean_IU
NUM_CHANNELS = 3
NUM_CLASSES = 22 #6 for brats
color_transform = Colorize()
image_transform = ToPILImage()
input_transform = Compose([
CenterCrop(256),
#Scale(240),
ToTensor(),
Normalize([.485, .456, .406], [.229, .224, .225]),
])
input_transform1 = Compose([
CenterCrop(256),
#Scale(136),
#ToTensor(),
])
target_transform = Compose([
CenterCrop(256),
#Scale(240),
ToLabel(),
