def test():
csv = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch/brats20_data.csv"
data, data_test = dataset.read_brats(csv)
modalities_to_use = {
BratsDataset.flair_idx: True,
BratsDataset.t1_idx: True,
BratsDataset.t2_idx: True,
BratsDataset.t1ce_idx: True
}
transforms = T.Compose([T.ToTensor()])
sampling_method = importlib.import_module(
"src.dataset.patching.random_tumor_distribution")
patch_size = (128, 128, 128)
n_patches = 10
data = data * n_patches
train_dataset = BratsDataset(data, modalities_to_use, sampling_method,
patch_size, transforms)
train_loader = DataLoader(dataset=train_dataset,
batch_size=16,
shuffle=True,
num_workers=1)
for idx, b_data, b_labels in train_loader:
print(b_data)
model_config = config.get_model_config()
dataset_config = config.get_dataset_config()
basic_config = config.get_basic_config()
unc_config = config.get_uncertainty_config()
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
task = "segmentation_task"
compute_metrics = False
flag_post_process = True
model, model_path = load_network(device, model_config, dataset_config,
model_config["network"])
setx = "train"
data, data_test = dataset.read_brats(dataset_config.get("train_csv"))
data.extend(data_test)
sampling = dataset_config.get("sampling_method").split(".")[-1]
uncertainty_flag = basic_config.getboolean("uncertainty_flag")
uncertainty_type = unc_config.get("uncertainty_type")
n_iterations = unc_config.getint("n_iterations")
use_dropout = unc_config.getboolean("use_dropout")
for idx in range(0, len(data)):
patch_size = data[idx].size
images = data[idx].load_mri_volumes(normalize=True)
brain_mask = data[idx].get_brain_mask()
from matplotlib import pyplot as plt
import numpy as np
def matplotlib_imshow(images, normalized=False):
img = torchvision.utils.make_grid(images, padding=10)
npimg = img.numpy()
# c h w
trans_im = np.transpose(npimg, (1, 2, 0))
plt.imshow(trans_im, cmap="gray")
plt.savefig("batch")
csv = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch/brats20_data.csv"
data, data_test = dataset.read_brats(csv)
modalities_to_use = {
BratsDataset.flair_idx: True,
BratsDataset.t1_idx: True,
BratsDataset.t2_idx: True,
BratsDataset.t1ce_idx: True
}
sampling_method = importlib.import_module(
"src.dataset.patching.random_tumor_distribution")
transforms = T.Compose([T.ToTensor()])
data_train = data * 100
batch_size = 4
train_dataset = BratsDataset(data_train, modalities_to_use, sampling_method,
(128, 128, 128), transforms)
task = f"segmentation_task/{setx}"
dataset_csv_path = f"{gen_path}/datasets/{setx}/no_patch/{csv}"
models = [
"model_1598550861", "model_1598639885", "model_1598640035",
"model_1598640005"
]
check_path = f"{gen_path}results/checkpoints/"
models = list(map(lambda x: os.path.join(check_path, x, task), models))
output_dir = os.path.join(check_path, f"{task}/ensemble_predictions/")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
data, _ = dataset.read_brats(dataset_csv_path, lgg_only=False)
for patient in tqdm(data, total=len(data), desc="Ensemble prediction"):
patient_name = patient.patient
seg_maps = read_preds_from_models(models, f"{patient_name}.nii.gz")
ensemble_map = majority_voting(seg_maps, patient.get_brain_mask())
output_path_with_name = os.path.join(output_dir,
f"{patient_name}.nii.gz")
save_segmask_as_nifi_volume(ensemble_map, patient.get_affine(),
output_path_with_name)
if compute_metrics:
patient_path = os.path.join(patient.data_path, patient.patch_name,
patient.seg)
basic_config = config.get_basic_config()
device = torch.device(
"cuda") if torch.cuda.is_available() else torch.device("cpu")
sampling = dataset_config.get("sampling_method").split(".")[-1]
models_gen_path = model_config.get("model_path")
task = "ensemble_segmentation"
compute_metrics = False
model_vnet = load_model_1598550861(models_gen_path)
model_2 = load_model_1598639885(models_gen_path)
model_3 = load_model_1598640035(models_gen_path)
model_4 = load_model_1598640005(models_gen_path)
data, _ = dataset.read_brats(dataset_config.get("val_csv"))
for idx in range(0, len(data)):
results = {}
images = data[idx].load_mri_volumes(normalize=True)
x_1, x_2, y_1, y_2, z_1, z_2, images, brain_mask, patch_size = crop_no_patch(
data[idx].size, images, data[idx].get_brain_mask(), sampling)
_, prediction_four_channels_1 = predict.predict(
model_vnet, images, device, False)
_, prediction_four_channels_2 = predict.predict(
model_2, images, device, False)
_, prediction_four_channels_3 = predict.predict(
model_3, images, device, False)
del self.patches_by_patient[patient]
yield batch_indices
def __len__(self):
return (len(self.dataset_indices) + self.batch_size -
1) // self.batch_size
if __name__ == "__main__":
from src.dataset.utils import dataset
from torchvision import transforms as T
import importlib
data, _ = dataset.read_brats(
"/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/random_tumor_distribution/brats20_data.csv"
)
data_train = data[:40]
data_val = data[:40]
modalities_to_use = {
BratsDataset.flair_idx: True,
BratsDataset.t1_idx: True,
BratsDataset.t2_idx: True,
BratsDataset.t1ce_idx: True
}
sampling_method = importlib.import_module(
"src.dataset.patching.random_tumor_distribution")
dataset = BratsDataset(data_train, modalities_to_use, sampling_method,
(64, 64, 64), T.Compose([T.ToTensor()]))
sampler = BratsPatchSampler(dataset, n_patients=2, n_samples=3)
patch_size = config.patch_size
tensorboard_logdir = basic_config.get("tensorboard_logs")
checkpoint_path = model_config.get("checkpoint")
batch_size = dataset_config.getint("batch_size")
n_patches = dataset_config.getint("n_patches")
n_classes = dataset_config.getint("classes")
loss = model_config.get("loss")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
"cpu")
logger.info(f"Device: {device}")
######## DATASET
logger.info("Creating Dataset...")
data, _ = dataset.read_brats(dataset_config.get("train_csv"),
lgg_only=dataset_config.getboolean("lgg_only"))
data_train, data_val = train_val_split(data, val_size=0.2)
data_train = data_train * n_patches
data_val = data_val * n_patches
n_modalities = dataset_config.getint("n_modalities") # like color channels
sampling_method = importlib.import_module(
dataset_config.get("sampling_method"))
transform = transforms.Compose([
color_augmentations.RandomIntensityShift(),
color_augmentations.RandomIntensityScale(),
spatial_augmentations.RandomMirrorFlip(p=0.5),
spatial_augmentations.RandomRotation90(p=0.5)
])
def unnorm(data, epsilon=1e-8):
non_zero = data[data > 0.0]
mean = non_zero.mean()
std = non_zero.std() + epsilon
out = data * std + mean
out[data == 0] = 0
return out
dataset_path = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch"
train_csv = os.path.join(dataset_path, "brats20_data.csv")
print("Loading dataset")
data, data_test = read_brats(train_csv)
data = data_test
sampling_method = importlib.import_module(
"src.dataset.patching.binary_distribution")
compute_patch = True
patch_size = (64, 64, 64)
batch_size = 4
print("Creating Dataset")
train_dataset = BratsDataset(data,
sampling_method,
patch_size,
compute_patch=compute_patch)
train_loader = DataLoader(dataset=train_dataset,
data = np.array(data)
for index, patient_patch in enumerate(data):
patient = patient_patch.patient
grade = patient_patch.grade
if grade == "LGG":
add_patch(patches_by_patient_lgg, index, patient)
elif grade == "HGG":
add_patch(patches_by_patient_hgg, index, patient)
else:
print("Unknown grade")
train_patients_lgg, val_patients_lgg = get_split_random(
data, patches_by_patient_lgg, val_size)
train, val = get_split_random(data, patches_by_patient_hgg, val_size)
train.extend(train_patients_lgg)
val.extend(val_patients_lgg)
return train, val
if __name__ == "__main__":
csv_path = "/Users/lauramora/Documents/MASTER/TFM/Data/2020/train/no_patch/brats20_data.csv"
data, _ = dataset.read_brats(csv_path)
train, val = train_val_split(data)
print()
