def get_dataset_from_option(options):
fin = options.image_in
dir_sample = options.sample_dir
add_affine_zoom, add_affine_rot = options.add_affine_zoom, options.add_affine_rot
batch_size, num_workers = options.batch_size, options.num_workers
doit = do_training('/tmp/', 'not_use', verbose=True)
# adding transformation
tc = []
name_suffix = ''
#Attention pas de _ dans le name_suffix
if options.add_cut_mask > 0:
target_shape, mask_key = (182, 218, 182), 'brain'
tc = [
CropOrPad(target_shape=target_shape, mask_name=mask_key),
]
name_suffix += '_tCropBrain'
if add_affine_rot > 0 or add_affine_zoom > 0:
if add_affine_zoom == 0: add_affine_zoom = 1 #0 -> no affine so 1
tc.append(
RandomAffine(scales=(add_affine_zoom, add_affine_zoom),
degrees=(add_affine_rot, add_affine_rot),
image_interpolation=Interpolation.NEAREST))
name_suffix += '_tAffineS{}R{}'.format(add_affine_zoom, add_affine_rot)
# for hcp should be before RescaleIntensity
mask_brain = False
if options.add_mask_brain:
tc.append(ApplyMask(masking_method='brain'))
name_suffix += '_tMaskBrain'
mask_brain = True
if options.add_rescal_Imax:
tc.append(RescaleIntensity(percentiles=(0, 99)))
name_suffix += '_tRescale-0-99'
if options.add_elastic1:
tc.append(get_motion_transform(type='elastic1'))
name_suffix += '_tElastic1'
if options.add_bias:
tc.append(RandomBiasField())
name_suffix += '_tBias'
if len(name_suffix) == 0:
name_suffix = '_Raw'
target = None
if len(tc) == 0: tc = None
add_to_load, add_to_load_regexp = None, None
if len(dir_sample) > 0:
print('loading from {}'.format(dir_sample))
if options.add_orig:
add_to_load, add_to_load_regexp = 'original', 'notused'
data_name = get_parent_path(dir_sample)[1]
if mask_brain and 'hcp' in data_name:
add_to_load_regexp = 'brain_T'
if add_to_load is None:
add_to_load = 'brain'
else:
add_to_load += 'brain'
doit.set_data_loader(batch_size=batch_size,
num_workers=num_workers,
load_from_dir=dir_sample,
transforms=tc,
add_to_load=add_to_load,
add_to_load_regexp=add_to_load_regexp)
name_suffix = 'On_' + data_name + name_suffix
target = options.target #'ssim' #suppose that if from sample, it should be simulation so set target
else:
print('working on ')
for ff in fin:
print(ff)
doit.set_data_loader_from_file_list(fin,
transforms=tc,
batch_size=batch_size,
num_workers=num_workers,
mask_key=mask_key,
mask_regex='^mask')
return doit, name_suffix, target
'dropout': 0,
'batch_norm': True,
'drop_conv': 0.1,
'validation_droupout': False,
'in_size': in_size,
'cuda': cuda,
'max_epochs': max_epochs
}
#'conv_block':[8, 16, 32, 64, 128]
dir_cache = get_cache_dir(root_fs=root_fs)
#load_from_dir = ['{}/{}/'.format(dir_cache, data_name_train), '{}/{}/'.format(dir_cache, data_name_val)]
res_name = '{}_{}'.format(base_name, data_name_train)
load_from_dir = [None]
doit = do_training(res_dir, res_name, verbose)
#transforms = get_motion_transform('random_noise_1')
transforms = get_motion_transform('AffFFT_random_noise')
if do_eval:
from torchio.transforms import CropOrPad, RandomAffine, RescaleIntensity, ApplyMask, RandomBiasField, RandomNoise, \
Interpolation, RandomAffineFFT
from utils_file import get_parent_path, gfile, gdir
from utils import get_ep_iter_from_res_name
tc = [RandomNoise(std=(0.020, 0.2))]
if add_affine_rot > 0 or add_affine_zoom > 0:
gpu = options.use_gpu
#let's force absolute path if not (weights[0] == '/') : weights1 = prefix + weights
cuda = True if gpu > 0 else False
#fixed param
batch_size, num_workers = 2, 0
fin = options.image_in
dir_sample = options.sample_dir
target_shape, mask_key = (182, 218, 182), 'brain'
tc = [
CropOrPad(target_shape=target_shape, mask_name=mask_key),
]
doit = do_training('/tmp/', 'not_use', verbose=True)
if len(dir_sample) > 0:
print('loading from {}'.format(dir_sample))
doit.set_data_loader(batch_size=batch_size,
num_workers=num_workers,
load_from_dir=dir_sample,
transforms=tc)
name += '_' + get_parent_path(dir_sample)[1]
else:
print('working on ')
for ff in fin:
print(ff)
doit.set_data_loader_from_file_list(fin,
transforms=tc,
