'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:
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 ))
load_from_dir = [
'{}/{}/'.format(dir_cache, data_name_train),
'{}/{}/'.format(dir_cache, data_name_val)
]
res_name = '{}_{}'.format(base_name, data_name_train)
doit = do_training(res_dir, res_name, verbose)
if do_save:
#rr test
load_from_dir = [None]
res_dir = res_name = '/data/romain/HCPdata'
train_csv_file, val_csv_file = res_name + '/healthy_brain_ms_train_hcp400.csv', res_name + '/healthy_brain_ms_val_hcp200.csv'
doit = do_training(res_dir, res_name, verbose)
transforms = get_motion_transform()
train_csv_file, val_csv_file = get_train_and_val_csv(data_name_train,
root_fs=root_fs)
doit.set_data_loader(train_csv_file=train_csv_file,
val_csv_file=val_csv_file,
transforms=transforms,
batch_size=batch_size,
num_workers=num_workers,
save_to_dir=load_from_dir[0],
replicate_suj=20)
doit.save_to_dir(
load_from_dir
) # no more use, because it is much faster on cluster with job created by
elif do_eval:
doit.set_data_loader(batch_size=batch_size,
res_name = '{}_{}'.format(base_name, data_name_train)
load_from_dir = [None]
if 'cati' in data_name_train:
target_shape, mask_key = (182, 218, 182), 'brain'
print('adding a CropOrPad {} with mask key {}'.format(
target_shape, mask_key))
tc = [
CropOrPad(target_shape=target_shape, mode='mask', mask_key=mask_key),
]
else:
tc = None
doit = do_training(res_dir, res_name, verbose)
transforms = get_motion_transform('random_noise_1')
if do_eval:
val_csv_file = train_csv_file
doit.set_data_loader(train_csv_file=train_csv_file,
val_csv_file=val_csv_file,
transforms=transforms,
batch_size=batch_size,
num_workers=num_workers,
save_to_dir=load_from_dir[0],
replicate_suj=nb_replicate)
doit.set_model(par_model)
if do_eval:
doit.val_dataloader = doit.train_dataloader
options.seed), options.res_dir
index, nb_sample = np.int(options.index_num), np.int(options.nb_sample)
plot_volume, keep_all, keep_brain = options.plot_volume, options.keep_all, options.keep_brain
motion_type = options.motion_type
import os
resdir_mvt = res_dir + '/mvt_param/'
resdir_fig = res_dir + '/fig/'
try: #on cluster, all job are doing the mkdir at the same time ...
if not os.path.isdir(resdir_mvt): os.mkdir(resdir_mvt)
if not os.path.isdir(resdir_fig): os.mkdir(resdir_fig)
except:
pass
transfo = get_motion_transform(type=motion_type)
torch.manual_seed(seed)
np.random.seed(seed)
dir_img = get_parent_path([fin])[0]
fm = gfile(dir_img, '^mask', {"items": 1})
fp1 = gfile(dir_img, '^p1', {"items": 1})
fp2 = gfile(dir_img, '^p2', {"items": 1})
if len(fm) == 0: #may be in cat12 subdir (like for HCP)
fm = gfile(dir_img, '^brain_T1', {"items": 1})
#dir_cat = gdir(dir_img,'cat12')
#fm = gfile(dir_cat, '^mask_brain', {"items": 1})
#fp1 = gfile(dir_cat, '^p1', {"items": 1})
#fp2 = gfile(dir_cat, '^p2', {"items": 1})
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