def output_mean(self, z_vol, folder_split, vol_num):
err_avg_emb = dict()
for yi, y in enumerate(self.output_modalities):
z_idx = [outi for outi, out in enumerate(self.mm.model.outputs) if self.output_modalities[yi] in out.name]
y_synth = [z_vol[zi][:, 0] for zi in z_idx]
y_truth_mod = self.data.select_for_ids(y, [vol_num])
if y_truth_mod.shape[1] > 1:
sh = y_truth_mod.shape
y_truth_mod = y_truth_mod[:, sh[1] / 2:sh[1] / 2 + 1]
y_synth_mod = np.mean(y_synth, axis=0)
scipy.misc.imsave(folder_split + '/avg_emb_ims/im_avg_emb' + str(vol_num) + '_' + str(90) + '.png',
np.concatenate([y_synth_mod[90], y_truth_mod[90, 0]], axis=1))
err = ErrorMetrics(np.expand_dims(y_synth_mod, axis=1), y_truth_mod)
err_avg_emb[y] = err
return err_avg_emb
def test_at_split(self, split_dict, folder_split):
ids_train = split_dict['train']
ids_valid = split_dict['validation']
ids_test = split_dict['test']
all_ids = sorted(ids_train + ids_valid + ids_test)
num_vols = len(all_ids)
metrics = [
'MSE_NBG', 'MSE', 'SSIM_NBG', 'PSNR_NBG', 'SSIM', 'PSNR',
'MSE_NBG_AVG_EMB'
]
print('testing model on all volumes...')
# create files
files_embs = {}
for emb in range(self.mm.num_emb):
files = {}
for mod in self.output_modalities:
csv_header = '#,' + ','.join(
metrics[:-1]) + ', volume_type, MSE_NBG_AVG_EMB\n'
csv_file = folder_split + '/individual_results_emb_' + str(
emb) + '_mod_' + mod + '.csv'
#python 'open' can't make the directory, so invoke 'os' to create the folder
if not os.path.exists(folder_split):
os.makedirs(folder_split)
fd = open(csv_file, "w")
fd.write(csv_header)
files[mod] = fd
files_embs[emb] = files
print 'Created ' + str(len(files_embs)) + ' test files'
if not os.path.exists(folder_split + '/avg_emb_ims'):
os.makedirs(folder_split + '/avg_emb_ims')
for vol_num in range(num_vols):
if vol_num not in ids_test:
continue
print('testing model on volume ' + str(vol_num) + '...')
X = [
self.data.select_for_ids(mod, [vol_num])
for mod in self.input_modalities
]
Z = self.mm.model.predict(X)
# compute emb average
err_avg_emb = self.output_mean(Z, folder_split, vol_num)
for emb in range(self.mm.num_emb):
files = files_embs[emb]
for yi, y in enumerate(self.output_modalities):
z_idx = [
outi for outi, out in enumerate(self.mm.model.outputs)
if self.output_modalities[yi] in out.name
]
y_synth = [Z[zi][:, 0] for zi in z_idx]
y_truth = self.data.select_for_ids(y, [vol_num])
if y_truth.shape[1] > 1:
sh = y_truth.shape
y_truth = y_truth[:, sh[1] / 2:sh[1] / 2 + 1]
err = ErrorMetrics(y_synth[emb], y_truth)
vol_type = ''
if vol_num in ids_test:
vol_type = 'test'
if vol_num in ids_valid:
vol_type = 'validation'
if vol_num in ids_train:
vol_type = 'training'
pattern = "%d" + ", %.3f" * (len(metrics) -
1) + ', %s, %.3f\n'
new_row = pattern % tuple(
[vol_num] + list([err[em] for em in metrics[:-1]]) +
[vol_type] + [err_avg_emb[y]['MSE_NBG']])
files[y].write(new_row)
for files in files_embs.values():
for fd in files.values():
fd.close()
cb_X = [
self.data.select_for_ids(mod, all_ids, as_array=False)
for mod in self.input_modalities
]
cb_Y = [
self.data.select_for_ids(mod, all_ids, as_array=False)
for mod in self.output_modalities
]
cb = ImageSaveCallback(cb_X, cb_Y, None, None, folder_split,
self.output_modalities)
cb.model = self.mm.model
for vol in ids_test + [1, 8, 12, 13]:
cb.saveImage(vol, [70, 80, 90, 100],
folder_split + '/test_im' + str(vol), cb_X, cb_Y)