self.csv_out[i] = self._initialise_empty_csv(
1 + location_init[0, :].shape[-1])
else:
window[i] = np.asarray(window[i])
try:
assert window[i].ndim <= 2
except (TypeError, AssertionError):
tf.logging.error(
"The output you are trying to "
"save as csv is more than "
"bidimensional. Did you want "
"to save an image instead? "
"Put the keyword window "
"in the output dictionary"
" in your application file")
if window[i].ndim < 2:
window[i] = np.expand_dims(window[i], 0)
self.csv_out[i] = self._initialise_empty_csv(
n_channel=window[i][0].shape[-1] + location_init
[0, :].shape[-1])
for i in window:
if "window" in i:
self.image_out[i][
x_start:x_end, y_start:y_end, z_start:z_end, ...] = \
window[i][batch_id, ...]
else:
if isinstance(window[i], (list, tuple, np.ndarray)):
window[i] = np.asarray(window[i])
try:
assert window[i].ndim <= 2
except (TypeError, AssertionError):
tf.logging.error(
"The output you are trying to "
"save as csv is more than "
"bidimensional. Did you want "
"to save an image instead? "
"Put the keyword window "
"in the output dictionary"
" in your application file")
if window[i].ndim < 2:
window[i] = np.expand_dims(window[i], 0)
window[i] = np.asarray(window[i])
window_loc = np.concatenate([
window[i], np.tile(
location_init[batch_id, ...],
[window[i].shape[0], 1])], 1)
else:
window_loc = np.concatenate([
np.reshape(window[i], [1, 1]), np.tile(
location_init[batch_id, ...], [1, 1])], 1)
self.csv_out[i] = np.concatenate([self.csv_out[i],
window_loc], 0)
return True
After Change
window[i][batch_id, ...]
else:
if isinstance(window[i], (list, tuple, np.ndarray)):
window_save = np.squeeze(np.asarray(window[i][batch_id,
...]))
try:
assert window_save.ndim <= 2
except (TypeError, AssertionError):
tf.logging.error(
"The output you are trying to "
"save as csv is more than "
"bidimensional. Did you want "
"to save an image instead? "
"Put the keyword window "
"in the output dictionary"
" in your application file")
while window_save.ndim < 2:
window_save = np.expand_dims(window_save, 0)
window_save = np.asarray(window_save)
window_loc = np.concatenate([
window_save, np.tile(
location_init[batch_id, ...],
[window_save.shape[0], 1])], 1)
else:
window_loc = np.concatenate([
np.reshape(window[i][batch_id, ...], [1, 1]), \
np.tile(
location_init[batch_id, ...], [1, 1])], 1)
self.csv_out[i] = np.concatenate([self.csv_out[i],
window_loc], 0)
return True
