def test_cropped_image(self, full_image):
image_size_np = [1] + list(
reversed(self.image_size
)) if self.data_format == 'channels_first' else list(
reversed(self.image_size)) + [1]
labels_size_np = [self.num_labels] + list(
reversed(self.image_size
)) if self.data_format == 'channels_first' else list(
reversed(self.image_size)) + [self.num_labels]
predictions_full_size_np = [self.num_labels] + list(
full_image.shape[1:]
) if self.data_format == 'channels_first' else list(
full_image.shape[:-1]) + [self.num_labels]
cropped_inc = [
0
] + self.cropped_inc if self.data_format == 'channels_first' else self.cropped_inc + [
0
]
image_tiler = ImageTiler(full_image.shape, image_size_np, cropped_inc,
True, -1)
prediction_tiler = ImageTiler(predictions_full_size_np, labels_size_np,
cropped_inc, True, 0)
for image_tiler, prediction_tiler in zip(image_tiler,
prediction_tiler):
current_image = image_tiler.get_current_data(full_image)
feed_dict = {self.data_val: np.expand_dims(current_image, axis=0)}
run_tuple = self.sess.run((self.prediction_val, ),
feed_dict=feed_dict)
prediction = np.squeeze(run_tuple[0], axis=0)
image_tiler.set_current_data(current_image)
prediction_tiler.set_current_data(prediction)
return prediction_tiler.output_image
def test_cropped_image(self,
dataset_entry,
return_all_intermediate_embeddings=False):
generators = dataset_entry['generators']
full_image = generators['image']
# initialize sizes based on data_format
fetches = self.embeddings_cropped_val
if self.data_format == 'channels_first':
image_size_np = [1, self.num_frames] + list(
reversed(self.image_size))
full_image_size_np = list(full_image.shape)
embeddings_size_np = [self.num_embeddings, self.num_frames] + list(
reversed(self.image_size))
full_embeddings_size_np = [self.num_embeddings] + list(
full_image.shape[1:])
inc = [0, 0] + list(reversed(self.tiled_increment))
else:
image_size_np = list(reversed(self.image_size)) + [1]
full_image_size_np = list(full_image.shape)
embeddings_size_np = list(reversed(
self.image_size)) + [self.num_embeddings]
full_embeddings_size_np = list(
full_image.shape[0:2]) + [self.num_embeddings]
inc = list(reversed(self.tiled_increment)) + [0]
# initialize on image tiler for the input and a list of image tilers for the embeddings
image_tiler = ImageTiler(full_image_size_np, image_size_np, inc, True,
-1)
embeddings_tilers = tuple([
ImageTiler(full_embeddings_size_np, embeddings_size_np, inc, True,
-1) for _ in range(len(self.embeddings_cropped_val))
])
all_intermediate_embeddings = []
for state_index, all_tilers in enumerate(
zip(*((image_tiler, ) + embeddings_tilers))):
image_tiler = all_tilers[0]
embeddings_tilers = all_tilers[1:]
current_image = image_tiler.get_current_data(full_image)
feed_dict = {self.data_val: np.expand_dims(current_image, axis=0)}
run_tuple = self.sess.run(fetches, feed_dict)
image_tiler.set_current_data(current_image)
for i, embeddings_tiler in enumerate(embeddings_tilers):
embeddings = np.squeeze(run_tuple[i], axis=0)
if return_all_intermediate_embeddings and i == len(
embeddings_tilers) - 1:
all_intermediate_embeddings.append(embeddings)
embeddings_tiler.set_current_data(embeddings)
embeddings = [
embeddings_tiler.output_image
for embeddings_tiler in embeddings_tilers
]
if return_all_intermediate_embeddings:
return embeddings, all_intermediate_embeddings
else:
return embeddings
def test_cropped_image(self, dataset_entry):
"""
Perform inference on a dataset_entry with the validation network. Performs cropped prediction and merges outputs as maxima.
:param dataset_entry: A dataset entry from the dataset.
:return: input image (np.array), target heatmaps (np.array), predicted heatmaps, transformation (sitk.Transform)
"""
generators = dataset_entry['generators']
transformations = dataset_entry['transformations']
transformation = transformations['image']
image_size_np = [1] + list(reversed(self.image_size))
labels_size_np = [self.num_landmarks] + list(reversed(self.image_size))
full_image = generators['image']
landmarks = generators['landmarks']
image_tiler = ImageTiler(full_image.shape, image_size_np,
self.cropped_inc, True, -1)
landmark_tiler = LandmarkTiler(full_image.shape, image_size_np,
self.cropped_inc)
prediction_tiler = ImageTiler(
(self.num_landmarks, ) + full_image.shape[1:], labels_size_np,
self.cropped_inc, True, 0)
for image_tiler, landmark_tiler, prediction_tiler in zip(
image_tiler, landmark_tiler, prediction_tiler):
current_image = image_tiler.get_current_data(full_image)
current_landmarks = landmark_tiler.get_current_data(landmarks)
if self.has_validation_groundtruth:
feed_dict = {
self.data_val:
np.expand_dims(current_image, axis=0),
self.target_landmarks_val:
np.expand_dims(current_landmarks, axis=0)
}
run_tuple = self.sess.run(
(self.prediction_val, self.loss_val) +
self.val_loss_aggregator.get_update_ops(),
feed_dict=feed_dict)
else:
feed_dict = {
self.data_val: np.expand_dims(current_image, axis=0)
}
run_tuple = self.sess.run((self.prediction_val, ),
feed_dict=feed_dict)
prediction = np.squeeze(run_tuple[0], axis=0)
image_tiler.set_current_data(current_image)
prediction_tiler.set_current_data(prediction)
return image_tiler.output_image, prediction_tiler.output_image, transformation
def test_cropped_image(self, dataset_entry):
"""
Perform inference on a dataset_entry with the validation network. Performs cropped prediction and merges outputs as maxima.
:param dataset_entry: A dataset entry from the dataset.
:return: input image (np.array), target heatmaps (np.array), predicted heatmaps, transformation (sitk.Transform)
"""
generators = dataset_entry['generators']
transformations = dataset_entry['transformations']
transformation = transformations['image']
full_image = generators['image']
if self.has_validation_groundtruth:
landmarks = generators['landmarks']
image_size_for_tilers = np.minimum(full_image.shape[1:], list(reversed(self.max_image_size_for_cropped_test))).tolist()
image_size_np = [1] + image_size_for_tilers
labels_size_np = [self.num_landmarks] + image_size_for_tilers
image_tiler = ImageTiler(full_image.shape, image_size_np, self.cropped_inc, True, -1)
landmark_tiler = LandmarkTiler(full_image.shape, image_size_np, self.cropped_inc)
prediction_tiler = ImageTiler((self.num_landmarks,) + full_image.shape[1:], labels_size_np, self.cropped_inc, True, -np.inf)
prediction_local_tiler = ImageTiler((self.num_landmarks,) + full_image.shape[1:], labels_size_np, self.cropped_inc, True, -np.inf)
prediction_spatial_tiler = ImageTiler((self.num_landmarks,) + full_image.shape[1:], labels_size_np, self.cropped_inc, True, -np.inf)
for image_tiler, landmark_tiler, prediction_tiler, prediction_local_tiler, prediction_spatial_tiler in zip(image_tiler, landmark_tiler, prediction_tiler, prediction_local_tiler, prediction_spatial_tiler):
current_image = image_tiler.get_current_data(full_image)
if self.has_validation_groundtruth:
current_landmarks = landmark_tiler.get_current_data(landmarks)
(prediction, prediction_local, prediction_spatial), losses = self.call_model_and_loss(np.expand_dims(current_image, axis=0),
np.expand_dims(current_landmarks, axis=0), training=False)
self.loss_metric_logger_val.update_metrics(losses)
else:
prediction, prediction_local, prediction_spatial = self.model(np.expand_dims(current_image, axis=0), training=False)
image_tiler.set_current_data(current_image)
prediction_tiler.set_current_data(np.squeeze(prediction, axis=0))
prediction_local_tiler.set_current_data(np.squeeze(prediction_local, axis=0))
prediction_spatial_tiler.set_current_data(np.squeeze(prediction_spatial, axis=0))
return image_tiler.output_image, prediction_tiler.output_image, prediction_local_tiler.output_image, prediction_spatial_tiler.output_image, transformation
def test_cropped_image(self, dataset_entry):
generators = dataset_entry['generators']
transformations = dataset_entry['transformations']
heatmap_transform = transformations['image']
image_size_np = [1] + list(reversed(self.image_size))
heatmap_size_np = [self.num_landmarks] + list(reversed(
self.image_size))
full_image = generators['image']
landmarks = generators['landmarks']
image_tiler = ImageTiler(full_image.shape, image_size_np,
self.cropped_inc, True, -1)
landmark_tiler = LandmarkTiler(full_image.shape, image_size_np,
self.cropped_inc)
heatmap_tiler = ImageTiler(
(self.num_landmarks, ) + full_image.shape[1:], heatmap_size_np,
self.cropped_inc, True, 0)
for image_tiler, landmark_tiler, heatmap_tiler in zip(
image_tiler, landmark_tiler, heatmap_tiler):
current_image = image_tiler.get_current_data(full_image)
current_landmarks = landmark_tiler.get_current_data(landmarks)
feed_dict = {
self.image_val:
np.expand_dims(current_image, axis=0),
self.target_landmarks_val:
np.expand_dims(current_landmarks, axis=0)
}
run_tuple = self.sess.run(
(self.heatmaps_val, self.target_heatmaps_val, self.loss_val) +
self.val_loss_aggregator.get_update_ops(), feed_dict)
prediction = np.squeeze(run_tuple[0], axis=0)
image_tiler.set_current_data(current_image)
heatmap_tiler.set_current_data(prediction)
return image_tiler.output_image, heatmap_tiler.output_image, heatmap_transform
def test_cropped_image(self,
dataset_entry,
current_lstm_states,
return_all_intermediate_embeddings=False):
"""
Tests the whole image by cropping the input image.
:param dataset_entry: The dataset entry.
:param current_lstm_states: The current lstm states per tile.
:param return_all_intermediate_embeddings: If true, return embeddings for all tiles.
:return: merged embeddings, (list of all intermediate embeddings), list of next lstm states per tile
"""
generators = dataset_entry['generators']
full_image = generators['image']
# initialize sizes based on data_format
fetches = self.embeddings_cropped_val + self.lstm_output_states_cropped_val
if self.data_format == 'channels_first':
image_size_np = [1] + list(reversed(self.image_size))
full_image_size_np = list(full_image.shape)
embeddings_size_np = [self.num_embeddings] + list(
reversed(self.image_size))
full_embeddings_size_np = [self.num_embeddings] + list(
full_image.shape[1:])
inc = [0] + list(reversed(self.tiled_increment))
else:
image_size_np = list(reversed(self.image_size)) + [1]
full_image_size_np = list(full_image.shape)
embeddings_size_np = list(reversed(
self.image_size)) + [self.num_embeddings]
full_embeddings_size_np = list(
full_image.shape[0:2]) + [self.num_embeddings]
inc = list(reversed(self.tiled_increment)) + [0]
# initialize on image tiler for the input and a list of image tilers for the embeddings
image_tiler = ImageTiler(full_image_size_np, image_size_np, inc, True,
-1)
embeddings_tilers = tuple([
ImageTiler(full_embeddings_size_np, embeddings_size_np, inc, True,
-1) for _ in range(len(self.embeddings_cropped_val))
])
next_lstm_states = []
all_intermediate_embeddings = []
for state_index, all_tilers in enumerate(
zip(*((image_tiler, ) + embeddings_tilers))):
image_tiler = all_tilers[0]
embeddings_tilers = all_tilers[1:]
current_image = image_tiler.get_current_data(full_image)
feed_dict = {
self.data_cropped_val: np.expand_dims(current_image, axis=0)
}
if len(current_lstm_states) > 0:
for i in range(len(self.lstm_input_states_cropped_val)):
feed_dict[self.lstm_input_states_cropped_val[
i]] = current_lstm_states[state_index][i]
run_tuple = self.sess.run(fetches, feed_dict)
image_tiler.set_current_data(current_image)
for i, embeddings_tiler in enumerate(embeddings_tilers):
embeddings = np.squeeze(run_tuple[i], axis=0)
if return_all_intermediate_embeddings and i == len(
embeddings_tilers) - 1:
all_intermediate_embeddings.append(embeddings)
embeddings_tiler.set_current_data(embeddings)
current_next_lstm_states = run_tuple[
len(self.embeddings_cropped_val
):len(self.embeddings_cropped_val) +
len(self.lstm_output_states_cropped_val)]
next_lstm_states.append(current_next_lstm_states)
embeddings = [
embeddings_tiler.output_image
for embeddings_tiler in embeddings_tilers
]
if return_all_intermediate_embeddings:
return embeddings, all_intermediate_embeddings, next_lstm_states
else:
return embeddings, next_lstm_states
def test_cropped_image(self, dataset_entry):
"""
Perform inference on a dataset_entry with the validation network. Performs cropped prediction and merges outputs as maxima.
:param dataset_entry: A dataset entry from the dataset.
:return: input image (np.array), target heatmaps (np.array), predicted heatmaps, transformation (sitk.Transform)
"""
generators = dataset_entry['generators']
transformations = dataset_entry['transformations']
transformation = transformations['image']
full_image = generators['image']
if self.data_format == 'channels_first':
image_size_for_tilers = np.minimum(
full_image.shape[1:],
list(reversed(self.max_image_size_for_cropped_test))).tolist()
image_size_np = [1] + image_size_for_tilers
labels_size_np = [self.num_landmarks] + image_size_for_tilers
image_tiler = ImageTiler(full_image.shape, image_size_np,
self.cropped_inc, True, -1)
prediction_tiler = ImageTiler(
(self.num_landmarks, ) + full_image.shape[1:], labels_size_np,
self.cropped_inc, True, -np.inf)
prediction_local_tiler = ImageTiler(
(self.num_landmarks, ) + full_image.shape[1:], labels_size_np,
self.cropped_inc, True, -np.inf)
prediction_spatial_tiler = ImageTiler(
(self.num_landmarks, ) + full_image.shape[1:], labels_size_np,
self.cropped_inc, True, -np.inf)
else:
image_size_for_tilers = np.minimum(
full_image.shape[:-1],
list(reversed(self.max_image_size_for_cropped_test))).tolist()
image_size_np = image_size_for_tilers + [1]
labels_size_np = image_size_for_tilers + [self.num_landmarks]
image_tiler = ImageTiler(full_image.shape, image_size_np,
self.cropped_inc, True, -1)
prediction_tiler = ImageTiler(
full_image.shape[:-1] + (self.num_landmarks, ), labels_size_np,
self.cropped_inc, True, -np.inf)
prediction_local_tiler = ImageTiler(
full_image.shape[:-1] + (self.num_landmarks, ), labels_size_np,
self.cropped_inc, True, -np.inf)
prediction_spatial_tiler = ImageTiler(
full_image.shape[:-1] + (self.num_landmarks, ), labels_size_np,
self.cropped_inc, True, -np.inf)
for image_tiler, prediction_tiler, prediction_local_tiler, prediction_spatial_tiler in zip(
image_tiler, prediction_tiler, prediction_local_tiler,
prediction_spatial_tiler):
current_image = image_tiler.get_current_data(full_image)
predictions = []
predictions_local = []
predictions_spatial = []
for load_model_filename in self.load_model_filenames:
if len(self.load_model_filenames) > 1:
self.load_model(load_model_filename)
prediction, prediction_local, prediction_spatial = self.call_model(
np.expand_dims(current_image, axis=0))
predictions.append(prediction.numpy())
predictions_local.append(prediction_local.numpy())
predictions_spatial.append(prediction_spatial.numpy())
prediction = np.mean(predictions, axis=0)
prediction_local = np.mean(predictions_local, axis=0)
prediction_spatial = np.mean(predictions_spatial, axis=0)
image_tiler.set_current_data(current_image)
prediction_tiler.set_current_data(np.squeeze(prediction, axis=0))
prediction_local_tiler.set_current_data(
np.squeeze(prediction_local, axis=0))
prediction_spatial_tiler.set_current_data(
np.squeeze(prediction_spatial, axis=0))
return image_tiler.output_image, prediction_tiler.output_image, prediction_local_tiler.output_image, prediction_spatial_tiler.output_image, transformation