def train(self):
if self.conf.reload_step > 0:
self.reload(self.conf.reload_step)
if self.conf.data_type == '2D':
train_reader = H5DataLoader(self.conf.data_dir +
self.conf.train_data)
valid_reader = H5DataLoader(self.conf.data_dir +
self.conf.valid_data)
else:
train_reader = H53DDataLoader(
self.conf.data_dir + self.conf.train_data, self.input_shape)
valid_reader = H53DDataLoader(
self.conf.data_dir + self.conf.valid_data, self.input_shape)
for epoch_num in range(self.conf.max_step):
if epoch_num % self.conf.test_interval == 0:
inputs, annotations = valid_reader.next_batch(self.conf.batch)
feed_dict = {
self.inputs: inputs,
self.annotations: annotations
}
loss, summary, accuracy, dice_accuracy = self.sess.run(
[
self.loss_op, self.valid_summary, self.accuracy_op,
self.dice_accuracy_op
],
feed_dict=feed_dict)
self.save_summary(summary, epoch_num + self.conf.reload_step)
print('----valid loss', loss)
print('----valid accuracy', accuracy)
print('----valid dice accuracy', dice_accuracy)
elif epoch_num % self.conf.summary_interval == 0:
inputs, annotations = train_reader.next_batch(self.conf.batch)
feed_dict = {
self.inputs: inputs,
self.annotations: annotations
}
loss, _, summary = self.sess.run(
[self.loss_op, self.train_op, self.train_summary],
feed_dict=feed_dict)
self.save_summary(summary, epoch_num + self.conf.reload_step)
else:
inputs, annotations = train_reader.next_batch(self.conf.batch)
feed_dict = {
self.inputs: inputs,
self.annotations: annotations
}
loss, summary, _, accuracy, dice_accuracy= self.sess.run(
[self.loss_op, self.train_summary, self.train_op, self.accuracy_op, \
self.dice_accuracy_op], feed_dict=feed_dict)
print('----train loss', loss)
print('----train accuracy', accuracy)
print('----train dice accuracy', dice_accuracy)
self.save_summary(summary, epoch_num + self.conf.reload_step)
if epoch_num % self.conf.save_interval == 0:
self.save(epoch_num + self.conf.reload_step)
def predict(self, test_step, data_index, sub_batch_index, test_type):
print('---->predicting ', test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
if test_type == 'valid':
test_reader = H53DDataLoader(self.conf.data_dir +
self.conf.valid_data,
self.input_shape,
is_train=False)
elif test_type == 'predict':
test_reader = H53DDataLoader(self.conf.data_dir +
self.conf.test_data,
self.input_shape,
is_train=False)
else:
print("invalid type")
return
predict_generator = test_reader.generate_data(
data_index, sub_batch_index,
[self.conf.depth, self.conf.height, self.conf.width],
[self.conf.d_gap, self.conf.w_gap, self.conf.h_gap])
s_predictions, s_labels = self.predict_func(predict_generator)
# process label and data
# process label
expand_annotations = tf.expand_dims(s_labels,
-1,
name='s_labels/expand_dims')
one_hot_annotations = tf.squeeze(expand_annotations,
axis=[self.channel_axis],
name='s_labels/squeeze')
one_hot_annotations = tf.one_hot(one_hot_annotations,
depth=self.conf.class_num,
axis=self.channel_axis,
name='s_labels/one_hot')
# process data
decoded_predictions = tf.argmax(s_predictions,
self.channel_axis,
name='accuracy/decode_pred')
correct_prediction = tf.equal(tf.cast(s_labels, tf.int64),
decoded_predictions,
name='accuracy/predict_correct_pred')
#accuracy
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32,
name='accuracy/cast'),
name='accuracy/accuracy_op')
#loss
loss = tf.reduce_mean(
tf.losses.softmax_cross_entropy(one_hot_annotations,
s_predictions))
return accuracy_op, loss
def test(self):
sig = True
print('---->testing ', self.conf.test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
if self.conf.data_type == '2D':
test_reader = H5DataLoader(
self.conf.data_dir + self.conf.test_data, False)
else:
test_reader = H53DDataLoader(
self.conf.data_dir + self.conf.test_data, self.input_shape)
self.sess.run(tf.local_variables_initializer())
count = 0
losses = []
accuracies = []
m_ious = []
while sig:
sig, inputs, labels = test_reader.next_batch(self.conf.batch)
if inputs is None:
break
feed_dict = {self.inputs: inputs, self.labels: labels}
loss, accuracy, m_iou, _ = self.sess.run(
[self.loss_op, self.accuracy_op, self.m_iou, self.miou_op],
feed_dict=feed_dict)
print('values----->', loss, accuracy, m_iou)
count += 1
losses.append(loss)
accuracies.append(accuracy)
m_ious.append(m_iou)
print('Loss: ', np.mean(losses))
print('Accuracy: ', np.mean(accuracies))
print('M_iou: ', m_ious[-1])
def test(self):
print('---->testing ', self.conf.test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
data_reader = H53DDataLoader(self.conf.data_dir, self.conf.patch_size,
self.conf.validation_id,
self.conf.overlap_stepsize)
self.sess.run(tf.local_variables_initializer())
# count = 0
losses = []
accuracies = []
for i in range(data_reader.num_of_valid_patches):
inputs, annotations, _ = data_reader.valid_next_batch()
# if inputs.shape[0] < self.conf.batch:
# break
# pseudo_inputs = np.zeros((1,32,32,32,2), dtype=np.float32)
# pseudo_labels = np.zeros((1,32,32,32), dtype=np.float32)
# CUT_MEAN = np.array((100.913811861, 121.187003401), dtype=np.float32)
# pseudo_inputs -= CUT_MEAN
feed_dict = {self.inputs: inputs, self.annotations: annotations}
loss, accuracy = self.sess.run([self.loss_op, self.accuracy_op],
feed_dict=feed_dict)
print('values----->', loss, accuracy)
# count += 1
losses.append(loss)
accuracies.append(accuracy)
print('Loss: ', np.mean(losses))
print('Accuracy: ', np.mean(accuracies))
def predict(self):
print('---->predicting ', self.conf.test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
data_reader = H53DDataLoader(self.conf.data_dir, self.conf.patch_size, self.conf.validation_id, self.conf.overlap_stepsize)
self.sess.run(tf.local_variables_initializer())
predictions = {}
for i in range(data_reader.num_of_valid_patches):
inputs, annotations, location = data_reader.valid_next_batch()
# if inputs.shape[0] < self.conf.batch:
# break
feed_dict = {self.inputs: inputs, self.annotations: annotations}
preds = self.sess.run(self.softmax_predictions, feed_dict=feed_dict)
print('--->processing results for: ', location)
for j in range(self.conf.patch_size):
for k in range(self.conf.patch_size):
for l in range(self.conf.patch_size):
key = (location[0]+j, location[1]+k, location[2]+l)
if key not in predictions.keys():
predictions[key] = []
predictions[key].append(preds[0, j, k, l, :])
print('--->averaging results')
results = np.zeros((data_reader.t_d, data_reader.t_h, data_reader.t_w, self.conf.class_num), dtype=np.float32)
for key in predictions.keys():
results[key[0], key[1], key[2]] = np.mean(predictions[key], axis=0)
print('--->saving results')
save_filename = 'results' + str(self.conf.test_step) + '_sub' + str(self.conf.validation_id) + '_overlap' + str(self.conf.overlap_stepsize) +'.npy'
save_file = os.path.join(self.conf.savedir, save_filename)
np.save(save_file, results)
def predict(self):
print('---->predicting ', self.conf.test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
if self.conf.data_type == '2D':
test_reader = H5DataLoader(
self.conf.data_dir+self.conf.test_data, False)
else:
test_reader = H53DDataLoader(
self.conf.data_dir+self.conf.test_data, self.input_shape)
predictions = []
while True:
inputs, labels = test_reader.next_batch(self.conf.batch)
if inputs.shape[0] < self.conf.batch:
break
feed_dict = {self.inputs: inputs, self.labels: labels}
predictions.append(self.sess.run(
self.decoded_preds, feed_dict=feed_dict))
print('----->saving predictions')
for index, prediction in enumerate(predictions):
for i in range(prediction.shape[0]):
imsave(prediction[i], self.conf.sampledir +
str(index*prediction.shape[0]+i)+'.png')
def predict(self,test_step,data_index,sub_batch_index, test_type):
print('---->predicting ', self.conf.test_step)
if test_type == 'valid':
test_reader = H53DDataLoader(
self.conf.data_dir+self.conf.valid_data, self.input_shape,is_train=False)
elif test_type == 'predict':
print("start get predict data")
test_reader = H53DDataLoader(
self.conf.data_dir+self.conf.test_data, self.input_shape,is_train=False)
print("finish get data")
else:
print("invalid type")
return
predict_generator = test_reader.generate_data(data_index,sub_batch_index,[self.conf.depth,self.conf.height,self.conf.width],[self.conf.d_gap,self.conf.w_gap,self.conf.h_gap])
s_predictions,s_labels = self.predict_func(predict_generator)
print("finish concate ---------+++++")
# process label and data
# process label
expand_annotations = tf.expand_dims(
s_labels, -1, name='s_labels/expand_dims')
one_hot_annotations = tf.squeeze(
expand_annotations, axis=[self.channel_axis],
name='s_labels/squeeze')
one_hot_annotations = tf.one_hot(
one_hot_annotations, depth=self.conf.class_num,
axis=self.channel_axis, name='s_labels/one_hot')
# process data
decoded_predictions = tf.argmax(
s_predictions, self.channel_axis, name='accuracy/decode_pred')
correct_prediction = tf.equal(
tf.cast(s_labels,tf.int64), decoded_predictions,
name='accuracy/predict_correct_pred')
#accuracy
accuracy_op = tf.reduce_mean(
tf.cast(correct_prediction, tf.float32, name='accuracy/cast'),
name='accuracy/accuracy_op')
#loss
loss = tf.reduce_mean(tf.losses.softmax_cross_entropy(one_hot_annotations, s_predictions))
#dice ratio
DiceRatio, sublist = ops.dice_accuracy(decoded_predictions, s_labels, self.conf.class_num)
print("session image labels-------vvvvvvv")
accuracy, Loss, diceRatio, Sublist = self.sess.run([accuracy_op, loss,DiceRatio,sublist])
del DiceRatio, sublist
gc.collect()
return accuracy, Loss, diceRatio, Sublist
def train(self):
if self.conf.reload_step > 0:
self.reload(self.conf.reload_step)
if self.conf.data_type == '2D':
train_reader = H5DataLoader(
self.conf.data_dir+self.conf.train_data)
valid_reader = H5DataLoader(
self.conf.data_dir+self.conf.valid_data)
else:
train_reader = H53DDataLoader(
self.conf.data_dir+self.conf.train_data, self.input_shape)
valid_reader = H53DDataLoader(
self.conf.data_dir+self.conf.valid_data, self.input_shape)
for epoch_num in range(self.conf.max_step+1):
if epoch_num and epoch_num % self.conf.test_interval == 0:
inputs, labels = valid_reader.next_batch(self.conf.batch)
feed_dict = {self.inputs: inputs,
self.labels: labels}
loss, summary = self.sess.run(
[self.loss_op, self.valid_summary], feed_dict=feed_dict)
self.save_summary(summary, epoch_num+self.conf.reload_step)
print('----testing loss', loss)
if epoch_num and epoch_num % self.conf.summary_interval == 0:
inputs, labels = train_reader.next_batch(self.conf.batch)
feed_dict = {self.inputs: inputs,
self.labels: labels}
loss, _, summary = self.sess.run(
[self.loss_op, self.train_op, self.train_summary],
feed_dict=feed_dict)
self.save_summary(summary, epoch_num+self.conf.reload_step)
else:
inputs, labels = train_reader.next_batch(self.conf.batch)
feed_dict = {self.inputs: inputs,
self.labels: labels}
loss, _ = self.sess.run(
[self.loss_op, self.train_op], feed_dict=feed_dict)
print('----training loss', loss)
if epoch_num and epoch_num % self.conf.save_interval == 0:
self.save(epoch_num+self.conf.reload_step)
def predict(self):
print('---->predicting ', self.conf.test_step)
if self.conf.test_step > 0:
self.reload(self.conf.test_step)
else:
print("please set a reasonable test_step")
return
if self.conf.data_type == '2D':
test_reader = H5DataLoader(
self.conf.data_dir + self.conf.test_data, False)
else:
test_reader = H53DDataLoader(
self.conf.data_dir + self.conf.test_data, self.input_shape)
predictions = []
sig = True
final_inputs = []
final_labels = []
while sig:
sig, inputs, labels = test_reader.next_batch(self.conf.batch)
if inputs is None:
break
final_inputs.append(inputs)
final_labels.append(labels)
feed_dict = {self.inputs: inputs, self.labels: labels}
predictions.append(
self.sess.run(self.decoded_preds, feed_dict=feed_dict))
final_inputs = np.array(final_inputs, 'uint8')
final_labels = np.array(final_labels, 'uint8')
print('----->saving predictions')
for index, prediction in enumerate(predictions):
for i in range(prediction.shape[0]):
imsave(
prediction[i], self.conf.sampledir +
str(index * prediction.shape[0] + i) + '.png')
scipy.misc.imsave(
self.conf.sampledir +
str(index * prediction.shape[0] + i) + '.jpg',
final_inputs[index][i])
scipy.misc.imsave(
self.conf.sampledir +
str(index * prediction.shape[0] + i) + '_label.png',
final_labels[index][i] * 45)
def train(self):
if self.conf.reload_step > 0:
self.reload(self.conf.reload_step)
data_reader = H53DDataLoader(self.conf.data_dir, self.conf.patch_size,
self.conf.validation_id,
self.conf.overlap_stepsize,
self.conf.aug_flip, self.conf.aug_rotate)
for train_step in range(1, self.conf.max_step + 1):
# if train_step % self.conf.test_interval == 0:
# inputs, annotations = data_reader.valid_next_batch()
# feed_dict = {self.inputs: inputs,
# self.annotations: annotations}
# loss, summary = self.sess.run(
# [self.loss_op, self.valid_summary], feed_dict=feed_dict)
# self.save_summary(summary, train_step+self.conf.reload_step)
# print('----testing loss', loss)
# el
if train_step % self.conf.summary_interval == 0:
inputs, annotations = data_reader.next_batch(self.conf.batch)
feed_dict = {
self.inputs: inputs,
self.annotations: annotations
}
loss, _, summary = self.sess.run(
[self.loss_op, self.train_op, self.train_summary],
feed_dict=feed_dict)
self.save_summary(summary, train_step + self.conf.reload_step)
else:
inputs, annotations = data_reader.next_batch(self.conf.batch)
feed_dict = {
self.inputs: inputs,
self.annotations: annotations
}
loss, _ = self.sess.run([self.loss_op, self.train_op],
feed_dict=feed_dict)
print('----training loss', loss)
if train_step % self.conf.save_interval == 0:
self.save(train_step + self.conf.reload_step)