Fix Lazy Logging (#3108)

examples/trials/ga_squad/trial.py

@@ -218,8 +218,7 @@ def run_epoch(batches, answer_net, is_training):
             loss, _, = sess.run(
                 [answer_net.loss, answer_net.train_op], feed_dict=feed_dict)
             if count % 100 == 0:
-                logger.debug('%d %g except:%g, loss:%g' %
-                             (count, used, used / count * len(batches), loss))
+                logger.debug('%d %g except:%g, loss:%g', count, used, used / count * len(batches), loss)
             loss_sum += loss
         else:
             feed_dict = {answer_net.query_word: query,
@@ -239,8 +238,7 @@ def run_epoch(batches, answer_net, is_training):
             contexts += context
             ids = np.concatenate((ids, sample_id))
             if count % 100 == 0:
-                logger.debug('%d %g except:%g' %
-                             (count, used, used / count * len(batches)))
+                logger.debug('%d %g except:%g', count, used, used / count * len(batches))
     loss = loss_sum / len(batches)
     if is_training:
         return loss
@@ -327,7 +325,7 @@ def train_with_graph(graph, qp_pairs, dev_qp_pairs):
                 train_batches = data.get_batches(qp_pairs, cfg.batch_size)
                 train_loss = run_epoch(train_batches, train_model, True)
                 logger.debug('epoch ' + str(epoch) +
-                             ' loss: ' + str(train_loss))
+                             ' loss: ', str(train_loss))
                 dev_batches = list(data.get_batches(
                     dev_qp_pairs, cfg.batch_size))
                 _, position1, position2, ids, contexts = run_epoch(
@@ -361,8 +359,7 @@ def train_with_graph(graph, qp_pairs, dev_qp_pairs):
                         with open(os.path.join(save_path, 'epoch%d.score' % epoch), 'wb') as file:
                             pickle.dump(
                                 (position1, position2, ids, contexts), file)
-                logger.debug('epoch %d acc %g bestacc %g' %
-                             (epoch, acc, bestacc))
+                logger.debug('epoch %d acc %g bestacc %g', epoch, acc, bestacc)
                 if patience <= iter:
                     break
             logger.debug('save done.')

examples/trials/mnist-pbt-tuner-pytorch/mnist.py

@@ -112,7 +112,7 @@ def main(args):
 
     if os.path.isfile(load_checkpoint_path):
         model_state_dict = load_checkpoint(load_checkpoint_path)
-        logger.info("test : " + load_checkpoint_path)
+        logger.info("test : ", load_checkpoint_path)
         logger.info(type(model_state_dict))
         model.load_state_dict(model_state_dict)
 

examples/trials/sklearn/classification/main.py

@@ -63,7 +63,7 @@ def run(X_train, X_test, y_train, y_test, model):
     '''Train model and predict result'''
     model.fit(X_train, y_train)
     score = model.score(X_test, y_test)
-    LOG.debug('score: %s' % score)
+    LOG.debug('score: %s', score)
     nni.report_final_result(score)
 
 if __name__ == '__main__':

examples/trials/sklearn/regression/main.py

@@ -74,7 +74,7 @@ def run(X_train, X_test, y_train, y_test, model):
     model.fit(X_train, y_train)
     predict_y = model.predict(X_test)
     score = r2_score(y_test, predict_y)
-    LOG.debug('r2 score: %s' % score)
+    LOG.debug('r2 score: %s', score)
     nni.report_final_result(score)
 
 if __name__ == '__main__':

examples/trials/systems_auto_tuning/opevo/src/algorithms/opevo.py

@@ -387,8 +387,7 @@ def update_search_space(self, search_space):
         self.population = Population(search_space,
                                      self.mutate_rate,
                                      self.optimize_mode)
-        self.logger.debug('Total search space volume: '
-                          + str(self.population.volume))
+        self.logger.debug('Total search space volume: ', str(self.population.volume))
 
         if not self.serve_list:
             self.serve_list = self.population.get_offspring(

examples/trials/weight_sharing/ga_squad/trial.py

@@ -219,8 +219,7 @@ def run_epoch(batches, answer_net, is_training):
             loss, _, = sess.run(
                 [answer_net.loss, answer_net.train_op], feed_dict=feed_dict)
             if count % 100 == 0:
-                logger.debug('%d %g except:%g, loss:%g' %
-                             (count, used, used / count * len(batches), loss))
+                logger.debug('%d %g except:%g, loss:%g', count, used, used / count * len(batches), loss)
             loss_sum += loss
         else:
             feed_dict = {answer_net.query_word: query,
@@ -240,8 +239,7 @@ def run_epoch(batches, answer_net, is_training):
             contexts += context
             ids = np.concatenate((ids, sample_id))
             if count % 100 == 0:
-                logger.debug('%d %g except:%g' %
-                             (count, used, used / count * len(batches)))
+                logger.debug('%d %g except:%g', count, used, used / count * len(batches))
     loss = loss_sum / len(batches)
     if is_training:
         return loss
@@ -333,7 +331,7 @@ def train_with_graph(p_graph, qp_pairs, dev_qp_pairs):
                 train_batches = data.get_batches(qp_pairs, cfg.batch_size)
                 train_loss = run_epoch(train_batches, train_model, True)
                 logger.debug('epoch ' + str(epoch) +
-                             ' loss: ' + str(train_loss))
+                             ' loss: ', str(train_loss))
                 dev_batches = list(data.get_batches(
                     dev_qp_pairs, cfg.batch_size))
                 _, position1, position2, ids, contexts = run_epoch(
@@ -369,8 +367,7 @@ def train_with_graph(p_graph, qp_pairs, dev_qp_pairs):
                         with open(os.path.join(save_path, 'epoch%d.score' % epoch), 'wb') as file:
                             pickle.dump(
                                 (position1, position2, ids, contexts), file)
-                logger.debug('epoch %d acc %g bestacc %g' %
-                             (epoch, acc, bestacc))
+                logger.debug('epoch %d acc %g bestacc %g', epoch, acc, bestacc)
                 if patience <= iter:
                     break
             logger.debug('save done.')

test/async_sharing_test/simple_tuner.py

@@ -35,8 +35,7 @@ def generate_parameters(self, parameter_id, **kwargs):
                 'checksum': None,
                 'path': '',
             }
-            _logger.info('generate parameter for father trial %s' %
-                         parameter_id)
+            _logger.info('generate parameter for father trial %s', parameter_id)
             self.thread_lock.release()
             return {
                 'prev_id': 0,

test/config/naive_test/naive_assessor.py

@@ -18,7 +18,7 @@ def __init__(self, optimize_mode):
         _logger.info('init')
 
     def assess_trial(self, trial_job_id, trial_history):
-        _logger.info('assess trial %s %s' % (trial_job_id, trial_history))
+        _logger.info('assess trial %s %s', trial_job_id, trial_history)
 
         id_ = trial_history[0]
         if id_ in self._killed:

test/config/naive_test/naive_tuner.py

@@ -21,17 +21,17 @@ def __init__(self, optimize_mode):
 
     def generate_parameters(self, parameter_id, **kwargs):
         self.cur += 1
-        _logger.info('generate parameters: %s' % self.cur)
+        _logger.info('generate parameters: %s', self.cur)
         return { 'x': self.cur }
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
         reward = extract_scalar_reward(value)
-        _logger.info('receive trial result: %s, %s, %s' % (parameter_id, parameters, reward))
+        _logger.info('receive trial result: %s, %s, %s', parameter_id, parameters, reward)
         _result.write('%d %d\n' % (parameters['x'], reward))
         _result.flush()
 
     def update_search_space(self, search_space):
-        _logger.info('update_search_space: %s' % search_space)
+        _logger.info('update_search_space: %s', search_space)
         with open(os.path.join(_pwd, 'tuner_search_space.json'), 'w') as file_:
             json.dump(search_space, file_)
 

test/ut/tools/annotation/testcase/annotated/mnist.py

@@ -38,8 +38,7 @@ def build_network(self):
                 input_dim = int(math.sqrt(self.x_dim))
             except:
                 logger.debug(
-                    'input dim cannot be sqrt and reshape. input dim: ' +
-                    str(self.x_dim))
+                    'input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim))
                 raise
             x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1])
         with tf.name_scope('conv1'):
@@ -132,7 +131,7 @@ def main():
     mnist_network.build_network()
     logger.debug('Mnist build network done.')
     graph_location = tempfile.mkdtemp()
-    logger.debug('Saving graph to: %s' % graph_location)
+    logger.debug('Saving graph to: %s', graph_location)
     train_writer = tf.summary.FileWriter(graph_location)
     train_writer.add_graph(tf.get_default_graph())
     test_acc = 0.0

test/ut/tools/annotation/testcase/usercode/mnist.py

@@ -53,7 +53,7 @@ def build_network(self):
                 input_dim = int(math.sqrt(self.x_dim))
             except:
                 #print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
-                logger.debug('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
+                logger.debug('input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim))
                 raise
             x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1])
 
@@ -147,7 +147,7 @@ def main():
 
     # Write log
     graph_location = tempfile.mkdtemp()
-    logger.debug('Saving graph to: %s' % graph_location)
+    logger.debug('Saving graph to: %s', graph_location)
     # print('Saving graph to: %s' % graph_location)
     train_writer = tf.summary.FileWriter(graph_location)
     train_writer.add_graph(tf.get_default_graph())