logs: replace prints to stdout with logging (#369)

* logs: move keras model summary to log.info

* logs: remove prints in favor of logging

deeppavlov/core/models/keras_model.py

@@ -99,8 +99,7 @@ def init_model_from_scratch(self, model_name: str, optimizer_name: str,
         Returns:
             compiled model with given network and learning parameters
         """
-        log.info("[initializing `{}` from scratch]".format(self.__class__.__name__))
-        print(model_name)
+        log.info(f'[initializing `{self.__class__.__name__}` from scratch as {model_name}]')
         model_func = getattr(self, model_name, None)
         if callable(model_func):
             model = model_func(**self.opt)

deeppavlov/models/classifiers/keras_classification_model.py

@@ -115,7 +115,7 @@ def __init__(self, text_size: int,
                   "lear_rate": self.opt.get('lear_rate'),
                   "lear_rate_decay": self.opt.get('lear_rate_decay')}
 
-        self.model = self.load(**params)
+        self.model: Model = self.load(**params)
         self._change_not_fixed_params(text_size=text_size, model_name=model_name,
                                       optimizer=optimizer, loss=loss,
                                       lear_rate=lear_rate, lear_rate_decay=lear_rate_decay,
@@ -133,7 +133,10 @@ def __init__(self, text_size: int,
             if self.opt['fasttext_md5'] != current_fasttext_md5:
                 raise ConfigError(
                     "Given fasttext model does NOT match fasttext model used previously to train loaded model")
-        print("Model was successfully initialized!\nModel summary:\n{}".format(self.model.summary()))
+
+        summary = ['Model was successfully initialized!', 'Model summary:']
+        self.model.summary(print_fn=summary.append)
+        log.info('\n'.join(summary))
 
     def _change_not_fixed_params(self, **kwargs) -> None:
         """

deeppavlov/models/classifiers/utils.py

@@ -104,8 +104,7 @@ def log_metrics(names: [list, np.ndarray], values: [list, np.ndarray],
     Returns:
         None
     """
-    sys.stdout.write("\r")  # back to previous line
-    log.info("{} -->\t".format(mode))
+    log.info("\r{} -->\t".format(mode))
     if updates is not None:
         log.info("updates: {}\t".format(updates))
 

deeppavlov/models/preprocessors/squad_preprocessor.py

@@ -138,7 +138,7 @@ def convert_idx(text: str, tokens: List[str]) -> List[Tuple[int, int]]:
         for token in tokens:
             current = text.find(token, current)
             if current < 0:
-                print("Token {} cannot be found".format(token))
+                logger.error("Token {} cannot be found".format(token))
                 raise Exception()
             spans.append((current, current + len(token)))
             current += len(token)

deeppavlov/models/ranking/ranking_model.py

@@ -231,7 +231,8 @@ def make_hard_triplets(self, x, y, net):
                         if not no_samples:
                             break
                     if no_samples:
-                        print("There is no negative examples with distances greater than positive examples distances.")
+                        log.error("There are no negative examples with distances"
+                                  " greater than positive examples distances.")
                         exit(0)
             else:
                 if self.num_hardest_negatives is not None:
@@ -285,7 +286,7 @@ def make_hard_triplets(self, x, y, net):
         rp = [el[1] for el in triplets]
         rn = [el[2] for el in triplets]
         ratio = sum(hrds) / len(hrds)
-        print("Ratio of semi-hard negative samples is %f" % ratio)
+        log.info("Ratio of semi-hard negative samples is %f" % ratio)
         return [(c, rp), (c, rn)]
 
     def get_semi_hard_negative_ind(self, i, j, k, distances, anchor_negative_dist, batch_size, num_samples):

deeppavlov/models/seq2seq_go_bot/bot.py

@@ -125,7 +125,7 @@ def _filter(tokens):
         preds = [list(_filter(self.tgt_vocab(utter_idxs)))
                  for utter_idxs in pred_idxs]
         if self.debug:
-            print("Dialog prediction = \"{}\"".format(preds[-1]))
+            log.debug("Dialog prediction = \"{}\"".format(preds[-1]))
         return preds
 
     def save(self):

deeppavlov/models/squad/utils.py

@@ -98,7 +98,6 @@ def __call__(self, inputs, seq_len, keep_prob=1.0, is_train=None, concat_layers=
             gru_fw, gru_bw = self.grus[layer]
             init_fw, init_bw = self.inits[layer]
             mask_fw, mask_bw = self.dropout_mask[layer]
-            print(outputs)
             with tf.variable_scope('fw_{}'.format(layer), reuse=tf.AUTO_REUSE):
                 with tf.variable_scope('cudnn_gru', reuse=tf.AUTO_REUSE):
                     out_fw, _ = tf.nn.dynamic_rnn(cell=gru_fw, inputs=outputs[-1] * mask_fw, time_major=True,