Changed nemo.logging to logging

Signed-off-by: VahidooX <[email protected]>
NVIDIA · Jan 31, 2020 · 0864e65 · 0864e65
1 parent 4f81260
commit 0864e65
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Showing 9 changed files with 47 additions and 48 deletions.
diff --git a/nemo/collections/nlp/callbacks/joint_intent_slot_callback.py b/nemo/collections/nlp/callbacks/joint_intent_slot_callback.py
@@ -5,7 +5,7 @@
 import numpy as np
 from sklearn.metrics import classification_report
 
-import nemo
+from nemo import logging
 from nemo.collections.nlp.utils.callback_utils import list2str, plot_confusion_matrix, tensor2list
 
 __all__ = ['eval_iter_callback', 'eval_epochs_done_callback']
@@ -77,26 +77,26 @@ def eval_epochs_done_callback(global_vars, graph_fold):
     i = 0
     if intent_preds.shape[0] > sample_size + 1:
         i = random.randint(0, intent_preds.shape[0] - sample_size - 1)
-    nemo.logging.info("Sampled i_preds: [%s]" % list2str(intent_preds[i : i + sample_size]))
-    nemo.logging.info("Sampled intents: [%s]" % list2str(intent_labels[i : i + sample_size]))
-    nemo.logging.info("Sampled s_preds: [%s]" % list2str(slot_preds[i : i + sample_size]))
-    nemo.logging.info("Sampled slots: [%s]" % list2str(slot_labels[i : i + sample_size]))
+    logging.info("Sampled i_preds: [%s]" % list2str(intent_preds[i : i + sample_size]))
+    logging.info("Sampled intents: [%s]" % list2str(intent_labels[i : i + sample_size]))
+    logging.info("Sampled s_preds: [%s]" % list2str(slot_preds[i : i + sample_size]))
+    logging.info("Sampled slots: [%s]" % list2str(slot_labels[i : i + sample_size]))
 
     plot_confusion_matrix(intent_labels, intent_preds, graph_fold)
 
-    nemo.logging.info('Intent prediction results')
+    logging.info('Intent prediction results')
     correct_preds = sum(intent_labels == intent_preds)
     intent_accuracy = correct_preds / intent_labels.shape[0]
-    nemo.logging.info(f'Intent accuracy: {intent_accuracy}')
-    nemo.logging.info(
+    logging.info(f'Intent accuracy: {intent_accuracy}')
+    logging.info(
         f'Classification report:\n \
         {classification_report(intent_labels, intent_preds)}'
     )
 
-    nemo.logging.info('Slot prediction results')
+    logging.info('Slot prediction results')
     slot_accuracy = sum(slot_labels == slot_preds) / slot_labels.shape[0]
-    nemo.logging.info(f'Slot accuracy: {slot_accuracy}')
-    nemo.logging.info(
+    logging.info(f'Slot accuracy: {slot_accuracy}')
+    logging.info(
         f'Classification report:\n \
         {classification_report(slot_labels[:-2], slot_preds[:-2])}'
     )

diff --git a/nemo/collections/nlp/callbacks/lm_bert_callback.py b/nemo/collections/nlp/callbacks/lm_bert_callback.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-import nemo
+from nemo import logging
 
 
 def eval_iter_callback(tensors, global_vars):
@@ -24,14 +24,14 @@ def eval_iter_callback(tensors, global_vars):
 def eval_epochs_done_callback(global_vars):
     if 'dev_mlm_loss' in global_vars:
         mlm_loss = np.mean(global_vars["dev_mlm_loss"])
-        nemo.logging.info("Dev MLM perplexity: {0}".format(np.round(np.exp(mlm_loss), 3)))
+        logging.info("Dev MLM perplexity: {0}".format(np.round(np.exp(mlm_loss), 3)))
         global_vars["dev_mlm_loss"] = []
     else:
         mlm_loss = -123.0
 
     if 'dev_nsp_loss' in global_vars:
         nsp_loss = np.mean(global_vars["dev_nsp_loss"])
-        nemo.logging.info("Dev NSP perplexity: {0}".format(np.round(np.exp(nsp_loss), 3)))
+        logging.info("Dev NSP perplexity: {0}".format(np.round(np.exp(nsp_loss), 3)))
         global_vars["dev_nsp_loss"] = []
     else:
         nsp_loss = -123.0

diff --git a/nemo/collections/nlp/callbacks/lm_transformer_callback.py b/nemo/collections/nlp/callbacks/lm_transformer_callback.py
@@ -3,7 +3,7 @@
 
 import numpy as np
 
-import nemo
+from nemo import logging
 
 GLOBAL_KEYS = ["eval_loss", "sys"]
 
@@ -23,10 +23,10 @@ def eval_epochs_done_callback(global_vars):
     eval_loss = np.mean(global_vars["eval_loss"])
     eval_ppl = np.exp(eval_loss)
 
-    nemo.logging.info("------------------------------------------------------")
-    nemo.logging.info("Eval loss: {0}".format(np.round(eval_loss, 3)))
-    nemo.logging.info("Eval  ppl: {0}".format(np.round(eval_ppl, 3)))
-    nemo.logging.info("------------------------------------------------------")
+    logging.info("------------------------------------------------------")
+    logging.info("Eval loss: {0}".format(np.round(eval_loss, 3)))
+    logging.info("Eval  ppl: {0}".format(np.round(eval_ppl, 3)))
+    logging.info("------------------------------------------------------")
     for key in GLOBAL_KEYS:
         global_vars[key] = []
     return dict({"Eval_loss": eval_loss, "Eval_ppl": eval_ppl})
diff --git a/nemo/collections/nlp/callbacks/punctuation_capitalization_callback.py b/nemo/collections/nlp/callbacks/punctuation_capitalization_callback.py
@@ -6,7 +6,7 @@
 import numpy as np
 from sklearn.metrics import classification_report
 
-import nemo
+from nemo import logging
 from nemo.collections.nlp.utils.callback_utils import list2str, plot_confusion_matrix, tensor2list
 
 

diff --git a/nemo/collections/nlp/callbacks/token_classification_callback.py b/nemo/collections/nlp/callbacks/token_classification_callback.py
@@ -6,7 +6,7 @@
 import numpy as np
 from sklearn.metrics import classification_report
 
-import nemo
+from nemo import logging
 from nemo.collections.nlp.utils.callback_utils import list2str, plot_confusion_matrix, tensor2list
 
 
@@ -51,21 +51,21 @@ def eval_epochs_done_callback(global_vars, label_ids, graph_fold=None, none_labe
     preds = preds[subtokens_mask]
 
     accuracy = sum(labels == preds) / labels.shape[0]
-    nemo.logging.info(f'Accuracy: {accuracy}')
+    logging.info(f'Accuracy: {accuracy}')
 
     # print predictions and labels for a small random subset of data
     sample_size = 20
     i = 0
     if preds.shape[0] > sample_size + 1:
         i = random.randint(0, preds.shape[0] - sample_size - 1)
-    nemo.logging.info("Sampled preds: [%s]" % list2str(preds[i : i + sample_size]))
-    nemo.logging.info("Sampled labels: [%s]" % list2str(labels[i : i + sample_size]))
+    logging.info("Sampled preds: [%s]" % list2str(preds[i : i + sample_size]))
+    logging.info("Sampled labels: [%s]" % list2str(labels[i : i + sample_size]))
 
     # remove labels from label_ids that don't appear in the dev set
     used_labels = set(labels) | set(preds)
     label_ids = {k: label_ids[k] for k, v in label_ids.items() if v in used_labels}
 
-    nemo.logging.info(classification_report(labels, preds, target_names=label_ids))
+    logging.info(classification_report(labels, preds, target_names=label_ids))
 
     # calculate and plot confusion_matrix
     if graph_fold:

diff --git a/nemo/collections/nlp/data/datasets/lm_bert_dataset.py b/nemo/collections/nlp/data/datasets/lm_bert_dataset.py
@@ -26,7 +26,7 @@
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-import nemo
+from nemo import logging
 from nemo.collections.nlp.data.datasets.datasets_utils import download_wkt2
 from nemo.collections.nlp.data.datasets.lm_transformer_dataset import create_vocab_mlm
 
@@ -385,7 +385,7 @@ def __init__(self, dataset_name, data_dir, vocab_size, sample_size, special_toke
                 data_dir, vocab_size, sample_size, special_tokens, train_file
             )
         else:
-            nemo.logging.warning(
+            logging.warning(
                 "Looks like you passed a dataset name that isn't "
                 "already supported by NeMo. Please make sure that "
                 "you build the preprocessing method for it."

diff --git a/nemo/collections/nlp/data/datasets/punctuation_capitalization_dataset.py b/nemo/collections/nlp/data/datasets/punctuation_capitalization_dataset.py
@@ -29,8 +29,8 @@
 import numpy as np
 from torch.utils.data import Dataset
 
-import nemo
 import nemo.collections.nlp.data.datasets.datasets_utils as utils
+from nemo import logging
 
 
 def get_features(
@@ -162,12 +162,12 @@ def get_features(
         logging.info("*** Example ***")
         logging.info("i: %s" % (i))
         logging.info("subtokens: %s" % " ".join(list(map(str, all_subtokens[i]))))
-        nemo.logging.info("loss_mask: %s" % " ".join(list(map(str, all_loss_mask[i]))))
-        nemo.logging.info("input_mask: %s" % " ".join(list(map(str, all_input_mask[i]))))
-        nemo.logging.info("subtokens_mask: %s" % " ".join(list(map(str, all_subtokens_mask[i]))))
+        logging.info("loss_mask: %s" % " ".join(list(map(str, all_loss_mask[i]))))
+        logging.info("input_mask: %s" % " ".join(list(map(str, all_input_mask[i]))))
+        logging.info("subtokens_mask: %s" % " ".join(list(map(str, all_subtokens_mask[i]))))
         if with_label:
-            nemo.logging.info("punct_labels: %s" % " ".join(list(map(str, punct_all_labels[i]))))
-            nemo.logging.info("capit_labels: %s" % " ".join(list(map(str, capit_all_labels[i]))))
+            logging.info("punct_labels: %s" % " ".join(list(map(str, punct_all_labels[i]))))
+            logging.info("capit_labels: %s" % " ".join(list(map(str, capit_all_labels[i]))))
 
     return (
         all_input_ids,
@@ -247,7 +247,7 @@ def __init__(
         if use_cache and os.path.exists(features_pkl):
             # If text_file was already processed, load from pickle
             features = pickle.load(open(features_pkl, 'rb'))
-            nemo.logging.info(f'features restored from {features_pkl}')
+            logging.info(f'features restored from {features_pkl}')
         else:
             if num_samples == 0:
                 raise ValueError("num_samples has to be positive", num_samples)
@@ -290,16 +290,16 @@ def __init__(
             # for dev/test sets use label mapping from training set
             if punct_label_ids:
                 if len(punct_label_ids) != len(punct_unique_labels):
-                    nemo.logging.info(
+                    logging.info(
                         'Not all labels from the specified'
                         + 'label_ids dictionary are present in the'
                         + 'current dataset. Using the provided'
                         + 'label_ids dictionary.'
                     )
                 else:
-                    nemo.logging.info('Using the provided label_ids dictionary.')
+                    logging.info('Using the provided label_ids dictionary.')
             else:
-                nemo.logging.info(
+                logging.info(
                     'Creating a new label to label_id dictionary.'
                     + ' It\'s recommended to use label_ids generated'
                     + ' during training for dev/test sets to avoid'
@@ -334,7 +334,7 @@ def create_label_ids(unique_labels, pad_label=pad_label):
 
             if use_cache:
                 pickle.dump(features, open(features_pkl, "wb"))
-                nemo.logging.info(f'features saved to {features_pkl}')
+                logging.info(f'features saved to {features_pkl}')
 
         self.all_input_ids = features[0]
         self.all_segment_ids = features[1]
@@ -350,14 +350,14 @@ def create_label_ids(unique_labels, pad_label=pad_label):
         def get_stats_and_save(all_labels, label_ids, name):
             infold = text_file[: text_file.rfind('/')]
             merged_labels = itertools.chain.from_iterable(all_labels)
-            nemo.logging.info('Three most popular labels')
+            logging.info('Three most popular labels')
             _, label_frequencies = utils.get_label_stats(merged_labels, infold + '/label_count_' + name + '.tsv')
 
             out = open(os.path.join(infold, name + '_label_ids.csv'), 'w')
             labels, _ = zip(*sorted(label_ids.items(), key=lambda x: x[1]))
             out.write('\n'.join(labels))
-            nemo.logging.info(f'Labels: {label_ids}')
-            nemo.logging.info(f'Labels mapping saved to : {out.name}')
+            logging.info(f'Labels: {label_ids}')
+            logging.info(f'Labels mapping saved to : {out.name}')
 
             return label_frequencies
 

diff --git a/nemo/collections/nlp/data/datasets/token_classification_dataset.py b/nemo/collections/nlp/data/datasets/token_classification_dataset.py
@@ -27,9 +27,8 @@
 import numpy as np
 from torch.utils.data import Dataset
 
-import nemo
 import nemo.collections.nlp.data.datasets.datasets_utils as datasets_utils
-import nemo.collections.nlp.data.datasets.joint_intent_slot_dataset
+from nemo import logging
 
 __all__ = ['BertTokenClassificationDataset', 'BertTokenClassificationInferDataset']
 

diff --git a/nemo/collections/nlp/utils/common_nlp_utils.py b/nemo/collections/nlp/utils/common_nlp_utils.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 
-import nemo
+from nemo import logging
 
 
 def _is_whitespace(c):
@@ -27,11 +27,11 @@ def read_intent_slot_outputs(
     pred_slots = np.argmax(slot_logits, axis=2)
     slot_masks = slot_masks > 0.5
     for i, query in enumerate(queries):
-        nemo.logging.info(f'Query: {query}')
+        logging.info(f'Query: {query}')
         pred = pred_intents[i]
-        nemo.logging.info(f'Predicted intent:\t{pred}\t{intent_dict[pred]}')
+        logging.info(f'Predicted intent:\t{pred}\t{intent_dict[pred]}')
         if intents is not None:
-            nemo.logging.info(f'True intent:\t{intents[i]}\t{intent_dict[intents[i]]}')
+            logging.info(f'True intent:\t{intents[i]}\t{intent_dict[intents[i]]}')
 
         pred_slot = pred_slots[i][slot_masks[i]]
         tokens = query.strip().split()
@@ -43,7 +43,7 @@ def read_intent_slot_outputs(
             output = f'{token}\t{slot_dict[pred_slot[j]]}'
             if slots is not None:
                 output = f'{output}\t{slot_dict[slots[i][j]]}'
-            nemo.logging.info(output)
+            logging.info(output)
 
 
 def get_vocab(file):