Merge pull request #12 from ChenXinhao/master

fix(bert) fix pylint error

official/nlp/bert/model.py

@@ -23,7 +23,6 @@ import copy
 import json
 import math
 import os
-import sys
 import urllib
 import urllib.request
 from io import open
@@ -39,7 +38,7 @@ from megengine.module.activation import Softmax
 
 
 def transpose(inp, a, b):
-    cur_shape = [i for i in range(0, len(inp.shape))]
+    cur_shape = list(range(0, len(inp.shape)))
     cur_shape[a], cur_shape[b] = cur_shape[b], cur_shape[a]
     return inp.dimshuffle(*cur_shape)
 
@@ -84,7 +83,7 @@ def gelu(x):
 ACT2FN = {"gelu": gelu, "relu": F.relu}
 
 
-class BertConfig(object):
+class BertConfig:
     """Configuration class to store the configuration of a `BertModel`.
     """
 
@@ -441,6 +440,7 @@ class BertModel(Module):
     """
 
     def __init__(self, config):
+        super().__init__()
         self.embeddings = BertEmbeddings(config)
         self.encoder = BertEncoder(config)
         self.pooler = BertPooler(config)
@@ -537,6 +537,7 @@ class BertForSequenceClassification(Module):
     """
 
     def __init__(self, config, num_labels, bert=None):
+        super().__init__()
         if bert is None:
             self.bert = BertModel(config)
         else:
@@ -577,10 +578,8 @@ MODEL_NAME = {
 
 
 def download_file(url, filename):
-    try:
-        urllib.URLopener().retrieve(url, filename)
-    except:
-        urllib.request.urlretrieve(url, filename)
+    # urllib.URLopener().retrieve(url, filename)
+    urllib.request.urlretrieve(url, filename)
 
 
 def create_hub_bert(model_name, pretrained):

official/nlp/bert/mrpc_dataset.py

@@ -20,7 +20,7 @@ from tokenization import BertTokenizer
 logger = mge.get_logger(__name__)
 
 
-class DataProcessor(object):
+class DataProcessor:
     """Base class for data converters for sequence classification data sets."""
 
     def get_train_examples(self, data_dir):
@@ -46,7 +46,7 @@ class DataProcessor(object):
             return lines
 
 
-class InputFeatures(object):
+class InputFeatures:
     """A single set of features of data."""
 
     def __init__(self, input_ids, input_mask, segment_ids, label_id):
@@ -56,7 +56,7 @@ class InputFeatures(object):
         self.label_id = label_id
 
 
-class InputExample(object):
+class InputExample:
     """A single training/test example for simple sequence classification."""
 
     def __init__(self, guid, text_a, text_b=None, label=None):
@@ -195,12 +195,12 @@ def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer
         label_id = label_map[example.label]
         if ex_index < 0:
             logger.info("*** Example ***")
-            logger.info("guid: %s" % (example.guid))
-            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
-            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
-            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
-            logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
-            logger.info("label: %s (id = %d)" % (example.label, label_id))
+            logger.info("guid: {}".format(example.guid))
+            logger.info("tokens: {}".format(" ".join([str(x) for x in tokens])))
+            logger.info("input_ids: {}".format(" ".join([str(x) for x in input_ids])))
+            logger.info("input_mask: {}".format(" ".join([str(x) for x in input_mask])))
+            logger.info("segment_ids: {}".format(" ".join([str(x) for x in segment_ids])))
+            logger.info("label: {} (id = {})".format(example.label, label_id))
 
         features.append(
             InputFeatures(

official/nlp/bert/test.py

@@ -12,16 +12,16 @@ import megengine.functional as F
 from megengine.jit import trace
 from tqdm import tqdm
 
-from config import get_args
 from model import BertForSequenceClassification, create_hub_bert
 from mrpc_dataset import MRPCDataset
-
-args = get_args()
+# pylint: disable=import-outside-toplevel
+import config_args
+args = config_args.get_args()
 logger = mge.get_logger(__name__)
 
 
 @trace(symbolic=True)
-def net_eval(input_ids, segment_ids, input_mask, label_ids, opt=None, net=None):
+def net_eval(input_ids, segment_ids, input_mask, label_ids, net=None):
     net.eval()
     results = net(input_ids, segment_ids, input_mask, label_ids)
     logits, loss = results
@@ -39,7 +39,7 @@ def eval(dataloader, net):
 
     sum_loss, sum_accuracy, total_steps, total_examples = 0, 0, 0, 0
 
-    for step, batch in enumerate(tqdm(dataloader, desc="Iteration")):
+    for _, batch in enumerate(tqdm(dataloader, desc="Iteration")):
         input_ids, input_mask, segment_ids, label_ids = tuple(
             mge.tensor(t) for t in batch
         )

official/nlp/bert/tokenization.py

@@ -22,7 +22,7 @@ import os
 import unicodedata
 from io import open
 
-import megengine as megengine
+import megengine
 
 logger = megengine.get_logger(__name__)
 
@@ -54,7 +54,7 @@ def whitespace_tokenize(text):
     return tokens
 
 
-class BertTokenizer(object):
+class BertTokenizer:
     """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
 
     def __init__(
@@ -150,7 +150,7 @@ class BertTokenizer(object):
         return vocab_file
 
 
-class BasicTokenizer(object):
+class BasicTokenizer:
     """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
 
     def __init__(
@@ -243,18 +243,19 @@ class BasicTokenizer(object):
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
-        if (
-            (cp >= 0x4E00 and cp <= 0x9FFF)
-            or (cp >= 0x3400 and cp <= 0x4DBF)  #
-            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
-            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
-            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
-            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
-            or (cp >= 0xF900 and cp <= 0xFAFF)
-            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
-        ):  #
-            return True
-
+        cp_range = [
+            (0x4E00, 0x9FFF),
+            (0x3400, 0x4DBF),
+            (0x20000, 0x2A6DF),
+            (0x2A700, 0x2B73F),
+            (0x2B740, 0x2B81F),
+            (0x2B820, 0x2CEAF),
+            (0xF900, 0xFAFF),
+            (0x2F800, 0x2FA1F),
+        ]
+        for min_cp, max_cp in cp_range:
+            if min_cp <= cp <= max_cp:
+                return True
         return False
 
     def _clean_text(self, text):
@@ -271,7 +272,7 @@ class BasicTokenizer(object):
         return "".join(output)
 
 
-class WordpieceTokenizer(object):
+class WordpieceTokenizer:
     """Runs WordPiece tokenization."""
 
     def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
@@ -335,7 +336,7 @@ def _is_whitespace(char):
     """Checks whether `chars` is a whitespace character."""
     # \t, \n, and \r are technically contorl characters but we treat them
     # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
+    if char in (" ", "\t", "\n", "\r"):
         return True
     cat = unicodedata.category(char)
     if cat == "Zs":
@@ -347,7 +348,7 @@ def _is_control(char):
     """Checks whether `chars` is a control character."""
     # These are technically control characters but we count them as whitespace
     # characters.
-    if char == "\t" or char == "\n" or char == "\r":
+    if char in ("\t", "\n", "\r"):
         return False
     cat = unicodedata.category(char)
     if cat.startswith("C"):
@@ -363,10 +364,10 @@ def _is_punctuation(char):
     # Punctuation class but we treat them as punctuation anyways, for
     # consistency.
     if (
-        (cp >= 33 and cp <= 47)
-        or (cp >= 58 and cp <= 64)
-        or (cp >= 91 and cp <= 96)
-        or (cp >= 123 and cp <= 126)
+        (33 <= cp <= 47)
+        or (58 <= cp <= 64)
+        or (91 <= cp <= 96)
+        or (123 <= cp <= 126)
     ):
         return True
     cat = unicodedata.category(char)

official/nlp/bert/train.py

@@ -13,16 +13,16 @@ import megengine.optimizer as optim
 from megengine.jit import trace
 from tqdm import tqdm
 
-from config import get_args
 from model import BertForSequenceClassification, create_hub_bert
 from mrpc_dataset import MRPCDataset
-
-args = get_args()
+# pylint: disable=import-outside-toplevel
+import config_args
+args = config_args.get_args()
 logger = mge.get_logger(__name__)
 
 
 @trace(symbolic=True)
-def net_eval(input_ids, segment_ids, input_mask, label_ids, opt=None, net=None):
+def net_eval(input_ids, segment_ids, input_mask, label_ids, net=None):
     net.eval()
     results = net(input_ids, segment_ids, input_mask, label_ids)
     logits, loss = results
@@ -49,7 +49,7 @@ def eval(dataloader, net):
 
     sum_loss, sum_accuracy, total_steps, total_examples = 0, 0, 0, 0
 
-    for step, batch in enumerate(tqdm(dataloader, desc="Iteration")):
+    for _, batch in enumerate(tqdm(dataloader, desc="Iteration")):
         input_ids, input_mask, segment_ids, label_ids = tuple(
             mge.tensor(t) for t in batch
         )
@@ -79,7 +79,7 @@ def train(dataloader, net, opt):
     logger.info("batch size = %d", args.train_batch_size)
     sum_loss, sum_accuracy, total_steps, total_examples = 0, 0, 0, 0
 
-    for step, batch in enumerate(tqdm(dataloader, desc="Iteration")):
+    for _, batch in enumerate(tqdm(dataloader, desc="Iteration")):
         input_ids, input_mask, segment_ids, label_ids = tuple(
             mge.tensor(t) for t in batch
         )