未验证 提交 e9286a5e 编写于 作者: C ChenXinhao 提交者: GitHub

Merge pull request #12 from ChenXinhao/master

fix(bert) fix pylint error
......@@ -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:
......@@ -577,9 +578,7 @@ MODEL_NAME = {
def download_file(url, filename):
try:
urllib.URLopener().retrieve(url, filename)
except:
# urllib.URLopener().retrieve(url, filename)
urllib.request.urlretrieve(url, filename)
......
......@@ -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(
......
......@@ -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
)
......
......@@ -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) #
): #
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)
......
......@@ -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
)
......
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