1.处理新测试例的import问题 2.将warnings.warn替换为logger.warn

fastNLP/core/metrics/accuracy.py

@@ -4,13 +4,13 @@ __all__ = [
 ]
 
 from typing import Union
-import warnings
 
 import numpy as np
 
 from fastNLP.core.metrics.metric import Metric
 from fastNLP.core.metrics.backend import Backend
 from fastNLP.core.utils.seq_len_to_mask import seq_len_to_mask
+from fastNLP.core.log import logger
 
 
 class Accuracy(Metric):
@@ -69,7 +69,7 @@ class Accuracy(Metric):
         elif pred.ndim == target.ndim + 1:
             pred = pred.argmax(axis=-1)
             if seq_len is None and target.ndim > 1:
-                warnings.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
+                logger.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
 
         else:
             raise RuntimeError(f"when pred have size:{pred.shape}, target should have size: {pred.shape} or "

fastNLP/core/metrics/classify_f1_pre_rec_metric.py

@@ -4,7 +4,6 @@ __all__ = [
 
 from typing import Union, List
 from collections import Counter
-import warnings
 import numpy as np
 
 from .metric import Metric
@@ -12,7 +11,7 @@ from .backend import Backend
 from fastNLP.core.vocabulary import Vocabulary
 from fastNLP.core.utils.seq_len_to_mask import seq_len_to_mask
 from .utils import _compute_f_pre_rec
-
+from fastNLP.core.log import logger
 
 class ClassifyFPreRecMetric(Metric):
     def __init__(self, tag_vocab: Vocabulary = None, ignore_labels: List[str] = None,
@@ -157,7 +156,7 @@ class ClassifyFPreRecMetric(Metric):
         elif pred.ndim == target.ndim + 1:
             pred = pred.argmax(axis=-1)
             if seq_len is None and target.ndim > 1:
-                warnings.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
+                logger.warn("You are not passing `seq_len` to exclude pad when calculate accuracy.")
         else:
             raise RuntimeError(f"when pred have "
                                f"size:{pred.shape}, target should have size: {pred.shape} or "

fastNLP/core/metrics/span_f1_pre_rec_metric.py

@@ -3,12 +3,12 @@ __all__ = [
 ]
 
 from typing import Union, List, Optional
-import warnings
 from collections import Counter
 
 from fastNLP.core.metrics.backend import Backend
 from fastNLP.core.metrics.metric import Metric
 from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.core.log import logger
 from .utils import _compute_f_pre_rec
 
 
@@ -39,7 +39,7 @@ def _check_tag_vocab_and_encoding_type(tag_vocab: Union[Vocabulary, dict], encod
                             f"encoding_type."
         tags = tags.replace(tag, '')  # 删除该值
     if tags:  # 如果不为空，说明出现了未使用的tag
-        warnings.warn(f"Tag:{tags} in encoding type:{encoding_type} is not presented in your Vocabulary. Check your "
+        logger.warn(f"Tag:{tags} in encoding type:{encoding_type} is not presented in your Vocabulary. Check your "
                       "encoding_type.")
 
 

fastNLP/core/utils/utils.py

@@ -2,7 +2,6 @@ import functools
 import inspect
 from inspect import Parameter
 import dataclasses
-import warnings
 from dataclasses import is_dataclass
 from copy import deepcopy
 from collections import defaultdict, OrderedDict
@@ -555,7 +554,7 @@ def deprecated(help_message: Optional[str] = None):
         def wrapper(*args, **kwargs):
             func_hash = hash(deprecated_function)
             if func_hash not in _emitted_deprecation_warnings:
-                warnings.warn(warning_msg, category=FutureWarning, stacklevel=2)
+                logger.warn(warning_msg, category=FutureWarning, stacklevel=2)
                 _emitted_deprecation_warnings.add(func_hash)
             return deprecated_function(*args, **kwargs)
 

fastNLP/embeddings/torch/static_embedding.py

@@ -7,7 +7,6 @@ __all__ = [
     "StaticEmbedding"
 ]
 import os
-import warnings
 from collections import defaultdict
 from copy import deepcopy
 import json
@@ -15,6 +14,7 @@ from typing import Union
 
 import numpy as np
 
+from fastNLP.core.log import logger
 from .embedding import TokenEmbedding
 from ...core import logger
 from ...core.vocabulary import Vocabulary
@@ -286,7 +286,7 @@ class StaticEmbedding(TokenEmbedding):
                     if word in vocab:
                         index = vocab.to_index(word)
                         if index in matrix:
-                            warnings.warn(f"Word has more than one vector in embedding file. Set logger level to "
+                            logger.warn(f"Word has more than one vector in embedding file. Set logger level to "
                                           f"DEBUG for detail.")
                             logger.debug(f"Word:{word} occurs again in line:{idx}(starts from 0)")
                         matrix[index] = torch.from_numpy(np.fromstring(' '.join(nums), sep=' ', dtype=dtype, count=dim))
@@ -295,7 +295,7 @@ class StaticEmbedding(TokenEmbedding):
                         found_count += 1
                 except Exception as e:
                     if error == 'ignore':
-                        warnings.warn("Error occurred at the {} line.".format(idx))
+                        logger.warn("Error occurred at the {} line.".format(idx))
                     else:
                         logger.error("Error occurred at the {} line.".format(idx))
                         raise e

fastNLP/io/embed_loader.py

@@ -9,12 +9,12 @@ __all__ = [
 
 import logging
 import os
-import warnings
 
 import numpy as np
 
 from fastNLP.core.utils.utils import Option
 from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.core.log import logger
 
 
 class EmbeddingOption(Option):
@@ -91,7 +91,7 @@ class EmbedLoader:
                         hit_flags[index] = True
                 except Exception as e:
                     if error == 'ignore':
-                        warnings.warn("Error occurred at the {} line.".format(idx))
+                        logger.warn("Error occurred at the {} line.".format(idx))
                     else:
                         logging.error("Error occurred at the {} line.".format(idx))
                         raise e
@@ -156,7 +156,7 @@ class EmbedLoader:
                         found_pad = True
                 except Exception as e:
                     if error == 'ignore':
-                        warnings.warn("Error occurred at the {} line.".format(idx))
+                        logger.warn("Error occurred at the {} line.".format(idx))
                         pass
                     else:
                         logging.error("Error occurred at the {} line.".format(idx))

fastNLP/io/loader/classification.py

@@ -25,11 +25,10 @@ import os
 import random
 import shutil
 import time
-import warnings
 
 from .loader import Loader
 from fastNLP.core.dataset import Instance, DataSet
-from ...core import logger
+from fastNLP.core.log import logger
 
 
 # from ...core._logger import log
@@ -346,7 +345,7 @@ class SST2Loader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if 'test' in os.path.split(path)[1]:
-                warnings.warn("SST2's test file has no target.")
+                logger.warn("SST2's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:

fastNLP/io/loader/matching.py

@@ -12,7 +12,6 @@ __all__ = [
 ]
 
 import os
-import warnings
 from typing import Union, Dict
 
 from .csv import CSVLoader
@@ -22,6 +21,7 @@ from fastNLP.io.data_bundle import DataBundle
 from ..utils import check_loader_paths
 # from ...core.const import Const
 from fastNLP.core.dataset import DataSet, Instance
+from fastNLP.core.log import logger
 
 
 class MNLILoader(Loader):
@@ -55,7 +55,7 @@ class MNLILoader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test_matched.tsv") or path.endswith('test_mismatched.tsv'):
-                warnings.warn("MNLI's test file has no target.")
+                logger.warn("MNLI's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:
@@ -227,7 +227,7 @@ class QNLILoader(JsonLoader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test.tsv"):
-                warnings.warn("QNLI's test file has no target.")
+                logger.warn("QNLI's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:
@@ -289,7 +289,7 @@ class RTELoader(Loader):
         with open(path, 'r', encoding='utf-8') as f:
             f.readline()  # 跳过header
             if path.endswith("test.tsv"):
-                warnings.warn("RTE's test file has no target.")
+                logger.warn("RTE's test file has no target.")
                 for line in f:
                     line = line.strip()
                     if line:

fastNLP/io/pipe/classification.py

@@ -16,7 +16,6 @@ __all__ = [
 ]
 
 import re
-import warnings
 
 try:
     from nltk import Tree
@@ -33,6 +32,7 @@ from ..loader.classification import IMDBLoader, YelpFullLoader, SSTLoader, SST2L
 # from ...core._logger import log
 # from ...core.const import Const
 from fastNLP.core.dataset import DataSet, Instance
+from fastNLP.core.log import logger
 
 
 class CLSBasePipe(Pipe):

fastNLP/io/pipe/matching.py

@@ -24,8 +24,7 @@ __all__ = [
     "MachingTruncatePipe",
 ]
 
-import warnings
-
+from fastNLP.core.log import logger
 from .pipe import Pipe
 from .utils import get_tokenizer
 from ..data_bundle import DataBundle
@@ -147,7 +146,7 @@ class MatchingBertPipe(Pipe):
             warn_msg = f"There are {len(target_vocab._no_create_word)} target labels" \
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
-            warnings.warn(warn_msg)
+            logger.warn(warn_msg)
             print(warn_msg)
         
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if
@@ -296,7 +295,7 @@ class MatchingPipe(Pipe):
             warn_msg = f"There are {len(target_vocab._no_create_word)} target labels" \
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!."
-            warnings.warn(warn_msg)
+            logger.warn(warn_msg)
             print(warn_msg)
         
         has_target_datasets = [dataset for name, dataset in data_bundle.datasets.items() if

fastNLP/io/pipe/utils.py

@@ -7,11 +7,11 @@ __all__ = [
 ]
 
 from typing import List
-import warnings
 
 # from ...core.const import Const
 from ...core.vocabulary import Vocabulary
 # from ...core._logger import log
+from fastNLP.core.log import logger
 from pkg_resources import parse_version
 
 
@@ -138,7 +138,7 @@ def _indexize(data_bundle, input_field_names='words', target_field_names='target
                        f" in {[name for name in data_bundle.datasets.keys() if 'train' not in name]} " \
                        f"data set but not in train data set!.\n" \
                        f"These label(s) are {tgt_vocab._no_create_word}"
-            warnings.warn(warn_msg)
+            logger.warn(warn_msg)
             # log.warning(warn_msg)
         tgt_vocab.index_dataset(*[ds for ds in data_bundle.datasets.values() if ds.has_field(target_field_name)], field_name=target_field_name)
         data_bundle.set_vocab(tgt_vocab, target_field_name)

fastNLP/modules/mix_modules/utils.py

@@ -1,4 +1,3 @@
-import warnings
 from typing import Any, Optional, Union
 
 import numpy as np
@@ -113,7 +112,7 @@ def _jittor2torch(jittor_var: 'jittor.Var', device: Optional[Union[str, int]] =
     # 如果outputs有_grad键，可以实现求导
     no_gradient = not jittor_var.requires_grad if no_gradient is None else no_gradient
     if no_gradient == False:
-        warnings.warn("The result tensor will not keep gradients due to differences between jittor and pytorch.")
+        logger.warn("The result tensor will not keep gradients due to differences between jittor and pytorch.")
     jittor_numpy = jittor_var.numpy()
     if not np.issubdtype(jittor_numpy.dtype, np.inexact):
         no_gradient = True

fastNLP/transformers/torch/models/auto/modeling_auto.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """ Auto Model class. """
 
-import warnings
 from collections import OrderedDict
 
 from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
@@ -307,7 +306,7 @@ AutoModelForSpeechSeq2Seq = auto_class_update(
 class AutoModelWithLMHead(_AutoModelWithLMHead):
     @classmethod
     def from_config(cls, config):
-        warnings.warn(
+        logger.warn(
             "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
             "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
             "`AutoModelForSeq2SeqLM` for encoder-decoder models.",
@@ -317,7 +316,7 @@ class AutoModelWithLMHead(_AutoModelWithLMHead):
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        warnings.warn(
+        logger.warn(
             "The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use "
             "`AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and "
             "`AutoModelForSeq2SeqLM` for encoder-decoder models.",

tests/modules/torch/encoder/test_seq2seq_encoder.py

@@ -10,6 +10,7 @@ if _NEED_IMPORT_TORCH:
     from fastNLP.embeddings.torch import StaticEmbedding
 
 
+@pytest.mark.torch
 class TestTransformerSeq2SeqEncoder:
     def test_case(self):
         vocab = Vocabulary().add_word_lst("This is a test .".split())

tests/modules/torch/generator/test_seq2seq_generator.py

@@ -43,7 +43,7 @@ class DummyState(State):
         super().__init__()
         self.decoder = decoder
 
-    def reorder_state(self, indices: torch.LongTensor):
+    def reorder_state(self, indices: "torch.LongTensor"):
         self.decoder.decoder_output = self._reorder_state(self.decoder.decoder_output, indices, dim=0)