SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

better error messages

Browse Source
This commit is contained in:
thomwolf
2019-10-10 11:31:01 +02:00
parent 43a237f15e
commit a5997dd81a
4 changed files with 57 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

19
transformers/configuration_utils.py
View File

@@ -130,20 +130,19 @@ class PretrainedConfig(object):
# redirect to the cache, if necessary
try:
resolved_config_file = cached_path(config_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
except EnvironmentError as e:
except EnvironmentError:
if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
logger.error(
"Couldn't reach server at '{}' to download pretrained model configuration file.".format(
config_file))
msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
config_file)
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
msg = "Model name '{}' was not found in model name list ({}). " \
"We assumed '{}' was a path or url to a configuration file named {} or " \
"a directory containing such a file but couldn't find any such file at this path or url.".format(
pretrained_model_name_or_path,
', '.join(cls.pretrained_config_archive_map.keys()),
config_file))
raise e
config_file, CONFIG_NAME)
raise EnvironmentError(msg)
if resolved_config_file == config_file:
logger.info("loading configuration file {}".format(config_file))
else:

20
transformers/modeling_utils.py
View File

@@ -316,20 +316,20 @@ class PreTrainedModel(nn.Module):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
except EnvironmentError as e:
except EnvironmentError:
if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
logger.error(
"Couldn't reach server at '{}' to download pretrained weights.".format(
archive_file))
msg = "Couldn't reach server at '{}' to download pretrained weights.".format(
archive_file)
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
msg = "Model name '{}' was not found in model name list ({}). " \
"We assumed '{}' was a path or url to model weight files named one of {} but " \
"couldn't find any such file at this path or url.".format(
pretrained_model_name_or_path,
', '.join(cls.pretrained_model_archive_map.keys()),
archive_file))
raise e
archive_file,
[WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME])
raise EnvironmentError(msg)
if resolved_archive_file == archive_file:
logger.info("loading weights file {}".format(archive_file))
else:

29
transformers/tokenization_utils.py
View File

@@ -337,13 +337,13 @@ class PreTrainedTokenizer(object):
vocab_files[file_id] = full_file_name
if all(full_file_name is None for full_file_name in vocab_files.values()):
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find tokenizer files"
"at this path or url.".format(
raise EnvironmentError(
"Model name '{}' was not found in tokenizers model name list ({}). "
"We assumed '{}' was a path or url to a directory containing vocabulary files "
"named {} but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path, ))
return None
pretrained_model_name_or_path,
list(cls.vocab_files_names.values())))
# Get files from url, cache, or disk depending on the case
try:
@@ -353,17 +353,18 @@ class PreTrainedTokenizer(object):
resolved_vocab_files[file_id] = None
else:
resolved_vocab_files[file_id] = cached_path(file_path, cache_dir=cache_dir, force_download=force_download, proxies=proxies)
except EnvironmentError as e:
except EnvironmentError:
if pretrained_model_name_or_path in s3_models:
logger.error("Couldn't reach server to download vocabulary.")
msg = "Couldn't reach server at '{}' to download vocabulary files."
else:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} "
"at this path or url.".format(
msg = "Model name '{}' was not found in tokenizers model name list ({}). " \
"We assumed '{}' was a path or url to a directory containing vocabulary files " \
"named {}, but couldn't find such vocabulary files at this path or url.".format(
pretrained_model_name_or_path, ', '.join(s3_models),
pretrained_model_name_or_path, str(vocab_files.keys())))
raise e
pretrained_model_name_or_path,
list(cls.vocab_files_names.values()))
raise EnvironmentError(msg)
for file_id, file_path in vocab_files.items():
if file_path == resolved_vocab_files[file_id]: