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Merge branch 'release/1.1' of gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib into release/1.1

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wenmeng.zwm
2022-12-07 20:02:01 +08:00
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commit 8f8dc97c3a
14 changed files with 48 additions and 27 deletions
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15
README.md
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@@ -1,3 +1,18 @@
<div align="center">
[![PyPI](https://img.shields.io/pypi/v/modelscope)](https://pypi.org/project/modelscope/)
<!-- [![Documentation Status](https://readthedocs.org/projects/easy-cv/badge/?version=latest)](https://easy-cv.readthedocs.io/en/latest/) -->
[![license](https://img.shields.io/github/license/modelscope/modelscope.svg)](https://github.com/modelscope/modelscope/blob/master/LICENSE)
[![open issues](https://isitmaintained.com/badge/open/modelscope/modelscope.svg)](https://github.com/modelscope/modelscope/issues)
[![GitHub pull-requests](https://img.shields.io/github/issues-pr/modelscope/modelscope.svg)](https://GitHub.com/modelscope/modelscope/pull/)
[![GitHub latest commit](https://badgen.net/github/last-commit/modelscope/modelscope)](https://GitHub.com/modelscope/modelscope/commit/)
<!-- [![GitHub contributors](https://img.shields.io/github/contributors/modelscope/modelscope.svg)](https://GitHub.com/modelscope/modelscope/graphs/contributors/) -->
<!-- [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) -->
</div>
# Introduction
[ModelScope]( https://www.modelscope.cn) is a “Model-as-a-Service” (MaaS) platform that seeks to bring together most advanced machine learning models from the AI community, and to streamline the process of leveraging AI models in real applications. The core ModelScope library enables developers to perform inference, training and evaluation, through rich layers of API designs that facilitate a unified experience across state-of-the-art models from different AI domains.

2
modelscope/hub/git.py
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@@ -94,7 +94,7 @@ class GitCommandWrapper(metaclass=Singleton):
return False
def git_lfs_install(self, repo_dir):
cmd = ['git', '-C', repo_dir, 'lfs', 'install']
cmd = ['-C', repo_dir, 'lfs', 'install']
try:
self._run_git_command(*cmd)
return True

2
modelscope/models/nlp/T5/text2text_generation.py
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@@ -57,7 +57,7 @@ class T5ForConditionalGeneration(T5PreTrainedModel):
r'decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight',
]
def __init__(self, config: T5Config):
def __init__(self, config: T5Config, **kwargs):
super().__init__(config)
self.model_dim = config.d_model

2
modelscope/models/nlp/bert/document_segmentation.py
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@@ -24,7 +24,7 @@ class BertForDocumentSegmentation(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r'pooler']
def __init__(self, config):
def __init__(self, config, **kwargs):
super().__init__(config)
self.num_labels = config.num_labels
self.sentence_pooler_type = None

2
modelscope/models/nlp/bert/sentence_embedding.py
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@@ -11,7 +11,7 @@ from .backbone import BertModel, BertPreTrainedModel
@MODELS.register_module(Tasks.sentence_embedding, module_name=Models.bert)
class BertForSentenceEmbedding(BertPreTrainedModel):
def __init__(self, config):
def __init__(self, config, **kwargs):
super().__init__(config)
self.config = config
setattr(self, self.base_model_prefix,

2
modelscope/models/nlp/bert/text_classification.py
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@@ -66,7 +66,7 @@ class BertForSequenceClassification(BertPreTrainedModel):
weights.
"""
def __init__(self, config):
def __init__(self, config, **kwargs):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config

2
modelscope/models/nlp/ponet/document_segmentation.py
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@@ -25,7 +25,7 @@ __all__ = ['PoNetForDocumentSegmentation']
class PoNetForDocumentSegmentation(PoNetPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r'pooler']
def __init__(self, config):
def __init__(self, config, **kwargs):
super().__init__(config)
self.num_labels = config.num_labels

2
modelscope/pipelines/cv/video_human_matting_pipeline.py
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@@ -50,7 +50,7 @@ class VideoHumanMattingPipeline(Pipeline):
masks = []
rec = [None] * 4
self.model = self.model.to(self.device)
logger.info('matting start using ', self.device)
logger.info('matting start using ' + self.device)
with torch.no_grad():
while True:
if frame is None:

8
modelscope/pipelines/nlp/translation_evaluation_pipeline.py
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@@ -77,14 +77,14 @@ class TranslationEvaluationPipeline(Pipeline):
self.preprocessor.eval_mode = eval_mode
return
def __call__(self, input_dict: Dict[str, Union[str, List[str]]], **kwargs):
def __call__(self, input: Dict[str, Union[str, List[str]]], **kwargs):
r"""Implementation of __call__ function.
Args:
input_dict: The formatted dict containing the inputted sentences.
input: The formatted dict containing the inputted sentences.
An example of the formatted dict:
```
input_dict = {
input = {
'hyp': [
'This is a sentence.',
'This is another sentence.',
@@ -100,7 +100,7 @@ class TranslationEvaluationPipeline(Pipeline):
}
```
"""
return super().__call__(input=input_dict, **kwargs)
return super().__call__(input=input, **kwargs)
def forward(self,
input_ids: List[torch.Tensor]) -> Dict[str, torch.Tensor]:

7
modelscope/trainers/hooks/checkpoint_hook.py
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@@ -215,6 +215,10 @@ class CheckpointHook(Hook):
# TODO a temp fix to avoid pipeline_name and task mismatch
config['pipeline'] = {'type': config['task']}
# remove parallel module that is not JSON serializable
if 'parallel' in config and 'module' in config['parallel']:
del config['parallel']['module']
class SaveConfig:
def __init__(self, output_dir, config):
@@ -422,4 +426,5 @@ class BestCkptSaverHook(CheckpointHook):
def after_run(self, trainer):
if self.restore_best:
self.load_checkpoint(self._best_ckpt_file, trainer)
if is_master():
self.load_checkpoint(self._best_ckpt_file, trainer)

4
modelscope/version.py
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@@ -1,5 +1,5 @@
# Make sure to modify __release_datetime__ to release time when making official release.
__version__ = '1.1.0'
__version__ = '1.1.1'
# default release datetime for branches under active development is set
# to be a time far-far-away-into-the-future
__release_datetime__ = '2022-11-30 16:13:12'
__release_datetime__ = '2022-12-08 00:00:00'

2
requirements/audio.txt
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@@ -1,6 +1,6 @@
easyasr>=0.0.2
espnet==202204
funasr>=0.1.3
funasr>=0.1.4
h5py
inflect
keras

9
setup.py
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@@ -195,8 +195,8 @@ if __name__ == '__main__':
long_description_content_type='text/markdown',
author='Alibaba ModelScope team',
author_email='modelscope@list.alibaba-inc.com',
keywords='',
url='TBD',
keywords='python,nlp,science,cv,speech,multi-modal',
url='https://github.com/modelscope/modelscope',
packages=find_packages(exclude=('configs', 'tools', 'demo')),
include_package_data=True,
classifiers=[
@@ -204,9 +204,10 @@ if __name__ == '__main__':
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
],
license='Apache License 2.0',
tests_require=parse_requirements('requirements/tests.txt'),

16
tests/pipelines/test_translation_evaluation.py
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@@ -18,7 +18,7 @@ class TranslationEvaluationTest(unittest.TestCase, DemoCompatibilityCheck):
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_model_name_for_unite_large(self):
input_dict = {
input = {
'hyp': [
'This is a sentence.',
'This is another sentence.',
@@ -34,17 +34,17 @@ class TranslationEvaluationTest(unittest.TestCase, DemoCompatibilityCheck):
}
pipeline_ins = pipeline(self.task, model=self.model_id_large)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
pipeline_ins.change_eval_mode(eval_mode=EvaluationMode.SRC)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
pipeline_ins.change_eval_mode(eval_mode=EvaluationMode.REF)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_model_name_for_unite_base(self):
input_dict = {
input = {
'hyp': [
'This is a sentence.',
'This is another sentence.',
@@ -60,13 +60,13 @@ class TranslationEvaluationTest(unittest.TestCase, DemoCompatibilityCheck):
}
pipeline_ins = pipeline(self.task, model=self.model_id_base)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
pipeline_ins.change_eval_mode(eval_mode=EvaluationMode.SRC)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
pipeline_ins.change_eval_mode(eval_mode=EvaluationMode.REF)
print(pipeline_ins(input_dict))
print(pipeline_ins(input=input))
if __name__ == '__main__':