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Merge branch merge_master_github_0626 into master

Browse Source 
Title: merge_master_github_0626 
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/13071322
This commit is contained in:
wenmeng.zwm
2023-06-26 23:31:22 +08:00
parent 7e28ca226b 341cdd20a8
commit e9fba3c042
10 changed files with 39 additions and 47 deletions
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1
.github/workflows/citest.yaml vendored
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@@ -40,6 +40,7 @@ jobs:
unittest:
# The type of runner that the job will run on
runs-on: [modelscope-self-hosted]
timeout-minutes: 240
steps:
- name: ResetFileMode
shell: bash

1
MANIFEST.in
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@@ -1 +1,2 @@
recursive-include modelscope/configs *.py *.cu *.h *.cpp
recursive-include modelscope/cli/template *.tpl

12
README.md
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@@ -203,12 +203,20 @@ To allow out-of-box usage for all the models on ModelScope, official docker imag
CPU docker image
```shell
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.3.0
# py37
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.6.1
# py38
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch1.11.0-tf1.15.5-1.6.1
```
GPU docker image
```shell
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.3.0
# py37
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.6.1
# py38
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py38-torch1.11.0-tf1.15.5-1.6.1
```
## Setup Local Python Environment

12
README_zh.md
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@@ -189,12 +189,20 @@ ModelScope Library目前支持tensorflow，pytorch深度学习框架进行模型
CPU镜像
```shell
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.3.0
# py37
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.6.1
# py38
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch1.11.0-tf1.15.5-1.6.1
```
GPU镜像
```shell
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.3.0
# py37
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.6.1
# py38
registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py38-torch1.11.0-tf1.15.5-1.6.1
```
## 搭建本地Python环境

4
modelscope/cli/modelcard.py
View File

@@ -13,8 +13,8 @@ from modelscope.utils.logger import get_logger
logger = get_logger()
curren_path = os.path.dirname(os.path.abspath(__file__))
template_path = os.path.join(curren_path, 'template')
current_path = os.path.dirname(os.path.abspath(__file__))
template_path = os.path.join(current_path, 'template')
def subparser_func(args):

4
modelscope/cli/pipeline.py
View File

@@ -8,8 +8,8 @@ from modelscope.utils.logger import get_logger
logger = get_logger()
curren_path = os.path.dirname(os.path.abspath(__file__))
template_path = os.path.join(curren_path, 'template')
current_path = os.path.dirname(os.path.abspath(__file__))
template_path = os.path.join(current_path, 'template')
def subparser_func(args):

2
modelscope/preprocessors/nlp/space/batch.py
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@@ -50,7 +50,7 @@ def batch(reader, batch_size, drop_last=False):
# Batch size check
batch_size = int(batch_size)
if batch_size <= 0:
raise ValueError('batch_size should be a positive integeral value, '
raise ValueError('batch_size should be a positive integer value, '
'but got batch_size={}'.format(batch_size))
return batch_reader

2
modelscope/preprocessors/nlp/space/fields/intent_field.py
View File

@@ -344,7 +344,7 @@ class BPETextField(object):
print(f'Saved {len(examples)} examples (elapsed {elapsed:.2f}s)')
else:
print(f"Saving examples to '{filename}' ...")
raise ValueError(f'Unsport file format: {filename}')
raise ValueError(f'Unsupport file format: {filename}')
def load_examples(self, filename):
start = time.time()

8
modelscope/tools/speech_tts_autolabel.py
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@@ -39,7 +39,7 @@ def run_auto_label(input_wav,
if not os.path.exists(work_dir):
raise ValueError(f'work_dir: {work_dir} not exists')
def _download_and_unzip_resousrce(model, model_revision=None):
def _download_and_unzip_resource(model, model_revision=None):
if os.path.exists(model):
model_cache_dir = model if os.path.isdir(
model) else os.path.dirname(model)
@@ -52,7 +52,7 @@ def run_auto_label(input_wav,
revision=model_revision,
user_agent={ThirdParty.KEY: 'speech_tts_autolabel'})
if not os.path.exists(model_cache_dir):
raise ValueError(f'mdoel_cache_dir: {model_cache_dir} not exists')
raise ValueError(f'model_cache_dir: {model_cache_dir} not exists')
zip_file = os.path.join(model_cache_dir, 'model.zip')
if not os.path.exists(zip_file):
raise ValueError(f'zip_file: {zip_file} not exists')
@@ -61,8 +61,8 @@ def run_auto_label(input_wav,
target_resource = os.path.join(model_cache_dir, 'model')
return target_resource
model_resource = _download_and_unzip_resousrce(resource_model_id,
resource_revision)
model_resource = _download_and_unzip_resource(resource_model_id,
resource_revision)
auto_labeling = AutoLabeling(
os.path.abspath(input_wav),
model_resource,

40
tests/pipelines/test_controllable_image_generation.py
View File

@@ -1,4 +1,5 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
import random
import tempfile
import unittest
@@ -27,40 +28,13 @@ class ControllableImageGenerationTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_run_with_model_from_modelhub(self):
output_image_path = tempfile.NamedTemporaryFile(suffix='.png').name
control_types = [
'canny', 'hough', 'hed', 'depth', 'normal', 'pose', 'seg',
'fake_scribble', 'scribble'
]
control_type = random.choice(control_types)
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='canny')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='hough')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='hed')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='depth')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='normal')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='pose')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='seg')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='fake_scribble')
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
pipeline_ins = pipeline(
self.task, model=self.model_id, control_type='scribble')
self.task, model=self.model_id, control_type=control_type)
output = pipeline_ins(input=self.input)[OutputKeys.OUTPUT_IMG]
cv2.imwrite(output_image_path, output)
print(