mirror of
https://github.com/modelscope/modelscope.git
synced 2026-07-13 13:59:40 +02:00
merge master
This commit is contained in:
ly119399
@@ -4,5 +4,5 @@ rm -rf build
|
||||
|
||||
# update api rst
|
||||
#rm -rf source/api/
|
||||
#sphinx-apidoc --module-first -o source/api/ ../maas_lib/
|
||||
#sphinx-apidoc --module-first -o source/api/ ../modelscope/
|
||||
make html
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
yapf -r -i maas_lib/ configs/ tests/ setup.py
|
||||
isort -rc maas_lib/ configs/ tests/ setup.py
|
||||
flake8 maas_lib/ configs/ tests/ setup.py
|
||||
yapf -r -i modelscope/ configs/ tests/ setup.py
|
||||
isort -rc modelscope/ configs/ tests/ setup.py
|
||||
flake8 modelscope/ configs/ tests/ setup.py
|
||||
|
||||
4
LICENSE
4
LICENSE
@@ -1,4 +1,4 @@
|
||||
Copyright 2022-2023 Alibaba MaaS. All rights reserved.
|
||||
Copyright 2022-2023 Alibaba ModelScope. All rights reserved.
|
||||
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
@@ -188,7 +188,7 @@ Copyright 2022-2023 Alibaba MaaS. All rights reserved.
|
||||
same "printed page" as the copyright notice for easier
|
||||
identification within third-party archives.
|
||||
|
||||
Copyright 2020-2022 Alibaba MaaS.
|
||||
Copyright 2020-2022 Alibaba ModelScope.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
|
||||
@@ -1 +1 @@
|
||||
recursive-include maas_lib/configs *.py
|
||||
recursive-include modelscope/configs *.py
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
# Introduction
|
||||
|
||||
MaaS library is targeted to support training, evaluation and inference for the state of the art models provided by Mind and further support third-party models provided by users outside alibaba.
|
||||
ModelScope library is targeted to support training, evaluation and inference for the state of the art models provided by Mind and further support third-party models provided by users outside alibaba.
|
||||
|
||||
# Design doc
|
||||
|
||||
|
||||
@@ -1 +1 @@
|
||||
This folder will host example configs for each model supported by maas_lib.
|
||||
This folder will host example configs for each model supported by modelscope.
|
||||
|
||||
@@ -1,34 +0,0 @@
|
||||
maas\_lib.fileio.format package
|
||||
===============================
|
||||
|
||||
.. automodule:: maas_lib.fileio.format
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.fileio.format.base module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.fileio.format.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.fileio.format.json module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.fileio.format.json
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.fileio.format.yaml module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.fileio.format.yaml
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,34 +0,0 @@
|
||||
maas\_lib.fileio package
|
||||
========================
|
||||
|
||||
.. automodule:: maas_lib.fileio
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib.fileio.format
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.fileio.file module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: maas_lib.fileio.file
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.fileio.io module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: maas_lib.fileio.io
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,18 +0,0 @@
|
||||
maas\_lib.models.nlp package
|
||||
============================
|
||||
|
||||
.. automodule:: maas_lib.models.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.models.nlp.sequence\_classification\_model module
|
||||
-----------------------------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.models.nlp.sequence_classification_model
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,34 +0,0 @@
|
||||
maas\_lib.models package
|
||||
========================
|
||||
|
||||
.. automodule:: maas_lib.models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib.models.nlp
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.models.base module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: maas_lib.models.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.models.builder module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: maas_lib.models.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,7 +0,0 @@
|
||||
maas\_lib.pipelines.audio package
|
||||
=================================
|
||||
|
||||
.. automodule:: maas_lib.pipelines.audio
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,18 +0,0 @@
|
||||
maas\_lib.pipelines.cv package
|
||||
==============================
|
||||
|
||||
.. automodule:: maas_lib.pipelines.cv
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.pipelines.cv.image\_matting module
|
||||
--------------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.pipelines.cv.image_matting
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,7 +0,0 @@
|
||||
maas\_lib.pipelines.multi\_modal package
|
||||
========================================
|
||||
|
||||
.. automodule:: maas_lib.pipelines.multi_modal
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,50 +0,0 @@
|
||||
maas\_lib.preprocessors package
|
||||
===============================
|
||||
|
||||
.. automodule:: maas_lib.preprocessors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.preprocessors.base module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.preprocessors.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.preprocessors.builder module
|
||||
--------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.preprocessors.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.preprocessors.common module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.preprocessors.common
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.preprocessors.image module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.preprocessors.image
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.preprocessors.nlp module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.preprocessors.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,30 +0,0 @@
|
||||
maas\_lib package
|
||||
=================
|
||||
|
||||
.. automodule:: maas_lib
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib.fileio
|
||||
maas_lib.models
|
||||
maas_lib.pipelines
|
||||
maas_lib.preprocessors
|
||||
maas_lib.utils
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.version module
|
||||
------------------------
|
||||
|
||||
.. automodule:: maas_lib.version
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,18 +0,0 @@
|
||||
maas\_lib.trainers.nlp package
|
||||
==============================
|
||||
|
||||
.. automodule:: maas_lib.trainers.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.trainers.nlp.sequence\_classification\_trainer module
|
||||
---------------------------------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.trainers.nlp.sequence_classification_trainer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,34 +0,0 @@
|
||||
maas\_lib.trainers package
|
||||
==========================
|
||||
|
||||
.. automodule:: maas_lib.trainers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib.trainers.nlp
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.trainers.base module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: maas_lib.trainers.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.trainers.builder module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: maas_lib.trainers.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,58 +0,0 @@
|
||||
maas\_lib.utils package
|
||||
=======================
|
||||
|
||||
.. automodule:: maas_lib.utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.utils.config module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.config
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.utils.constant module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.constant
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.utils.logger module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.logger
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.utils.pymod module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.pymod
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.utils.registry module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.registry
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.utils.type\_assert module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.utils.type_assert
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
34
docs/source/api/modelscope.fileio.format.rst
Normal file
34
docs/source/api/modelscope.fileio.format.rst
Normal file
@@ -0,0 +1,34 @@
|
||||
modelscope.fileio.format package
|
||||
================================
|
||||
|
||||
.. automodule:: modelscope.fileio.format
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.fileio.format.base module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: modelscope.fileio.format.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.fileio.format.json module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: modelscope.fileio.format.json
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.fileio.format.yaml module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: modelscope.fileio.format.yaml
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
34
docs/source/api/modelscope.fileio.rst
Normal file
34
docs/source/api/modelscope.fileio.rst
Normal file
@@ -0,0 +1,34 @@
|
||||
modelscope.fileio package
|
||||
=========================
|
||||
|
||||
.. automodule:: modelscope.fileio
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.fileio.format
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.fileio.file module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: modelscope.fileio.file
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.fileio.io module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: modelscope.fileio.io
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
18
docs/source/api/modelscope.models.cv.cartoon.facelib.LK.rst
Normal file
18
docs/source/api/modelscope.models.cv.cartoon.facelib.LK.rst
Normal file
@@ -0,0 +1,18 @@
|
||||
modelscope.models.cv.cartoon.facelib.LK package
|
||||
===============================================
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.LK
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.LK.lk module
|
||||
-------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.LK.lk
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
50
docs/source/api/modelscope.models.cv.cartoon.facelib.rst
Normal file
50
docs/source/api/modelscope.models.cv.cartoon.facelib.rst
Normal file
@@ -0,0 +1,50 @@
|
||||
modelscope.models.cv.cartoon.facelib package
|
||||
============================================
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.LK
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.config module
|
||||
--------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.config
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.face\_detector module
|
||||
----------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.face_detector
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.face\_landmark module
|
||||
----------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.face_landmark
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.cv.cartoon.facelib.facer module
|
||||
-------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.facelib.facer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -0,0 +1,15 @@
|
||||
modelscope.models.cv.cartoon.mtcnn\_pytorch package
|
||||
===================================================
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.mtcnn_pytorch
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.models.cv.cartoon.mtcnn_pytorch.src
|
||||
@@ -0,0 +1,26 @@
|
||||
modelscope.models.cv.cartoon.mtcnn\_pytorch.src package
|
||||
=======================================================
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.mtcnn_pytorch.src
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.cv.cartoon.mtcnn\_pytorch.src.align\_trans module
|
||||
-------------------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.mtcnn_pytorch.src.align_trans
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.cv.cartoon.mtcnn\_pytorch.src.matlab\_cp2tform module
|
||||
-----------------------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.mtcnn_pytorch.src.matlab_cp2tform
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
27
docs/source/api/modelscope.models.cv.cartoon.rst
Normal file
27
docs/source/api/modelscope.models.cv.cartoon.rst
Normal file
@@ -0,0 +1,27 @@
|
||||
modelscope.models.cv.cartoon package
|
||||
====================================
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.models.cv.cartoon.facelib
|
||||
modelscope.models.cv.cartoon.mtcnn_pytorch
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.cv.cartoon.utils module
|
||||
-----------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.cv.cartoon.utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
15
docs/source/api/modelscope.models.cv.rst
Normal file
15
docs/source/api/modelscope.models.cv.rst
Normal file
@@ -0,0 +1,15 @@
|
||||
modelscope.models.cv package
|
||||
============================
|
||||
|
||||
.. automodule:: modelscope.models.cv
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.models.cv.cartoon
|
||||
26
docs/source/api/modelscope.models.nlp.rst
Normal file
26
docs/source/api/modelscope.models.nlp.rst
Normal file
@@ -0,0 +1,26 @@
|
||||
modelscope.models.nlp package
|
||||
=============================
|
||||
|
||||
.. automodule:: modelscope.models.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.nlp.sequence\_classification\_model module
|
||||
------------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.nlp.sequence_classification_model
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.nlp.text\_generation\_model module
|
||||
----------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.nlp.text_generation_model
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
35
docs/source/api/modelscope.models.rst
Normal file
35
docs/source/api/modelscope.models.rst
Normal file
@@ -0,0 +1,35 @@
|
||||
modelscope.models package
|
||||
=========================
|
||||
|
||||
.. automodule:: modelscope.models
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.models.cv
|
||||
modelscope.models.nlp
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.models.base module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: modelscope.models.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.models.builder module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: modelscope.models.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
7
docs/source/api/modelscope.pipelines.audio.rst
Normal file
7
docs/source/api/modelscope.pipelines.audio.rst
Normal file
@@ -0,0 +1,7 @@
|
||||
modelscope.pipelines.audio package
|
||||
==================================
|
||||
|
||||
.. automodule:: modelscope.pipelines.audio
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
26
docs/source/api/modelscope.pipelines.cv.rst
Normal file
26
docs/source/api/modelscope.pipelines.cv.rst
Normal file
@@ -0,0 +1,26 @@
|
||||
modelscope.pipelines.cv package
|
||||
===============================
|
||||
|
||||
.. automodule:: modelscope.pipelines.cv
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.pipelines.cv.image\_cartoon\_pipeline module
|
||||
-------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.cv.image_cartoon_pipeline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.pipelines.cv.image\_matting\_pipeline module
|
||||
-------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.cv.image_matting_pipeline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
18
docs/source/api/modelscope.pipelines.multi_modal.rst
Normal file
18
docs/source/api/modelscope.pipelines.multi_modal.rst
Normal file
@@ -0,0 +1,18 @@
|
||||
modelscope.pipelines.multi\_modal package
|
||||
=========================================
|
||||
|
||||
.. automodule:: modelscope.pipelines.multi_modal
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.pipelines.multi\_modal.image\_captioning module
|
||||
----------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.multi_modal.image_captioning
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
26
docs/source/api/modelscope.pipelines.nlp.rst
Normal file
26
docs/source/api/modelscope.pipelines.nlp.rst
Normal file
@@ -0,0 +1,26 @@
|
||||
modelscope.pipelines.nlp package
|
||||
================================
|
||||
|
||||
.. automodule:: modelscope.pipelines.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.pipelines.nlp.sequence\_classification\_pipeline module
|
||||
------------------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.nlp.sequence_classification_pipeline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.pipelines.nlp.text\_generation\_pipeline module
|
||||
----------------------------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.nlp.text_generation_pipeline
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
53
docs/source/api/modelscope.pipelines.rst
Normal file
53
docs/source/api/modelscope.pipelines.rst
Normal file
@@ -0,0 +1,53 @@
|
||||
modelscope.pipelines package
|
||||
============================
|
||||
|
||||
.. automodule:: modelscope.pipelines
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.pipelines.audio
|
||||
modelscope.pipelines.cv
|
||||
modelscope.pipelines.multi_modal
|
||||
modelscope.pipelines.nlp
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.pipelines.base module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.pipelines.builder module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.pipelines.default module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.default
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.pipelines.util module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: modelscope.pipelines.util
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
50
docs/source/api/modelscope.preprocessors.rst
Normal file
50
docs/source/api/modelscope.preprocessors.rst
Normal file
@@ -0,0 +1,50 @@
|
||||
modelscope.preprocessors package
|
||||
================================
|
||||
|
||||
.. automodule:: modelscope.preprocessors
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.preprocessors.base module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: modelscope.preprocessors.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.preprocessors.builder module
|
||||
---------------------------------------
|
||||
|
||||
.. automodule:: modelscope.preprocessors.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.preprocessors.common module
|
||||
--------------------------------------
|
||||
|
||||
.. automodule:: modelscope.preprocessors.common
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.preprocessors.image module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: modelscope.preprocessors.image
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.preprocessors.nlp module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: modelscope.preprocessors.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
18
docs/source/api/modelscope.pydatasets.rst
Normal file
18
docs/source/api/modelscope.pydatasets.rst
Normal file
@@ -0,0 +1,18 @@
|
||||
modelscope.pydatasets package
|
||||
=============================
|
||||
|
||||
.. automodule:: modelscope.pydatasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.pydatasets.py\_dataset module
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: modelscope.pydatasets.py_dataset
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
32
docs/source/api/modelscope.rst
Normal file
32
docs/source/api/modelscope.rst
Normal file
@@ -0,0 +1,32 @@
|
||||
modelscope package
|
||||
==================
|
||||
|
||||
.. automodule:: modelscope
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
modelscope.fileio
|
||||
modelscope.models
|
||||
modelscope.pipelines
|
||||
modelscope.preprocessors
|
||||
modelscope.pydatasets
|
||||
modelscope.trainers
|
||||
modelscope.utils
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.version module
|
||||
-------------------------
|
||||
|
||||
.. automodule:: modelscope.version
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,7 +1,7 @@
|
||||
maas\_lib.pipelines.nlp package
|
||||
modelscope.trainers.nlp package
|
||||
===============================
|
||||
|
||||
.. automodule:: maas_lib.pipelines.nlp
|
||||
.. automodule:: modelscope.trainers.nlp
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -9,10 +9,10 @@ maas\_lib.pipelines.nlp package
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.pipelines.nlp.sequence\_classification\_pipeline module
|
||||
-----------------------------------------------------------------
|
||||
modelscope.trainers.nlp.sequence\_classification\_trainer module
|
||||
----------------------------------------------------------------
|
||||
|
||||
.. automodule:: maas_lib.pipelines.nlp.sequence_classification_pipeline
|
||||
.. automodule:: modelscope.trainers.nlp.sequence_classification_trainer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,7 +1,7 @@
|
||||
maas\_lib.pipelines package
|
||||
modelscope.trainers package
|
||||
===========================
|
||||
|
||||
.. automodule:: maas_lib.pipelines
|
||||
.. automodule:: modelscope.trainers
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -12,25 +12,23 @@ Subpackages
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib.pipelines.cv
|
||||
maas_lib.pipelines.multi_modal
|
||||
maas_lib.pipelines.nlp
|
||||
modelscope.trainers.nlp
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
maas\_lib.pipelines.base module
|
||||
modelscope.trainers.base module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: maas_lib.pipelines.base
|
||||
.. automodule:: modelscope.trainers.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
maas\_lib.pipelines.builder module
|
||||
modelscope.trainers.builder module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: maas_lib.pipelines.builder
|
||||
.. automodule:: modelscope.trainers.builder
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
66
docs/source/api/modelscope.utils.rst
Normal file
66
docs/source/api/modelscope.utils.rst
Normal file
@@ -0,0 +1,66 @@
|
||||
modelscope.utils package
|
||||
========================
|
||||
|
||||
.. automodule:: modelscope.utils
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
modelscope.utils.config module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.config
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.constant module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.constant
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.hub module
|
||||
---------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.hub
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.logger module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.logger
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.pymod module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.pymod
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.registry module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.registry
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
modelscope.utils.type\_assert module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: modelscope.utils.type_assert
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
@@ -1,7 +1,7 @@
|
||||
maas_lib
|
||||
========
|
||||
modelscope
|
||||
==========
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
maas_lib
|
||||
modelscope
|
||||
|
||||
@@ -18,10 +18,10 @@ import sphinx_rtd_theme
|
||||
sys.path.insert(0, os.path.abspath('../../'))
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = 'maas_lib'
|
||||
copyright = '2022-2023, Alibaba MaaS'
|
||||
author = 'maas_lib Authors'
|
||||
version_file = '../../maas_lib/version.py'
|
||||
project = 'modelscope'
|
||||
copyright = '2022-2023, Alibaba ModelScope'
|
||||
author = 'modelscope Authors'
|
||||
version_file = '../../modelscope/version.py'
|
||||
|
||||
|
||||
def get_version():
|
||||
@@ -88,7 +88,7 @@ html_static_path = ['_static']
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'maas_lib_doc'
|
||||
htmlhelp_basename = 'modelscope_doc'
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
# Ignore >>> when copying code
|
||||
|
||||
@@ -10,39 +10,86 @@ We use the following toolsseed isortseed isortseed isort for linting and formatt
|
||||
|
||||
Style configurations of yapf and isort can be found in [setup.cfg](../../setup.cfg).
|
||||
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `seed-isort-config`, `isort`, `trailing whitespaces`,
|
||||
fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
|
||||
The config for a pre-commit hook is stored in [.pre-commit-config](../../.pre-commit-config.yaml).
|
||||
After you clone the repository, you will need to install initialize pre-commit hook.
|
||||
```bash
|
||||
pip install -r requirements/tests.txt
|
||||
```
|
||||
From the repository folder
|
||||
```bash
|
||||
pre-commit install
|
||||
```
|
||||
fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
|
||||
The config for a pre-commit hook is stored in [.pre-commit-config](../../.pre-commit-config.yaml).
|
||||
After you clone the repository, you will need to install initialize pre-commit hook.
|
||||
```bash
|
||||
pip install -r requirements/tests.txt
|
||||
```
|
||||
From the repository folder
|
||||
```bash
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
After this on every commit check code linters and formatter will be enforced.
|
||||
After this on every commit check code linters and formatter will be enforced.
|
||||
|
||||
If you want to use pre-commit to check all the files, you can run
|
||||
```bash
|
||||
pre-commit run --all-files
|
||||
```
|
||||
If you want to use pre-commit to check all the files, you can run
|
||||
```bash
|
||||
pre-commit run --all-files
|
||||
```
|
||||
|
||||
If you only want to format and lint your code, you can run
|
||||
```bash
|
||||
make linter
|
||||
```
|
||||
If you only want to format and lint your code, you can run
|
||||
```bash
|
||||
make linter
|
||||
```
|
||||
|
||||
## 2. Test
|
||||
### 2.1 Unit test
|
||||
```bash
|
||||
make test
|
||||
```
|
||||
## 2. Test
|
||||
### 2.1 Unit test
|
||||
```bash
|
||||
make test
|
||||
```
|
||||
|
||||
### 2.2 Test data
|
||||
TODO
|
||||
### 2.2 Test data
|
||||
TODO
|
||||
|
||||
## 3. Build pip package
|
||||
```bash
|
||||
make whl
|
||||
```
|
||||
## Code Review
|
||||
|
||||
1. Run following command to create an aone CR, replace `TARGET_BRANCH` and `CR_NAME` with the one you want.
|
||||
```shell
|
||||
git push origin HEAD:refs/for/TARGET_BRANCH/CR_NAME
|
||||
```
|
||||
|
||||
Please refer to [https://yuque.antfin.com/aone/platform/lcg8yr](https://yuque.antfin.com/aone/platform/lcg8yr) for more details.
|
||||
|
||||
The following output is expected.
|
||||
```shell
|
||||
Counting objects: 5, done.
|
||||
Delta compression using up to 96 threads.
|
||||
Compressing objects: 100% (5/5), done.
|
||||
Writing objects: 100% (5/5), 543 bytes | 0 bytes/s, done.
|
||||
Total 5 (delta 4), reused 0 (delta 0)
|
||||
remote: +------------------------------------------------------------------------+
|
||||
remote: | Merge Request #8949062 was created or updated. |
|
||||
remote: | View merge request at URL: |
|
||||
remote: | https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8949062 |
|
||||
remote: +------------------------------------------------------------------------+
|
||||
To git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git
|
||||
* [new branch] HEAD -> refs/for/master/support_kwargs_pipeline
|
||||
```
|
||||
|
||||
2. Open the remote url `https://code.aone.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/ID` and edit the title of CR with following format before merging your code:
|
||||
* Feature
|
||||
```shell
|
||||
[to #AONE_ID] feat: commit title
|
||||
|
||||
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8949062
|
||||
|
||||
* commit msg1
|
||||
* commit msg2
|
||||
```
|
||||
* Bugfix
|
||||
```shell
|
||||
[to #AONE_ID] fix: commit title
|
||||
|
||||
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8949062
|
||||
|
||||
* commit msg1
|
||||
* commit msg2
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Build pip package
|
||||
```bash
|
||||
make whl
|
||||
```
|
||||
|
||||
@@ -1,11 +1,11 @@
|
||||
.. maas_lib documentation file,
|
||||
.. modelscope documentation file,
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
MaasLib DOCUMENTATION
|
||||
ModelScope DOCUMENTATION
|
||||
=======================================
|
||||
|
||||
MaasLib doc
|
||||
ModelScope doc
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
@@ -30,11 +30,11 @@ MaasLib doc
|
||||
:maxdepth: 10
|
||||
:caption: API Doc
|
||||
|
||||
api/maas_lib.preprocessors
|
||||
api/maas_lib.models
|
||||
api/maas_lib.pipelines
|
||||
api/maas_lib.fileio
|
||||
api/maas_lib.utils
|
||||
api/modelscope.preprocessors
|
||||
api/modelscope.models
|
||||
api/modelscope.pipelines
|
||||
api/modelscope.fileio
|
||||
api/modelscope.utils
|
||||
|
||||
|
||||
Indices and tables
|
||||
|
||||
@@ -5,39 +5,39 @@
|
||||
|
||||
安装完成后，执行如下命令为maas library创建对应的python环境。
|
||||
```shell
|
||||
conda create -n maas python=3.6
|
||||
conda activate maas
|
||||
conda create -n modelscope python=3.6
|
||||
conda activate modelscope
|
||||
```
|
||||
检查python和pip命令是否切换到conda环境下。
|
||||
```shell
|
||||
which python
|
||||
# ~/workspace/anaconda3/envs/maas/bin/python
|
||||
# ~/workspace/anaconda3/envs/modelscope/bin/python
|
||||
|
||||
which pip
|
||||
# ~/workspace/anaconda3/envs/maas/bin/pip
|
||||
# ~/workspace/anaconda3/envs/modelscope/bin/pip
|
||||
```
|
||||
注： 本项目只支持`python3`环境，请勿使用python2环境。
|
||||
|
||||
## 第三方依赖安装
|
||||
|
||||
MaaS Library目前支持tensorflow，pytorch两大深度学习框架进行模型训练、推理， 在Python 3.6+, Pytorch 1.8+, Tensorflow 2.6上测试可运行，用户可以根据所选模型对应的计算框架进行安装，可以参考如下链接进行安装所需框架:
|
||||
ModelScope Library目前支持tensorflow，pytorch两大深度学习框架进行模型训练、推理， 在Python 3.6+, Pytorch 1.8+, Tensorflow 2.6上测试可运行，用户可以根据所选模型对应的计算框架进行安装，可以参考如下链接进行安装所需框架:
|
||||
|
||||
* [Pytorch安装指导](https://pytorch.org/get-started/locally/)
|
||||
* [Tensorflow安装指导](https://www.tensorflow.org/install/pip)
|
||||
|
||||
|
||||
## MaaS library 安装
|
||||
## ModelScope library 安装
|
||||
|
||||
注： 如果在安装过程中遇到错误，请前往[常见问题](faq.md)查找解决方案。
|
||||
|
||||
### pip安装
|
||||
```shell
|
||||
pip install -r http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/maas.txt
|
||||
pip install -r http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/modelscope.txt
|
||||
```
|
||||
|
||||
安装成功后，可以执行如下命令进行验证安装是否正确
|
||||
```shell
|
||||
python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
|
||||
python -c "from modelscope.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
|
||||
```
|
||||
|
||||
|
||||
@@ -45,11 +45,11 @@ python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting
|
||||
|
||||
适合本地开发调试使用，修改源码后可以直接执行
|
||||
```shell
|
||||
git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git maaslib
|
||||
git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git modelscope
|
||||
git fetch origin master
|
||||
git checkout master
|
||||
|
||||
cd maaslib
|
||||
cd modelscope
|
||||
|
||||
#安装依赖
|
||||
pip install -r requirements.txt
|
||||
@@ -60,7 +60,7 @@ export PYTHONPATH=`pwd`
|
||||
|
||||
安装成功后，可以执行如下命令进行验证安装是否正确
|
||||
```shell
|
||||
python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
|
||||
python -c "from modelscope.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
|
||||
```
|
||||
|
||||
|
||||
@@ -79,8 +79,8 @@ pipeline函数提供了简洁的推理接口，示例如下， 更多pipeline介
|
||||
```python
|
||||
import cv2
|
||||
import os.path as osp
|
||||
from maas_lib.pipelines import pipeline
|
||||
from maas_lib.utils.constant import Tasks
|
||||
from modelscope.pipelines import pipeline
|
||||
from modelscope.utils.constant import Tasks
|
||||
|
||||
# 根据任务名创建pipeline
|
||||
img_matting = pipeline(Tasks.image_matting, model='damo/image-matting-person')
|
||||
@@ -95,12 +95,13 @@ print(f'Output written to {osp.abspath("result.png")}')
|
||||
```
|
||||
|
||||
此外，pipeline接口也能接收Dataset作为输入，上面的代码同样可以实现为
|
||||
|
||||
```python
|
||||
import cv2
|
||||
import os.path as osp
|
||||
from maas_lib.pipelines import pipeline
|
||||
from maas_lib.utils.constant import Tasks
|
||||
from ali_maas_datasets import PyDataset
|
||||
from modelscope.pipelines import pipeline
|
||||
from modelscope.utils.constant import Tasks
|
||||
from modelscope.pydatasets import PyDataset
|
||||
|
||||
# 使用图像url构建PyDataset，此处也可通过 input_location = '/dir/to/images' 来使用本地文件夹
|
||||
input_location = [
|
||||
|
||||
@@ -19,7 +19,7 @@
|
||||
1. pipeline函数支持指定特定任务名称，加载任务默认模型，创建对应Pipeline对象
|
||||
执行如下python代码
|
||||
```python
|
||||
>>> from maas_lib.pipelines import pipeline
|
||||
>>> from modelscope.pipelines import pipeline
|
||||
>>> img_matting = pipeline(task='image-matting', model='damo/image-matting-person')
|
||||
```
|
||||
|
||||
@@ -65,8 +65,8 @@ wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/easynlp_modelz
|
||||
|
||||
创建tokenizer和模型
|
||||
```python
|
||||
>>> from maas_lib.models import Model
|
||||
>>> from maas_lib.preprocessors import SequenceClassificationPreprocessor
|
||||
>>> from modelscope.models import Model
|
||||
>>> from modelscope.preprocessors import SequenceClassificationPreprocessor
|
||||
>>> model = Model.from_pretrained('damo/bert-base-sst2')
|
||||
>>> tokenizer = SequenceClassificationPreprocessor(
|
||||
model.model_dir, first_sequence='sentence', second_sequence=None)
|
||||
@@ -74,7 +74,7 @@ wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/easynlp_modelz
|
||||
|
||||
使用tokenizer和模型对象创建pipeline
|
||||
```python
|
||||
>>> from maas_lib.pipelines import pipeline
|
||||
>>> from modelscope.pipelines import pipeline
|
||||
>>> semantic_cls = pipeline('text-classification', model=model, preprocessor=tokenizer)
|
||||
>>> semantic_cls("Hello world!")
|
||||
```
|
||||
|
||||
@@ -1,87 +0,0 @@
|
||||
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||||
|
||||
import os.path as osp
|
||||
from typing import Union
|
||||
|
||||
import json
|
||||
from maas_hub.file_download import model_file_download
|
||||
|
||||
from maas_lib.models.base import Model
|
||||
from maas_lib.utils.config import Config, ConfigDict
|
||||
from maas_lib.utils.constant import CONFIGFILE, Tasks
|
||||
from maas_lib.utils.registry import Registry, build_from_cfg
|
||||
from .base import Pipeline
|
||||
from .util import is_model_name
|
||||
|
||||
PIPELINES = Registry('pipelines')
|
||||
|
||||
|
||||
def build_pipeline(cfg: ConfigDict,
|
||||
task_name: str = None,
|
||||
default_args: dict = None):
|
||||
""" build pipeline given model config dict.
|
||||
|
||||
Args:
|
||||
cfg (:obj:`ConfigDict`): config dict for model object.
|
||||
task_name (str, optional): task name, refer to
|
||||
:obj:`Tasks` for more details.
|
||||
default_args (dict, optional): Default initialization arguments.
|
||||
"""
|
||||
return build_from_cfg(
|
||||
cfg, PIPELINES, group_key=task_name, default_args=default_args)
|
||||
|
||||
|
||||
def pipeline(task: str = None,
|
||||
model: Union[str, Model] = None,
|
||||
preprocessor=None,
|
||||
config_file: str = None,
|
||||
pipeline_name: str = None,
|
||||
framework: str = None,
|
||||
device: int = -1,
|
||||
**kwargs) -> Pipeline:
|
||||
""" Factory method to build a obj:`Pipeline`.
|
||||
|
||||
|
||||
Args:
|
||||
task (str): Task name defining which pipeline will be returned.
|
||||
model (str or obj:`Model`): model name or model object.
|
||||
preprocessor: preprocessor object.
|
||||
config_file (str, optional): path to config file.
|
||||
pipeline_name (str, optional): pipeline class name or alias name.
|
||||
framework (str, optional): framework type.
|
||||
device (int, optional): which device is used to do inference.
|
||||
|
||||
Return:
|
||||
pipeline (obj:`Pipeline`): pipeline object for certain task.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
>>> p = pipeline('image-classification')
|
||||
>>> p = pipeline('text-classification', model='distilbert-base-uncased')
|
||||
>>> # Using model object
|
||||
>>> resnet = Model.from_pretrained('Resnet')
|
||||
>>> p = pipeline('image-classification', model=resnet)
|
||||
"""
|
||||
if task is None and pipeline_name is None:
|
||||
raise ValueError('task or pipeline_name is required')
|
||||
|
||||
if pipeline_name is None:
|
||||
# get default pipeline for this task
|
||||
assert task in PIPELINES.modules, f'No pipeline is registerd for Task {task}'
|
||||
pipeline_name = get_default_pipeline(task)
|
||||
|
||||
cfg = ConfigDict(type=pipeline_name)
|
||||
|
||||
if model:
|
||||
assert isinstance(model, (str, Model)), \
|
||||
f'model should be either str or Model, but got {type(model)}'
|
||||
cfg.model = model
|
||||
|
||||
if preprocessor is not None:
|
||||
cfg.preprocessor = preprocessor
|
||||
|
||||
return build_pipeline(cfg, task_name=task)
|
||||
|
||||
|
||||
def get_default_pipeline(task):
|
||||
return list(PIPELINES.modules[task].keys())[0]
|
||||
@@ -1 +0,0 @@
|
||||
from .image_matting import ImageMatting
|
||||
@@ -1,29 +0,0 @@
|
||||
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||||
import os.path as osp
|
||||
|
||||
import json
|
||||
from maas_hub.file_download import model_file_download
|
||||
|
||||
from maas_lib.utils.constant import CONFIGFILE
|
||||
|
||||
|
||||
def is_model_name(model):
|
||||
if osp.exists(model):
|
||||
if osp.exists(osp.join(model, CONFIGFILE)):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
else:
|
||||
# try:
|
||||
# cfg_file = model_file_download(model, CONFIGFILE)
|
||||
# except Exception:
|
||||
# cfg_file = None
|
||||
# TODO @wenmeng.zwm use exception instead of
|
||||
# following tricky logic
|
||||
cfg_file = model_file_download(model, CONFIGFILE)
|
||||
with open(cfg_file, 'r') as infile:
|
||||
cfg = json.load(infile)
|
||||
if 'Code' in cfg:
|
||||
return False
|
||||
else:
|
||||
return True
|
||||
@@ -2,4 +2,4 @@
|
||||
|
||||
from .base import Model
|
||||
from .builder import MODELS, build_model
|
||||
from .nlp import SequenceClassificationModel
|
||||
from .nlp import BertForSequenceClassification
|
||||
@@ -7,9 +7,10 @@ from typing import Dict, List, Tuple, Union
|
||||
from maas_hub.file_download import model_file_download
|
||||
from maas_hub.snapshot_download import snapshot_download
|
||||
|
||||
from maas_lib.models.builder import build_model
|
||||
from maas_lib.utils.config import Config
|
||||
from maas_lib.utils.constant import CONFIGFILE
|
||||
from modelscope.models.builder import build_model
|
||||
from modelscope.utils.config import Config
|
||||
from modelscope.utils.constant import CONFIGFILE
|
||||
from modelscope.utils.hub import get_model_cache_dir
|
||||
|
||||
Tensor = Union['torch.Tensor', 'tf.Tensor']
|
||||
|
||||
@@ -39,8 +40,9 @@ class Model(ABC):
|
||||
if osp.exists(model_name_or_path):
|
||||
local_model_dir = model_name_or_path
|
||||
else:
|
||||
|
||||
local_model_dir = snapshot_download(model_name_or_path)
|
||||
cache_path = get_model_cache_dir(model_name_or_path)
|
||||
local_model_dir = cache_path if osp.exists(
|
||||
cache_path) else snapshot_download(model_name_or_path)
|
||||
# else:
|
||||
# raise ValueError(
|
||||
# 'Remote model repo {model_name_or_path} does not exists')
|
||||
@@ -48,7 +50,7 @@ class Model(ABC):
|
||||
cfg = Config.from_file(osp.join(local_model_dir, CONFIGFILE))
|
||||
task_name = cfg.task
|
||||
model_cfg = cfg.model
|
||||
# TODO @wenmeng.zwm may should mannually initialize model after model building
|
||||
# TODO @wenmeng.zwm may should manually initialize model after model building
|
||||
if hasattr(model_cfg, 'model_type') and not hasattr(model_cfg, 'type'):
|
||||
model_cfg.type = model_cfg.model_type
|
||||
model_cfg.model_dir = local_model_dir
|
||||
@@ -1,7 +1,7 @@
|
||||
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||||
|
||||
from maas_lib.utils.config import ConfigDict
|
||||
from maas_lib.utils.registry import Registry, build_from_cfg
|
||||
from modelscope.utils.config import ConfigDict
|
||||
from modelscope.utils.registry import Registry, build_from_cfg
|
||||
|
||||
MODELS = Registry('models')
|
||||
|
||||
4
modelscope/models/cv/cartoon/facelib/LICENSE
Normal file
4
modelscope/models/cv/cartoon/facelib/LICENSE
Normal file
@@ -0,0 +1,4 @@
|
||||
|
||||
Copyright (c) Peppa_Pig_Face_Engine
|
||||
|
||||
https://github.com/610265158/Peppa_Pig_Face_Engine
|
||||
97
modelscope/models/cv/cartoon/facelib/LK/lk.py
Normal file
97
modelscope/models/cv/cartoon/facelib/LK/lk.py
Normal file
@@ -0,0 +1,97 @@
|
||||
import numpy as np
|
||||
|
||||
from ..config import config as cfg
|
||||
|
||||
|
||||
class GroupTrack():
|
||||
|
||||
def __init__(self):
|
||||
self.old_frame = None
|
||||
self.previous_landmarks_set = None
|
||||
self.with_landmark = True
|
||||
self.thres = cfg.TRACE.pixel_thres
|
||||
self.alpha = cfg.TRACE.smooth_landmark
|
||||
self.iou_thres = cfg.TRACE.iou_thres
|
||||
|
||||
def calculate(self, img, current_landmarks_set):
|
||||
if self.previous_landmarks_set is None:
|
||||
self.previous_landmarks_set = current_landmarks_set
|
||||
result = current_landmarks_set
|
||||
else:
|
||||
previous_lm_num = self.previous_landmarks_set.shape[0]
|
||||
if previous_lm_num == 0:
|
||||
self.previous_landmarks_set = current_landmarks_set
|
||||
result = current_landmarks_set
|
||||
return result
|
||||
else:
|
||||
result = []
|
||||
for i in range(current_landmarks_set.shape[0]):
|
||||
not_in_flag = True
|
||||
for j in range(previous_lm_num):
|
||||
if self.iou(current_landmarks_set[i],
|
||||
self.previous_landmarks_set[j]
|
||||
) > self.iou_thres:
|
||||
result.append(
|
||||
self.smooth(current_landmarks_set[i],
|
||||
self.previous_landmarks_set[j]))
|
||||
not_in_flag = False
|
||||
break
|
||||
if not_in_flag:
|
||||
result.append(current_landmarks_set[i])
|
||||
|
||||
result = np.array(result)
|
||||
self.previous_landmarks_set = result
|
||||
|
||||
return result
|
||||
|
||||
def iou(self, p_set0, p_set1):
|
||||
rec1 = [
|
||||
np.min(p_set0[:, 0]),
|
||||
np.min(p_set0[:, 1]),
|
||||
np.max(p_set0[:, 0]),
|
||||
np.max(p_set0[:, 1])
|
||||
]
|
||||
rec2 = [
|
||||
np.min(p_set1[:, 0]),
|
||||
np.min(p_set1[:, 1]),
|
||||
np.max(p_set1[:, 0]),
|
||||
np.max(p_set1[:, 1])
|
||||
]
|
||||
|
||||
# computing area of each rectangles
|
||||
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
|
||||
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
|
||||
|
||||
# computing the sum_area
|
||||
sum_area = S_rec1 + S_rec2
|
||||
|
||||
# find the each edge of intersect rectangle
|
||||
x1 = max(rec1[0], rec2[0])
|
||||
y1 = max(rec1[1], rec2[1])
|
||||
x2 = min(rec1[2], rec2[2])
|
||||
y2 = min(rec1[3], rec2[3])
|
||||
|
||||
# judge if there is an intersect
|
||||
intersect = max(0, x2 - x1) * max(0, y2 - y1)
|
||||
|
||||
iou = intersect / (sum_area - intersect)
|
||||
return iou
|
||||
|
||||
def smooth(self, now_landmarks, previous_landmarks):
|
||||
result = []
|
||||
for i in range(now_landmarks.shape[0]):
|
||||
x = now_landmarks[i][0] - previous_landmarks[i][0]
|
||||
y = now_landmarks[i][1] - previous_landmarks[i][1]
|
||||
dis = np.sqrt(np.square(x) + np.square(y))
|
||||
if dis < self.thres:
|
||||
result.append(previous_landmarks[i])
|
||||
else:
|
||||
result.append(
|
||||
self.do_moving_average(now_landmarks[i],
|
||||
previous_landmarks[i]))
|
||||
|
||||
return np.array(result)
|
||||
|
||||
def do_moving_average(self, p_now, p_previous):
|
||||
p = self.alpha * p_now + (1 - self.alpha) * p_previous
|
||||
return p
|
||||
23
modelscope/models/cv/cartoon/facelib/config.py
Normal file
23
modelscope/models/cv/cartoon/facelib/config.py
Normal file
@@ -0,0 +1,23 @@
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from easydict import EasyDict as edict
|
||||
|
||||
config = edict()
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
||||
|
||||
config.DETECT = edict()
|
||||
config.DETECT.topk = 10
|
||||
config.DETECT.thres = 0.8
|
||||
config.DETECT.input_shape = (512, 512, 3)
|
||||
config.KEYPOINTS = edict()
|
||||
config.KEYPOINTS.p_num = 68
|
||||
config.KEYPOINTS.base_extend_range = [0.2, 0.3]
|
||||
config.KEYPOINTS.input_shape = (160, 160, 3)
|
||||
config.TRACE = edict()
|
||||
config.TRACE.pixel_thres = 1
|
||||
config.TRACE.smooth_box = 0.3
|
||||
config.TRACE.smooth_landmark = 0.95
|
||||
config.TRACE.iou_thres = 0.5
|
||||
config.DATA = edict()
|
||||
config.DATA.pixel_means = np.array([123., 116., 103.]) # RGB
|
||||
116
modelscope/models/cv/cartoon/facelib/face_detector.py
Normal file
116
modelscope/models/cv/cartoon/facelib/face_detector.py
Normal file
@@ -0,0 +1,116 @@
|
||||
import time
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
from .config import config as cfg
|
||||
|
||||
if tf.__version__ >= '2.0':
|
||||
tf = tf.compat.v1
|
||||
|
||||
|
||||
class FaceDetector:
|
||||
|
||||
def __init__(self, dir):
|
||||
|
||||
self.model_path = dir + '/detector.pb'
|
||||
self.thres = cfg.DETECT.thres
|
||||
self.input_shape = cfg.DETECT.input_shape
|
||||
|
||||
self._graph = tf.Graph()
|
||||
|
||||
with self._graph.as_default():
|
||||
self._graph, self._sess = self.init_model(self.model_path)
|
||||
|
||||
self.input_image = tf.get_default_graph().get_tensor_by_name(
|
||||
'tower_0/images:0')
|
||||
self.training = tf.get_default_graph().get_tensor_by_name(
|
||||
'training_flag:0')
|
||||
self.output_ops = [
|
||||
tf.get_default_graph().get_tensor_by_name('tower_0/boxes:0'),
|
||||
tf.get_default_graph().get_tensor_by_name('tower_0/scores:0'),
|
||||
tf.get_default_graph().get_tensor_by_name(
|
||||
'tower_0/num_detections:0'),
|
||||
]
|
||||
|
||||
def __call__(self, image):
|
||||
|
||||
image, scale_x, scale_y = self.preprocess(
|
||||
image,
|
||||
target_width=self.input_shape[1],
|
||||
target_height=self.input_shape[0])
|
||||
|
||||
image = np.expand_dims(image, 0)
|
||||
|
||||
boxes, scores, num_boxes = self._sess.run(
|
||||
self.output_ops,
|
||||
feed_dict={
|
||||
self.input_image: image,
|
||||
self.training: False
|
||||
})
|
||||
|
||||
num_boxes = num_boxes[0]
|
||||
boxes = boxes[0][:num_boxes]
|
||||
|
||||
scores = scores[0][:num_boxes]
|
||||
|
||||
to_keep = scores > self.thres
|
||||
boxes = boxes[to_keep]
|
||||
scores = scores[to_keep]
|
||||
|
||||
y1 = self.input_shape[0] / scale_y
|
||||
x1 = self.input_shape[1] / scale_x
|
||||
y2 = self.input_shape[0] / scale_y
|
||||
x2 = self.input_shape[1] / scale_x
|
||||
scaler = np.array([y1, x1, y2, x2], dtype='float32')
|
||||
boxes = boxes * scaler
|
||||
|
||||
scores = np.expand_dims(scores, 0).reshape([-1, 1])
|
||||
|
||||
for i in range(boxes.shape[0]):
|
||||
boxes[i] = np.array(
|
||||
[boxes[i][1], boxes[i][0], boxes[i][3], boxes[i][2]])
|
||||
return np.concatenate([boxes, scores], axis=1)
|
||||
|
||||
def preprocess(self, image, target_height, target_width, label=None):
|
||||
|
||||
h, w, c = image.shape
|
||||
|
||||
bimage = np.zeros(
|
||||
shape=[target_height, target_width, c],
|
||||
dtype=image.dtype) + np.array(
|
||||
cfg.DATA.pixel_means, dtype=image.dtype)
|
||||
long_side = max(h, w)
|
||||
|
||||
scale_x = scale_y = target_height / long_side
|
||||
|
||||
image = cv2.resize(image, None, fx=scale_x, fy=scale_y)
|
||||
|
||||
h_, w_, _ = image.shape
|
||||
bimage[:h_, :w_, :] = image
|
||||
|
||||
return bimage, scale_x, scale_y
|
||||
|
||||
def init_model(self, *args):
|
||||
pb_path = args[0]
|
||||
|
||||
def init_pb(model_path):
|
||||
config = tf.ConfigProto()
|
||||
config.gpu_options.per_process_gpu_memory_fraction = 0.2
|
||||
compute_graph = tf.Graph()
|
||||
compute_graph.as_default()
|
||||
sess = tf.Session(config=config)
|
||||
with tf.gfile.GFile(model_path, 'rb') as fid:
|
||||
graph_def = tf.GraphDef()
|
||||
graph_def.ParseFromString(fid.read())
|
||||
tf.import_graph_def(graph_def, name='')
|
||||
|
||||
return (compute_graph, sess)
|
||||
|
||||
model = init_pb(pb_path)
|
||||
|
||||
graph = model[0]
|
||||
sess = model[1]
|
||||
|
||||
return graph, sess
|
||||
154
modelscope/models/cv/cartoon/facelib/face_landmark.py
Normal file
154
modelscope/models/cv/cartoon/facelib/face_landmark.py
Normal file
@@ -0,0 +1,154 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
import tensorflow as tf
|
||||
|
||||
from .config import config as cfg
|
||||
|
||||
if tf.__version__ >= '2.0':
|
||||
tf = tf.compat.v1
|
||||
|
||||
|
||||
class FaceLandmark:
|
||||
|
||||
def __init__(self, dir):
|
||||
self.model_path = dir + '/keypoints.pb'
|
||||
self.min_face = 60
|
||||
self.keypoint_num = cfg.KEYPOINTS.p_num * 2
|
||||
|
||||
self._graph = tf.Graph()
|
||||
|
||||
with self._graph.as_default():
|
||||
|
||||
self._graph, self._sess = self.init_model(self.model_path)
|
||||
self.img_input = tf.get_default_graph().get_tensor_by_name(
|
||||
'tower_0/images:0')
|
||||
self.embeddings = tf.get_default_graph().get_tensor_by_name(
|
||||
'tower_0/prediction:0')
|
||||
self.training = tf.get_default_graph().get_tensor_by_name(
|
||||
'training_flag:0')
|
||||
|
||||
self.landmark = self.embeddings[:, :self.keypoint_num]
|
||||
self.headpose = self.embeddings[:, -7:-4] * 90.
|
||||
self.state = tf.nn.sigmoid(self.embeddings[:, -4:])
|
||||
|
||||
def __call__(self, img, bboxes):
|
||||
landmark_result = []
|
||||
state_result = []
|
||||
for i, bbox in enumerate(bboxes):
|
||||
landmark, state = self._one_shot_run(img, bbox, i)
|
||||
if landmark is not None:
|
||||
landmark_result.append(landmark)
|
||||
state_result.append(state)
|
||||
return np.array(landmark_result), np.array(state_result)
|
||||
|
||||
def simple_run(self, cropped_img):
|
||||
with self._graph.as_default():
|
||||
|
||||
cropped_img = np.expand_dims(cropped_img, axis=0)
|
||||
landmark, p, states = self._sess.run(
|
||||
[self.landmark, self.headpose, self.state],
|
||||
feed_dict={
|
||||
self.img_input: cropped_img,
|
||||
self.training: False
|
||||
})
|
||||
|
||||
return landmark, states
|
||||
|
||||
def _one_shot_run(self, image, bbox, i):
|
||||
|
||||
bbox_width = bbox[2] - bbox[0]
|
||||
bbox_height = bbox[3] - bbox[1]
|
||||
if (bbox_width <= self.min_face and bbox_height <= self.min_face):
|
||||
return None, None
|
||||
add = int(max(bbox_width, bbox_height))
|
||||
bimg = cv2.copyMakeBorder(
|
||||
image,
|
||||
add,
|
||||
add,
|
||||
add,
|
||||
add,
|
||||
borderType=cv2.BORDER_CONSTANT,
|
||||
value=cfg.DATA.pixel_means)
|
||||
bbox += add
|
||||
|
||||
one_edge = (1 + 2 * cfg.KEYPOINTS.base_extend_range[0]) * bbox_width
|
||||
center = [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2]
|
||||
|
||||
bbox[0] = center[0] - one_edge // 2
|
||||
bbox[1] = center[1] - one_edge // 2
|
||||
bbox[2] = center[0] + one_edge // 2
|
||||
bbox[3] = center[1] + one_edge // 2
|
||||
|
||||
bbox = bbox.astype(np.int)
|
||||
crop_image = bimg[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
|
||||
h, w, _ = crop_image.shape
|
||||
crop_image = cv2.resize(
|
||||
crop_image,
|
||||
(cfg.KEYPOINTS.input_shape[1], cfg.KEYPOINTS.input_shape[0]))
|
||||
crop_image = crop_image.astype(np.float32)
|
||||
|
||||
keypoints, state = self.simple_run(crop_image)
|
||||
|
||||
res = keypoints[0][:self.keypoint_num].reshape((-1, 2))
|
||||
res[:, 0] = res[:, 0] * w / cfg.KEYPOINTS.input_shape[1]
|
||||
res[:, 1] = res[:, 1] * h / cfg.KEYPOINTS.input_shape[0]
|
||||
|
||||
landmark = []
|
||||
for _index in range(res.shape[0]):
|
||||
x_y = res[_index]
|
||||
landmark.append([
|
||||
int(x_y[0] * cfg.KEYPOINTS.input_shape[0] + bbox[0] - add),
|
||||
int(x_y[1] * cfg.KEYPOINTS.input_shape[1] + bbox[1] - add)
|
||||
])
|
||||
|
||||
landmark = np.array(landmark, np.float32)
|
||||
|
||||
return landmark, state
|
||||
|
||||
def init_model(self, *args):
|
||||
|
||||
if len(args) == 1:
|
||||
use_pb = True
|
||||
pb_path = args[0]
|
||||
else:
|
||||
use_pb = False
|
||||
meta_path = args[0]
|
||||
restore_model_path = args[1]
|
||||
|
||||
def ini_ckpt():
|
||||
graph = tf.Graph()
|
||||
graph.as_default()
|
||||
configProto = tf.ConfigProto()
|
||||
configProto.gpu_options.allow_growth = True
|
||||
sess = tf.Session(config=configProto)
|
||||
# load_model(model_path, sess)
|
||||
saver = tf.train.import_meta_graph(meta_path)
|
||||
saver.restore(sess, restore_model_path)
|
||||
|
||||
print('Model restred!')
|
||||
return (graph, sess)
|
||||
|
||||
def init_pb(model_path):
|
||||
config = tf.ConfigProto()
|
||||
config.gpu_options.per_process_gpu_memory_fraction = 0.2
|
||||
compute_graph = tf.Graph()
|
||||
compute_graph.as_default()
|
||||
sess = tf.Session(config=config)
|
||||
with tf.gfile.GFile(model_path, 'rb') as fid:
|
||||
graph_def = tf.GraphDef()
|
||||
graph_def.ParseFromString(fid.read())
|
||||
tf.import_graph_def(graph_def, name='')
|
||||
|
||||
# saver = tf.train.Saver(tf.global_variables())
|
||||
# saver.save(sess, save_path='./tmp.ckpt')
|
||||
return (compute_graph, sess)
|
||||
|
||||
if use_pb:
|
||||
model = init_pb(pb_path)
|
||||
else:
|
||||
model = ini_ckpt()
|
||||
|
||||
graph = model[0]
|
||||
sess = model[1]
|
||||
|
||||
return graph, sess
|
||||
150
modelscope/models/cv/cartoon/facelib/facer.py
Normal file
150
modelscope/models/cv/cartoon/facelib/facer.py
Normal file
@@ -0,0 +1,150 @@
|
||||
import time
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from .config import config as cfg
|
||||
from .face_detector import FaceDetector
|
||||
from .face_landmark import FaceLandmark
|
||||
from .LK.lk import GroupTrack
|
||||
|
||||
|
||||
class FaceAna():
|
||||
'''
|
||||
by default the top3 facea sorted by area will be calculated for time reason
|
||||
'''
|
||||
|
||||
def __init__(self, model_dir):
|
||||
self.face_detector = FaceDetector(model_dir)
|
||||
self.face_landmark = FaceLandmark(model_dir)
|
||||
self.trace = GroupTrack()
|
||||
|
||||
self.track_box = None
|
||||
self.previous_image = None
|
||||
self.previous_box = None
|
||||
|
||||
self.diff_thres = 5
|
||||
self.top_k = cfg.DETECT.topk
|
||||
self.iou_thres = cfg.TRACE.iou_thres
|
||||
self.alpha = cfg.TRACE.smooth_box
|
||||
|
||||
def run(self, image):
|
||||
|
||||
boxes = self.face_detector(image)
|
||||
|
||||
if boxes.shape[0] > self.top_k:
|
||||
boxes = self.sort(boxes)
|
||||
|
||||
boxes_return = np.array(boxes)
|
||||
landmarks, states = self.face_landmark(image, boxes)
|
||||
|
||||
if 1:
|
||||
track = []
|
||||
for i in range(landmarks.shape[0]):
|
||||
track.append([
|
||||
np.min(landmarks[i][:, 0]),
|
||||
np.min(landmarks[i][:, 1]),
|
||||
np.max(landmarks[i][:, 0]),
|
||||
np.max(landmarks[i][:, 1])
|
||||
])
|
||||
tmp_box = np.array(track)
|
||||
|
||||
self.track_box = self.judge_boxs(boxes_return, tmp_box)
|
||||
|
||||
self.track_box, landmarks = self.sort_res(self.track_box, landmarks)
|
||||
return self.track_box, landmarks, states
|
||||
|
||||
def sort_res(self, bboxes, points):
|
||||
area = []
|
||||
for bbox in bboxes:
|
||||
bbox_width = bbox[2] - bbox[0]
|
||||
bbox_height = bbox[3] - bbox[1]
|
||||
area.append(bbox_height * bbox_width)
|
||||
|
||||
area = np.array(area)
|
||||
picked = area.argsort()[::-1]
|
||||
sorted_bboxes = [bboxes[x] for x in picked]
|
||||
sorted_points = [points[x] for x in picked]
|
||||
return np.array(sorted_bboxes), np.array(sorted_points)
|
||||
|
||||
def diff_frames(self, previous_frame, image):
|
||||
if previous_frame is None:
|
||||
return True
|
||||
else:
|
||||
_diff = cv2.absdiff(previous_frame, image)
|
||||
diff = np.sum(
|
||||
_diff) / previous_frame.shape[0] / previous_frame.shape[1] / 3.
|
||||
return diff > self.diff_thres
|
||||
|
||||
def sort(self, bboxes):
|
||||
if self.top_k > 100:
|
||||
return bboxes
|
||||
area = []
|
||||
for bbox in bboxes:
|
||||
|
||||
bbox_width = bbox[2] - bbox[0]
|
||||
bbox_height = bbox[3] - bbox[1]
|
||||
area.append(bbox_height * bbox_width)
|
||||
|
||||
area = np.array(area)
|
||||
|
||||
picked = area.argsort()[-self.top_k:][::-1]
|
||||
sorted_bboxes = [bboxes[x] for x in picked]
|
||||
return np.array(sorted_bboxes)
|
||||
|
||||
def judge_boxs(self, previuous_bboxs, now_bboxs):
|
||||
|
||||
def iou(rec1, rec2):
|
||||
|
||||
# computing area of each rectangles
|
||||
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
|
||||
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
|
||||
|
||||
# computing the sum_area
|
||||
sum_area = S_rec1 + S_rec2
|
||||
|
||||
# find the each edge of intersect rectangle
|
||||
x1 = max(rec1[0], rec2[0])
|
||||
y1 = max(rec1[1], rec2[1])
|
||||
x2 = min(rec1[2], rec2[2])
|
||||
y2 = min(rec1[3], rec2[3])
|
||||
|
||||
# judge if there is an intersect
|
||||
intersect = max(0, x2 - x1) * max(0, y2 - y1)
|
||||
|
||||
return intersect / (sum_area - intersect)
|
||||
|
||||
if previuous_bboxs is None:
|
||||
return now_bboxs
|
||||
|
||||
result = []
|
||||
|
||||
for i in range(now_bboxs.shape[0]):
|
||||
contain = False
|
||||
for j in range(previuous_bboxs.shape[0]):
|
||||
if iou(now_bboxs[i], previuous_bboxs[j]) > self.iou_thres:
|
||||
result.append(
|
||||
self.smooth(now_bboxs[i], previuous_bboxs[j]))
|
||||
contain = True
|
||||
break
|
||||
if not contain:
|
||||
result.append(now_bboxs[i])
|
||||
|
||||
return np.array(result)
|
||||
|
||||
def smooth(self, now_box, previous_box):
|
||||
|
||||
return self.do_moving_average(now_box[:4], previous_box[:4])
|
||||
|
||||
def do_moving_average(self, p_now, p_previous):
|
||||
p = self.alpha * p_now + (1 - self.alpha) * p_previous
|
||||
return p
|
||||
|
||||
def reset(self):
|
||||
'''
|
||||
reset the previous info used foe tracking,
|
||||
:return:
|
||||
'''
|
||||
self.track_box = None
|
||||
self.previous_image = None
|
||||
self.previous_box = None
|
||||
21
modelscope/models/cv/cartoon/mtcnn_pytorch/LICENSE
Normal file
21
modelscope/models/cv/cartoon/mtcnn_pytorch/LICENSE
Normal file
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2017 Dan Antoshchenko
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
26
modelscope/models/cv/cartoon/mtcnn_pytorch/README.md
Normal file
26
modelscope/models/cv/cartoon/mtcnn_pytorch/README.md
Normal file
@@ -0,0 +1,26 @@
|
||||
# MTCNN
|
||||
|
||||
`pytorch` implementation of **inference stage** of face detection algorithm described in
|
||||
[Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://arxiv.org/abs/1604.02878).
|
||||
|
||||
## Example
|
||||
|
||||
|
||||
## How to use it
|
||||
Just download the repository and then do this
|
||||
```python
|
||||
from src import detect_faces
|
||||
from PIL import Image
|
||||
|
||||
image = Image.open('image.jpg')
|
||||
bounding_boxes, landmarks = detect_faces(image)
|
||||
```
|
||||
For examples see `test_on_images.ipynb`.
|
||||
|
||||
## Requirements
|
||||
* pytorch 0.2
|
||||
* Pillow, numpy
|
||||
|
||||
## Credit
|
||||
This implementation is heavily inspired by:
|
||||
* [pangyupo/mxnet_mtcnn_face_detection](https://github.com/pangyupo/mxnet_mtcnn_face_detection)
|
||||
187
modelscope/models/cv/cartoon/mtcnn_pytorch/src/align_trans.py
Normal file
187
modelscope/models/cv/cartoon/mtcnn_pytorch/src/align_trans.py
Normal file
@@ -0,0 +1,187 @@
|
||||
"""
|
||||
Created on Mon Apr 24 15:43:29 2017
|
||||
@author: zhaoy
|
||||
"""
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from .matlab_cp2tform import get_similarity_transform_for_cv2
|
||||
|
||||
# reference facial points, a list of coordinates (x,y)
|
||||
dx = 1
|
||||
dy = 1
|
||||
REFERENCE_FACIAL_POINTS = [
|
||||
[30.29459953 + dx, 51.69630051 + dy], # left eye
|
||||
[65.53179932 + dx, 51.50139999 + dy], # right eye
|
||||
[48.02519989 + dx, 71.73660278 + dy], # nose
|
||||
[33.54930115 + dx, 92.3655014 + dy], # left mouth
|
||||
[62.72990036 + dx, 92.20410156 + dy] # right mouth
|
||||
]
|
||||
|
||||
DEFAULT_CROP_SIZE = (96, 112)
|
||||
|
||||
global FACIAL_POINTS
|
||||
|
||||
|
||||
class FaceWarpException(Exception):
|
||||
|
||||
def __str__(self):
|
||||
return 'In File {}:{}'.format(__file__, super.__str__(self))
|
||||
|
||||
|
||||
def get_reference_facial_points(output_size=None,
|
||||
inner_padding_factor=0.0,
|
||||
outer_padding=(0, 0),
|
||||
default_square=False):
|
||||
|
||||
tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
|
||||
tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
|
||||
|
||||
# 0) make the inner region a square
|
||||
if default_square:
|
||||
size_diff = max(tmp_crop_size) - tmp_crop_size
|
||||
tmp_5pts += size_diff / 2
|
||||
tmp_crop_size += size_diff
|
||||
|
||||
h_crop = tmp_crop_size[0]
|
||||
w_crop = tmp_crop_size[1]
|
||||
if (output_size):
|
||||
if (output_size[0] == h_crop and output_size[1] == w_crop):
|
||||
return tmp_5pts
|
||||
|
||||
if (inner_padding_factor == 0 and outer_padding == (0, 0)):
|
||||
if output_size is None:
|
||||
return tmp_5pts
|
||||
else:
|
||||
raise FaceWarpException(
|
||||
'No paddings to do, output_size must be None or {}'.format(
|
||||
tmp_crop_size))
|
||||
|
||||
# check output size
|
||||
if not (0 <= inner_padding_factor <= 1.0):
|
||||
raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
|
||||
|
||||
factor = inner_padding_factor > 0 or outer_padding[0] > 0
|
||||
factor = factor or outer_padding[1] > 0
|
||||
if (factor and output_size is None):
|
||||
output_size = tmp_crop_size * \
|
||||
(1 + inner_padding_factor * 2).astype(np.int32)
|
||||
output_size += np.array(outer_padding)
|
||||
|
||||
cond1 = outer_padding[0] < output_size[0]
|
||||
cond2 = outer_padding[1] < output_size[1]
|
||||
if not (cond1 and cond2):
|
||||
raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
|
||||
'and outer_padding[1] < output_size[1])')
|
||||
|
||||
# 1) pad the inner region according inner_padding_factor
|
||||
if inner_padding_factor > 0:
|
||||
size_diff = tmp_crop_size * inner_padding_factor * 2
|
||||
tmp_5pts += size_diff / 2
|
||||
tmp_crop_size += np.round(size_diff).astype(np.int32)
|
||||
|
||||
# 2) resize the padded inner region
|
||||
size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
|
||||
|
||||
if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[
|
||||
1] * tmp_crop_size[0]:
|
||||
raise FaceWarpException(
|
||||
'Must have (output_size - outer_padding)'
|
||||
'= some_scale * (crop_size * (1.0 + inner_padding_factor)')
|
||||
|
||||
scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
|
||||
tmp_5pts = tmp_5pts * scale_factor
|
||||
|
||||
# 3) add outer_padding to make output_size
|
||||
reference_5point = tmp_5pts + np.array(outer_padding)
|
||||
|
||||
return reference_5point
|
||||
|
||||
|
||||
def get_affine_transform_matrix(src_pts, dst_pts):
|
||||
|
||||
tfm = np.float32([[1, 0, 0], [0, 1, 0]])
|
||||
n_pts = src_pts.shape[0]
|
||||
ones = np.ones((n_pts, 1), src_pts.dtype)
|
||||
src_pts_ = np.hstack([src_pts, ones])
|
||||
dst_pts_ = np.hstack([dst_pts, ones])
|
||||
|
||||
A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
|
||||
|
||||
if rank == 3:
|
||||
tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]],
|
||||
[A[0, 1], A[1, 1], A[2, 1]]])
|
||||
elif rank == 2:
|
||||
tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
|
||||
|
||||
return tfm
|
||||
|
||||
|
||||
def warp_and_crop_face(src_img,
|
||||
facial_pts,
|
||||
ratio=0.84,
|
||||
reference_pts=None,
|
||||
crop_size=(96, 112),
|
||||
align_type='similarity'
|
||||
'',
|
||||
return_trans_inv=False):
|
||||
|
||||
if reference_pts is None:
|
||||
if crop_size[0] == 96 and crop_size[1] == 112:
|
||||
reference_pts = REFERENCE_FACIAL_POINTS
|
||||
else:
|
||||
default_square = False
|
||||
inner_padding_factor = 0
|
||||
outer_padding = (0, 0)
|
||||
output_size = crop_size
|
||||
|
||||
reference_pts = get_reference_facial_points(
|
||||
output_size, inner_padding_factor, outer_padding,
|
||||
default_square)
|
||||
|
||||
ref_pts = np.float32(reference_pts)
|
||||
|
||||
factor = ratio
|
||||
ref_pts = (ref_pts - 112 / 2) * factor + 112 / 2
|
||||
ref_pts *= crop_size[0] / 112.
|
||||
|
||||
ref_pts_shp = ref_pts.shape
|
||||
if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
|
||||
raise FaceWarpException(
|
||||
'reference_pts.shape must be (K,2) or (2,K) and K>2')
|
||||
|
||||
if ref_pts_shp[0] == 2:
|
||||
ref_pts = ref_pts.T
|
||||
|
||||
src_pts = np.float32(facial_pts)
|
||||
src_pts_shp = src_pts.shape
|
||||
if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
|
||||
raise FaceWarpException(
|
||||
'facial_pts.shape must be (K,2) or (2,K) and K>2')
|
||||
|
||||
if src_pts_shp[0] == 2:
|
||||
src_pts = src_pts.T
|
||||
|
||||
if src_pts.shape != ref_pts.shape:
|
||||
raise FaceWarpException(
|
||||
'facial_pts and reference_pts must have the same shape')
|
||||
|
||||
if align_type == 'cv2_affine':
|
||||
tfm = cv2.getAffineTransform(src_pts, ref_pts)
|
||||
tfm_inv = cv2.getAffineTransform(ref_pts, src_pts)
|
||||
|
||||
elif align_type == 'affine':
|
||||
tfm = get_affine_transform_matrix(src_pts, ref_pts)
|
||||
tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)
|
||||
else:
|
||||
tfm, tfm_inv = get_similarity_transform_for_cv2(src_pts, ref_pts)
|
||||
|
||||
face_img = cv2.warpAffine(
|
||||
src_img,
|
||||
tfm, (crop_size[0], crop_size[1]),
|
||||
borderValue=(255, 255, 255))
|
||||
|
||||
if return_trans_inv:
|
||||
return face_img, tfm_inv
|
||||
else:
|
||||
return face_img
|
||||
@@ -0,0 +1,339 @@
|
||||
"""
|
||||
Created on Tue Jul 11 06:54:28 2017
|
||||
|
||||
@author: zhaoyafei
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import inv, lstsq
|
||||
from numpy.linalg import matrix_rank as rank
|
||||
from numpy.linalg import norm
|
||||
|
||||
|
||||
class MatlabCp2tormException(Exception):
|
||||
|
||||
def __str__(self):
|
||||
return 'In File {}:{}'.format(__file__, super.__str__(self))
|
||||
|
||||
|
||||
def tformfwd(trans, uv):
|
||||
"""
|
||||
Function:
|
||||
----------
|
||||
apply affine transform 'trans' to uv
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
@trans: 3x3 np.array
|
||||
transform matrix
|
||||
@uv: Kx2 np.array
|
||||
each row is a pair of coordinates (x, y)
|
||||
|
||||
Returns:
|
||||
----------
|
||||
@xy: Kx2 np.array
|
||||
each row is a pair of transformed coordinates (x, y)
|
||||
"""
|
||||
uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
|
||||
xy = np.dot(uv, trans)
|
||||
xy = xy[:, 0:-1]
|
||||
return xy
|
||||
|
||||
|
||||
def tforminv(trans, uv):
|
||||
"""
|
||||
Function:
|
||||
----------
|
||||
apply the inverse of affine transform 'trans' to uv
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
@trans: 3x3 np.array
|
||||
transform matrix
|
||||
@uv: Kx2 np.array
|
||||
each row is a pair of coordinates (x, y)
|
||||
|
||||
Returns:
|
||||
----------
|
||||
@xy: Kx2 np.array
|
||||
each row is a pair of inverse-transformed coordinates (x, y)
|
||||
"""
|
||||
Tinv = inv(trans)
|
||||
xy = tformfwd(Tinv, uv)
|
||||
return xy
|
||||
|
||||
|
||||
def findNonreflectiveSimilarity(uv, xy, options=None):
|
||||
|
||||
options = {'K': 2}
|
||||
|
||||
K = options['K']
|
||||
M = xy.shape[0]
|
||||
x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
|
||||
y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
|
||||
# print('--->x, y:\n', x, y
|
||||
|
||||
tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
|
||||
tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
|
||||
X = np.vstack((tmp1, tmp2))
|
||||
# print('--->X.shape: ', X.shape
|
||||
# print('X:\n', X
|
||||
|
||||
u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
|
||||
v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
|
||||
U = np.vstack((u, v))
|
||||
# print('--->U.shape: ', U.shape
|
||||
# print('U:\n', U
|
||||
|
||||
# We know that X * r = U
|
||||
if rank(X) >= 2 * K:
|
||||
r, _, _, _ = lstsq(X, U)
|
||||
r = np.squeeze(r)
|
||||
else:
|
||||
raise Exception('cp2tform:twoUniquePointsReq')
|
||||
|
||||
# print('--->r:\n', r
|
||||
|
||||
sc = r[0]
|
||||
ss = r[1]
|
||||
tx = r[2]
|
||||
ty = r[3]
|
||||
|
||||
Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
|
||||
|
||||
# print('--->Tinv:\n', Tinv
|
||||
|
||||
T = inv(Tinv)
|
||||
# print('--->T:\n', T
|
||||
|
||||
T[:, 2] = np.array([0, 0, 1])
|
||||
|
||||
return T, Tinv
|
||||
|
||||
|
||||
def findSimilarity(uv, xy, options=None):
|
||||
|
||||
options = {'K': 2}
|
||||
|
||||
# uv = np.array(uv)
|
||||
# xy = np.array(xy)
|
||||
|
||||
# Solve for trans1
|
||||
trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
|
||||
|
||||
# Solve for trans2
|
||||
|
||||
# manually reflect the xy data across the Y-axis
|
||||
xyR = xy
|
||||
xyR[:, 0] = -1 * xyR[:, 0]
|
||||
|
||||
trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
|
||||
|
||||
# manually reflect the tform to undo the reflection done on xyR
|
||||
TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
|
||||
|
||||
trans2 = np.dot(trans2r, TreflectY)
|
||||
|
||||
# Figure out if trans1 or trans2 is better
|
||||
xy1 = tformfwd(trans1, uv)
|
||||
norm1 = norm(xy1 - xy)
|
||||
|
||||
xy2 = tformfwd(trans2, uv)
|
||||
norm2 = norm(xy2 - xy)
|
||||
|
||||
if norm1 <= norm2:
|
||||
return trans1, trans1_inv
|
||||
else:
|
||||
trans2_inv = inv(trans2)
|
||||
return trans2, trans2_inv
|
||||
|
||||
|
||||
def get_similarity_transform(src_pts, dst_pts, reflective=True):
|
||||
"""
|
||||
Function:
|
||||
----------
|
||||
Find Similarity Transform Matrix 'trans':
|
||||
u = src_pts[:, 0]
|
||||
v = src_pts[:, 1]
|
||||
x = dst_pts[:, 0]
|
||||
y = dst_pts[:, 1]
|
||||
[x, y, 1] = [u, v, 1] * trans
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
@src_pts: Kx2 np.array
|
||||
source points, each row is a pair of coordinates (x, y)
|
||||
@dst_pts: Kx2 np.array
|
||||
destination points, each row is a pair of transformed
|
||||
coordinates (x, y)
|
||||
@reflective: True or False
|
||||
if True:
|
||||
use reflective similarity transform
|
||||
else:
|
||||
use non-reflective similarity transform
|
||||
|
||||
Returns:
|
||||
----------
|
||||
@trans: 3x3 np.array
|
||||
transform matrix from uv to xy
|
||||
trans_inv: 3x3 np.array
|
||||
inverse of trans, transform matrix from xy to uv
|
||||
"""
|
||||
|
||||
if reflective:
|
||||
trans, trans_inv = findSimilarity(src_pts, dst_pts)
|
||||
else:
|
||||
trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
|
||||
|
||||
return trans, trans_inv
|
||||
|
||||
|
||||
def cvt_tform_mat_for_cv2(trans):
|
||||
"""
|
||||
Function:
|
||||
----------
|
||||
Convert Transform Matrix 'trans' into 'cv2_trans' which could be
|
||||
directly used by cv2.warpAffine():
|
||||
u = src_pts[:, 0]
|
||||
v = src_pts[:, 1]
|
||||
x = dst_pts[:, 0]
|
||||
y = dst_pts[:, 1]
|
||||
[x, y].T = cv_trans * [u, v, 1].T
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
@trans: 3x3 np.array
|
||||
transform matrix from uv to xy
|
||||
|
||||
Returns:
|
||||
----------
|
||||
@cv2_trans: 2x3 np.array
|
||||
transform matrix from src_pts to dst_pts, could be directly used
|
||||
for cv2.warpAffine()
|
||||
"""
|
||||
cv2_trans = trans[:, 0:2].T
|
||||
|
||||
return cv2_trans
|
||||
|
||||
|
||||
def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
|
||||
"""
|
||||
Function:
|
||||
----------
|
||||
Find Similarity Transform Matrix 'cv2_trans' which could be
|
||||
directly used by cv2.warpAffine():
|
||||
u = src_pts[:, 0]
|
||||
v = src_pts[:, 1]
|
||||
x = dst_pts[:, 0]
|
||||
y = dst_pts[:, 1]
|
||||
[x, y].T = cv_trans * [u, v, 1].T
|
||||
|
||||
Parameters:
|
||||
----------
|
||||
@src_pts: Kx2 np.array
|
||||
source points, each row is a pair of coordinates (x, y)
|
||||
@dst_pts: Kx2 np.array
|
||||
destination points, each row is a pair of transformed
|
||||
coordinates (x, y)
|
||||
reflective: True or False
|
||||
if True:
|
||||
use reflective similarity transform
|
||||
else:
|
||||
use non-reflective similarity transform
|
||||
|
||||
Returns:
|
||||
----------
|
||||
@cv2_trans: 2x3 np.array
|
||||
transform matrix from src_pts to dst_pts, could be directly used
|
||||
for cv2.warpAffine()
|
||||
"""
|
||||
trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
|
||||
cv2_trans = cvt_tform_mat_for_cv2(trans)
|
||||
cv2_trans_inv = cvt_tform_mat_for_cv2(trans_inv)
|
||||
|
||||
return cv2_trans, cv2_trans_inv
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
"""
|
||||
u = [0, 6, -2]
|
||||
v = [0, 3, 5]
|
||||
x = [-1, 0, 4]
|
||||
y = [-1, -10, 4]
|
||||
|
||||
# In Matlab, run:
|
||||
#
|
||||
# uv = [u'; v'];
|
||||
# xy = [x'; y'];
|
||||
# tform_sim=cp2tform(uv,xy,'similarity');
|
||||
#
|
||||
# trans = tform_sim.tdata.T
|
||||
# ans =
|
||||
# -0.0764 -1.6190 0
|
||||
# 1.6190 -0.0764 0
|
||||
# -3.2156 0.0290 1.0000
|
||||
# trans_inv = tform_sim.tdata.Tinv
|
||||
# ans =
|
||||
#
|
||||
# -0.0291 0.6163 0
|
||||
# -0.6163 -0.0291 0
|
||||
# -0.0756 1.9826 1.0000
|
||||
# xy_m=tformfwd(tform_sim, u,v)
|
||||
#
|
||||
# xy_m =
|
||||
#
|
||||
# -3.2156 0.0290
|
||||
# 1.1833 -9.9143
|
||||
# 5.0323 2.8853
|
||||
# uv_m=tforminv(tform_sim, x,y)
|
||||
#
|
||||
# uv_m =
|
||||
#
|
||||
# 0.5698 1.3953
|
||||
# 6.0872 2.2733
|
||||
# -2.6570 4.3314
|
||||
"""
|
||||
u = [0, 6, -2]
|
||||
v = [0, 3, 5]
|
||||
x = [-1, 0, 4]
|
||||
y = [-1, -10, 4]
|
||||
|
||||
uv = np.array((u, v)).T
|
||||
xy = np.array((x, y)).T
|
||||
|
||||
print('\n--->uv:')
|
||||
print(uv)
|
||||
print('\n--->xy:')
|
||||
print(xy)
|
||||
|
||||
trans, trans_inv = get_similarity_transform(uv, xy)
|
||||
|
||||
print('\n--->trans matrix:')
|
||||
print(trans)
|
||||
|
||||
print('\n--->trans_inv matrix:')
|
||||
print(trans_inv)
|
||||
|
||||
print('\n---> apply transform to uv')
|
||||
print('\nxy_m = uv_augmented * trans')
|
||||
uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
|
||||
xy_m = np.dot(uv_aug, trans)
|
||||
print(xy_m)
|
||||
|
||||
print('\nxy_m = tformfwd(trans, uv)')
|
||||
xy_m = tformfwd(trans, uv)
|
||||
print(xy_m)
|
||||
|
||||
print('\n---> apply inverse transform to xy')
|
||||
print('\nuv_m = xy_augmented * trans_inv')
|
||||
xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
|
||||
uv_m = np.dot(xy_aug, trans_inv)
|
||||
print(uv_m)
|
||||
|
||||
print('\nuv_m = tformfwd(trans_inv, xy)')
|
||||
uv_m = tformfwd(trans_inv, xy)
|
||||
print(uv_m)
|
||||
|
||||
uv_m = tforminv(trans, xy)
|
||||
print('\nuv_m = tforminv(trans, xy)')
|
||||
print(uv_m)
|
||||
91
modelscope/models/cv/cartoon/utils.py
Normal file
91
modelscope/models/cv/cartoon/utils.py
Normal file
@@ -0,0 +1,91 @@
|
||||
import os
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
|
||||
def resize_size(image, size=720):
|
||||
h, w, c = np.shape(image)
|
||||
if min(h, w) > size:
|
||||
if h > w:
|
||||
h, w = int(size * h / w), size
|
||||
else:
|
||||
h, w = size, int(size * w / h)
|
||||
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
|
||||
return image
|
||||
|
||||
|
||||
def padTo16x(image):
|
||||
h, w, c = np.shape(image)
|
||||
if h % 16 == 0 and w % 16 == 0:
|
||||
return image, h, w
|
||||
nh, nw = (h // 16 + 1) * 16, (w // 16 + 1) * 16
|
||||
img_new = np.ones((nh, nw, 3), np.uint8) * 255
|
||||
img_new[:h, :w, :] = image
|
||||
|
||||
return img_new, h, w
|
||||
|
||||
|
||||
def get_f5p(landmarks, np_img):
|
||||
eye_left = find_pupil(landmarks[36:41], np_img)
|
||||
eye_right = find_pupil(landmarks[42:47], np_img)
|
||||
if eye_left is None or eye_right is None:
|
||||
print('cannot find 5 points with find_puil, used mean instead.!')
|
||||
eye_left = landmarks[36:41].mean(axis=0)
|
||||
eye_right = landmarks[42:47].mean(axis=0)
|
||||
nose = landmarks[30]
|
||||
mouth_left = landmarks[48]
|
||||
mouth_right = landmarks[54]
|
||||
f5p = [[eye_left[0], eye_left[1]], [eye_right[0], eye_right[1]],
|
||||
[nose[0], nose[1]], [mouth_left[0], mouth_left[1]],
|
||||
[mouth_right[0], mouth_right[1]]]
|
||||
return f5p
|
||||
|
||||
|
||||
def find_pupil(landmarks, np_img):
|
||||
h, w, _ = np_img.shape
|
||||
xmax = int(landmarks[:, 0].max())
|
||||
xmin = int(landmarks[:, 0].min())
|
||||
ymax = int(landmarks[:, 1].max())
|
||||
ymin = int(landmarks[:, 1].min())
|
||||
|
||||
if ymin >= ymax or xmin >= xmax or ymin < 0 or xmin < 0 or ymax > h or xmax > w:
|
||||
return None
|
||||
eye_img_bgr = np_img[ymin:ymax, xmin:xmax, :]
|
||||
eye_img = cv2.cvtColor(eye_img_bgr, cv2.COLOR_BGR2GRAY)
|
||||
eye_img = cv2.equalizeHist(eye_img)
|
||||
n_marks = landmarks - np.array([xmin, ymin]).reshape([1, 2])
|
||||
eye_mask = cv2.fillConvexPoly(
|
||||
np.zeros_like(eye_img), n_marks.astype(np.int32), 1)
|
||||
ret, thresh = cv2.threshold(eye_img, 100, 255,
|
||||
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
|
||||
thresh = (1 - thresh / 255.) * eye_mask
|
||||
cnt = 0
|
||||
xm = []
|
||||
ym = []
|
||||
for i in range(thresh.shape[0]):
|
||||
for j in range(thresh.shape[1]):
|
||||
if thresh[i, j] > 0.5:
|
||||
xm.append(j)
|
||||
ym.append(i)
|
||||
cnt += 1
|
||||
if cnt != 0:
|
||||
xm.sort()
|
||||
ym.sort()
|
||||
xm = xm[cnt // 2]
|
||||
ym = ym[cnt // 2]
|
||||
else:
|
||||
xm = thresh.shape[1] / 2
|
||||
ym = thresh.shape[0] / 2
|
||||
|
||||
return xm + xmin, ym + ymin
|
||||
|
||||
|
||||
def all_file(file_dir):
|
||||
L = []
|
||||
for root, dirs, files in os.walk(file_dir):
|
||||
for file in files:
|
||||
extend = os.path.splitext(file)[1]
|
||||
if extend == '.png' or extend == '.jpg' or extend == '.jpeg':
|
||||
L.append(os.path.join(root, file))
|
||||
return L
|
||||
@@ -1,3 +1,4 @@
|
||||
from .sequence_classification_model import * # noqa F403
|
||||
from .space.dialog_generation_model import * # noqa F403
|
||||
from .space.dialog_intent_model import *
|
||||
from .space.dialog_intent_model import * # noqa F403
|
||||
from .text_generation_model import * # noqa F403
|
||||
@@ -1,17 +1,17 @@
|
||||
from typing import Any, Dict, Optional, Union
|
||||
from typing import Any, Dict
|
||||
|
||||
import numpy as np
|
||||
|
||||
from maas_lib.utils.constant import Tasks
|
||||
from modelscope.utils.constant import Tasks
|
||||
from ..base import Model
|
||||
from ..builder import MODELS
|
||||
|
||||
__all__ = ['SequenceClassificationModel']
|
||||
__all__ = ['BertForSequenceClassification']
|
||||
|
||||
|
||||
@MODELS.register_module(
|
||||
Tasks.text_classification, module_name=r'bert-sentiment-analysis')
|
||||
class SequenceClassificationModel(Model):
|
||||
class BertForSequenceClassification(Model):
|
||||
|
||||
def __init__(self, model_dir: str, *args, **kwargs):
|
||||
# Model.__init__(self, model_dir, model_cls, first_sequence, *args, **kwargs)
|
||||
@@ -1,7 +1,7 @@
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from maas_lib.trainers.nlp.space.trainers.gen_trainer import MultiWOZTrainer
|
||||
from maas_lib.utils.constant import Tasks
|
||||
from modelscope.trainers.nlp.space.trainers.gen_trainer import MultiWOZTrainer
|
||||
from modelscope.utils.constant import Tasks
|
||||
from ...base import Model, Tensor
|
||||
from ...builder import MODELS
|
||||
from .model.generator import Generator
|
||||
@@ -68,13 +68,13 @@ class DialogGenerationModel(Model):
|
||||
from numpy import array, float32
|
||||
import torch
|
||||
|
||||
turn_1 = {
|
||||
'user': [
|
||||
13, 1045, 2052, 2066, 1037, 10095, 2013, 3002, 2198, 1005,
|
||||
1055, 2267, 2000, 10733, 12570, 21713, 4487, 15474, 1012, 7
|
||||
]
|
||||
}
|
||||
old_pv_turn_1 = {}
|
||||
# turn_1 = {
|
||||
# 'user': [
|
||||
# 13, 1045, 2052, 2066, 1037, 10095, 2013, 3002, 2198, 1005,
|
||||
# 1055, 2267, 2000, 10733, 12570, 21713, 4487, 15474, 1012, 7
|
||||
# ]
|
||||
# }
|
||||
# old_pv_turn_1 = {}
|
||||
|
||||
turn_2 = {
|
||||
'user':
|
||||
@@ -1,7 +1,7 @@
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from maas_lib.trainers.nlp.space.trainers.intent_trainer import IntentTrainer
|
||||
from maas_lib.utils.constant import Tasks
|
||||
from modelscope.trainers.nlp.space.trainers.intent_trainer import IntentTrainer
|
||||
from modelscope.utils.constant import Tasks
|
||||
from ...base import Model, Tensor
|
||||
from ...builder import MODELS
|
||||
from .model.generator import Generator
|
||||
@@ -3,7 +3,7 @@ IntentUnifiedTransformer
|
||||
"""
|
||||
import torch
|
||||
|
||||
from maas_lib.models.nlp.space.model.unified_transformer import \
|
||||
from modelscope.models.nlp.space.model.unified_transformer import \
|
||||
UnifiedTransformer
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from maas_lib.utils.nlp.space.criterions import compute_kl_loss
|
||||
from modelscope.utils.nlp.space.criterions import compute_kl_loss
|
||||
from .unified_transformer import UnifiedTransformer
|
||||
|
||||
|
||||
@@ -7,9 +7,9 @@ import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from maas_lib.models.nlp.space.model.model_base import ModelBase
|
||||
from maas_lib.models.nlp.space.modules.embedder import Embedder
|
||||
from maas_lib.models.nlp.space.modules.transformer_block import \
|
||||
from modelscope.models.nlp.space.model.model_base import ModelBase
|
||||
from modelscope.models.nlp.space.modules.embedder import Embedder
|
||||
from modelscope.models.nlp.space.modules.transformer_block import \
|
||||
TransformerBlock
|
||||
|
||||
|
||||
@@ -171,7 +171,7 @@ class UnifiedTransformer(ModelBase):
|
||||
batch_size = mask1.shape[0]
|
||||
seq_len1 = mask1.shape[1]
|
||||
seq_len2 = mask2.shape[1]
|
||||
seq_len = seq_len1 + seq_len2
|
||||
# seq_len = seq_len1 + seq_len2
|
||||
|
||||
mask_lu = mask1
|
||||
mask_ru = torch.ones(batch_size, seq_len1, seq_len2)
|
||||
@@ -5,8 +5,8 @@ TransformerBlock class.
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from maas_lib.models.nlp.space.modules.feedforward import FeedForward
|
||||
from maas_lib.models.nlp.space.modules.multihead_attention import \
|
||||
from modelscope.models.nlp.space.modules.feedforward import FeedForward
|
||||
from modelscope.models.nlp.space.modules.multihead_attention import \
|
||||
MultiheadAttention
|
||||
|
||||
|
||||
52
modelscope/models/nlp/text_generation_model.py
Normal file
52
modelscope/models/nlp/text_generation_model.py
Normal file
@@ -0,0 +1,52 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
from modelscope.utils.constant import Tasks
|
||||
from ..base import Model, Tensor
|
||||
from ..builder import MODELS
|
||||
|
||||
__all__ = ['PalmForTextGenerationModel']
|
||||
|
||||
|
||||
@MODELS.register_module(Tasks.text_generation, module_name=r'palm')
|
||||
class PalmForTextGenerationModel(Model):
|
||||
|
||||
def __init__(self, model_dir: str, *args, **kwargs):
|
||||
"""initialize the text generation model from the `model_dir` path.
|
||||
|
||||
Args:
|
||||
model_dir (str): the model path.
|
||||
model_cls (Optional[Any], optional): model loader, if None, use the
|
||||
default loader to load model weights, by default None.
|
||||
"""
|
||||
from sofa import PalmTokenizer
|
||||
|
||||
super().__init__(model_dir, *args, **kwargs)
|
||||
self.model_dir = model_dir
|
||||
|
||||
from sofa.models.palm import PalmForConditionalGeneration, TextGenerator
|
||||
tokenizer = kwargs.pop('tokenizer',
|
||||
PalmTokenizer.from_pretrained(model_dir))
|
||||
model = PalmForConditionalGeneration.from_pretrained(model_dir)
|
||||
self.generator = TextGenerator(model, tokenizer)
|
||||
|
||||
def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
|
||||
"""return the result by the model
|
||||
|
||||
Args:
|
||||
input (Dict[str, Any]): the preprocessed data
|
||||
|
||||
Returns:
|
||||
Dict[str, np.ndarray]: results
|
||||
Example:
|
||||
{
|
||||
'predictions': array([1]), # lable 0-negative 1-positive
|
||||
'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
|
||||
'logits': array([[-0.53860897, 1.5029076 ]], dtype=float32) # true value
|
||||
}
|
||||
"""
|
||||
|
||||
encoder_inputs = [
|
||||
input['input_ids'], input['token_type_ids'],
|
||||
input['attention_mask']
|
||||
]
|
||||
return self.generator(encoder_inputs)
|
||||
@@ -2,67 +2,86 @@
|
||||
|
||||
import os.path as osp
|
||||
from abc import ABC, abstractmethod
|
||||
from multiprocessing.sharedctypes import Value
|
||||
from typing import Any, Dict, Generator, List, Tuple, Union
|
||||
from typing import Any, Dict, Generator, List, Union
|
||||
|
||||
from ali_maas_datasets import PyDataset
|
||||
from maas_hub.snapshot_download import snapshot_download
|
||||
|
||||
from maas_lib.models import Model
|
||||
from maas_lib.preprocessors import Preprocessor
|
||||
from maas_lib.utils.config import Config
|
||||
from maas_lib.utils.constant import CONFIGFILE
|
||||
from modelscope.models.base import Model
|
||||
from modelscope.preprocessors import Preprocessor
|
||||
from modelscope.pydatasets import PyDataset
|
||||
from modelscope.utils.config import Config
|
||||
from modelscope.utils.hub import get_model_cache_dir
|
||||
from modelscope.utils.logger import get_logger
|
||||
from .util import is_model_name
|
||||
|
||||
Tensor = Union['torch.Tensor', 'tf.Tensor']
|
||||
Input = Union[str, PyDataset, 'PIL.Image.Image', 'numpy.ndarray']
|
||||
InputModel = Union[str, Model]
|
||||
|
||||
output_keys = [
|
||||
] # 对于不同task的pipeline，规定标准化的输出key，用以对接postprocess,同时也用来标准化postprocess后输出的key
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class Pipeline(ABC):
|
||||
|
||||
def initiate_single_model(self, model):
|
||||
logger.info(f'initiate model from {model}')
|
||||
# TODO @wenmeng.zwm replace model.startswith('damo/') with get_model
|
||||
if isinstance(model, str) and model.startswith('damo/'):
|
||||
if not osp.exists(model):
|
||||
cache_path = get_model_cache_dir(model)
|
||||
model = cache_path if osp.exists(
|
||||
cache_path) else snapshot_download(model)
|
||||
return Model.from_pretrained(model) if is_model_name(
|
||||
model) else model
|
||||
elif isinstance(model, Model):
|
||||
return model
|
||||
else:
|
||||
if model and not isinstance(model, str):
|
||||
raise ValueError(
|
||||
f'model type for single model is either str or Model, but got type {type(model)}'
|
||||
)
|
||||
return model
|
||||
|
||||
def initiate_multiple_models(self, input_models: List[InputModel]):
|
||||
models = []
|
||||
for model in input_models:
|
||||
models.append(self.initiate_single_model(model))
|
||||
return models
|
||||
|
||||
def __init__(self,
|
||||
config_file: str = None,
|
||||
model: Union[Model, str] = None,
|
||||
preprocessor: Preprocessor = None,
|
||||
model: Union[InputModel, List[InputModel]] = None,
|
||||
preprocessor: Union[Preprocessor, List[Preprocessor]] = None,
|
||||
**kwargs):
|
||||
""" Base class for pipeline.
|
||||
|
||||
If config_file is provided, model and preprocessor will be
|
||||
instantiated from corresponding config. Otherwise model
|
||||
instantiated from corresponding config. Otherwise, model
|
||||
and preprocessor will be constructed separately.
|
||||
|
||||
Args:
|
||||
config_file(str, optional): Filepath to configuration file.
|
||||
model: Model name or model object
|
||||
preprocessor: Preprocessor object
|
||||
model: (list of) Model name or model object
|
||||
preprocessor: (list of) Preprocessor object
|
||||
"""
|
||||
if config_file is not None:
|
||||
self.cfg = Config.from_file(config_file)
|
||||
|
||||
if isinstance(model, str):
|
||||
if not osp.exists(model):
|
||||
model = snapshot_download(model)
|
||||
|
||||
if is_model_name(model):
|
||||
self.model = Model.from_pretrained(model)
|
||||
else:
|
||||
self.model = model
|
||||
elif isinstance(model, Model):
|
||||
self.model = model
|
||||
if not isinstance(model, List):
|
||||
self.model = self.initiate_single_model(model)
|
||||
self.models = [self.model]
|
||||
else:
|
||||
if model:
|
||||
raise ValueError(
|
||||
f'model type is either str or Model, but got type {type(model)}'
|
||||
)
|
||||
self.models = self.initiate_multiple_models(model)
|
||||
|
||||
self.has_multiple_models = len(self.models) > 1
|
||||
self.preprocessor = preprocessor
|
||||
|
||||
def __call__(self, input: Union[Input, List[Input]], *args,
|
||||
**post_kwargs) -> Union[Dict[str, Any], Generator]:
|
||||
# moodel provider should leave it as it is
|
||||
# maas library developer will handle this function
|
||||
# model provider should leave it as it is
|
||||
# modelscope library developer will handle this function
|
||||
|
||||
# simple showcase, need to support iterator type for both tensorflow and pytorch
|
||||
# input_dict = self._handle_input(input)
|
||||
@@ -91,15 +110,17 @@ class Pipeline(ABC):
|
||||
|
||||
def preprocess(self, inputs: Input) -> Dict[str, Any]:
|
||||
""" Provide default implementation based on preprocess_cfg and user can reimplement it
|
||||
|
||||
"""
|
||||
assert self.preprocessor is not None, 'preprocess method should be implemented'
|
||||
assert not isinstance(self.preprocessor, List),\
|
||||
'default implementation does not support using multiple preprocessors.'
|
||||
return self.preprocessor(inputs)
|
||||
|
||||
def forward(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
|
||||
""" Provide default implementation using self.model and user can reimplement it
|
||||
"""
|
||||
assert self.model is not None, 'forward method should be implemented'
|
||||
assert not self.has_multiple_models, 'default implementation does not support multiple models in a pipeline.'
|
||||
return self.model(inputs)
|
||||
|
||||
@abstractmethod
|
||||
171
modelscope/pipelines/builder.py
Normal file
171
modelscope/pipelines/builder.py
Normal file
@@ -0,0 +1,171 @@
|
||||
# Copyright (c) Alibaba, Inc. and its affiliates.
|
||||
|
||||
import os.path as osp
|
||||
from typing import List, Union
|
||||
|
||||
import json
|
||||
from maas_hub.file_download import model_file_download
|
||||
|
||||
from modelscope.models.base import Model
|
||||
from modelscope.utils.config import Config, ConfigDict
|
||||
from modelscope.utils.constant import CONFIGFILE, Tasks
|
||||
from modelscope.utils.registry import Registry, build_from_cfg
|
||||
from .base import Pipeline
|
||||
from .util import is_model_name
|
||||
|
||||
PIPELINES = Registry('pipelines')
|
||||
|
||||
DEFAULT_MODEL_FOR_PIPELINE = {
|
||||
# TaskName: (pipeline_module_name, model_repo)
|
||||
Tasks.image_matting: ('image-matting', 'damo/image-matting-person'),
|
||||
Tasks.text_classification:
|
||||
('bert-sentiment-analysis', 'damo/bert-base-sst2'),
|
||||
Tasks.text_generation: ('palm', 'damo/nlp_palm_text-generation_chinese'),
|
||||
Tasks.image_captioning: ('ofa', None),
|
||||
Tasks.image_generation:
|
||||
('person-image-cartoon',
|
||||
'damo/cv_unet_person-image-cartoon_compound-models'),
|
||||
}
|
||||
|
||||
|
||||
def build_pipeline(cfg: ConfigDict,
|
||||
task_name: str = None,
|
||||
default_args: dict = None):
|
||||
""" build pipeline given model config dict.
|
||||
|
||||
Args:
|
||||
cfg (:obj:`ConfigDict`): config dict for model object.
|
||||
task_name (str, optional): task name, refer to
|
||||
:obj:`Tasks` for more details.
|
||||
default_args (dict, optional): Default initialization arguments.
|
||||
"""
|
||||
return build_from_cfg(
|
||||
cfg, PIPELINES, group_key=task_name, default_args=default_args)
|
||||
|
||||
|
||||
def pipeline(task: str = None,
|
||||
model: Union[str, List[str], Model, List[Model]] = None,
|
||||
preprocessor=None,
|
||||
config_file: str = None,
|
||||
pipeline_name: str = None,
|
||||
framework: str = None,
|
||||
device: int = -1,
|
||||
**kwargs) -> Pipeline:
|
||||
""" Factory method to build a obj:`Pipeline`.
|
||||
|
||||
|
||||
Args:
|
||||
task (str): Task name defining which pipeline will be returned.
|
||||
model (str or List[str] or obj:`Model` or obj:list[`Model`]): (list of) model name or model object.
|
||||
preprocessor: preprocessor object.
|
||||
config_file (str, optional): path to config file.
|
||||
pipeline_name (str, optional): pipeline class name or alias name.
|
||||
framework (str, optional): framework type.
|
||||
device (int, optional): which device is used to do inference.
|
||||
|
||||
Return:
|
||||
pipeline (obj:`Pipeline`): pipeline object for certain task.
|
||||
|
||||
Examples:
|
||||
```python
|
||||
>>> # Using default model for a task
|
||||
>>> p = pipeline('image-classification')
|
||||
>>> # Using pipeline with a model name
|
||||
>>> p = pipeline('text-classification', model='damo/distilbert-base-uncased')
|
||||
>>> # Using pipeline with a model object
|
||||
>>> resnet = Model.from_pretrained('Resnet')
|
||||
>>> p = pipeline('image-classification', model=resnet)
|
||||
>>> # Using pipeline with a list of model names
|
||||
>>> p = pipeline('audio-kws', model=['damo/audio-tts', 'damo/auto-tts2'])
|
||||
"""
|
||||
if task is None and pipeline_name is None:
|
||||
raise ValueError('task or pipeline_name is required')
|
||||
|
||||
if pipeline_name is None:
|
||||
# get default pipeline for this task
|
||||
if isinstance(model, str) \
|
||||
or (isinstance(model, list) and isinstance(model[0], str)):
|
||||
|
||||
# if is_model_name(model):
|
||||
if (isinstance(model, str) and model.startswith('damo/')) \
|
||||
or (isinstance(model, list) and model[0].startswith('damo/')) \
|
||||
or (isinstance(model, str) and osp.exists(model)):
|
||||
# TODO @wenmeng.zwm add support when model is a str of modelhub address
|
||||
# read pipeline info from modelhub configuration file.
|
||||
pipeline_name, default_model_repo = get_default_pipeline_info(
|
||||
task)
|
||||
else:
|
||||
pipeline_name = get_pipeline_by_model_name(task, model)
|
||||
else:
|
||||
pipeline_name, default_model_repo = get_default_pipeline_info(task)
|
||||
|
||||
if model is None:
|
||||
model = default_model_repo
|
||||
|
||||
assert isinstance(model, (type(None), str, Model, list)), \
|
||||
f'model should be either None, str, List[str], Model, or List[Model], but got {type(model)}'
|
||||
|
||||
cfg = ConfigDict(type=pipeline_name, model=model)
|
||||
|
||||
if kwargs:
|
||||
cfg.update(kwargs)
|
||||
|
||||
if preprocessor is not None:
|
||||
cfg.preprocessor = preprocessor
|
||||
|
||||
return build_pipeline(cfg, task_name=task)
|
||||
|
||||
|
||||
def add_default_pipeline_info(task: str,
|
||||
model_name: str,
|
||||
modelhub_name: str = None,
|
||||
overwrite: bool = False):
|
||||
""" Add default model for a task.
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
model_name (str): model_name.
|
||||
modelhub_name (str): name for default modelhub.
|
||||
overwrite (bool): overwrite default info.
|
||||
"""
|
||||
if not overwrite:
|
||||
assert task not in DEFAULT_MODEL_FOR_PIPELINE, \
|
||||
f'task {task} already has default model.'
|
||||
|
||||
DEFAULT_MODEL_FOR_PIPELINE[task] = (model_name, modelhub_name)
|
||||
|
||||
|
||||
def get_default_pipeline_info(task):
|
||||
""" Get default info for certain task.
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
|
||||
Return:
|
||||
A tuple: first element is pipeline name(model_name), second element
|
||||
is modelhub name.
|
||||
"""
|
||||
|
||||
if task not in DEFAULT_MODEL_FOR_PIPELINE:
|
||||
# support pipeline which does not register default model
|
||||
pipeline_name = list(PIPELINES.modules[task].keys())[0]
|
||||
default_model = None
|
||||
else:
|
||||
pipeline_name, default_model = DEFAULT_MODEL_FOR_PIPELINE[task]
|
||||
return pipeline_name, default_model
|
||||
|
||||
|
||||
def get_pipeline_by_model_name(task: str, model: Union[str, List[str]]):
|
||||
""" Get pipeline name by task name and model name
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
model (str| list[str]): model names
|
||||
"""
|
||||
if isinstance(model, str):
|
||||
model_key = model
|
||||
else:
|
||||
model_key = '_'.join(model)
|
||||
assert model_key in PIPELINES.modules[task], \
|
||||
f'pipeline for task {task} model {model_key} not found.'
|
||||
return model_key
|
||||
2
modelscope/pipelines/cv/__init__.py
Normal file
2
modelscope/pipelines/cv/__init__.py
Normal file
@@ -0,0 +1,2 @@
|
||||
from .image_cartoon_pipeline import ImageCartoonPipeline
|
||||
from .image_matting_pipeline import ImageMattingPipeline
|
||||
148
modelscope/pipelines/cv/image_cartoon_pipeline.py
Normal file
148
modelscope/pipelines/cv/image_cartoon_pipeline.py
Normal file
@@ -0,0 +1,148 @@
|
||||
import os
|
||||
from typing import Any, Dict
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import PIL
|
||||
import tensorflow as tf
|
||||
|
||||
from modelscope.models.cv.cartoon.facelib.facer import FaceAna
|
||||
from modelscope.models.cv.cartoon.mtcnn_pytorch.src.align_trans import (
|
||||
get_reference_facial_points, warp_and_crop_face)
|
||||
from modelscope.models.cv.cartoon.utils import get_f5p, padTo16x, resize_size
|
||||
from modelscope.pipelines.base import Input
|
||||
from modelscope.preprocessors import load_image
|
||||
from modelscope.utils.constant import Tasks
|
||||
from modelscope.utils.logger import get_logger
|
||||
from ..base import Pipeline
|
||||
from ..builder import PIPELINES
|
||||
|
||||
if tf.__version__ >= '2.0':
|
||||
tf = tf.compat.v1
|
||||
tf.disable_eager_execution()
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
@PIPELINES.register_module(
|
||||
Tasks.image_generation, module_name='person-image-cartoon')
|
||||
class ImageCartoonPipeline(Pipeline):
|
||||
|
||||
def __init__(self, model: str):
|
||||
super().__init__(model=model)
|
||||
self.facer = FaceAna(self.model)
|
||||
self.sess_anime_head = self.load_sess(
|
||||
os.path.join(self.model, 'cartoon_anime_h.pb'), 'model_anime_head')
|
||||
self.sess_anime_bg = self.load_sess(
|
||||
os.path.join(self.model, 'cartoon_anime_bg.pb'), 'model_anime_bg')
|
||||
|
||||
self.box_width = 288
|
||||
global_mask = cv2.imread(os.path.join(self.model, 'alpha.jpg'))
|
||||
global_mask = cv2.resize(
|
||||
global_mask, (self.box_width, self.box_width),
|
||||
interpolation=cv2.INTER_AREA)
|
||||
self.global_mask = cv2.cvtColor(
|
||||
global_mask, cv2.COLOR_BGR2GRAY).astype(np.float32) / 255.0
|
||||
|
||||
def load_sess(self, model_path, name):
|
||||
config = tf.ConfigProto(allow_soft_placement=True)
|
||||
config.gpu_options.allow_growth = True
|
||||
sess = tf.Session(config=config)
|
||||
logger.info(f'loading model from {model_path}')
|
||||
with tf.gfile.FastGFile(model_path, 'rb') as f:
|
||||
graph_def = tf.GraphDef()
|
||||
graph_def.ParseFromString(f.read())
|
||||
sess.graph.as_default()
|
||||
tf.import_graph_def(graph_def, name=name)
|
||||
sess.run(tf.global_variables_initializer())
|
||||
logger.info(f'load model {model_path} done.')
|
||||
return sess
|
||||
|
||||
def preprocess(self, input: Input) -> Dict[str, Any]:
|
||||
if isinstance(input, str):
|
||||
img = np.array(load_image(input))
|
||||
elif isinstance(input, PIL.Image.Image):
|
||||
img = np.array(input.convert('RGB'))
|
||||
elif isinstance(input, np.ndarray):
|
||||
if len(input.shape) == 2:
|
||||
input = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
|
||||
img = input[:, :, ::-1]
|
||||
else:
|
||||
raise TypeError(f'input should be either str, PIL.Image,'
|
||||
f' np.array, but got {type(input)}')
|
||||
img = img.astype(np.float)
|
||||
result = {'img': img}
|
||||
return result
|
||||
|
||||
def detect_face(self, img):
|
||||
src_h, src_w, _ = img.shape
|
||||
boxes, landmarks, _ = self.facer.run(img)
|
||||
if boxes.shape[0] == 0:
|
||||
return None
|
||||
else:
|
||||
return landmarks
|
||||
|
||||
def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
|
||||
img = input['img'].astype(np.uint8)
|
||||
ori_h, ori_w, _ = img.shape
|
||||
img = resize_size(img, size=720)
|
||||
|
||||
img_brg = img[:, :, ::-1]
|
||||
|
||||
landmarks = self.detect_face(img)
|
||||
if landmarks is None:
|
||||
print('No face detected!')
|
||||
return {'output_png': None}
|
||||
|
||||
# background process
|
||||
pad_bg, pad_h, pad_w = padTo16x(img_brg)
|
||||
|
||||
bg_res = self.sess_anime_bg.run(
|
||||
self.sess_anime_bg.graph.get_tensor_by_name(
|
||||
'model_anime_bg/output_image:0'),
|
||||
feed_dict={'model_anime_bg/input_image:0': pad_bg})
|
||||
res = bg_res[:pad_h, :pad_w, :]
|
||||
|
||||
for landmark in landmarks:
|
||||
# get facial 5 points
|
||||
f5p = get_f5p(landmark, img_brg)
|
||||
|
||||
# face alignment
|
||||
head_img, trans_inv = warp_and_crop_face(
|
||||
img,
|
||||
f5p,
|
||||
ratio=0.75,
|
||||
reference_pts=get_reference_facial_points(default_square=True),
|
||||
crop_size=(self.box_width, self.box_width),
|
||||
return_trans_inv=True)
|
||||
|
||||
# head process
|
||||
head_res = self.sess_anime_head.run(
|
||||
self.sess_anime_head.graph.get_tensor_by_name(
|
||||
'model_anime_head/output_image:0'),
|
||||
feed_dict={
|
||||
'model_anime_head/input_image:0': head_img[:, :, ::-1]
|
||||
})
|
||||
|
||||
# merge head and background
|
||||
head_trans_inv = cv2.warpAffine(
|
||||
head_res,
|
||||
trans_inv, (np.size(img, 1), np.size(img, 0)),
|
||||
borderValue=(0, 0, 0))
|
||||
|
||||
mask = self.global_mask
|
||||
mask_trans_inv = cv2.warpAffine(
|
||||
mask,
|
||||
trans_inv, (np.size(img, 1), np.size(img, 0)),
|
||||
borderValue=(0, 0, 0))
|
||||
mask_trans_inv = np.expand_dims(mask_trans_inv, 2)
|
||||
|
||||
res = mask_trans_inv * head_trans_inv + (1 - mask_trans_inv) * res
|
||||
|
||||
res = cv2.resize(res, (ori_w, ori_h), interpolation=cv2.INTER_AREA)
|
||||
|
||||
return {'output_png': res}
|
||||
|
||||
def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
|
||||
return inputs
|
||||
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Reference in New Issue
Block a user