merge master

.dev_scripts/build_docs.sh

@@ -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

.dev_scripts/linter.sh

@@ -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

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.

MANIFEST.in

@@ -1 +1 @@
-recursive-include maas_lib/configs *.py
+recursive-include modelscope/configs *.py

README.md

@@ -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
 

configs/README.md

@@ -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.

docs/source/api/maas_lib.fileio.format.rst

@@ -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:

docs/source/api/maas_lib.fileio.rst

@@ -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:

docs/source/api/maas_lib.models.nlp.rst

@@ -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:

docs/source/api/maas_lib.models.rst

@@ -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:

docs/source/api/maas_lib.pipelines.audio.rst

@@ -1,7 +0,0 @@
-maas\_lib.pipelines.audio package
-=================================
-
-.. automodule:: maas_lib.pipelines.audio
-   :members:
-   :undoc-members:
-   :show-inheritance:

docs/source/api/maas_lib.pipelines.cv.rst

@@ -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:

docs/source/api/maas_lib.pipelines.multi_modal.rst

@@ -1,7 +0,0 @@
-maas\_lib.pipelines.multi\_modal package
-========================================
-
-.. automodule:: maas_lib.pipelines.multi_modal
-   :members:
-   :undoc-members:
-   :show-inheritance:

docs/source/api/maas_lib.preprocessors.rst

@@ -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:

docs/source/api/maas_lib.rst

@@ -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:

docs/source/api/maas_lib.trainers.nlp.rst

@@ -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:

docs/source/api/maas_lib.trainers.rst

@@ -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:

docs/source/api/maas_lib.utils.rst

@@ -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:

docs/source/api/modelscope.fileio.format.rst

@@ -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:

docs/source/api/modelscope.fileio.rst

@@ -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:

docs/source/api/modelscope.models.cv.cartoon.facelib.LK.rst

@@ -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:

docs/source/api/modelscope.models.cv.cartoon.facelib.rst

@@ -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:

docs/source/api/modelscope.models.cv.cartoon.mtcnn_pytorch.rst

@@ -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

docs/source/api/modelscope.models.cv.cartoon.mtcnn_pytorch.src.rst

@@ -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:

docs/source/api/modelscope.models.cv.cartoon.rst

@@ -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:

docs/source/api/modelscope.models.cv.rst

@@ -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

docs/source/api/modelscope.models.nlp.rst

@@ -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:

docs/source/api/modelscope.models.rst

@@ -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:

docs/source/api/modelscope.pipelines.audio.rst

@@ -0,0 +1,7 @@
+modelscope.pipelines.audio package
+==================================
+
+.. automodule:: modelscope.pipelines.audio
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/api/modelscope.pipelines.cv.rst

@@ -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:

docs/source/api/modelscope.pipelines.multi_modal.rst

@@ -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:

docs/source/api/modelscope.pipelines.nlp.rst

@@ -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:

docs/source/api/modelscope.pipelines.rst

@@ -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:

docs/source/api/modelscope.preprocessors.rst

@@ -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:

docs/source/api/modelscope.pydatasets.rst

@@ -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:

docs/source/api/modelscope.rst

@@ -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:

docs/source/api/maas_lib.pipelines.nlp.rst → docs/source/api/modelscope.trainers.nlp.rst

@@ -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:

docs/source/api/maas_lib.pipelines.rst → docs/source/api/modelscope.trainers.rst

@@ -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:

docs/source/api/modelscope.utils.rst

@@ -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:

docs/source/api/modules.rst

@@ -1,7 +1,7 @@
-maas_lib
-========
+modelscope
+==========
 
 .. toctree::
    :maxdepth: 4
 
-   maas_lib
+   modelscope

docs/source/conf.py

@@ -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

docs/source/develop.md

@@ -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
- ```
-
- 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 only want to format and lint your code, you can run
- ```bash
- make linter
- ```
-
- ## 2. Test
- ### 2.1 Unit test
- ```bash
- make test
- ```
-
- ### 2.2 Test data
- TODO
-
- ## 3. Build pip package
- ```bash
- make whl
- ```
+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.
+
+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
+```
+
+## 2. Test
+### 2.1 Unit test
+```bash
+make test
+```
+
+### 2.2 Test data
+TODO
+
+## 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
+```

docs/source/index.rst

@@ -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

docs/source/quick_start.md

@@ -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 = [

docs/source/tutorials/pipeline.md

@@ -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!")
 ```

maas_lib/pipelines/builder.py

@@ -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]

maas_lib/pipelines/cv/__init__.py

@@ -1 +0,0 @@
-from .image_matting import ImageMatting

maas_lib/pipelines/util.py

@@ -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

maas_lib/models/__init__.py → modelscope/models/__init__.py

@@ -2,4 +2,4 @@
 
 from .base import Model
 from .builder import MODELS, build_model
-from .nlp import SequenceClassificationModel
+from .nlp import BertForSequenceClassification

maas_lib/models/base.py → modelscope/models/base.py

@@ -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

maas_lib/models/builder.py → modelscope/models/builder.py

@@ -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')
 

modelscope/models/cv/cartoon/facelib/LICENSE

@@ -0,0 +1,4 @@
+
+Copyright (c) Peppa_Pig_Face_Engine
+
+https://github.com/610265158/Peppa_Pig_Face_Engine

modelscope/models/cv/cartoon/facelib/LK/lk.py

@@ -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

modelscope/models/cv/cartoon/facelib/config.py

@@ -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

modelscope/models/cv/cartoon/facelib/face_detector.py

@@ -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

modelscope/models/cv/cartoon/facelib/face_landmark.py

@@ -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

modelscope/models/cv/cartoon/facelib/facer.py

@@ -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

modelscope/models/cv/cartoon/mtcnn_pytorch/LICENSE

@@ -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.

modelscope/models/cv/cartoon/mtcnn_pytorch/README.md

@@ -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
+![example of a face detection](images/example.png)
+
+## 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)

modelscope/models/cv/cartoon/mtcnn_pytorch/src/align_trans.py

@@ -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

modelscope/models/cv/cartoon/mtcnn_pytorch/src/matlab_cp2tform.py

@@ -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)

modelscope/models/cv/cartoon/utils.py

@@ -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

maas_lib/models/nlp/__init__.py → modelscope/models/nlp/__init__.py

@@ -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

maas_lib/models/nlp/sequence_classification_model.py → modelscope/models/nlp/sequence_classification_model.py

@@ -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)

maas_lib/models/nlp/space/dialog_generation_model.py → modelscope/models/nlp/space/dialog_generation_model.py

@@ -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':

maas_lib/models/nlp/space/dialog_intent_model.py → modelscope/models/nlp/space/dialog_intent_model.py

@@ -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

maas_lib/models/nlp/space/model/gen_unified_transformer.py → modelscope/models/nlp/space/model/gen_unified_transformer.py

@@ -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
 
 

maas_lib/models/nlp/space/model/intent_unified_transformer.py → modelscope/models/nlp/space/model/intent_unified_transformer.py

@@ -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
 
 

maas_lib/models/nlp/space/model/unified_transformer.py → modelscope/models/nlp/space/model/unified_transformer.py

@@ -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)

maas_lib/models/nlp/space/modules/transformer_block.py → modelscope/models/nlp/space/modules/transformer_block.py

@@ -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
 
 

modelscope/models/nlp/text_generation_model.py

@@ -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)

maas_lib/pipelines/base.py → modelscope/pipelines/base.py

@@ -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

modelscope/pipelines/builder.py

@@ -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

modelscope/pipelines/cv/__init__.py

@@ -0,0 +1,2 @@
+from .image_cartoon_pipeline import ImageCartoonPipeline
+from .image_matting_pipeline import ImageMattingPipeline

modelscope/pipelines/cv/image_cartoon_pipeline.py

@@ -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