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[Model] Refactor insightface models (#919)

Browse Source 
* 重构insightface代码

* 重写insightface example代码

* 重写insightface example代码

* 删除多余代码

* 修改预处理代码

* 修改文档

* 修改文档

* 恢复误删除的文件

* 修改cpp example

* 修改cpp example

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 测试python代码

* 跑通python代码

* 修复重复初始化的bug

* 更新adaface的python代码

* 修复c++重复初始化的问题

* 修复c++重复初始化的问题

* 修复Python重复初始化的问题

* 新增preprocess的几个参数的获取方式

* 修复注释的错误

* 按照要求修改

* 修改文档中的图片为图片压缩包

* 修改编译完成后程序的提示

* 更新错误include

* 删除无用文件

* 更新文档
This commit is contained in:
Zheng_Bicheng
2022-12-26 21:01:58 +08:00
committed by GitHub
parent df940b750f
commit ec67f8ee6d
62 changed files with 1750 additions and 2100 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
python/fastdeploy/vision/faceid/__init__.py
View File

@@ -13,9 +13,4 @@
# limitations under the License.
from __future__ import absolute_import
from .contrib.adaface import AdaFace
from .contrib.arcface import ArcFace
from .contrib.cosface import CosFace
from .contrib.insightface_rec import InsightFaceRecognitionModel
from .contrib.partial_fc import PartialFC
from .contrib.vpl import VPL
from .contrib import *

2
python/fastdeploy/vision/faceid/contrib/__init__.py
View File

@@ -13,3 +13,5 @@
# limitations under the License.
from __future__ import absolute_import
from .insightface import *
from .adaface import *

126
python/fastdeploy/vision/faceid/contrib/adaface.py
View File

@@ -1,126 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class AdaFace(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a AdaFace model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./adaface.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(AdaFace, self).__init__(runtime_option)
self._model = C.vision.faceid.AdaFace(
model_file, params_file, self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "AdaFace initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)), \
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2, \
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)), \
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3, \
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)), \
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3, \
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value

109
python/fastdeploy/vision/faceid/contrib/adaface/__init__.py Normal file
View File

@@ -0,0 +1,109 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from ..... import FastDeployModel, ModelFormat
from ..... import c_lib_wrap as C
class AdaFacePreprocessor:
def __init__(self):
"""Create a preprocessor for AdaFace Model
"""
self._preprocessor = C.vision.faceid.AdaFacePreprocessor()
def run(self, input_ims):
"""Preprocess input images for AdaFace Model
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor, include image, scale_factor, im_shape
"""
return self._preprocessor.run(input_ims)
class AdaFacePostprocessor:
def __init__(self):
"""Create a postprocessor for AdaFace Model
"""
self._postprocessor = C.vision.faceid.AdaFacePostprocessor()
def run(self, runtime_results):
"""Postprocess the runtime results for PaddleClas Model
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
:return: list of FaceRecognitionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
"""
return self._postprocessor.run(runtime_results)
@property
def l2_normalize(self):
"""
confidence threshold for postprocessing, default is 0.5
"""
return self._postprocessor.l2_normalize
class AdaFace(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a AdaFace model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g adaface/model.pdmodel
:param params_file: (str)Path of parameters file, e.g adaface/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(AdaFace, self).__init__(runtime_option)
self._model = C.vision.faceid.AdaFace(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "AdaFace model initialize failed."
def predict(self, im):
"""Detect an input image
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: DetectionResult
"""
assert im is not None, "The input image data is None."
return self._model.predict(im)
def batch_predict(self, images):
"""Detect a batch of input image list
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of DetectionResult
"""
return self._model.batch_predict(images)
@property
def preprocessor(self):
"""Get AdaFacePreprocessor object of the loaded model
:return AdaFacePreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get AdaFacePostprocessor object of the loaded model
:return AdaFacePostprocessor
"""
return self._model.postprocessor

127
python/fastdeploy/vision/faceid/contrib/arcface.py
View File

@@ -1,127 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
from ..contrib.insightface_rec import InsightFaceRecognitionModel
class ArcFace(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a ArcFace model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./arcface.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(ArcFace, self).__init__(runtime_option)
self._model = C.vision.faceid.ArcFace(
model_file, params_file, self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "ArcFace initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value

126
python/fastdeploy/vision/faceid/contrib/cosface.py
View File

@@ -1,126 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class CosFace(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a CosFace model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./cosface.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(CosFace, self).__init__(runtime_option)
self._model = C.vision.faceid.CosFace(
model_file, params_file, self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "CosFace initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value

222
python/fastdeploy/vision/faceid/contrib/insightface/__init__.py Normal file
View File

@@ -0,0 +1,222 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from ..... import FastDeployModel, ModelFormat
from ..... import c_lib_wrap as C
class InsightFaceRecognitionPreprocessor:
def __init__(self):
"""Create a preprocessor for InsightFaceRecognition Model
"""
self._preprocessor = C.vision.faceid.InsightFaceRecognitionPreprocessor(
)
def run(self, input_ims):
"""Preprocess input images for InsightFaceRecognition Model
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor, include image, scale_factor, im_shape
"""
return self._preprocessor.run(input_ims)
@property
def size(self):
"""
Argument for image preprocessing step, tuple of (width, height),
decide the target size after resize, default (112, 112)
"""
return self._preprocessor.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha values for normalization,
default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
"""
return self._preprocessor.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization,
default beta = {-1.f, -1.f, -1.f}
"""
return self._preprocessor.beta
@property
def permute(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel,
such as BGR->RGB, default true.
"""
return self._preprocessor.permute
class InsightFaceRecognitionPostprocessor:
def __init__(self):
"""Create a postprocessor for InsightFaceRecognition Model
"""
self._postprocessor = C.vision.faceid.InsightFaceRecognitionPostprocessor(
)
def run(self, runtime_results):
"""Postprocess the runtime results for PaddleClas Model
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
:return: list of FaceRecognitionResult(If the runtime_results is predict by batched samples, the length of this list equals to the batch size)
"""
return self._postprocessor.run(runtime_results)
@property
def l2_normalize(self):
"""
confidence threshold for postprocessing, default is 0.5
"""
return self._postprocessor.l2_normalize
class InsightFaceRecognitionBase(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a InsightFaceRecognitionBase model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g InsightFaceRecognitionBase/model.pdmodel
:param params_file: (str)Path of parameters file, e.g InsightFaceRecognitionBase/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.InsightFaceRecognitionBase(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "InsightFaceRecognitionBase model initialize failed."
def predict(self, im):
"""Detect an input image
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: DetectionResult
"""
assert im is not None, "The input image data is None."
return self._model.predict(im)
def batch_predict(self, images):
"""Detect a batch of input image list
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of DetectionResult
"""
return self._model.batch_predict(images)
@property
def preprocessor(self):
"""Get InsightFaceRecognitionPreprocessor object of the loaded model
:return InsightFaceRecognitionPreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get InsightFaceRecognitionPostprocessor object of the loaded model
:return InsightFaceRecognitionPostprocessor
"""
return self._model.postprocessor
class ArcFace(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a ArcFace model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g ArcFace/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ArcFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.ArcFace(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "ArcFace model initialize failed."
class CosFace(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a CosFace model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g CosFace/model.pdmodel
:param params_file: (str)Path of parameters file, e.g CosFace/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.CosFace(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "CosFace model initialize failed."
class PartialFC(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a PartialFC model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g PartialFC/model.pdmodel
:param params_file: (str)Path of parameters file, e.g PartialFC/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.PartialFC(
model_file, params_file, self._runtime_option, model_format)
assert self.initialized, "PartialFC model initialize failed."
class VPL(InsightFaceRecognitionBase):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a VPL model exported by PaddleClas.
:param model_file: (str)Path of model file, e.g VPL/model.pdmodel
:param params_file: (str)Path of parameters file, e.g VPL/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
super(InsightFaceRecognitionBase, self).__init__(runtime_option)
self._model = C.vision.faceid.VPL(model_file, params_file,
self._runtime_option, model_format)
assert self.initialized, "VPL model initialize failed."

126
python/fastdeploy/vision/faceid/contrib/insightface_rec.py
View File

@@ -1,126 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class InsightFaceRecognitionModel(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a InsightFace model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./arcface.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(InsightFaceRecognitionModel, self).__init__(runtime_option)
self._model = C.vision.faceid.InsightFaceRecognitionModel(
model_file, params_file, self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "InsightFaceRecognitionModel initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟InsightFaceRecognitionModel模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value

126
python/fastdeploy/vision/faceid/contrib/partial_fc.py
View File

@@ -1,126 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class PartialFC(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a PartialFC model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./partial_fc.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(PartialFC, self).__init__(runtime_option)
self._model = C.vision.faceid.PartialFC(
model_file, params_file, self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "PartialFC initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value

126
python/fastdeploy/vision/faceid/contrib/vpl.py
View File

@@ -1,126 +0,0 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class VPL(FastDeployModel):
def __init__(self,
model_file,
params_file="",
runtime_option=None,
model_format=ModelFormat.ONNX):
"""Load a VPL model exported by InsigtFace.
:param model_file: (str)Path of model file, e.g ./vpl.onnx
:param params_file: (str)Path of parameters file, e.g yolox/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
"""
# 调用基函数进行backend_option的初始化
# 初始化后的option保存在self._runtime_option
super(VPL, self).__init__(runtime_option)
self._model = C.vision.faceid.VPL(model_file, params_file,
self._runtime_option, model_format)
# 通过self.initialized判断整个模型的初始化是否成功
assert self.initialized, "VPL initialize failed."
def predict(self, input_image):
""" Predict the face recognition result for an input image
:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: FaceRecognitionResult
"""
return self._model.predict(input_image)
# 一些跟模型有关的属性封装
# 多数是预处理相关，可通过修改如model.size = [112, 112]改变预处理时resize的大小（前提是模型支持）
@property
def size(self):
"""
Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (112, 112)
"""
return self._model.size
@property
def alpha(self):
"""
Argument for image preprocessing step, alpha value for normalization, default alpha = [1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f]
"""
return self._model.alpha
@property
def beta(self):
"""
Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
"""
return self._model.beta
@property
def swap_rb(self):
"""
Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default True.
"""
return self._model.swap_rb
@property
def l2_normalize(self):
"""
Argument for image preprocessing step, whether to apply l2 normalize to embedding values, default False;
"""
return self._model.l2_normalize
@size.setter
def size(self, wh):
assert isinstance(wh, (list, tuple)),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self._model.size = wh
@alpha.setter
def alpha(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `alpha` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.alpha = value
@beta.setter
def beta(self, value):
assert isinstance(value, (list, tuple)),\
"The value to set `beta` must be type of tuple or list."
assert len(value) == 3,\
"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
len(value))
self._model.beta = value
@swap_rb.setter
def swap_rb(self, value):
assert isinstance(
value, bool), "The value to set `swap_rb` must be type of bool."
self._model.swap_rb = value
@l2_normalize.setter
def l2_normalize(self, value):
assert isinstance(
value,
bool), "The value to set `l2_normalize` must be type of bool."
self._model.l2_normalize = value