[vision] Add AdaFace model support (#301)

* 新增adaface模型

* 新增adaface模型python代码

* 新增adaface模型example代码

* 删除无用的import

* update

* 修正faceid文档的错误

* 修正faceid文档的错误

* 删除无用文件

* 新增adaface模型paddleinference推理代码,模型文件先提交方便测试后期会删除

* 新增adaface模型paddleinference推理代码,模型文件先提交方便测试后期会删除

* 按照要求修改并跑通cpp example

* 测试python example

* python cpu测试通过，修改了文档

* 修正文档，替换了模型下载地址

* 修正文档

* 修正文档

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

python/fastdeploy/vision/faceid/__init__.py

@@ -13,6 +13,7 @@
 # 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

python/fastdeploy/vision/faceid/contrib/adaface.py

@@ -0,0 +1,98 @@
+# 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):
+        # 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):
+        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):
+        return self._model.size
+
+    @property
+    def alpha(self):
+        return self._model.alpha
+
+    @property
+    def beta(self):
+        return self._model.beta
+
+    @property
+    def swap_rb(self):
+        return self._model.swap_rb
+
+    @property
+    def l2_normalize(self):
+        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