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* 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>
99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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class AdaFace(FastDeployModel):
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def __init__(self,
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model_file,
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params_file="",
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runtime_option=None,
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model_format=ModelFormat.PADDLE):
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# 调用基函数进行backend_option的初始化
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# 初始化后的option保存在self._runtime_option
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super(AdaFace, self).__init__(runtime_option)
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self._model = C.vision.faceid.AdaFace(
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model_file, params_file, self._runtime_option, model_format)
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# 通过self.initialized判断整个模型的初始化是否成功
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assert self.initialized, "AdaFace initialize failed."
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def predict(self, input_image):
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return self._model.predict(input_image)
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# 一些跟模型有关的属性封装
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# 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）
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@property
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def size(self):
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return self._model.size
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@property
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def alpha(self):
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return self._model.alpha
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@property
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def beta(self):
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return self._model.beta
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@property
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def swap_rb(self):
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return self._model.swap_rb
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@property
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def l2_normalize(self):
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return self._model.l2_normalize
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@size.setter
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def size(self, wh):
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assert isinstance(wh, (list, tuple)), \
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"The value to set `size` must be type of tuple or list."
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assert len(wh) == 2, \
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"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
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len(wh))
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self._model.size = wh
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@alpha.setter
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def alpha(self, value):
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assert isinstance(value, (list, tuple)), \
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"The value to set `alpha` must be type of tuple or list."
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assert len(value) == 3, \
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"The value to set `alpha` must contatins 3 elements for each channels, but now it contains {} elements.".format(
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len(value))
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self._model.alpha = value
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@beta.setter
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def beta(self, value):
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assert isinstance(value, (list, tuple)), \
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"The value to set `beta` must be type of tuple or list."
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assert len(value) == 3, \
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"The value to set `beta` must contatins 3 elements for each channels, but now it contains {} elements.".format(
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len(value))
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self._model.beta = value
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@swap_rb.setter
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def swap_rb(self, value):
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assert isinstance(
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value, bool), "The value to set `swap_rb` must be type of bool."
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self._model.swap_rb = value
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@l2_normalize.setter
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def l2_normalize(self, value):
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assert isinstance(
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value,
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bool), "The value to set `l2_normalize` must be type of bool."
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self._model.l2_normalize = value
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