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* add style transfer model * add examples for generation model * add unit test * add speed comparison * add speed comparison * add variable for constant * add preprocessor and postprocessor * add preprocessor and postprocessor * fix * fix according to review Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
103 lines
3.8 KiB
Python
103 lines
3.8 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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import logging
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from .... import FastDeployModel, ModelFormat
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from .... import c_lib_wrap as C
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class AnimeGANPreprocessor:
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def __init__(self, config_file):
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"""Create a preprocessor for AnimeGAN.
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"""
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self._preprocessor = C.vision.generation.AnimeGANPreprocessor()
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def run(self, input_ims):
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"""Preprocess input images for AnimeGAN.
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:param: input_ims: (list of numpy.ndarray)The input image
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:return: list of FDTensor
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"""
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return self._preprocessor.run(input_ims)
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class AnimeGANPostprocessor:
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def __init__(self):
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"""Create a postprocessor for AnimeGAN.
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"""
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self._postprocessor = C.vision.generation.AnimeGANPostprocessor()
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def run(self, runtime_results):
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"""Postprocess the runtime results for AnimeGAN
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:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
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:return: results: (list) Final results
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"""
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return self._postprocessor.run(runtime_results)
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class AnimeGAN(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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"""Load a AnimeGAN model.
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:param model_file: (str)Path of model file, e.g ./model.pdmodel
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:param params_file: (str)Path of parameters file, e.g ./model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
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:param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
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:param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
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"""
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# call super constructor to initialize self._runtime_option
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super(AnimeGAN, self).__init__(runtime_option)
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self._model = C.vision.generation.AnimeGAN(
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model_file, params_file, self._runtime_option, model_format)
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# assert self.initialized to confirm initialization successfully.
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assert self.initialized, "AnimeGAN initialize failed."
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def predict(self, input_image):
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""" Predict the style transfer result for an input image
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:param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
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:return: style transfer result
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"""
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return self._model.predict(input_image)
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def batch_predict(self, input_images):
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""" Predict the style transfer result for multiple input images
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:param input_images: (list of numpy.ndarray)The list of input image data, each image is a 3-D array with layout HWC, BGR format
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:return: a list of style transfer results
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"""
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return self._model.batch_predict(input_images)
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@property
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def preprocessor(self):
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"""Get AnimeGANPreprocessor object of the loaded model
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:return AnimeGANPreprocessor
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"""
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return self._model.preprocessor
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@property
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def postprocessor(self):
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"""Get AnimeGANPostprocessor object of the loaded model
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:return AnimeGANPostprocessor
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"""
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return self._model.postprocessor
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