# 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
[docs]class PaddleSegModel(FastDeployModel):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a image segmentation model exported by PaddleSeg.
:param model_file: (str)Path of model file, e.g unet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g unet/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
:param config_file: (str) Path of configuration file for deploy, e.g unet/deploy.yml
: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(PaddleSegModel, self).__init__(runtime_option)
# assert model_format == ModelFormat.PADDLE, "PaddleSeg only support model format of ModelFormat.Paddle now."
self._model = C.vision.segmentation.PaddleSegModel(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleSeg model initialize failed."
[docs] def predict(self, image):
"""Predict the segmentation result for an input image
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: SegmentationResult
"""
return self._model.predict(image)
[docs] def batch_predict(self, image_list):
"""Predict the segmentation results for a batch of input images
:param image_list: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return: list of SegmentationResult
"""
return self._model.batch_predict(image_list)
[docs] def clone(self):
"""Clone PaddleSegModel object
:return: a new PaddleSegModel object
"""
class PaddleSegCloneModel(PaddleSegModel):
def __init__(self, model):
self._model = model
clone_model = PaddleSegCloneModel(self._model.clone())
return clone_model
@property
def preprocessor(self):
"""Get PaddleSegPreprocessor object of the loaded model
:return: PaddleSegPreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get PaddleSegPostprocessor object of the loaded model
:return: PaddleSegPostprocessor
"""
return self._model.postprocessor
[docs]class PaddleSegPreprocessor:
def __init__(self, config_file):
"""Create a preprocessor for PaddleSegModel from configuration file
:param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
"""
self._preprocessor = C.vision.segmentation.PaddleSegPreprocessor(
config_file)
[docs] def run(self, input_ims):
"""Preprocess input images for PaddleSegModel
:param input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
[docs] def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
[docs] def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
@property
def is_vertical_screen(self):
"""Atrribute of PP-HumanSeg model. Stating Whether the input image is vertical image(height > width), default value is False
:return: value of is_vertical_screen(bool)
"""
return self._preprocessor.is_vertical_screen
@is_vertical_screen.setter
def is_vertical_screen(self, value):
"""Set attribute is_vertical_screen of PP-HumanSeg model.
:param value: (bool)The value to set is_vertical_screen
"""
assert isinstance(
value,
bool), "The value to set `is_vertical_screen` must be type of bool."
self._preprocessor.is_vertical_screen = value
[docs]class PaddleSegPostprocessor:
def __init__(self, config_file):
"""Create a postprocessor for PaddleSegModel from configuration file
:param config_file: (str)Path of configuration file, e.g ppliteseg/deploy.yaml
"""
self._postprocessor = C.vision.segmentation.PaddleSegPostprocessor(
config_file)
[docs] def run(self, runtime_results, imgs_info):
"""Postprocess the runtime results for PaddleSegModel
:param runtime_results: (list of FDTensor)The output FDTensor results from runtime
:param imgs_info: The original input images shape info map, key is "shape_info", value is [[image_height, image_width]]
:return: list of SegmentationResult(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, imgs_info)
@property
def apply_softmax(self):
"""Atrribute of PaddleSeg model. Stating Whether applying softmax operator in the postprocess, default value is False
:return: value of apply_softmax(bool)
"""
return self._postprocessor.apply_softmax
@apply_softmax.setter
def apply_softmax(self, value):
"""Set attribute apply_softmax of PaddleSeg model.
:param value: (bool)The value to set apply_softmax
"""
assert isinstance(
value,
bool), "The value to set `apply_softmax` must be type of bool."
self._postprocessor.apply_softmax = value
@property
def store_score_map(self):
"""Atrribute of PaddleSeg model. Stating Whether storing score map in the SegmentationResult, default value is False
:return: value of store_score_map(bool)
"""
return self._postprocessor.store_score_map
@store_score_map.setter
def store_score_map(self, value):
"""Set attribute store_score_map of PaddleSeg model.
:param value: (bool)The value to set store_score_map
"""
assert isinstance(
value,
bool), "The value to set `store_score_map` must be type of bool."
self._postprocessor.store_score_map = value