mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +08:00
89 lines
3.6 KiB
Python
89 lines
3.6 KiB
Python
# 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 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."
|
|
|
|
def predict(self, input_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(input_image)
|
|
|
|
@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._model.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._model.apply_softmax = value
|
|
|
|
@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._model.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._model.is_vertical_screen = value
|