# 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 typing import Union, List
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
from ...common import ProcessorManager
[docs]class PaddleDetPreprocessor(ProcessorManager):
def __init__(self, config_file):
"""Create a preprocessor for PaddleDetection Model from configuration file
:param config_file: (str)Path of configuration file, e.g ppyoloe/infer_cfg.yml
"""
self._manager = C.vision.detection.PaddleDetPreprocessor(
config_file)
[docs] def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._manager.disable_normalize()
[docs] def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._manager.disable_permute()
class NMSOption:
def __init__(self):
self.nms_option = C.vision.detection.NMSOption()
@property
def background_label(self):
return self.nms_option.background_label
[docs]class PaddleDetPostprocessor:
def __init__(self):
"""Create a postprocessor for PaddleDetection Model
"""
self._postprocessor = C.vision.detection.PaddleDetPostprocessor()
[docs] def run(self, runtime_results):
"""Postprocess the runtime results for PaddleDetection Model
:param: runtime_results: (list of FDTensor)The output FDTensor results from runtime
:return: list of ClassifyResult(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)
def apply_nms(self):
self._postprocessor.apply_nms()
[docs] def set_nms_option(self, nms_option=None):
"""This function will enable decode and nms in postprocess step.
"""
if nms_option is None:
nms_option = NMSOption()
self._postprocessor.set_nms_option(self, nms_option.nms_option)
[docs]class PPYOLOE(FastDeployModel):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a PPYOLOE model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g ppyoloe/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ppyoloe/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
self._model = C.vision.detection.PPYOLOE(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PPYOLOE model initialize failed."
[docs] def predict(self, im):
"""Detect an input image
:param im: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
:return: DetectionResult
"""
assert im is not None, "The input image data is None."
return self._model.predict(im)
[docs] def batch_predict(self, images):
"""Detect a batch of input image list
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of DetectionResult
"""
return self._model.batch_predict(images)
[docs] def clone(self):
"""Clone PPYOLOE object
:return: a new PPYOLOE object
"""
class PPYOLOEClone(PPYOLOE):
def __init__(self, model):
self._model = model
clone_model = PPYOLOEClone(self._model.clone())
return clone_model
@property
def preprocessor(self):
"""Get PaddleDetPreprocessor object of the loaded model
:return PaddleDetPreprocessor
"""
return self._model.preprocessor
@property
def postprocessor(self):
"""Get PaddleDetPostprocessor object of the loaded model
:return PaddleDetPostprocessor
"""
return self._model.postprocessor
[docs]class PPYOLO(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a PPYOLO model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g ppyolo/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ppyolo/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
: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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PPYOLO model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PPYOLO(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PPYOLO model initialize failed."
[docs] def clone(self):
"""Clone PPYOLO object
:return: a new PPYOLO object
"""
class PPYOLOClone(PPYOLO):
def __init__(self, model):
self._model = model
clone_model = PPYOLOClone(self._model.clone())
return clone_model
[docs]class PaddleYOLOX(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOX model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolox/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolox/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PaddleYOLOX model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PaddleYOLOX(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleYOLOX model initialize failed."
[docs] def clone(self):
"""Clone PaddleYOLOX object
:return: a new PaddleYOLOX object
"""
class PaddleYOLOXClone(PaddleYOLOX):
def __init__(self, model):
self._model = model
clone_model = PaddleYOLOXClone(self._model.clone())
return clone_model
[docs]class PicoDet(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a PicoDet model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g picodet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g picodet/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
self._model = C.vision.detection.PicoDet(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PicoDet model initialize failed."
[docs] def clone(self):
"""Clone PicoDet object
:return: a new PicoDet object
"""
class PicoDetClone(PicoDet):
def __init__(self, model):
self._model = model
clone_model = PicoDetClone(self._model.clone())
return clone_model
[docs]class FasterRCNN(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a FasterRCNN model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g fasterrcnn/model.pdmodel
:param params_file: (str)Path of parameters file, e.g fasterrcnn/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "FasterRCNN model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.FasterRCNN(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "FasterRCNN model initialize failed."
[docs] def clone(self):
"""Clone FasterRCNN object
:return: a new FasterRCNN object
"""
class FasterRCNNClone(FasterRCNN):
def __init__(self, model):
self._model = model
clone_model = FasterRCNNClone(self._model.clone())
return clone_model
class YOLOv3(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOv3 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolov3/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolov3/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "YOLOv3 model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.YOLOv3(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "YOLOv3 model initialize failed."
def clone(self):
"""Clone YOLOv3 object
:return: a new YOLOv3 object
"""
class YOLOv3Clone(YOLOv3):
def __init__(self, model):
self._model = model
clone_model = YOLOv3Clone(self._model.clone())
return clone_model
class SOLOv2(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a SOLOv2 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g solov2/model.pdmodel
:param params_file: (str)Path of parameters file, e.g solov2/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 deployment, e.g solov2/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "SOLOv2 model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.SOLOv2(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "SOLOv2 model initialize failed."
def clone(self):
"""Clone SOLOv2 object
:return: a new SOLOv2 object
"""
class SOLOv2Clone(SOLOv2):
def __init__(self, model):
self._model = model
clone_model = SOLOv2Clone(self._model.clone())
return clone_model
[docs]class MaskRCNN(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a MaskRCNN model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g fasterrcnn/model.pdmodel
:param params_file: (str)Path of parameters file, e.g fasterrcnn/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "MaskRCNN model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.MaskRCNN(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "MaskRCNN model initialize failed."
[docs] def batch_predict(self, images):
"""Detect a batch of input image list, batch_predict is not supported for maskrcnn now.
:param im: (list of numpy.ndarray) The input image list, each element is a 3-D array with layout HWC, BGR format
:return list of DetectionResult
"""
raise Exception(
"batch_predict is not supported for MaskRCNN model now.")
[docs] def clone(self):
"""Clone MaskRCNN object
:return: a new MaskRCNN object
"""
class MaskRCNNClone(MaskRCNN):
def __init__(self, model):
self._model = model
clone_model = MaskRCNNClone(self._model.clone())
return clone_model
class SSD(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a SSD model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g ssd/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ssd/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "SSD model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.SSD(model_file, params_file,
config_file, self._runtime_option,
model_format)
assert self.initialized, "SSD model initialize failed."
def clone(self):
"""Clone SSD object
:return: a new SSD object
"""
class SSDClone(SSD):
def __init__(self, model):
self._model = model
clone_model = SSDClone(self._model.clone())
return clone_model
class PaddleYOLOv5(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOv5 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolov5/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolov5/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PaddleYOLOv5 model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PaddleYOLOv5(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleYOLOv5 model initialize failed."
class PaddleYOLOv6(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOv6 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolov6/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolov6/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PaddleYOLOv6 model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PaddleYOLOv6(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleYOLOv6 model initialize failed."
class PaddleYOLOv7(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOv7 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolov7/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolov7/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PaddleYOLOv7 model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PaddleYOLOv7(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleYOLOv7 model initialize failed."
class PaddleYOLOv8(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a YOLOv8 model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g yolov8/model.pdmodel
:param params_file: (str)Path of parameters file, e.g yolov8/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 deployment, e.g yolov8/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
self._model = C.vision.detection.PaddleYOLOv8(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PaddleYOLOv8 model initialize failed."
class RTMDet(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a RTMDet model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g rtmdet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g rtmdet/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "RTMDet model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.RTMDet(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "RTMDet model initialize failed."
class CascadeRCNN(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a CascadeRCNN model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g cascadercnn/model.pdmodel
:param params_file: (str)Path of parameters file, e.g cascadercnn/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "CascadeRCNN model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.CascadeRCNN(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "CascadeRCNN model initialize failed."
class PSSDet(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a PSSDet model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g pssdet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g pssdet/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PSSDet model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PSSDet(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PSSDet model initialize failed."
class RetinaNet(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a RetinaNet model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g retinanet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g retinanet/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "RetinaNet model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.RetinaNet(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "RetinaNet model initialize failed."
class PPYOLOESOD(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a PPYOLOESOD model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g ppyoloesod/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ppyoloesod/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "PPYOLOESOD model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.PPYOLOESOD(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "PPYOLOESOD model initialize failed."
class FCOS(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a FCOS model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g fcos/model.pdmodel
:param params_file: (str)Path of parameters file, e.g fcos/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "FCOS model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.FCOS(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "FCOS model initialize failed."
class TTFNet(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a TTFNet model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g ttfnet/model.pdmodel
:param params_file: (str)Path of parameters file, e.g ttfnet/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "TTFNet model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.TTFNet(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "TTFNet model initialize failed."
class TOOD(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a TOOD model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g tood/model.pdmodel
:param params_file: (str)Path of parameters file, e.g tood/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "TOOD model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.TOOD(
model_file, params_file, config_file, self._runtime_option,
model_format)
assert self.initialized, "TOOD model initialize failed."
class GFL(PPYOLOE):
def __init__(self,
model_file,
params_file,
config_file,
runtime_option=None,
model_format=ModelFormat.PADDLE):
"""Load a GFL model exported by PaddleDetection.
:param model_file: (str)Path of model file, e.g gfl/model.pdmodel
:param params_file: (str)Path of parameters file, e.g gfl/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 deployment, e.g ppyoloe/infer_cfg.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(PPYOLOE, self).__init__(runtime_option)
assert model_format == ModelFormat.PADDLE, "GFL model only support model format of ModelFormat.Paddle now."
self._model = C.vision.detection.GFL(model_file, params_file,
config_file, self._runtime_option,
model_format)
assert self.initialized, "GFL model initialize failed."