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[CVCUDA] PP-OCR and PPDet preprocessor support use_cuda python API(#1734)

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add cvcuda support in ppocr and ppdet
This commit is contained in:
guxukai
2023-03-29 20:57:36 +08:00
committed by GitHub
parent 3ef48f7746
commit 38f056d1c1
2 changed files with 34 additions and 60 deletions
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18
python/fastdeploy/vision/detection/ppdet/__init__.py
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@@ -17,36 +17,28 @@ from typing import Union, List
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
from ...common import ProcessorManager
class PaddleDetPreprocessor:
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._preprocessor = C.vision.detection.PaddleDetPreprocessor(
config_file)
def run(self, input_ims):
"""Preprocess input images for PaddleDetection Model
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor, include image, scale_factor, im_shape
"""
return self._preprocessor.run(input_ims)
self._manager = C.vision.detection.PaddleDetPreprocessor(config_file)
def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
self._manager.disable_normalize()
def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
self._manager.disable_permute()
class NMSOption:

76
python/fastdeploy/vision/ocr/ppocr/__init__.py
View File

@@ -16,32 +16,26 @@ from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
from ...common import ProcessorManager
def sort_boxes(boxes):
return C.vision.ocr.sort_boxes(boxes)
class DBDetectorPreprocessor:
class DBDetectorPreprocessor(ProcessorManager):
def __init__(self):
"""
Create a preprocessor for DBDetectorModel
"""
self._preprocessor = C.vision.ocr.DBDetectorPreprocessor()
def run(self, input_ims):
"""Preprocess input images for DBDetectorModel
:param: input_ims: (list of numpy.ndarray) The input image
:return: pair(list of FDTensor, list of std::array<int, 4>)
"""
return self._preprocessor.run(input_ims)
super(DBDetectorPreprocessor, self).__init__()
self._manager = C.vision.ocr.DBDetectorPreprocessor()
@property
def max_side_len(self):
"""Get max_side_len value.
"""
return self._preprocessor.max_side_len
return self._manager.max_side_len
@max_side_len.setter
def max_side_len(self, value):
@@ -50,7 +44,7 @@ class DBDetectorPreprocessor:
"""
assert isinstance(
value, int), "The value to set `max_side_len` must be type of int."
self._preprocessor.max_side_len = value
self._manager.max_side_len = value
def set_normalize(self, mean, std, is_scale):
"""Set preprocess normalize parameters, please call this API to
@@ -60,30 +54,30 @@ class DBDetectorPreprocessor:
:param: std: (list of float) std values
:param: is_scale: (boolean) whether to scale
"""
self._preprocessor.set_normalize(mean, std, is_scale)
self._manager.set_normalize(mean, std, is_scale)
@property
def static_shape_infer(self):
return self._preprocessor.static_shape_infer
return self._manager.static_shape_infer
@static_shape_infer.setter
def static_shape_infer(self, value):
assert isinstance(
value,
bool), "The value to set `static_shape_infer` must be type of bool."
self._preprocessor.static_shape_infer = value
self._manager.static_shape_infer = value
def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
self._manager.disable_normalize()
def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
self._manager.disable_permute()
class DBDetectorPostprocessor:
@@ -324,18 +318,12 @@ class DBDetector(FastDeployModel):
self._model.postprocessor.use_dilation = value
class ClassifierPreprocessor:
class ClassifierPreprocessor(ProcessorManager):
def __init__(self):
"""Create a preprocessor for ClassifierModel
"""
self._preprocessor = C.vision.ocr.ClassifierPreprocessor()
def run(self, input_ims):
"""Preprocess input images for ClassifierModel
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
super(ClassifierPreprocessor, self).__init__()
self._manager = C.vision.ocr.ClassifierPreprocessor()
def set_normalize(self, mean, std, is_scale):
"""Set preprocess normalize parameters, please call this API to
@@ -345,30 +333,30 @@ class ClassifierPreprocessor:
:param: std: (list of float) std values
:param: is_scale: (boolean) whether to scale
"""
self._preprocessor.set_normalize(mean, std, is_scale)
self._manager.set_normalize(mean, std, is_scale)
@property
def cls_image_shape(self):
return self._preprocessor.cls_image_shape
return self._manager.cls_image_shape
@cls_image_shape.setter
def cls_image_shape(self, value):
assert isinstance(
value,
list), "The value to set `cls_image_shape` must be type of list."
self._preprocessor.cls_image_shape = value
self._manager.cls_image_shape = value
def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
self._manager.disable_normalize()
def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
self._manager.disable_permute()
class ClassifierPostprocessor:
@@ -497,29 +485,23 @@ class Classifier(FastDeployModel):
self._model.postprocessor.cls_thresh = value
class RecognizerPreprocessor:
class RecognizerPreprocessor(ProcessorManager):
def __init__(self):
"""Create a preprocessor for RecognizerModel
"""
self._preprocessor = C.vision.ocr.RecognizerPreprocessor()
def run(self, input_ims):
"""Preprocess input images for RecognizerModel
:param: input_ims: (list of numpy.ndarray)The input image
:return: list of FDTensor
"""
return self._preprocessor.run(input_ims)
super(RecognizerPreprocessor, self).__init__()
self._manager = C.vision.ocr.RecognizerPreprocessor()
@property
def static_shape_infer(self):
return self._preprocessor.static_shape_infer
return self._manager.static_shape_infer
@static_shape_infer.setter
def static_shape_infer(self, value):
assert isinstance(
value,
bool), "The value to set `static_shape_infer` must be type of bool."
self._preprocessor.static_shape_infer = value
self._manager.static_shape_infer = value
def set_normalize(self, mean, std, is_scale):
"""Set preprocess normalize parameters, please call this API to
@@ -529,30 +511,30 @@ class RecognizerPreprocessor:
:param: std: (list of float) std values
:param: is_scale: (boolean) whether to scale
"""
self._preprocessor.set_normalize(mean, std, is_scale)
self._manager.set_normalize(mean, std, is_scale)
@property
def rec_image_shape(self):
return self._preprocessor.rec_image_shape
return self._manager.rec_image_shape
@rec_image_shape.setter
def rec_image_shape(self, value):
assert isinstance(
value,
list), "The value to set `rec_image_shape` must be type of list."
self._preprocessor.rec_image_shape = value
self._manager.rec_image_shape = value
def disable_normalize(self):
"""
This function will disable normalize in preprocessing step.
"""
self._preprocessor.disable_normalize()
self._manager.disable_normalize()
def disable_permute(self):
"""
This function will disable hwc2chw in preprocessing step.
"""
self._preprocessor.disable_permute()
self._manager.disable_permute()
class RecognizerPostprocessor: