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[CVCUDA] PP-OCR detector preprocessor integrate CV-CUDA (#1382)

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* move manager initialized_ flag to ppcls

* update dbdetector preprocess api

* declare processor op

* ppocr detector preprocessor support cvcuda

* move cvcuda op to class member

* ppcls use manager register api

* refactor det preprocessor init api

* add set preprocessor api

* add create processor macro

* new processor call api

* ppcls preprocessor init resize on cpu

* ppocr detector preprocessor set normalize api

* revert ppcls pybind

* remove dbdetector set preprocessor

* refine dbdetector preprocessor includes

* remove mean std in py constructor

* add comments

* update comment

* Update __init__.py
This commit is contained in:
Wang Xinyu
2023-02-22 19:39:11 +08:00
committed by GitHub
parent 2f8d9c9a57
commit 91a1c72f98
24 changed files with 448 additions and 330 deletions
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93
python/fastdeploy/vision/ocr/ppocr/__init__.py
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@@ -37,43 +37,31 @@ class DBDetectorPreprocessor:
@property
def max_side_len(self):
"""Get max_side_len value.
"""
return self._preprocessor.max_side_len
@max_side_len.setter
def max_side_len(self, value):
"""Set max_side_len value.
:param: value: (int) max_side_len value
"""
assert isinstance(
value, int), "The value to set `max_side_len` must be type of int."
self._preprocessor.max_side_len = value
@property
def is_scale(self):
return self._preprocessor.is_scale
@is_scale.setter
def is_scale(self, value):
assert isinstance(
value, bool), "The value to set `is_scale` must be type of bool."
self._preprocessor.is_scale = value
@property
def scale(self):
return self._preprocessor.scale
@scale.setter
def scale(self, value):
assert isinstance(
value, list), "The value to set `scale` must be type of list."
self._preprocessor.scale = value
@property
def mean(self):
return self._preprocessor.mean
@mean.setter
def mean(self, value):
assert isinstance(
value, list), "The value to set `mean` must be type of list."
self._preprocessor.mean = value
def set_normalize(self,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
is_scale=True):
"""Set preprocess normalize parameters, please call this API to
customize the normalize parameters, otherwise it will use the default
normalize parameters.
:param: mean: (list of float) mean values
:param: std: (list of float) std values
:param: is_scale: (boolean) whether to scale
"""
self._preprocessor.set_normalize(mean, std, is_scale)
class DBDetectorPostprocessor:
@@ -174,6 +162,7 @@ class DBDetector(FastDeployModel):
"""Clone OCR detection model object
:return: a new OCR detection model object
"""
class DBDetectorClone(DBDetector):
def __init__(self, model):
self._model = model
@@ -203,18 +192,10 @@ class DBDetector(FastDeployModel):
def preprocessor(self):
return self._model.preprocessor
@preprocessor.setter
def preprocessor(self, value):
self._model.preprocessor = value
@property
def postprocessor(self):
return self._model.postprocessor
@postprocessor.setter
def postprocessor(self, value):
self._model.postprocessor = value
# Det Preprocessor Property
@property
def max_side_len(self):
@@ -226,36 +207,6 @@ class DBDetector(FastDeployModel):
value, int), "The value to set `max_side_len` must be type of int."
self._model.preprocessor.max_side_len = value
@property
def is_scale(self):
return self._model.preprocessor.is_scale
@is_scale.setter
def is_scale(self, value):
assert isinstance(
value, bool), "The value to set `is_scale` must be type of bool."
self._model.preprocessor.is_scale = value
@property
def scale(self):
return self._model.preprocessor.scale
@scale.setter
def scale(self, value):
assert isinstance(
value, list), "The value to set `scale` must be type of list."
self._model.preprocessor.scale = value
@property
def mean(self):
return self._model.preprocessor.mean
@mean.setter
def mean(self, value):
assert isinstance(
value, list), "The value to set `mean` must be type of list."
self._model.preprocessor.mean = value
# Det Ppstprocessor Property
@property
def det_db_thresh(self):
@@ -421,6 +372,7 @@ class Classifier(FastDeployModel):
"""Clone OCR classification model object
:return: a new OCR classification model object
"""
class ClassifierClone(Classifier):
def __init__(self, model):
self._model = model
@@ -629,6 +581,7 @@ class Recognizer(FastDeployModel):
"""Clone OCR recognition model object
:return: a new OCR recognition model object
"""
class RecognizerClone(Recognizer):
def __init__(self, model):
self._model = model
@@ -734,7 +687,7 @@ class PPOCRv3(FastDeployModel):
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
if cls_model is None:
self.system_ = C.vision.ocr.PPOCRv3(det_model._model,
rec_model._model)
rec_model._model)
else:
self.system_ = C.vision.ocr.PPOCRv3(
det_model._model, cls_model._model, rec_model._model)
@@ -743,6 +696,7 @@ class PPOCRv3(FastDeployModel):
"""Clone PPOCRv3 pipeline object
:return: a new PPOCRv3 pipeline object
"""
class PPOCRv3Clone(PPOCRv3):
def __init__(self, system):
self.system_ = system
@@ -809,7 +763,7 @@ class PPOCRv2(FastDeployModel):
assert det_model is not None and rec_model is not None, "The det_model and rec_model cannot be None."
if cls_model is None:
self.system_ = C.vision.ocr.PPOCRv2(det_model._model,
rec_model._model)
rec_model._model)
else:
self.system_ = C.vision.ocr.PPOCRv2(
det_model._model, cls_model._model, rec_model._model)
@@ -818,6 +772,7 @@ class PPOCRv2(FastDeployModel):
"""Clone PPOCRv3 pipeline object
:return: a new PPOCRv3 pipeline object
"""
class PPOCRv2Clone(PPOCRv2):
def __init__(self, system):
self.system_ = system