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https://github.com/PaddlePaddle/FastDeploy.git
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c6480de736
* bind success * bind success fix * FDMat pybind, ResizeByShort pybind * FDMat pybind, ResizeByShort pybind, remove initialized_ * override BindProcessorManager::Run in python is available * PyProcessorManager done * vision_pybind fix * manager.py fix * add tutorials * remove Apply() bind * remove Apply() bind and fix * fix reviewed problem * fix reviewed problem * fix reviewed problem readme * fix reviewed problem readme etc * apply return outputs * nits * update readme * fix FDMatbatch * add op pybind: CenterCrop, Pad * add op overload for pass FDMatBatch --------- Co-authored-by: Wang Xinyu <shaywxy@gmail.com>
70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from abc import ABC, abstractmethod
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from ... import c_lib_wrap as C
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class ProcessorManager:
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def __init__(self):
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self._manager = None
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def run(self, input_ims):
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"""Process input image
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:param: input_ims: (list of numpy.ndarray) The input images
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:return: list of FDTensor
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"""
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return self._manager.run(input_ims)
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def use_cuda(self, enable_cv_cuda=False, gpu_id=-1):
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"""Use CUDA processors
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:param: enable_cv_cuda: Ture: use CV-CUDA, False: use CUDA only
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:param: gpu_id: GPU device id
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"""
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return self._manager.use_cuda(enable_cv_cuda, gpu_id)
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class PyProcessorManager(ABC):
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"""
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PyProcessorManager is used to define a customized processor in python
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"""
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def __init__(self):
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self._manager = C.vision.processors.ProcessorManager()
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def use_cuda(self, enable_cv_cuda=False, gpu_id=-1):
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"""Use CUDA processors
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:param: enable_cv_cuda: Ture: use CV-CUDA, False: use CUDA only
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:param: gpu_id: GPU device id
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"""
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return self._manager.use_cuda(enable_cv_cuda, gpu_id)
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def __call__(self, images):
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image_batch = C.vision.FDMatBatch()
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image_batch.from_mats(images)
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self._manager.pre_apply(image_batch)
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outputs = self.apply(image_batch)
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self._manager.post_apply()
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return outputs
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@abstractmethod
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def apply(self, image_batch):
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print("This function has to be implemented.")
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return []
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