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- # copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
- #
- # 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 .base import BasePipeline
- class _SingleModelPipeline(BasePipeline):
- def __init__(self, model, batch_size=1, device=None, predictor_kwargs=None):
- super().__init__(device, predictor_kwargs)
- self._build_predictor(model)
- self.set_predictor(batch_size=batch_size)
- def _build_predictor(self, model):
- self.model = self._create(model)
- def set_predictor(self, batch_size=None, device=None):
- if batch_size:
- self.model.set_predictor(batch_size=batch_size)
- if device:
- self.model.set_predictor(device=device)
- def predict(self, input, **kwargs):
- self.set_predictor(**kwargs)
- yield from self.model(input)
- class ImageClassification(_SingleModelPipeline):
- entities = "image_classification"
- class ObjectDetection(_SingleModelPipeline):
- entities = "object_detection"
- class InstanceSegmentation(_SingleModelPipeline):
- entities = "instance_segmentation"
- class SemanticSegmentation(_SingleModelPipeline):
- entities = "semantic_segmentation"
- class TSFc(_SingleModelPipeline):
- entities = "ts_fc"
- class TSAd(_SingleModelPipeline):
- entities = "ts_ad"
- class TSCls(_SingleModelPipeline):
- entities = "ts_cls"
- class MultiLableImageClas(_SingleModelPipeline):
- entities = "multi_label_image_classification"
- class SmallObjDet(_SingleModelPipeline):
- entities = "small_object_detection"
- class AnomalyDetection(_SingleModelPipeline):
- entities = "anomaly_detection"
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