forked from PulseFocusPlatform/PulseFocusPlatform
85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
# Copyright (c) 2020 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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import os.path as osp
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import pkg_resources
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try:
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from collections.abc import Sequence
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except:
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from collections import Sequence
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from ppdet.core.workspace import load_config, create
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from ppdet.utils.checkpoint import load_weight
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from ppdet.utils.download import get_config_path
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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__all__ = [
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'list_model', 'get_config_file', 'get_weights_url', 'get_model',
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'MODEL_ZOO_FILENAME'
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]
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MODEL_ZOO_FILENAME = 'MODEL_ZOO'
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def list_model(filters=[]):
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model_zoo_file = pkg_resources.resource_filename('ppdet.model_zoo',
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MODEL_ZOO_FILENAME)
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with open(model_zoo_file) as f:
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model_names = f.read().splitlines()
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# filter model_name
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def filt(name):
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for f in filters:
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if name.find(f) < 0:
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return False
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return True
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if isinstance(filters, str) or not isinstance(filters, Sequence):
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filters = [filters]
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model_names = [name for name in model_names if filt(name)]
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if len(model_names) == 0 and len(filters) > 0:
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raise ValueError("no model found, please check filters seeting, "
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"filters can be set as following kinds:\n"
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"\tDataset: coco, voc ...\n"
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"\tArchitecture: yolo, rcnn, ssd ...\n"
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"\tBackbone: resnet, vgg, darknet ...\n")
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model_str = "Available Models:\n"
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for model_name in model_names:
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model_str += "\t{}\n".format(model_name)
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logger.info(model_str)
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# models and configs save on bcebos under dygraph directory
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def get_config_file(model_name):
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return get_config_path("ppdet://configs/{}.yml".format(model_name))
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def get_weights_url(model_name):
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return "ppdet://models/{}.pdparams".format(osp.split(model_name)[-1])
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def get_model(model_name, pretrained=True):
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cfg_file = get_config_file(model_name)
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cfg = load_config(cfg_file)
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model = create(cfg.architecture)
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if pretrained:
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load_weight(model, get_weights_url(model_name))
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return model
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