114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 paddle
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import paddle.nn.functional as F
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import argparse
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
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from ppcls.utils import logger
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from ppcls.utils.save_load import init_model
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from ppcls.utils.config import get_config
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from ppcls.utils import multi_hot_encode
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from ppcls.utils import accuracy_score
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from ppcls.utils import mean_average_precision
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from ppcls.utils import precision_recall_fscore
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from ppcls.data import Reader
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import program
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import numpy as np
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def parse_args():
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parser = argparse.ArgumentParser("PaddleClas eval script")
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parser.add_argument(
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'-c',
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'--config',
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type=str,
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default='./configs/eval.yaml',
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help='config file path')
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parser.add_argument(
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'-o',
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'--override',
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action='append',
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default=[],
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help='config options to be overridden')
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args = parser.parse_args()
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return args
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def main(args, return_dict={}):
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config = get_config(args.config, overrides=args.override, show=True)
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config.mode = "valid"
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# assign place
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use_gpu = config.get("use_gpu", True)
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place = paddle.set_device('gpu' if use_gpu else 'cpu')
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multilabel = config.get("multilabel", False)
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trainer_num = paddle.distributed.get_world_size()
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use_data_parallel = trainer_num != 1
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config["use_data_parallel"] = use_data_parallel
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if config["use_data_parallel"]:
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paddle.distributed.init_parallel_env()
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net = program.create_model(config.ARCHITECTURE, config.classes_num)
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if config["use_data_parallel"]:
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net = paddle.DataParallel(net)
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init_model(config, net, optimizer=None)
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valid_dataloader = Reader(config, 'valid', places=place)()
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net.eval()
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with paddle.no_grad():
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if not multilabel:
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top1_acc = program.run(valid_dataloader, config, net, None, None, 0,
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'valid')
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return_dict["top1_acc"] = top1_acc
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return top1_acc
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else:
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all_outs = []
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targets = []
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for idx, batch in enumerate(valid_dataloader()):
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feeds = program.create_feeds(batch, False, config.classes_num, multilabel)
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out = net(feeds["image"])
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out = F.sigmoid(out)
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use_distillation = config.get("use_distillation", False)
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if use_distillation:
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out = out[1]
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all_outs.extend(list(out.numpy()))
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targets.extend(list(feeds["label"].numpy()))
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all_outs = np.array(all_outs)
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targets = np.array(targets)
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mAP = mean_average_precision(all_outs, targets)
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return_dict["mean average precision"] = mAP
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return mAP
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if __name__ == '__main__':
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args = parse_args()
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return_dict = {}
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main(args, return_dict)
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print(return_dict)
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