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# Copyright (c) 2021 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 os
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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class Topk(object):
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def __init__(self, topk=1, class_id_map_file=None):
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assert isinstance(topk, (int, ))
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self.class_id_map = self.parse_class_id_map(class_id_map_file)
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self.topk = topk
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def parse_class_id_map(self, class_id_map_file):
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if class_id_map_file is None:
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return None
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if not os.path.exists(class_id_map_file):
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print(
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"Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
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)
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return None
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try:
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class_id_map = {}
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with open(class_id_map_file, "r") as fin:
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lines = fin.readlines()
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for line in lines:
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partition = line.split("\n")[0].partition(" ")
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class_id_map[int(partition[0])] = str(partition[-1])
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except Exception as ex:
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print(ex)
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class_id_map = None
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return class_id_map
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def __call__(self, x, file_names=None, multilabel=False):
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assert isinstance(x, paddle.Tensor)
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if file_names is not None:
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assert x.shape[0] == len(file_names)
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x = F.softmax(x, axis=-1) if not multilabel else F.sigmoid(x)
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x = x.numpy()
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y = []
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for idx, probs in enumerate(x):
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index = probs.argsort(axis=0)[-self.topk:][::-1].astype(
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"int32") if not multilabel else np.where(
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probs >= 0.5)[0].astype("int32")
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clas_id_list = []
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score_list = []
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label_name_list = []
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for i in index:
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clas_id_list.append(i.item())
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score_list.append(probs[i].item())
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if self.class_id_map is not None:
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label_name_list.append(self.class_id_map[i.item()])
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result = {
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"class_ids": clas_id_list,
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"scores": np.around(
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score_list, decimals=5).tolist(),
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}
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if file_names is not None:
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result["file_name"] = file_names[idx]
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if label_name_list is not None:
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result["label_names"] = label_name_list
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y.append(result)
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return y
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class MultiLabelTopk(Topk):
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def __init__(self, topk=1, class_id_map_file=None):
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super().__init__()
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def __call__(self, x, file_names=None):
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return super().__call__(x, file_names, multilabel=True)
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