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85 lines
3.1 KiB
85 lines
3.1 KiB
# 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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