91 lines
3.3 KiB
Python
91 lines
3.3 KiB
Python
# copyright (c) 2022 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.nn.functional as F
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class ThreshOutput(object):
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def __init__(self, threshold, label_0="0", label_1="1"):
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self.threshold = threshold
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self.label_0 = label_0
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self.label_1 = label_1
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def __call__(self, x, file_names=None):
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y = []
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x = F.softmax(x, axis=-1).numpy()
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for idx, probs in enumerate(x):
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score = probs[1]
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if score < self.threshold:
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result = {"class_ids": [0], "scores": [1 - score], "label_names": [self.label_0]}
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else:
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result = {"class_ids": [1], "scores": [score], "label_names": [self.label_1]}
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if file_names is not None:
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result["file_name"] = file_names[idx]
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y.append(result)
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return y
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class MultiLabelThreshOutput(object):
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def __init__(self, threshold=0.5, class_id_map_file=None, delimiter=None):
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self.threshold = threshold
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self.delimiter = delimiter if delimiter is not None else " "
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self.class_id_map = self.parse_class_id_map(class_id_map_file)
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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(self.delimiter)
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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):
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y = []
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x = F.sigmoid(x).numpy()
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for idx, probs in enumerate(x):
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index = np.where(probs >= self.threshold)[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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"label_names": label_name_list
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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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y.append(result)
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return y
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