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57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
# Copyright (c) 2022 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 numpy as np
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import cv2
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import os
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import re
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from tqdm import trange
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def topk_accuracy(topk_list, label_list):
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match_array = np.logical_or.reduce(topk_list == label_list, axis=1)
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topk_acc_score = match_array.sum() / match_array.shape[0]
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return topk_acc_score
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def eval_classify(model, image_file_path, label_file_path, topk=5):
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result_list = []
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label_list = []
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image_label_dict = {}
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assert os.path.isdir(
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image_file_path), "The image_file_path:{} is not a directory.".format(
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image_file_path)
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assert os.path.isfile(
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label_file_path), "The label_file_path:{} is not a file.".format(
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label_file_path)
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assert isinstance(topk, int), "The tok:{} is not int type".format(topk)
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with open(label_file_path, 'r') as file:
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lines = file.readlines()
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for line in lines:
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items = line.strip().split()
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image_name = items[0]
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label = items[1]
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image_label_dict[image_name] = int(label)
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images_num = len(image_label_dict)
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for (image, label), i in zip(image_label_dict.items(),
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trange(
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images_num, desc='Inference Progress')):
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label_list.append([label])
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image_path = os.path.join(image_file_path, image)
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im = cv2.imread(image_path)
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result = model.predict(im, topk)
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result_list.append(result.label_ids)
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topk_acc_score = topk_accuracy(np.array(result_list), np.array(label_list))
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return topk_acc_score
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