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79 lines
3.1 KiB
Python
79 lines
3.1 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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from tqdm import trange
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import numpy as np
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import collections
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import os
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import math
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import time
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def eval_segmentation(model, data_dir):
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import cv2
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from .utils import Cityscapes
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from .utils import f1_score, calculate_area, mean_iou, accuracy, kappa
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assert os.path.isdir(
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data_dir), "The image_file_path:{} is not a directory.".format(
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data_dir)
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eval_dataset = Cityscapes(dataset_root=data_dir, mode="val")
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file_list = eval_dataset.file_list
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image_num = eval_dataset.num_samples
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num_classes = eval_dataset.num_classes
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intersect_area_all = 0
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pred_area_all = 0
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label_area_all = 0
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conf_mat_all = []
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twenty_percent_image_num = math.ceil(image_num * 0.2)
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start_time = 0
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end_time = 0
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average_inference_time = 0
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for image_label_path, i in zip(file_list,
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trange(
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image_num, desc="Inference Progress")):
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if i == twenty_percent_image_num:
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start_time = time.time()
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im = cv2.imread(image_label_path[0])
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label = cv2.imread(image_label_path[1], cv2.IMREAD_GRAYSCALE)
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result = model.predict(im)
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if i == image_num - 1:
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end_time = time.time()
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average_inference_time = round(
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(end_time - start_time) / (image_num - twenty_percent_image_num),
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4)
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pred = np.array(result.label_map).reshape(result.shape[0],
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result.shape[1])
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intersect_area, pred_area, label_area = calculate_area(pred, label,
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num_classes)
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intersect_area_all = intersect_area_all + intersect_area
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pred_area_all = pred_area_all + pred_area
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label_area_all = label_area_all + label_area
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class_iou, miou = mean_iou(intersect_area_all, pred_area_all,
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label_area_all)
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class_acc, oacc = accuracy(intersect_area_all, pred_area_all)
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kappa_res = kappa(intersect_area_all, pred_area_all, label_area_all)
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category_f1score = f1_score(intersect_area_all, pred_area_all,
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label_area_all)
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eval_metrics = collections.OrderedDict(
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zip([
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'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
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'category_F1-score', 'average_inference_time(s)'
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], [
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miou, class_iou, oacc, class_acc, kappa_res, category_f1score,
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average_inference_time
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]))
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return eval_metrics
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