mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +08:00
312e1b097d
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs Co-authored-by: Jason <jiangjiajun@baidu.com>
93 lines
3.7 KiB
Python
93 lines
3.7 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from tqdm import trange
|
|
import numpy as np
|
|
import collections
|
|
import os
|
|
import math
|
|
import time
|
|
|
|
|
|
def eval_segmentation(model, data_dir, batch_size=1):
|
|
import cv2
|
|
from .utils import Cityscapes
|
|
from .utils import f1_score, calculate_area, mean_iou, accuracy, kappa
|
|
assert os.path.isdir(
|
|
data_dir), "The image_file_path:{} is not a directory.".format(
|
|
data_dir)
|
|
eval_dataset = Cityscapes(dataset_root=data_dir, mode="val")
|
|
file_list = eval_dataset.file_list
|
|
image_num = eval_dataset.num_samples
|
|
num_classes = eval_dataset.num_classes
|
|
intersect_area_all = 0
|
|
pred_area_all = 0
|
|
label_area_all = 0
|
|
conf_mat_all = []
|
|
twenty_percent_image_num = math.ceil(image_num * 0.2)
|
|
start_time = 0
|
|
end_time = 0
|
|
average_inference_time = 0
|
|
im_list = []
|
|
label_list = []
|
|
for image_label_path, i in zip(file_list,
|
|
trange(
|
|
image_num, desc="Inference Progress")):
|
|
if i == twenty_percent_image_num:
|
|
start_time = time.time()
|
|
im = cv2.imread(image_label_path[0])
|
|
label = cv2.imread(image_label_path[1], cv2.IMREAD_GRAYSCALE)
|
|
label_list.append(label)
|
|
if batch_size == 1:
|
|
result = model.predict(im)
|
|
results = [result]
|
|
else:
|
|
im_list.append(im)
|
|
# If the batch_size is not satisfied, the remaining pictures are formed into a batch
|
|
if (i + 1) % batch_size != 0 and i != image_num - 1:
|
|
continue
|
|
results = model.batch_predict(im_list)
|
|
if i == image_num - 1:
|
|
end_time = time.time()
|
|
average_inference_time = round(
|
|
(end_time - start_time) /
|
|
(image_num - twenty_percent_image_num), 4)
|
|
for result, label in zip(results, label_list):
|
|
pred = np.array(result.label_map).reshape(result.shape[0],
|
|
result.shape[1])
|
|
intersect_area, pred_area, label_area = calculate_area(pred, label,
|
|
num_classes)
|
|
intersect_area_all = intersect_area_all + intersect_area
|
|
pred_area_all = pred_area_all + pred_area
|
|
label_area_all = label_area_all + label_area
|
|
im_list.clear()
|
|
label_list.clear()
|
|
|
|
class_iou, miou = mean_iou(intersect_area_all, pred_area_all,
|
|
label_area_all)
|
|
class_acc, oacc = accuracy(intersect_area_all, pred_area_all)
|
|
kappa_res = kappa(intersect_area_all, pred_area_all, label_area_all)
|
|
category_f1score = f1_score(intersect_area_all, pred_area_all,
|
|
label_area_all)
|
|
|
|
eval_metrics = collections.OrderedDict(
|
|
zip([
|
|
'miou', 'category_iou', 'oacc', 'category_acc', 'kappa',
|
|
'category_F1-score', 'average_inference_time(s)'
|
|
], [
|
|
miou, class_iou, oacc, class_acc, kappa_res, category_f1score,
|
|
average_inference_time
|
|
]))
|
|
return eval_metrics
|