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evaluate.py
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evaluate.py
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''' AdapNet: Adaptive Semantic Segmentation
in Adverse Environmental Conditions
Copyright (C) 2018 Abhinav Valada, Johan Vertens , Ankit Dhall and Wolfram Burgard
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.'''
import argparse
import datetime
import importlib
import os
import numpy as np
import tensorflow as tf
import yaml
from dataset.helper import *
PARSER = argparse.ArgumentParser()
PARSER.add_argument('-c', '--config', default='config/cityscapes_test.config')
def test_func(config):
os.environ['CUDA_VISIBLE_DEVICES'] = config['gpu_id']
module = importlib.import_module('models.' + config['model'])
model_func = getattr(module, config['model'])
data_list, iterator = get_test_data(config)
resnet_name = 'resnet_v1_50'
with tf.variable_scope(resnet_name):
model = model_func(num_classes=config['num_classes'], training=False)
images_pl = tf.placeholder(tf.float32, [None, config['height'], config['width'], 3])
model.build_graph(images_pl)
config1 = tf.ConfigProto()
config1.gpu_options.allow_growth = True
sess = tf.Session(config=config1)
sess.run(tf.global_variables_initializer())
import_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
print 'total_variables_loaded:', len(import_variables)
mean = np.load(config['mean'])
saver = tf.train.Saver(import_variables)
saver.restore(sess, config['checkpoint'])
sess.run(iterator.initializer)
step = 0
total_num = 0
output_matrix = np.zeros([config['num_classes'], 3])
while 1:
try:
img, label = sess.run([data_list[0], data_list[1]])
img = img - mean
feed_dict = {images_pl : img}
probabilities = sess.run([model.softmax], feed_dict=feed_dict)
prediction = np.argmax(probabilities[0], 3)
gt = np.argmax(label, 3)
prediction[gt == 0] = 0
output_matrix = compute_output_matrix(gt, prediction, output_matrix)
total_num += label.shape[0]
if (step+1) % config['skip_step'] == 0:
print '%s %s] %d. iou updating' \
% (str(datetime.datetime.now()), str(os.getpid()), total_num)
print 'mIoU: ', compute_iou(output_matrix)
step += 1
except tf.errors.OutOfRangeError:
print 'mIoU: ', compute_iou(output_matrix), 'total_data: ', total_num
break
def main():
args = PARSER.parse_args()
if args.config:
file_address = open(args.config)
config = yaml.load(file_address)
else:
print '--config config_file_address missing'
test_func(config)
if __name__ == '__main__':
main()