test_Oxford.py

import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
# os.environ["MKL_NUM_THREADS"] = "4" 
# os.environ["NUMEXPR_NUM_THREADS"] = "4" 
# os.environ["OMP_NUM_THREADS"] = "4" 
import cv2
import random
import numpy as np
import copy
import tensorflow as tf
from tensorflow.python.ops.gen_math_ops import *
from model import CVML
from readdata_Oxford import DataLoader

load_model_path = './model.ckpt'


batch_size = 16
dimension = 8
is_training = False
tf.reset_default_graph()
input_data = DataLoader('test')
    
# Define placeholers
sat = tf.placeholder(tf.float32, [None, 512, 512, 3], name='sat')
grd = tf.placeholder(tf.float32, [None, 154, 231, 3], name='grd')
gt = tf.placeholder(tf.float32, [None, 512, 512, 1], name='gt')

keep_prob = tf.placeholder(tf.float32)
training = tf.placeholder(tf.bool)

# Build model
logits, matching_score = CVML(sat, grd, keep_prob, dimension, is_training, False)
heatmap = tf.reshape(tf.nn.softmax(tf.reshape(logits, [tf.shape(logits)[0], 512*512])), tf.shape(logits))

saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print('load model...')
    saver.restore(sess, load_model_path)
    print('processing...')
    input_data.reset_scan()
    distance = []
    probability_at_gt = []
    while True:
        batch_sat, batch_grd, batch_gt = input_data.next_batch_scan(batch_size)
        if batch_sat is None:
            break
        feed_dict = {sat: batch_sat, grd: batch_grd, keep_prob: 1.0, training: False}
        heatmap_val = sess.run(heatmap, feed_dict=feed_dict)  
        for batch_idx in range(batch_gt.shape[0]):
            current_gt = batch_gt[batch_idx, :, :, :]
            loc_gt = np.unravel_index(current_gt.argmax(), current_gt.shape)
            current_pred = heatmap_val[batch_idx, :, :, :]
            probability_at_gt.append(current_pred[loc_gt])
            loc_pred = np.unravel_index(current_pred.argmax(), current_pred.shape)
            distance.append(np.sqrt((loc_gt[0]-loc_pred[0])**2+(loc_gt[1]-loc_pred[1])**2)) 


print('mean distance error (m): ', np.mean(distance)*0.0924/512*800) 
print('median distance error (m): ', np.median(distance)*0.0924/512*800)
print('mean probability at GT: ', np.mean(probability_at_gt))
print('median probability at GT: ', np.median(probability_at_gt))