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train_Oxford.py
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train_Oxford.py
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import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
# os.environ["MKL_NUM_THREADS"] = "3"
# os.environ["NUMEXPR_NUM_THREADS"] = "3"
# os.environ["OMP_NUM_THREADS"] = "3"
import cv2
import random
import numpy as np
import copy
import scipy
import scipy.stats as stats
import tensorflow as tf
from tensorflow.python.ops.gen_math_ops import *
from model import CVML
from readdata_Oxford import DataLoader
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--learning_rate', type=float, help='learning rate', default=1e-5)
parser.add_argument('-e', '--start_epoch', type=int, help='start epoch', default=0)
parser.add_argument('-b', '--batch_size', type=int, help='batch size', default=8)
args = vars(parser.parse_args())
learning_rate_val = args['learning_rate']
start_epoch = args['start_epoch']
batch_size = args['batch_size']
is_training = True
number_of_epoch = 70
keep_prob_val = 0.8
dimension = 8
beta = 1e4
temperature=0.1
label = 'OxfordRobotCar'
save_model_path = './models/'
def contrastive_loss(scores, labels, temperature=1.0):
"""Contrastive loss over matching score. Adapted from https://arxiv.org/pdf/2004.11362.pdf Eq.2
We extraly weigh the positive samples using the ground truth likelihood on those positions
loss = - 1/sum(weights) * sum(inner_element*weights)
inner_element = log( exp(score_pos/temperature) / sum(exp(score/temperature)) )
"""
exp_scores = tf.math.exp(scores / temperature)
bool_mask = tf.cast(labels>1e-2, tf.bool) # only keep positive samples, we set a threshod on the likelihood in GT
denominator = tf.reduce_sum(exp_scores, [1, 2, 3], keepdims=True)
inner_element = tf.math.log(tf.boolean_mask(exp_scores/denominator, bool_mask))
return -tf.reduce_sum(inner_element*tf.boolean_mask(labels, bool_mask)) / tf.reduce_sum(tf.boolean_mask(labels, bool_mask))
def train(start_epoch=0, learning_rate_val=learning_rate_val):
'''
Train the model from epoch N. Default is 0.
'''
# Import data
input_data = DataLoader('train')
# 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')
gt_bottleneck = tf.nn.max_pool(gt, ksize=[1, 64, 64, 1], strides=[1, 64, 64, 1], padding='SAME', name='gt_bottleneck')
keep_prob = tf.placeholder(tf.float32)
learning_rate = tf.placeholder(tf.float32)
tf.summary.scalar('learning_rate', learning_rate)
training = tf.placeholder(tf.bool)
# Build model
logits, matching_score = CVML(sat, grd, keep_prob, dimension, is_training, False)
logits_reshaped = tf.reshape(logits, [tf.shape(logits)[0], 512*512])
gt_reshaped = tf.reshape(gt, [tf.shape(logits)[0], 512*512])
gt_reshaped = gt_reshaped / tf.reduce_sum(gt_reshaped, axis=1, keepdims=True)
loss_heatmap = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=gt_reshaped, logits=logits_reshaped))
loss_bottleneck = contrastive_loss(matching_score, gt_bottleneck, temperature)
loss = loss_heatmap + loss_bottleneck*beta
loss_summary = tf.summary.scalar('loss', loss)
heatmap = tf.reshape(tf.nn.softmax(logits_reshaped), tf.shape(logits))
# Get all summaries
summary = tf.summary.merge_all()
# set training
global_step = tf.Variable(0, trainable=False)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(learning_rate, 0.9, 0.999).minimize(loss, global_step=global_step)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)
# run model
print('run model...')
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 1
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
print('load model...')
if start_epoch == 0:
load_model_path_init = save_model_path+'Initialize/initial_model.ckpt'
variables_to_restore_init = tf.contrib.framework.get_variables_to_restore(include=['VGG_grd','VGG_sat'])
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(load_model_path_init, variables_to_restore_init)
init_fn(sess)
print(" Model initialized from: %s" % load_model_path_init)
else:
load_model_path = save_model_path+label+'/' + str(start_epoch - 1) + '/model.ckpt'
saver.restore(sess, load_model_path)
print(" Model loaded from: %s" % load_model_path)
print('load model...FINISHED')
# Define tensorboard writer
logdir = './graph/'+label
if not os.path.exists(logdir):
os.makedirs(logdir)
graph = tf.get_default_graph()
writer = tf.summary.FileWriter(logdir, sess.graph)
# Train
for epoch in range(start_epoch, number_of_epoch):
iter = 0
while True:
# train
batch_sat, batch_grd, batch_gt = input_data.next_pair_batch(batch_size)
if batch_sat is None:
break
global_step_val = tf.train.global_step(sess, global_step)
feed_dict = {sat: batch_sat, grd: batch_grd, gt: batch_gt,
learning_rate: learning_rate_val,
keep_prob: keep_prob_val, training: True}
_, loss_val, heatmap_val , summary_val = sess.run([train_step, loss, heatmap, summary], feed_dict=feed_dict)
# Write to tensorboard
writer.add_summary(summary_val, global_step_val)
if iter % 200 == 0:
print('global %d, epoch %d, iter %d: loss : %.8f' %
(global_step_val, epoch, iter, loss_val))
iter += 1
# Save model
if epoch % 5 == 0:
model_dir = save_model_path+label+'/' + str(epoch) + '/'
if not os.path.exists(model_dir):
os.makedirs(model_dir)
save_path = saver.save(sess, model_dir + 'model.ckpt')
print("Model saved in file: %s" % save_path)
# ---------------------- validation ----------------------
if epoch % 5 == 0:
print('validate...')
print('go through all the ground images in the validation set...')
input_data.reset_scan()
distance = []
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, gt: batch_gt, 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, :, :, :]
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))
distance_error = np.mean(distance)
print('mean distance error on validation set: ', distance_error)
file = 'results/'+label+'_error.txt'
with open(file,'ab') as f:
np.savetxt(f, [distance_error], fmt='%4f', header='validation_set_mean_distance_error_in_pixels:', comments=str(epoch)+'_')
tf.reset_default_graph()
train(0)