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train.py
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import argparse
import tensorflow as tf
from network import *
import numpy as np
import random
import math
import os
from data_processing import DataLoader_atten_polar
from sklearn.metrics import roc_auc_score
import platform
from icecream import ic
from datetime import datetime
mode='Glim-net'
test_num = 348
lr_change= [3*1e-8, 1*1e-7, 6*1e-6, 6*1e-6, 1*1e-5, 5*1e-7]
strategy_num=5
strategy_list = [680, 172, 172, 88, 80, 48]
strategy_epoch_duration=[5,5,5,5,5,5]
Epoch = 400
epoch_test = 1
batch_size= 4
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str, help='exp name')
parser.add_argument('--seed', type=int,default=1, help='seed')
parser.add_argument('--strategy_list', type=int,nargs='+', help='strategy list')
parser.add_argument('--strategy_num', type=int,default=5, help='strategy_num')
parser.add_argument('--epoch', type=int,default=400, help='epoch number')
parser.add_argument('--batch_size', type=int,default=4, help='batch_size')
args = parser.parse_args()
random_seed = args.seed
strategy_list=args.strategy_list
strategy_num=args.strategy_num
Epoch = args.epoch
batch_size = args.batch_size
exp_name = args.name
run_id = datetime.now().strftime(r'%m%d_%H%M%S')
save_path=f'{exp_name}/{run_id}'
if not os.path.exists(save_path):
os.makedirs(save_path)
acc_txt=f"{save_path}/acc.txt"
log_txt=f"{save_path}/log.txt"
test_details=f"{save_path}/test_detail.txt"
config_txt=f"{save_path}/config.txt"
throw_txt=f"{save_path}/throw.txt"
with open(config_txt, "w+")as f0:
f0.write('mode: ' + mode)
f0.write('\n')
f0.write('strategy_num: %d ' % strategy_num)
f0.write('\n')
f0.write('epoch_test: %d' % epoch_test)
f0.write('\n')
f0.write('All epoch: %d ' % Epoch)
f0.write('\n')
for i in (strategy_list):
f0.write('strategy_list: %d ' % i)
f0.write('\n')
for i in (lr_change):
f0.write('lr_change: %.9f ' % i)
f0.write('\n')
f0.flush()
def remove_all_file(path):
if os.path.isdir(path):
for i in os.listdir(path):
path_file = os.path.join(path, i)
os.remove(path_file)
def main():
g1 = tf.Graph()
with g1.as_default():
n_steps = 5
best_model = -1
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
tf.set_random_seed(random_seed)
np.random.seed(random_seed)
random.seed(random_seed)
batch_size_val = batch_size
input = tf.placeholder(tf.float32, (batch_size, n_steps, 224, 224, 3), name='inputx')
GT_label = tf.placeholder(tf.int64, (batch_size, n_steps), name='GT_labelx') #size = [batch, n_steps
label_polar_map = tf.placeholder(tf.float32, (batch_size, n_steps, 224, 224, 3), name='label_polar_mapx')
delta_year=tf.placeholder(tf.float32, (batch_size, n_steps), name='delta_yearx')
# time matrix
time_matrix=tf.placeholder(tf.float32, (batch_size, n_steps, n_steps), name='time_matrix')
is_training = tf.placeholder(tf.bool, name='is_training')
label_predict_op = inference_model(input,GT_label,label_polar_map,delta_year,time_matrix, is_training)
lr = tf.placeholder(tf.float32, shape=[])
loss_per_batch=Loss_per_batch(label_predict_op, GT_label)
loss_op = loss_liliu(label_predict_op, GT_label) #[batch,n_steps]
acc_op = top_k_accuracy(label_predict_op[0], GT_label)
extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(extra_update_ops):
train_op = tf.train.AdamOptimizer(lr, beta1=0.9, beta2=0.999, epsilon=1e-08).minimize(loss_op)
saver = tf.train.Saver(max_to_keep=50)
with tf.Session(config=tf_config) as sess:
sess.run(tf.global_variables_initializer())
list_img_path_train_init = os.listdir('./data/train/image/all/')
list_img_path_train_init.sort()
list_img_path_train=list_img_path_train_init
list_img_path_test = os.listdir('./data/test/image/all/')
list_img_path_test.sort()
"""train"""
dataloader_train = DataLoader_atten_polar(batch_size=batch_size, list_img_path= list_img_path_train,state='train')
ic(dataloader_train.size)
"""test"""
dataloader_test = DataLoader_atten_polar(batch_size=batch_size,list_img_path=list_img_path_test, state='test')
"""train_strategy"""
dataloader_strategy = DataLoader_atten_polar(batch_size=batch_size, list_img_path=list_img_path_train, state='train')
count = 0
count_strategy=0
flag_strategy = True
print("Start Training!")
with open(acc_txt, "w+") as f:
with open(log_txt, "w+")as f2:
for epoch in range(0, Epoch):
print('\nEpoch: %d' % (epoch + 1))
"""-----------------------------------------throw------------------------------------------"""
if flag_strategy and (epoch % strategy_epoch_duration[count_strategy]) == 0 and epoch>0:
if count_strategy == (strategy_num-1):
flag_strategy = False
count_strategy += 1 # 1~10
train_leftnum_before = strategy_list[count_strategy-1]
train_leftnum_now = strategy_list[count_strategy]
loss_trainingset = [0 for i_loss_train in range(train_leftnum_before)]
list_img_path_train_before = list_img_path_train
for jj in range(int(train_leftnum_before / batch_size)):
image_train_test, year_train_test, _, Polar_train_test, GTlabel_train_test,my_time_matrix = dataloader_strategy.get_batch(shuffle=False)
loss_per_batch_1 = sess.run(loss_per_batch, feed_dict={input: image_train_test,
GT_label: GTlabel_train_test,
label_polar_map: Polar_train_test,
delta_year: year_train_test,
time_matrix: my_time_matrix,
lr:lr_change[count_strategy],
is_training:False})
for i_loss_chosen in range(batch_size):
loss_trainingset[jj * 4 + i_loss_chosen] = loss_per_batch_1[i_loss_chosen]
matrix1 = np.zeros([train_leftnum_before, 2], dtype=np.float)
matrix2 = np.zeros([train_leftnum_now, 2], dtype=np.float)
for i_1 in range(train_leftnum_before):
matrix1[i_1] = [loss_trainingset[i_1], i_1]
matrix1 = sorted(matrix1, key=lambda cus: cus[0], reverse=True) # big-->small
for i_2 in range(train_leftnum_now):
matrix2[i_2] = matrix1[i_2]
for i_3 in range(train_leftnum_now):
if i_3 == 0:
list_img_path_train0 = [list_img_path_train_before[int(matrix2[i_3][1])]]
else:
list_img_path_train0.append(list_img_path_train_before[int(matrix2[i_3][1])])
list_img_path_train = list_img_path_train0
list_img_path_train.sort()
with open(throw_txt, "a")as f3:
f3.write('strategy: %d ' % count_strategy)
f3.write('\n')
f3.flush()
for i_f3 in range(train_leftnum_before-train_leftnum_now):
f3.write(list_img_path_train_before[int(matrix1[train_leftnum_now+i_f3][1])]+' : ')
f3.flush()
with open('data/train/label/all/'+ list_img_path_train_before[int(matrix1[train_leftnum_now+i_f3][1])] + '.txt', 'r') as f4:
K = f4.readlines()
for i_line in range(5):
line = K[i_line + 1]
line = line.strip('\n')
line = int(line)
f3.write(str(line))
f3.flush()
f3.write('\n')
f3.flush()
if count_strategy == strategy_num:
f3.write('Last left: ')
f3.write('\n')
f3.flush()
for i_f3_lastleft in range(train_leftnum_now):
f3.write(list_img_path_train[i_f3_lastleft] + ' : ')
f3.flush()
with open('data/train/label/all/' + list_img_path_train[i_f3_lastleft] + '.txt', 'r') as f5:
K = f5.readlines()
for i_line in range(5):
line = K[i_line + 1]
line = line.strip('\n')
line = int(line)
f3.write(str(line))
f3.flush()
f3.write('\n')
f3.flush()
dataloader_train = DataLoader_atten_polar(batch_size=batch_size,list_img_path=list_img_path_train, state='train')
dataloader_strategy = DataLoader_atten_polar(batch_size=batch_size,list_img_path=list_img_path_train, state='train')
print('\nEpoch: %d, train_num: %d' % (epoch + 1, dataloader_train.size))
"""-----------------------------------------train------------------------------------------"""
for i in range(int(strategy_list[count_strategy]/batch_size)):
# sota
image1,year1,GTmap1,Polar1, GTlabel1, time_matrix1= dataloader_train.get_batch()
loss_train,_, acc,label_predict = sess.run([ loss_op,train_op, acc_op,label_predict_op],
feed_dict={input: image1,
GT_label: GTlabel1,
label_polar_map:Polar1,
delta_year:year1,
time_matrix:time_matrix1,
lr:lr_change[count_strategy],
is_training:True})
f2.write('strategy:%d, epoch:%03d %05d |Loss: %.03f | Acc: %.3f%%' % (
count_strategy ,epoch , (i + 1), loss_train, 100. * acc))
print('strategy:%d, epoch:%03d %05d |Loss: %.03f | Acc: %.3f%%' % (
count_strategy ,epoch , (i + 1), loss_train, 100. * acc))
f2.write('\n')
f2.flush()
count +=1
"""-----------------------------------------test------------------------------------------"""
ic(list_img_path_test[:3])
with open(test_details, "a")as f6:
f6.write('epoch: %d' % epoch)
f6.write('\n')
f6.flush()
if epoch % epoch_test == 0:
print("testing")
tp = 0.0
fn = 0.0
tn = 0.0
fp = 0.0
y_true = [0.0 for _ in range(test_num * n_steps)]
y_scores = [0.0 for _ in range(test_num * n_steps)]
for j in range(int(test_num / batch_size_val)):
imagev, yearv, GTmapv, Polarv, GTlabelv,my_time_matrix = dataloader_test.get_batch(shuffle=False)
label_predict = sess.run(label_predict_op,
feed_dict={input: imagev, GT_label: GTlabelv,
label_polar_map: Polarv,
delta_year: yearv,
time_matrix: my_time_matrix,
is_training:False
})
GTlabelv_test = np.reshape(GTlabelv, [-1]) # batch_size* n_steps
label_predict_test = np.reshape(label_predict[0], [-1, 2]) # batch_size*n_steps,2
label_predict_0 = label_predict_test[:, 0] # batch_size* n_steps
label_predict_1 = label_predict_test[:, 1] # batch_size* n_steps
"""----------------------------tptn---------------------------------"""
for nb in range(batch_size_val * n_steps):
if GTlabelv_test[nb] == 1 and (label_predict_1[nb] > label_predict_0[nb]):
tp = tp + 1
if GTlabelv_test[nb] == 0 and (label_predict_1[nb] < label_predict_0[nb]):
tn = tn + 1
if GTlabelv_test[nb] == 1 and (label_predict_1[nb] < label_predict_0[nb]):
fn = fn + 1
if GTlabelv_test[nb] == 0 and (label_predict_1[nb] > label_predict_0[nb]):
fp = fp + 1
"""----------------------------AUC---------------------------------"""
for nb in range(batch_size_val * n_steps): # 20
y_true[j * (batch_size_val * n_steps) + nb] = GTlabelv_test[nb]
y_scores[j * (batch_size_val * n_steps) + nb] = (math.exp(
label_predict_1[nb])) / (math.exp(label_predict_1[nb]) + math.exp(
label_predict_0[nb]))
"""----------------------------print all result of 384---------------------------------"""
for batch in range(batch_size_val):
if j * 4 + batch == test_num:
break
f6.write('%s :' % list_img_path_test[j * 4 + batch])
f6.flush()
for img_perimgpath in range(5):
f6.write('%d' % GTlabelv[batch][img_perimgpath])
f6.flush()
f6.write(' ')
f6.flush()
for img_perimgpath in range(5):
if label_predict[0][batch][img_perimgpath][1] > \
label_predict[0][batch][img_perimgpath][0]:
f6.write('1')
f6.flush()
if label_predict[0][batch][img_perimgpath][1] < \
label_predict[0][batch][img_perimgpath][0]:
f6.write('0')
f6.flush()
f6.write(' ')
f6.flush()
for img_perimgpath in range(5):
p_glau = math.exp(label_predict[0][batch][img_perimgpath][1]) / (
math.exp(label_predict[0][batch][img_perimgpath][1]) + math.exp(
label_predict[0][batch][img_perimgpath][0]))
f6.write('%.03f%% ' % (100. * p_glau))
f6.flush()
f6.write('\n')
f6.flush()
acc = (tp + tn) / (tp + tn + fp + fn)
Sen = tp / (tp + fn)
Spe = tn / (tn + fp)
AUC = roc_auc_score(y_true, y_scores)
print("test accuracy: %.03f%% |test sen: %.03f%% |test spe: %.03f%% |test AUC: %.03f%%" % (
100. * acc, 100. * Sen, 100. * Spe, 100. * AUC))
f.write(
' epoch %03d | Acc: %.3f%% | sen: %.3f%% | spe: %.3f%% |AUC: %.3f%%|tp: %.3f | tn: %.3f| fp: %.3f | fn: %.3f' % (
epoch, 100. * acc, 100. * Sen, 100. * Spe, 100. * AUC, tp, tn,
fp, fn))
f.write('\n')
f.flush()
if epoch>0 and AUC > best_model and Sen > 0.857 and Spe > 0.856:
best_model = AUC
saver.save(sess, f'{save_path}/best_model.ckpt')
if Sen>0.86 and Spe >0.87:
saver.save(sess, f'{save_path}/{epoch}.ckpt')
if __name__ == '__main__':
# remove_all_file(save_path)
if platform.system() =='Linux':
if (os.path.exists(throw_txt)):
os.remove(throw_txt)
os.mknod(throw_txt)
if (os.path.exists(test_details)):
os.remove(test_details)
os.mknod(test_details)
main()