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g_train.py
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import os
import numpy as np
import tensorflow as tf
from util.util import tf_imgri2ssos, myNumExt, tf_kri2imgri, tf_pad0, DIM2CH2
import time
from ipdb import set_trace as st
from options.baseOptions import BaseOptions
from math import ceil
from model.myRNN import myDoubleLSTM as metaLearner
from model.FewShot import FewShotG as myModel
# options to init
opt = BaseOptions().parse()
dtype= tf.float32
clip = 0.00001
nB = opt.batchSize
if opt.dataset=='7T':
from data.DB7T import DB7T as myDB
else:
st()
if opt.model == 'Gnet_':
from model.learner import Gnet_ as Learner
elif opt.model == 'tmp_net':
from model.learner import tmp_net as Learner
else:
st()
# init. DB first
DB_train = myDB(opt,'train')
DB_valid = myDB(opt,'valid')
opt = DB_train.getInfo(opt)
#
opt.nStep_train = ceil(len(DB_train)/opt.batchSize)
opt.nStep_valid = ceil(len(DB_valid)/opt.batchSize)
if opt.debug_mode:
opt.nStep_train=1
opt.nStep_valid=1
disp_step_train = ceil(opt.nStep_train/opt.disp_div_N)
disp_step_valid = ceil(opt.nStep_valid/opt.disp_div_N)
## dummy data info
model_id = 'nCh'+str(opt.ngf)
opt.d_spath1 = './model/dummy/'+opt.model+'_theta_shapes_'+model_id+'.npy'
opt.d_spath2 = './model/dummy/'+opt.model+'_ntheta_'+model_id+'.npy'
if os.path.isfile(opt.d_spath1):
opt.dummy_theta_shapes=np.load(opt.d_spath1)
opt.ntheta =np.load(opt.d_spath2)
else:
opt.dummy_theta_shapes=[]
opt.ntheta =[]
st()
##
start_time = time.time()
myFS = myModel(opt, metaLearner, Learner)
saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
#config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
latest_ckpt = tf.train.latest_checkpoint(opt.ckpt_dir)
if latest_ckpt==None:
print("Start! initially!")
tf.global_variables_initializer().run()
epoch_start=0
else:
print("Start from saved model -"+latest_ckpt)
saver.restore(sess, latest_ckpt)
epoch_start=myNumExt(latest_ckpt)+1
summary_writer = tf.summary.FileWriter(opt.log_dir, sess.graph)
summary_writer_v = tf.summary.FileWriter(opt.log_dir_v, sess.graph)
t_init = time.time()
print("------- %d sec to initialize network-----" % (t_init-start_time) )
# save the initailzed (Xavier) c2 to c2_init
myFS.save_state(sess)
nE_update=(opt.nEpoch_state_update + opt.nEpoch_Wb_update)
## Fisrt, train the learner network
for iEpoch in range(epoch_start, opt.nEpoch):
if not opt.debug_mode:
DB_train.shuffle()
#if iEpoch>40:
# nE_update = opt.nEpoch_state_update + opt.nEpoch_Wb_update + int(iEpoch/10)
disp_cnt = 0
sum_loss_train = 0.0
t_i_1 = time.time()
#out_argm = [myFS.optimizer_pre, myFS.loss_test, myFS.merged_all, myFS.gvs_pre]
#out_arg = [myFS.optimizer_pre, myFS.loss_test]
tag_state_update = ((iEpoch % nE_update) < opt.nEpoch_state_update)
if tag_state_update:
func_getBatch = DB_train.getBatch_G#_train
out_argm = [myFS.optimizer_state, myFS.loss_test, myFS.merged_all]
out_arg = [myFS.optimizer_state, myFS.loss_test]
else:
func_getBatch = DB_train.getBatch_G
out_argm = [myFS.optimizer, myFS.loss_test, myFS.merged_all]
out_arg = [myFS.optimizer, myFS.loss_test]
myFS.k_shot = int(opt.k_shot+iEpoch/500)
for step in range(opt.nStep_train):
_input_ACSk, _target_ACSk, _input_k, _target_k = func_getBatch(step*nB, (step+1)*nB)
myFS.restore_state(sess)
feed_dict={myFS.input_node_train: _input_ACSk, myFS.target_node_train: _target_ACSk, myFS.input_node_test: _input_k, myFS.target_node_test:_target_k,myFS.is_Training:True}
if step%disp_step_train==0 or step==0:
_,loss_test_train, merged = sess.run(out_argm,feed_dict=feed_dict)
summary_writer.add_summary(merged, iEpoch*opt.disp_div_N+disp_cnt)
disp_cnt+=1
else:
_,loss_test_train = sess.run(out_arg,feed_dict=feed_dict)
#if tag_state_update:
myFS.save_state(sess) # save the c to c_init
sum_loss_train += loss_test_train
t_i_v = time.time()
print('%d epoch -- loss : %.4f e-3, %d sec' %(iEpoch, sum_loss_train/opt.nStep_train*1000, t_i_v-t_i_1))
disp_cnt = 0
sum_loss_valid = 0.0
for step in range(opt.nStep_valid):
myFS.restore_state(sess)
_input_ACSk, _target_ACSk, _input_k, _target_k = DB_valid.getBatch_G(step*opt.batchSize, (step+1)*opt.batchSize)
feed_dict = {myFS.input_node_train: _input_ACSk, myFS.target_node_train: _target_ACSk, myFS.input_node_test: _input_k, myFS.target_node_test:_target_k,myFS.is_Training:False}
if step%disp_step_valid==0 or step==0:
loss_test_valid, merged = sess.run([myFS.loss_test, myFS.merged_all], feed_dict=feed_dict)
summary_writer_v.add_summary(merged, iEpoch*opt.disp_div_N+disp_cnt)
disp_cnt+=1
else:
loss_test_valid = sess.run(myFS.loss_test, feed_dict=feed_dict)
sum_loss_valid += loss_test_valid
t_i = time.time()
print('%d epoch -- loss : %.4f e-3, %d sec' %(iEpoch, sum_loss_valid/opt.nStep_valid*1000, t_i-t_i_v))
if (iEpoch%50==0):
path_saved = saver.save(sess, os.path.join(opt.ckpt_dir, "model.ckpt"), global_step=iEpoch)
print(' Total time elpased : %d sec' %(t_init-time.time()))