def evaluator_thread(cnn_file, hp, inputs, outputs):
# start tensorflow session
import numpy as np
import tensorflow as tf
# sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
session_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=hp.config['log_device_placement'])
sess = tf.InteractiveSession(config=session_config)
# import network
from cnn_autoencoder_v9 import create_cnn
cnn = create_cnn(hp)
# init session and load network state
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, cnn_file)
# little chat
print('lf cnn evaluator waiting for inputs')
terminated = 0
while not terminated:
batch = inputs.get()
if batch == ():
terminated = 1
else:
out = dict()
# default params for network input
net_in = cnn.prepare_net_input(batch)
net_in[cnn.noise_sigma] = 0.0
# FOR SPECIFIC DECODER PATH (todo: make less of a hack)
decoder_path = batch['decoder_path']
if decoder_path == 'depth':
decoder = cnn.decoders_2D[decoder_path]
(sv, loss) = sess.run([decoder['upconv'], decoder['loss']],
feed_dict=net_in)
else:
decoder = cnn.decoders_3D[decoder_path]
(sv_v,
loss_v) = sess.run([decoder['upconv_v'], decoder['loss']],
feed_dict=net_in)
(sv_h,
loss_h) = sess.run([decoder['upconv_h'], decoder['loss']],
feed_dict=net_in)
sv_h = np.transpose(sv_h, [0, 1, 3, 2, 4])
sv = sv_v[:, :, :, :, :]
out['cv_v'] = sv_v
out['cv_h'] = sv_h
out['cv'] = sv
outputs.put((out, batch))
inputs.task_done()
def trainer_thread(model_path, hp, inputs):
# start tensorflow session
session_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=hp.config['log_device_placement'])
sess = tf.InteractiveSession(config=session_config)
# import network
from cnn_autoencoder_v9 import create_cnn
cnn = create_cnn(hp)
# add optimizers (will be saved with the network)
cnn.add_training_ops()
# start session
print(' initialising TF session')
sess.run(tf.global_variables_initializer())
print(' ... done')
# save object
print(' checking for model ' + model_path)
if os.path.exists(model_path + 'model.ckpt.index'):
print(' restoring model ' + model_path)
tft.optimistic_restore(sess, model_path + 'model.ckpt')
print(' ... done.')
else:
print(' ... not found.')
writerTensorboard = tf.summary.FileWriter(
hp.tf_log_path + hp.network_model, sess.graph)
# writerTensorboard = tf.summary.FileWriter('./visual_' + hp.network_model, sess.graph)
# new saver object with complete network
saver = tf.train.Saver()
# statistics
count = 0.0
print('lf cnn trainer waiting for inputs')
terminated = 0
while not terminated:
batch = inputs.get()
if batch == ():
terminated = 1
else:
niter = batch['niter']
ep = batch['epoch']
# default params for network input
net_in = cnn.prepare_net_input(batch)
# evaluate current network performance on mini-batch
if batch['logging']:
print()
sys.stdout.write(' dataset(%d:%s) ep(%d) batch(%d) : ' %
(batch['nfeed'], batch['feed_id'], ep, niter))
#loss_average = (count * loss_average + loss) / (count + 1.0)
count = count + 1.0
fields = [
'%s' % (datetime.datetime.now()), batch['feed_id'],
batch['nfeed'], niter, ep
]
for id in cnn.decoders_2D:
# if id in batch:
(loss) = sess.run(cnn.decoders_2D[id]['loss'],
feed_dict=net_in)
sys.stdout.write(' %s %g ' % (id, loss))
fields.append(id)
fields.append(loss)
# else:
# fields.append( '' )
# fields.append( '' )
import csv
with open(model_path + batch['logfile'], 'a+') as f:
writer = csv.writer(f)
writer.writerow(fields)
summary = sess.run(cnn.merged, feed_dict=net_in)
writerTensorboard.add_summary(summary, niter)
print('')
#code.interact( local=locals() )
if batch['niter'] % hp.training[
'save_interval'] == 0 and niter != 0 and batch[
'nfeed'] == 0 and batch['training']:
# epochs now take too long, save every few 100 steps
# Save the variables to disk.
save_path = saver.save(sess, model_path + 'model.ckpt')
print('NEXT EPOCH')
print(" model saved in file: %s" % save_path)
# statistics
#print(" past epoch average loss %g"%(loss_average))
count = 0.0
# run training step
if batch['training']:
net_in[cnn.phase] = True
#code.interact( local=locals() )
sys.stdout.write('.') #T%i ' % int(count) )
for id in cnn.minimizers:
# check if all channels required for minimizer are present in batch
ok = True
for r in cnn.minimizers[id]['requires']:
if not (r in batch):
ok = False
if ok:
sys.stdout.write(cnn.minimizers[id]['id'] + ' ')
sess.run(cnn.minimizers[id]['train_step'],
feed_dict=net_in)
sys.stdout.flush()
inputs.task_done()
def trainer_thread(model_path, hp, inputs):
# start tensorflow session
session_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=hp.config['log_device_placement'])
sess = tf.InteractiveSession(config=session_config)
# import network
from cnn_autoencoder_v9 import create_cnn
cnn = create_cnn(hp)
# add optimizers (will be saved with the network)
cnn.add_training_ops()
# start session
print(' initialising TF session')
sess.run(tf.global_variables_initializer())
# save object
print(' checking for model ' + model_path)
if os.path.exists(model_path + 'model.ckpt.index'):
print(' restoring model ' + model_path)
tft.optimistic_restore(sess, model_path + 'model.ckpt')
print(' ... done.')
else:
print(' ... not found.')
writerTensorboard = tf.summary.FileWriter(
hp.tf_log_path + hp.network_model, sess.graph)
# writerTensorboard = tf.summary.FileWriter('/home/mz/HD data/Tensorboard Logs/' + hp.network_model, sess.graph)
# writerTensorboard = tf.summary.FileWriter('./visual_' + hp.network_model, sess.graph)
# new saver object with complete network
saver = tf.train.Saver()
# statistics
count = 0.0
print('lf cnn trainer waiting for inputs')
terminated = 0
counter = 0
iterGan = hp.iterGan
if len(hp.discriminator) > 0:
use_gan = True
train_gan = True
else:
use_gan = False
train_gan = False
while not terminated:
batch = inputs.get()
if batch == ():
terminated = 1
else:
niter = batch['niter']
ep = batch['epoch']
# default params for network input
net_in = cnn.prepare_net_input(batch)
# evaluate current network performance on mini-batch
# (tst_h,tst_v) = sess.run([cnn.refinement_3D['lf']['stack_h'],cnn.refinement_3D['lf']['stack_v']], feed_dict=net_in)
if batch['logging']:
summary_image = sess.run(cnn.merged_images, feed_dict=net_in)
writerTensorboard.add_summary(summary_image, niter)
print()
sys.stdout.write(' dataset(%d:%s) ep(%d) batch(%d) : ' %
(batch['nfeed'], batch['feed_id'], ep, niter))
#loss_average = (count * loss_average + loss) / (count + 1.0)
count = count + 1.0
fields = [
'%s' % (datetime.datetime.now()), batch['feed_id'],
batch['nfeed'], niter, ep
]
# for id in cnn.decoders_3D:
(loss) = sess.run(cnn.LFLSTM['losses'], feed_dict=net_in)
print('adv_percep_loss: %s' % str(
sess.run(cnn.LFLSTM['loss_percep_adv'], feed_dict=net_in)))
# print('gan_logit_sr %s' %str(sigmoid(sess.run( cnn.discriminator['logits'], feed_dict=net_in ))))
# print('gan_logit_gt %s' %str(sigmoid(sess.run( cnn.discriminator['logits_gt'], feed_dict=net_in ))))
sys.stdout.write(' %s %g ' % ('LFLSTM', loss))
fields.append('LFLSTM')
fields.append(loss)
for id in cnn.minimizers:
ok = True
if ok:
summary = sess.run(cnn.merged_lstm, feed_dict=net_in)
if id == 'GAN':
summary = sess.run(cnn.merged_gan,
feed_dict=net_in)
writerTensorboard.add_summary(summary, niter)
# (tst) = sess.run( cnn.tst, feed_dict=net_in )
import csv
with open(model_path + batch['logfile'], 'a+') as f:
writer = csv.writer(f)
writer.writerow(fields)
print('')
#code.interact( local=locals() )
if batch['niter'] % hp.training[
'save_interval'] == 0 and niter != 0 and batch[
'nfeed'] == 0 and batch['training']:
# epochs now take too long, save every few 100 steps
# Save the variables to disk.
# save_path = saver.save(sess, model_path + 'model_'+ str(batch[ 'niter' ])+'.ckpt' )
save_path = saver.save(sess, model_path + 'model' + '.ckpt')
print('NEXT EPOCH')
print(" model saved in file: %s" % save_path)
# statistics
#print(" past epoch average loss %g"%(loss_average))
count = 0.0
# run training step
if batch['training']:
net_in[cnn.phase] = True
#code.interact( local=locals() )
sys.stdout.write('.') #T%i ' % int(count) )
if train_gan:
ok = True
for r in cnn.minimizers['GAN']['requires']:
if not ('lf_patches' + r in batch):
ok = False
if ok:
sys.stdout.write(cnn.minimizers['GAN']['id'] + ' ')
sess.run(cnn.minimizers['GAN']['train_step'],
feed_dict=net_in)
counter += 1
if counter == iterGan:
train_gan = False
else:
for id in cnn.minimizers:
if id != 'GAN':
ok = True
for r in cnn.minimizers[id]['requires']:
if not ('lf_patches' + r in batch):
ok = False
if ok:
sys.stdout.write(cnn.minimizers[id]['id'] +
' ')
sess.run(cnn.minimizers[id]['train_step'],
feed_dict=net_in)
if use_gan:
train_gan = True
counter = 0
sys.stdout.flush()
inputs.task_done()
def trainer_thread(model_path, hp, inputs):
# start tensorflow session
session_config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=hp.config['log_device_placement'])
sess = tf.InteractiveSession(config=session_config)
# import network
from cnn_autoencoder_v9 import create_cnn
cnn = create_cnn(hp)
# add optimizers (will be saved with the network)
cnn.add_training_ops()
# start session
print(' initialising TF session')
sess.run(tf.global_variables_initializer())
print('... restoring resnet50 ')
# print('... restoring vgg19 ')
# print('... restoring moobilenet ')
# print('... restoring inception ')
ckpt_resnet = 'resnet_v1_50.ckpt'
# ckpt_vgg = 'vgg_19.ckpt'
# ckpt_mobilenet = 'mobilenet/mobilenet_v1_0.5_192.ckpt'
# ckpt_inception = 'inception/inception_v3.ckpt'
# reader = tf.train.NewCheckpointReader(ckpt_mobilenet)
# tst = reader.get_variable_to_shape_map()
# reader.get_variable_to_shape_map()['MobilenetV2/expanded_conv_10/expand/weights']
# import numpy as np
# print(np.transpose(sorted(tst)))
# resnet_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope='resnet_v1_50'))
# resnet_saver.restore(sess, ckpt_resnet)
# vgg_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope='vgg_19'))
# vgg_saver.restore(sess, ckpt_vgg)
# mobilenet_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope='MobilenetV1'))
# mobilenet_saver.restore(sess, ckpt_mobilenet)
# inception_saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES, scope='InceptionV3'))
# inception_saver.restore(sess, ckpt_inception)
# var_list = [op.name+':0' for op in sess.graph.get_operations() if
# 'variable' in op.type.lower() and 'resnet' in op.name.lower()]
# var_list = [sess.graph.get_tensor_by_name(t) for t in var_list]
# saver = tf.train.Saver(var_list=var_list)
# saver.restore(sess, ckpt_path)
# test
# (tst) = sess.run('resnet_v1_50/block1/unit_1/bottleneck_v1/conv3/weights:0')
print(' ... done')
# save object
print(' checking for model ' + model_path)
if os.path.exists(model_path + 'model.ckpt.index'):
print(' restoring model ' + model_path)
tft.optimistic_restore(sess, model_path + 'model.ckpt')
print(' ... done.')
else:
print(' ... not found.')
writerTensorboard = tf.summary.FileWriter(
hp.tf_log_path + hp.network_model, sess.graph)
# writerTensorboard = tf.summary.FileWriter('/home/mz/HD data/Tensorboard Logs/' + hp.network_model, sess.graph)
# writerTensorboard = tf.summary.FileWriter('./visual_' + hp.network_model, sess.graph)
# new saver object with complete network
saver = tf.train.Saver()
# statistics
count = 0.0
print('lf cnn trainer waiting for inputs')
terminated = 0
counter = 0
iterGan = hp.iterGan
if len(hp.discriminator) > 0:
use_gan = True
train_gan = True
else:
use_gan = False
train_gan = False
while not terminated:
batch = inputs.get()
if batch == ():
terminated = 1
else:
niter = batch['niter']
ep = batch['epoch']
# default params for network input
net_in = cnn.prepare_net_input(batch)
# evaluate current network performance on mini-batch
# (tst_h,tst_v) = sess.run([cnn.refinement_3D['lf']['stack_h'],cnn.refinement_3D['lf']['stack_v']], feed_dict=net_in)
if batch['logging']:
summary_image = sess.run(cnn.merged_images, feed_dict=net_in)
writerTensorboard.add_summary(summary_image, niter)
print()
sys.stdout.write(' dataset(%d:%s) ep(%d) batch(%d) : ' %
(batch['nfeed'], batch['feed_id'], ep, niter))
#loss_average = (count * loss_average + loss) / (count + 1.0)
count = count + 1.0
fields = [
'%s' % (datetime.datetime.now()), batch['feed_id'],
batch['nfeed'], niter, ep
]
for id in cnn.decoders_3D:
(loss) = sess.run(cnn.decoders_3D[id]['loss_s2'],
feed_dict=net_in)
sys.stdout.write(' %s %g ' % (id, loss))
fields.append(id)
fields.append(loss)
for id in cnn.minimizers:
ok = True
for r in cnn.minimizers[id]['requires']:
if not ('stacks_v_' + r in batch):
ok = False
if ok:
if id.endswith('s2'):
summary = sess.run(cnn.merged_s2, feed_dict=net_in)
if id.endswith('s4'):
summary = sess.run(cnn.merged_s4, feed_dict=net_in)
if id == 'GAN':
summary = sess.run(cnn.merged_gan,
feed_dict=net_in)
writerTensorboard.add_summary(summary, niter)
# (tst) = sess.run( cnn.tst, feed_dict=net_in )
import csv
with open(model_path + batch['logfile'], 'a+') as f:
writer = csv.writer(f)
writer.writerow(fields)
print('')
#code.interact( local=locals() )
if batch['niter'] % hp.training[
'save_interval'] == 0 and niter != 0 and batch[
'nfeed'] == 0 and batch['training']:
# epochs now take too long, save every few 100 steps
# Save the variables to disk.
save_path = saver.save(
sess,
model_path + 'model_' + str(batch['niter']) + '.ckpt')
print('NEXT EPOCH')
print(" model saved in file: %s" % save_path)
# statistics
#print(" past epoch average loss %g"%(loss_average))
count = 0.0
# run training step
if batch['training']:
net_in[cnn.phase] = True
#code.interact( local=locals() )
sys.stdout.write('.') #T%i ' % int(count) )
if train_gan:
ok = True
for r in cnn.minimizers['GAN']['requires']:
if not ('stacks_v_' + r in batch):
ok = False
if ok:
sys.stdout.write(cnn.minimizers['GAN']['id'] + ' ')
sess.run(cnn.minimizers['GAN']['train_step'],
feed_dict=net_in)
counter += 1
if counter == iterGan:
train_gan = False
else:
for id in cnn.minimizers:
if id != 'GAN':
ok = True
for r in cnn.minimizers[id]['requires']:
if not ('stacks_v_' + r in batch):
ok = False
if ok:
sys.stdout.write(cnn.minimizers[id]['id'] +
' ')
sess.run(cnn.minimizers[id]['train_step'],
feed_dict=net_in)
if use_gan:
train_gan = True
counter = 0
sys.stdout.flush()
inputs.task_done()
