def select_serial_port(self, action):
# un-check all menu actions
for item in self.serial_port_action_list:
item.setChecked(False)
# check selected serial port action
action.setChecked(True)
self.serial_port = action.text()
save_value('serial', 'port', self.serial_port)
def train(M, src=None, trg=None, has_disc=True, saver=None, model_name=None):
"""Main training function
Creates log file, manages datasets, trains model
M - (TensorDict) the model
src - (obj) source domain. Contains train/test Data obj
trg - (obj) target domain. Contains train/test Data obj
has_disc - (bool) whether model requires a discriminator update
saver - (Saver) saves models during training
model_name - (str) name of the model being run with relevant parms info
"""
# Training settings
bs = 64
iterep = 1000
itersave = 20000
n_epoch = 80
epoch = 0
feed_dict = {}
# Create a log directory and FileWriter
log_dir = os.path.join(args.logdir, model_name)
delete_existing(log_dir)
train_writer = tf.summary.FileWriter(log_dir)
# Create a save directory
if saver:
model_dir = os.path.join('checkpoints', model_name)
delete_existing(model_dir)
os.makedirs(model_dir)
# Replace src domain with psuedolabeled trg
if args.dirt > 0:
print("Setting backup and updating backup model")
src = PseudoData(args.trg, trg, M.teacher)
M.sess.run(M.update_teacher)
# Sanity check model
print_list = []
if src:
save_value(M.fn_ema_acc,
'test/src_test_ema_1k',
src.test,
train_writer,
0,
print_list,
full=False)
if trg:
save_value(M.fn_ema_acc, 'test/trg_test_ema', trg.test,
train_writer, 0, print_list)
save_value(M.fn_ema_acc,
'test/trg_train_ema_1k',
trg.train,
train_writer,
0,
print_list,
full=False)
print print_list
if src: get_info(args.src, src)
if trg: get_info(args.trg, trg)
print("Batch size:", bs)
print("Iterep:", iterep)
print("Total iterations:", n_epoch * iterep)
print("Log directory:", log_dir)
for i in range(n_epoch * iterep):
# Run discriminator optimizer
if has_disc:
update_dict(M, feed_dict, src, trg, bs)
summary, _ = M.sess.run(M.ops_disc, feed_dict)
train_writer.add_summary(summary, i + 1)
# Run main optimizer
update_dict(M, feed_dict, src, trg, bs)
summary, _ = M.sess.run(M.ops_main, feed_dict)
train_writer.add_summary(summary, i + 1)
train_writer.flush()
end_epoch, epoch = tb.utils.progbar(i,
iterep,
message='{}/{}'.format(epoch, i),
display=args.run >= 999)
# Update pseudolabeler
if args.dirt and (i + 1) % args.dirt == 0:
print "Updating teacher model"
M.sess.run(M.update_teacher)
# Log end-of-epoch values
if end_epoch:
print_list = M.sess.run(M.ops_print, feed_dict)
if src:
save_value(M.fn_ema_acc,
'test/src_test_ema_1k',
src.test,
train_writer,
i + 1,
print_list,
full=False)
if trg:
save_value(M.fn_ema_acc, 'test/trg_test_ema', trg.test,
train_writer, i + 1, print_list)
save_value(M.fn_ema_acc,
'test/trg_train_ema_1k',
trg.train,
train_writer,
i + 1,
print_list,
full=False)
print_list += ['epoch', epoch]
print print_list
if saver and (i + 1) % itersave == 0:
save_model(saver, M, model_dir, i + 1)
# Saving final model
if saver:
save_model(saver, M, model_dir, i + 1)
def train(M, src=None, trg=None, saver=None, model_name=None):
"""Main training function
Creates log file, manages datasets, trains model
M - (TensorDict) the model
src - (obj) source domain. Contains train/test Data obj
trg - (obj) target domain. Contains train/test Data obj
saver - (Saver) saves models during training
model_name - (str) name of the model being run with relevant parms info
"""
# Training settings
bs = 64
iterep = 1000
iterviz = 5000 if args.run < 999 else 1000
itersave = 20000
n_epoch = 200
epoch = 0
feed_dict = {}
# Create a log directory and FileWriter
log_dir = os.path.join(args.logdir, model_name)
delete_existing(log_dir)
train_writer = tf.summary.FileWriter(log_dir)
# Create a save directory
if saver:
model_dir = os.path.join('checkpoints', model_name)
delete_existing(model_dir)
os.makedirs(model_dir)
if src: get_info(args.src, src)
if trg: get_info(args.trg, trg)
print "Batch size:", bs
print "Iterep:", iterep
print "Total iterations:", n_epoch * iterep
print "Log directory:", log_dir
for i in xrange(n_epoch * iterep):
# Run main optimizer
update_dict(M, feed_dict, src, trg, bs)
summary, _ = M.sess.run(M.ops_main, feed_dict)
train_writer.add_summary(summary, i + 1)
train_writer.flush()
end_epoch, epoch = tb.utils.progbar(i, iterep,
message='{}/{}'.format(epoch, i),
display=args.run >= 999)
# Log end-of-epoch values
if end_epoch:
print_list = M.sess.run(M.ops_print, feed_dict)
if src:
save_value(M.fn_loss, 'test/src_loss',
src.test, train_writer, i + 1, print_list, full=False)
if trg:
save_value(M.fn_loss, 'test/trg_loss',
trg.test, train_writer, i + 1, print_list, full=False)
print_list += ['epoch', epoch]
print print_list
# Visualize images
if (i + 1) % iterviz == 0 and getattr(M, 'ops_image', None) is not None:
summary = M.sess.run(M.ops_image)
train_writer.add_summary(summary, i + 1)
if saver and (i + 1) % itersave == 0:
save_model(saver, M, model_dir, i + 1)
# Saving final model
if saver:
save_model(saver, M, model_dir, i + 1)
def train(M, src=None, trg=None, has_disc=True, saver=None, model_name=None):
"""Main training function
Creates log file, manages datasets, trains model
M - (TensorDict) the model
src - (obj) source domain. Contains train/test Data obj
trg - (obj) target domain. Contains train/test Data obj
has_disc - (bool) whether model requires a discriminator update
saver - (Saver) saves models during training
model_name - (str) name of the model being run with relevant parms info
"""
# Training settings
bs = 64
iterep = 1000
itersave = 20000
n_epoch = 80
epoch = 0
feed_dict = {}
# Create a log directory and FileWriter
log_dir = os.path.join(args.logdir, model_name)
delete_existing(log_dir)
train_writer = tf.summary.FileWriter(log_dir)
# Create a save directory
if saver:
model_dir = os.path.join('checkpoints', model_name)
delete_existing(model_dir)
os.makedirs(model_dir)
# Replace src domain with psuedolabeled trg
if args.dirt > 0:
print "Setting backup and updating backup model"
src = PseudoData(args.trg, trg, M.teacher)
M.sess.run(M.update_teacher)
# Sanity check model
print_list = []
if src:
save_value(M.fn_ema_acc, 'test/src_test_ema_1k',
src.test, train_writer, 0, print_list, full=False)
if trg:
save_value(M.fn_ema_acc, 'test/trg_test_ema',
trg.test, train_writer, 0, print_list)
save_value(M.fn_ema_acc, 'test/trg_train_ema_1k',
trg.train, train_writer, 0, print_list, full=False)
print print_list
if src: get_info(args.src, src)
if trg: get_info(args.trg, trg)
print "Batch size:", bs
print "Iterep:", iterep
print "Total iterations:", n_epoch * iterep
print "Log directory:", log_dir
for i in xrange(n_epoch * iterep):
# Run discriminator optimizer
if has_disc:
update_dict(M, feed_dict, src, trg, bs)
summary, _ = M.sess.run(M.ops_disc, feed_dict)
train_writer.add_summary(summary, i + 1)
# Run main optimizer
update_dict(M, feed_dict, src, trg, bs)
summary, _ = M.sess.run(M.ops_main, feed_dict)
train_writer.add_summary(summary, i + 1)
train_writer.flush()
end_epoch, epoch = tb.utils.progbar(i, iterep,
message='{}/{}'.format(epoch, i),
display=args.run >= 999)
# Update pseudolabeler
if args.dirt and (i + 1) % args.dirt == 0:
print "Updating teacher model"
M.sess.run(M.update_teacher)
# Log end-of-epoch values
if end_epoch:
print_list = M.sess.run(M.ops_print, feed_dict)
if src:
save_value(M.fn_ema_acc, 'test/src_test_ema_1k',
src.test, train_writer, i + 1, print_list, full=False)
if trg:
save_value(M.fn_ema_acc, 'test/trg_test_ema',
trg.test, train_writer, i + 1, print_list)
save_value(M.fn_ema_acc, 'test/trg_train_ema_1k',
trg.train, train_writer, i + 1, print_list, full=False)
print_list += ['epoch', epoch]
print print_list
if saver and (i + 1) % itersave == 0:
save_model(saver, M, model_dir, i + 1)
# Saving final model
if saver:
save_model(saver, M, model_dir, i + 1)
def train(M, src=None, trg=None, has_disc=True, saver=None, model_name=None):
"""Main training function
Creates log file, manages datasets, trains model
M - (TensorDict) the model
src - (obj) source domain. Contains train/test Data obj
trg - (obj) target domain. Contains train/test Data obj
has_disc - (bool) whether model requires a discriminator update
saver - (Saver) saves models during training
model_name - (str) name of the model being run with relevant parms info
"""
# Training settings
iterep = 1000
itersave = 20000
n_epoch = 200
epoch = 0
feed_dict = {}
# Create a log directory and FileWriter
log_dir = os.path.join(args.logdir, model_name)
delete_existing(log_dir)
train_writer = tf.summary.FileWriter(log_dir)
# Create a directory to save generated images
gen_img_path = os.path.join(args.gendir, model_name)
delete_existing(gen_img_path)
os.makedirs(gen_img_path)
# Create a save directory
if saver:
model_dir = os.path.join('checkpoints', model_name)
delete_existing(model_dir)
os.makedirs(model_dir)
# Replace src domain with psuedolabeled trg
if args.dirt > 0:
print "Setting backup and updating backup model"
src = PseudoData(args.trg, trg, M.teacher)
M.sess.run(M.update_teacher)
# Sanity check model
print_list = []
if src:
save_value(M.fn_ema_acc,
'test/src_test_ema_1k',
src.test,
train_writer,
0,
print_list,
full=False)
if trg:
save_value(M.fn_ema_acc, 'test/trg_test_ema', trg.test,
train_writer, 0, print_list)
save_value(M.fn_ema_acc,
'test/trg_train_ema_1k',
trg.train,
train_writer,
0,
print_list,
full=False)
print print_list
if src: get_info(args.src, src)
if trg: get_info(args.trg, trg)
print "Batch size:", args.bs
print "Iterep:", iterep
print "Total iterations:", n_epoch * iterep
print "Log directory:", log_dir
best_acc = -1.
trg_acc = -1.
for i in xrange(n_epoch * iterep):
if has_disc:
# Run discriminator optimizer
update_dict(M, feed_dict, src, trg, args.bs)
summary, _ = M.sess.run(M.ops_disc, feed_dict)
train_writer.add_summary(summary, i + 1)
# Run generator optimizer
update_dict(M, feed_dict, None, trg, args.bs, noise=True)
summary, _ = M.sess.run(M.ops_gen, feed_dict)
train_writer.add_summary(summary, i + 1)
# Run main optimizer
update_dict(M, feed_dict, src, trg, args.bs, noise=True)
summary, _ = M.sess.run(M.ops_main, feed_dict)
train_writer.add_summary(summary, i + 1)
train_writer.flush()
end_epoch, epoch = tb.utils.progbar(i,
iterep,
message='{}/{}'.format(epoch, i),
display=args.run >= 999)
# Update pseudolabeler
if args.dirt and (i + 1) % args.dirt == 0:
print "Updating teacher model"
M.sess.run(M.update_teacher)
if (i + 1) % iterep == 0:
gen_imgs = M.sess.run(M.trg_gen_x, feed_dict)
manifold_h = int(np.floor(np.sqrt(args.bs)))
manifold_w = int(np.floor(np.sqrt(args.bs)))
visualize_results(
gen_imgs, [manifold_h, manifold_w],
os.path.join(gen_img_path, 'epoch_{}.png'.format(
(i + 1) / iterep)))
# Log end-of-epoch values
if end_epoch:
print_list = M.sess.run(M.ops_print, feed_dict)
if src:
save_value(M.fn_ema_acc,
'test/src_test_ema_1k',
src.test,
train_writer,
i + 1,
print_list,
full=False)
if trg:
trg_acc = save_value(M.fn_ema_acc, 'test/trg_test_ema',
trg.test, train_writer, i + 1, print_list)
save_value(M.fn_ema_acc,
'test/trg_train_ema_1k',
trg.train,
train_writer,
i + 1,
print_list,
full=False)
print_list += ['epoch', epoch]
print print_list
if saver and trg_acc > best_acc:
print("Saving new best model")
saver.save(M.sess, os.path.join(model_dir, 'model_best'))
best_acc = trg_acc
# Saving final model
if saver:
save_model(saver, M, model_dir, i + 1)
def save(self):
name = self.parent.saveMacroNameLineEdit.text()
save_value('macro', name, self.macroData)