def train(self):
self.net_mode(train=True)
self.C_max = Variable(
cuda(torch.FloatTensor([self.C_max]), self.use_cuda))
out = False
pbar = tqdm(total=self.max_iter)
pbar.update(self.global_iter)
## write log to log file
outfile = os.path.join(self.ckpt_dir, "train.log")
fw_log = open(outfile, "w")
## init PID control
PID = PIDControl()
Kp = 0.01
Ki = -0.0001
Kd = 0.0
fw_log.write("Kp:{0:.5f} Ki: {1:.6f}\n".format(Kp, Ki))
fw_log.flush()
while not out:
for x in self.data_loader:
# print('shape>>', x.size())
self.global_iter += 1
pbar.update(1)
x = Variable(cuda(x, self.use_cuda))
x_recon, mu, logvar = self.net(x)
recon_loss = reconstruction_loss(x, x_recon, self.decoder_dist)
total_kld, dim_wise_kld, mean_kld = kl_divergence(mu, logvar)
if self.is_PID:
# print(self.beta)
self.beta, _ = PID.pid(self.KL_loss, total_kld.item(), Kp,
Ki, Kd)
beta_vae_loss = recon_loss + self.beta * total_kld
else:
beta_vae_loss = recon_loss + 1.0 * total_kld
### tricks for C
# C = torch.clamp(self.C_max/self.C_stop_iter*self.global_iter, 0, self.C_max.data[0])
# beta_vae_loss = recon_loss + self.gamma*(total_kld-C).abs()
self.optim.zero_grad()
beta_vae_loss.backward()
self.optim.step()
if self.viz_on and self.global_iter % self.gather_step == 0:
self.gather.insert(iter=self.global_iter,
mu=mu.mean(0).data,
var=logvar.exp().mean(0).data,
recon_loss=recon_loss.data,
total_kld=total_kld.data,
mean_kld=mean_kld.data,
beta=self.beta)
self.gather2.insert(iter=self.global_iter,
mu=mu.mean(0).data,
var=logvar.exp().mean(0).data,
recon_loss=recon_loss.data,
total_kld=total_kld.data,
mean_kld=mean_kld.data,
beta=self.beta)
if self.global_iter % 20 == 0:
## write log to file
fw_log.write(
'[{}] recon_loss:{:.3f} total_kld:{:.3f} mean_kld:{:.3f} beta:{:.4f}\n'
.format(self.global_iter, recon_loss.item(),
total_kld.item(), mean_kld.item(), self.beta))
fw_log.flush()
# if self.global_iter%self.display_step == 0:
# pbar.write('[{}] recon_loss:{:.3f} total_kld:{:.3f} mean_kld:{:.3f} beta:{:.4f}'.format(
# self.global_iter, recon_loss.item(), total_kld.item(), mean_kld.item(), self.beta))
if self.viz_on and self.global_iter % self.save_step == 0:
self.gather.insert(images=x.data)
self.gather.insert(images=F.sigmoid(x_recon).data)
self.viz_reconstruction()
self.viz_lines()
self.gather.flush()
if (self.viz_on
or self.save_output) and self.global_iter % 50000 == 0:
self.viz_traverse()
if self.global_iter % self.save_step == 0:
self.save_checkpoint('last')
pbar.write('Saved checkpoint(iter:{})'.format(
self.global_iter))
if self.global_iter % 20000 == 0:
self.save_checkpoint(str(self.global_iter))
if self.global_iter >= self.max_iter:
out = True
break
pbar.write("[Training Finished]")
pbar.close()
fw_log.close()
def main():
"""Entrypoint.
"""
config: Any = importlib.import_module(args.config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# train_data = tx.data.MonoTextData(config.train_data_hparams, device=device)
# val_data = tx.data.MonoTextData(config.val_data_hparams, device=device)
# test_data = tx.data.MonoTextData(config.test_data_hparams, device=device)
train_data = tx.data.MonoTextData(config.train_data_hparams,
device=torch.device("cpu"))
val_data = tx.data.MonoTextData(config.val_data_hparams,
device=torch.device("cpu"))
test_data = tx.data.MonoTextData(config.test_data_hparams,
device=torch.device("cpu"))
iterator = tx.data.DataIterator({
"train": train_data,
"valid": val_data,
"test": test_data
})
opt_vars = {
'learning_rate': config.lr_decay_hparams["init_lr"],
'best_valid_nll': 1e100,
'steps_not_improved': 0,
'kl_weight': config.kl_anneal_hparams["start"]
}
decay_cnt = 0
max_decay = config.lr_decay_hparams["max_decay"]
decay_factor = config.lr_decay_hparams["decay_factor"]
decay_ts = config.lr_decay_hparams["threshold"]
if 'pid' in args.model_name:
save_dir = args.model_name + '_' + str(config.dataset) + '_KL' + str(
args.exp_kl)
elif 'cost' in args.model_name:
save_dir = args.model_name + '_' + str(config.dataset) + '_step' + str(
args.anneal_steps)
elif 'cyclical' in args.model_name:
save_dir = args.model_name + '_' + str(config.dataset) + '_cyc_' + str(
args.cycle)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
suffix = f"{config.dataset}_{config.decoder_type}Decoder.ckpt"
save_path = os.path.join(save_dir, suffix)
# KL term annealing rate warm_up=10
## replace it with sigmoid function
anneal_r = 1.0 / (config.kl_anneal_hparams["warm_up"] *
(len(train_data) / config.batch_size))
vocab = train_data.vocab
model = VAE(train_data.vocab.size, config)
model.to(device)
start_tokens = torch.full((config.batch_size, ),
vocab.bos_token_id,
dtype=torch.long).to(device)
end_token = vocab.eos_token_id
optimizer = tx.core.get_optimizer(params=model.parameters(),
hparams=config.opt_hparams)
scheduler = ExponentialLR(optimizer, decay_factor)
## max iteration
max_iter = config.num_epochs * len(train_data) / config.batch_size
max_iter = min(max_iter, args.max_steps)
print('max steps:', max_iter)
pbar = tqdm(total=int(max_iter))
if args.mode == "train":
outFile = os.path.join(save_dir, 'train.log')
fw_log = open(outFile, "w")
global_steps = {}
global_steps['step'] = 0
pid = PIDControl()
opt_vars["kl_weight"] = 0.0
Kp = args.Kp
Ki = args.Ki
exp_kl = args.exp_kl
## train model
def _run_epoch(epoch: int, mode: str, display: int = 10) \
-> Tuple[Tensor, float]:
iterator.switch_to_dataset(mode)
if mode == 'train':
model.train()
kl_weight = opt_vars["kl_weight"]
else:
model.eval()
kl_weight = 1.0
# kl_weight = opt_vars["kl_weight"]
start_time = time.time()
num_words = 0
nll_total = 0.
avg_rec = tx.utils.AverageRecorder()
for batch in iterator:
## run model to get loss function
if global_steps['step'] >= args.max_steps:
break
ret = model(batch, kl_weight, start_tokens, end_token)
if mode == "train":
pbar.update(1)
global_steps['step'] += 1
kl_loss = ret['kl_loss'].item()
rec_loss = ret['rc_loss'].item()
total_loss = ret["nll"].item()
if 'cost' in args.model_name:
kl_weight = _cost_annealing(global_steps['step'], 1.0,
args.anneal_steps)
elif 'pid' in args.model_name:
kl_weight = pid.pid(exp_kl, kl_loss, Kp, Ki)
elif 'cyclical' in args.model_name:
kl_weight = _cyclical_annealing(global_steps['step'],
max_iter / args.cycle)
opt_vars["kl_weight"] = kl_weight
## total loss
ret["nll"].backward()
optimizer.step()
optimizer.zero_grad()
fw_log.write('epoch:{0} global_step:{1} total_loss:{2:.3f} kl_loss:{3:.3f} rec_loss:{4:.3f} kl_weight:{5:.4f}\n'\
.format(epoch, global_steps['step'], total_loss, kl_loss, rec_loss, kl_weight))
fw_log.flush()
batch_size = len(ret["lengths"])
num_words += torch.sum(ret["lengths"]).item()
nll_total += ret["nll"].item() * batch_size
avg_rec.add([
ret["nll"].item(), ret["kl_loss"].item(),
ret["rc_loss"].item()
], batch_size)
if global_steps['step'] % display == 1 and mode == 'train':
nll = avg_rec.avg(0)
klw = opt_vars["kl_weight"]
KL = avg_rec.avg(1)
rc = avg_rec.avg(2)
writer.add_scalar(f'Loss/Rec_loss_{args.model_name}', rc,
global_steps['step'])
writer.add_scalar(f'Loss/KL_diverg_{args.model_name}', KL,
global_steps['step'])
writer.add_scalar(f'Loss/KL_weight_{args.model_name}', klw,
global_steps['step'])
nll = avg_rec.avg(0)
KL = avg_rec.avg(1)
rc = avg_rec.avg(2)
if num_words > 0:
log_ppl = nll_total / num_words
ppl = math.exp(log_ppl)
else:
log_ppl = 100
ppl = math.exp(log_ppl)
nll = 1000
KL = args.exp_kl
print(f"\n{mode}: epoch {epoch}, nll {nll:.4f}, KL {KL:.4f}, "
f"rc {rc:.4f}, log_ppl {log_ppl:.4f}, ppl {ppl:.4f}")
return nll, ppl # type: ignore
args.model = save_path
@torch.no_grad()
def _generate(start_tokens: torch.LongTensor,
end_token: int,
filename: Optional[str] = None):
ckpt = torch.load(args.model)
model.load_state_dict(ckpt['model'])
model.eval()
batch_size = train_data.batch_size
dst = MultivariateNormalDiag(loc=torch.zeros(batch_size,
config.latent_dims),
scale_diag=torch.ones(
batch_size, config.latent_dims))
# latent_z = dst.rsample().to(device)
latent_z = torch.FloatTensor(batch_size,
config.latent_dims).uniform_(-1,
1).to(device)
# latent_z = torch.randn(batch_size, config.latent_dims).to(device)
helper = model.decoder.create_helper(decoding_strategy='infer_sample',
start_tokens=start_tokens,
end_token=end_token)
outputs = model.decode(helper=helper,
latent_z=latent_z,
max_decoding_length=100)
if config.decoder_type == "transformer":
outputs = outputs[0]
sample_tokens = vocab.map_ids_to_tokens_py(outputs.sample_id.cpu())
if filename is None:
fh = sys.stdout
else:
fh = open(filename, 'a', encoding='utf-8')
for sent in sample_tokens:
sent = tx.utils.compat_as_text(list(sent))
end_id = len(sent)
if vocab.eos_token in sent:
end_id = sent.index(vocab.eos_token)
fh.write(' '.join(sent[:end_id + 1]) + '\n')
print('Output done')
fh.close()
if args.mode == "predict":
out_path = os.path.join(save_dir, 'results.txt')
for _ in range(10):
_generate(start_tokens, end_token, out_path)
return
# Counts trainable parameters
total_parameters = sum(param.numel() for param in model.parameters())
print(f"{total_parameters} total parameters")
best_nll = best_ppl = 0.
## start running model
for epoch in range(config.num_epochs):
_, _ = _run_epoch(epoch, 'train', display=200)
val_nll, _ = _run_epoch(epoch, 'valid')
test_nll, test_ppl = _run_epoch(epoch, 'test')
if val_nll < opt_vars['best_valid_nll']:
opt_vars['best_valid_nll'] = val_nll
opt_vars['steps_not_improved'] = 0
best_nll = test_nll
best_ppl = test_ppl
states = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"scheduler": scheduler.state_dict()
}
torch.save(states, save_path)
else:
opt_vars['steps_not_improved'] += 1
if opt_vars['steps_not_improved'] == decay_ts:
old_lr = opt_vars['learning_rate']
opt_vars['learning_rate'] *= decay_factor
opt_vars['steps_not_improved'] = 0
new_lr = opt_vars['learning_rate']
ckpt = torch.load(save_path)
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
scheduler.load_state_dict(ckpt['scheduler'])
scheduler.step()
print(f"-----\nchange lr, old lr: {old_lr}, "
f"new lr: {new_lr}\n-----")
decay_cnt += 1
if decay_cnt == max_decay:
break
if global_steps['step'] >= args.max_steps:
break
print(f"\nbest testing nll: {best_nll:.4f},"
f"best testing ppl {best_ppl:.4f}\n")
if args.mode == "train":
fw_log.write(f"\nbest testing nll: {best_nll:.4f},"
f"best testing ppl {best_ppl:.4f}\n")
fw_log.close()
def main():
## random seeds
seed = FLAGS.seed
# tf.random.set_seed(seed)
np.random.seed(seed)
## config for training
config = Config()
pid = PIDControl(FLAGS.exp_KL)
# config for validation
valid_config = Config()
valid_config.keep_prob = 1.0
valid_config.dec_keep_prob = 1.0
valid_config.batch_size = 60
# configuration for testing
test_config = Config()
test_config.keep_prob = 1.0
test_config.dec_keep_prob = 1.0
test_config.batch_size = 1
pp(config)
# get data set
api = SWDADialogCorpus(FLAGS.data_dir, word2vec=FLAGS.word2vec_path, word2vec_dim=config.embed_size)
dial_corpus = api.get_dialog_corpus()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get("train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get("train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, config)
if FLAGS.forward_only or FLAGS.resume:
# log_dir = os.path.join(FLAGS.work_dir, FLAGS.test_path)
log_dir = os.path.join(FLAGS.work_dir, FLAGS.model_name)
else:
log_dir = os.path.join(FLAGS.work_dir, FLAGS.model_name)
## begin training
with tf.Session() as sess:
initializer = tf.random_uniform_initializer(-1.0 * config.init_w, config.init_w)
scope = "model"
with tf.variable_scope(scope, reuse=None, initializer=initializer):
model = KgRnnCVAE(sess, config, api, log_dir=None if FLAGS.forward_only else log_dir, forward=False, pid_control=pid, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
valid_model = KgRnnCVAE(sess, valid_config, api, log_dir=None, forward=False, pid_control=pid, scope=scope)
with tf.variable_scope(scope, reuse=True, initializer=initializer):
test_model = KgRnnCVAE(sess, test_config, api, log_dir=None, forward=True, pid_control=pid, scope=scope)
print("Created computation graphs")
if api.word2vec is not None and not FLAGS.forward_only:
print("Loaded word2vec")
sess.run(model.embedding.assign(np.array(api.word2vec)))
# write config to a file for logging
if not FLAGS.forward_only:
with open(os.path.join(log_dir, "configure.log"), "wb") as f:
f.write(pp(config, output=False))
# create a folder by force
ckp_dir = os.path.join(log_dir, "checkpoints")
print("*******checkpoint path: ", ckp_dir)
if not os.path.exists(ckp_dir):
os.mkdir(ckp_dir)
ckpt = tf.train.get_checkpoint_state(ckp_dir)
print("Created models with fresh parameters.")
sess.run(tf.global_variables_initializer())
if ckpt:
print("Reading dm models parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
### save log when running
if not FLAGS.forward_only:
logfileName = "train.log"
else:
logfileName = "test.log"
fw_log = open(os.path.join(log_dir, logfileName), "w")
print("log directory >>> : ", os.path.join(log_dir, "run.log"))
if not FLAGS.forward_only:
print('--start training now---')
dm_checkpoint_path = os.path.join(ckp_dir, model.__class__.__name__+ ".ckpt")
global_t = 1
patience = 20 # wait for at least 10 epoch before stop
dev_loss_threshold = np.inf
best_dev_loss = np.inf
pbar = tqdm(total = config.max_epoch)
## epoch start training
for epoch in range(config.max_epoch):
pbar.update(1)
print(">> Epoch %d with lr %f" % (epoch, model.learning_rate.eval()))
## begin training
FLAGS.mode = 'train'
if train_feed.num_batch is None or train_feed.ptr >= train_feed.num_batch:
train_feed.epoch_init(config.batch_size, config.backward_size,
config.step_size, shuffle=True)
global_t, train_loss = model.train(global_t, sess, train_feed, update_limit=config.update_limit)
FLAGS.mode = 'valid'
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
elbo, nll, ppl, au_count, kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('middle test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('epoch:{} testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(epoch, nll, ppl, au_count, kl_loss, elbo))
fw_log.flush()
'''
## begin validation
FLAGS.mode = 'valid'
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
valid_loss = valid_model.valid("ELBO_VALID", sess, valid_feed)
## test model
FLAGS.mode = 'test'
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=True, intra_shuffle=False)
test_model.test(sess, test_feed, num_batch=5)
done_epoch = epoch + 1
# only save a models if the dev loss is smaller
# Decrease learning rate if no improvement was seen over last 3 times.
if config.op == "sgd" and done_epoch > config.lr_hold:
sess.run(model.learning_rate_decay_op)
if valid_loss < best_dev_loss:
if valid_loss <= dev_loss_threshold * config.improve_threshold:
patience = max(patience, done_epoch * config.patient_increase)
dev_loss_threshold = valid_loss
# still save the best train model
if FLAGS.save_model:
print("Save model!!")
model.saver.save(sess, dm_checkpoint_path, global_step=epoch)
best_dev_loss = valid_loss
if config.early_stop and patience <= done_epoch:
print("!!Early stop due to run out of patience!!")
break
## print("Best validation loss %f" % best_dev_loss)
'''
print("Done training and save checkpoint")
if FLAGS.save_model:
print("Save model!!")
model.saver.save(sess, dm_checkpoint_path, global_step=epoch)
# begin validation
print('--------after training to testing now-----')
FLAGS.mode = 'test'
# valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
# valid_config.step_size, shuffle=False, intra_shuffle=False)
# valid_model.valid("ELBO_VALID", sess, valid_feed)
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
elbo, nll, ppl, au_count,kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('final test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('Final testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(nll, ppl, au_count, kl_loss, elbo))
dest_f = open(os.path.join(log_dir, FLAGS.test_res), "wb")
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=False, intra_shuffle=False)
test_model.test(sess, test_feed, num_batch=None, repeat=10, dest=dest_f)
dest_f.close()
print("****testing done****")
else:
# begin validation
# begin validation
print('*'*89)
print('--------testing now-----')
print('*'*89)
FLAGS.mode = 'test'
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
# valid_model.valid("ELBO_VALID", sess, valid_feed)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
elbo, nll, ppl, au_count, kl_loss = valid_model.valid("ELBO_TEST", sess, valid_feed, test_feed)
print('final test nll: {} ppl: {} ActiveUnit: {} kl_loss:{}\n'.format(nll, ppl,au_count,kl_loss))
fw_log.write('Final testing nll:{} ppl:{} ActiveUnit:{} kl_loss:{} elbo:{}\n'.\
format(nll, ppl, au_count, kl_loss, elbo))
# dest_f = open(os.path.join(log_dir, FLAGS.test_res), "wb")
# test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
# test_config.step_size, shuffle=False, intra_shuffle=False)
# test_model.test(sess, test_feed, num_batch=None, repeat=10, dest=dest_f)
# dest_f.close()
print("****testing done****")
fw_log.close()
#Evan Racah #tests PID on whole system from PID import PIDControl from BBB.roboclaw import Roboclaw from cv2 import KalmanFilter #import vijay/oliver's ultrasonic function pidCoefficients = [1, 0, 0] #still need to figure these out maxSpeed = 100 #we need to test what the max rotational velocity of the wheels on the robot are pid = PIDControl(0,[1,0,0],maxSpeed) #encProt = EncoderProtractor(0, wheelCircum, robotDiam, encoderResolution) """INITIALIZE KALMAN FILTER""" #we might need to use numpy based matrices for kalman implementation kalman = KalmanFilter(nstateValues, nMeasurementValues, nControlInputs) #kalman.transition_matrix = .... #kalman.measurement_matrix = .... #kalman.control_matrix = .... """#################""" #########initiate ultrasonic object here####### claw = Roboclaw(0x80, "/dev/ttyO1") curM1 = 50 curM2 = 50 claw.m1_forward(curM1) #I dont know what a good speed is yet claw.m2_forward(curM2)