def __init__(self, run_id=1, output_path="", env_path_root=""):
super().__init__()
self.cvae = CVAE(run_id=run_id)
self.device = torch.device('cuda' if CUDA_AVAILABLE else 'cpu')
self.output_path = output_path
self.env_path_root = env_path_root
if self.output_path is not None:
if os.path.exists(self.output_path):
shutil.rmtree(self.output_path)
os.mkdir(self.output_path)
def train(args, train_dataloader, valid_dataloader):
torch.manual_seed(87)
torch.cuda.manual_seed(87)
autoencoder = cc(CVAE())
criterion = torch.nn.MSELoss()
opt = torch.optim.Adam(autoencoder.parameters(), lr=args.lr)
best_loss = 1e100
for epoch in range(args.epoch):
print(f' Epoch {epoch}')
loss = _run_train(autoencoder, criterion, opt, train_dataloader)
print('\t [Info] Avg training loss:{:.5f}'.format(
loss / len(train_dataloader.dataset)))
loss = _run_eval(autoencoder, criterion, valid_dataloader)
print('\t [Info] Avg valid loss:{:.5f}'.format(
loss / len(valid_dataloader.dataset)))
if True or loss < best_loss:
best_loss = loss
save_path = "{}/epoch_{}_loss_{:.4f}".format(
args.save_path, epoch, loss / len(valid_dataloader.dataset))
torch.save({'state_dict': autoencoder.state_dict()},
f"{save_path}_autoencoder_.pt")
print(f'\t [Info] save weights at {save_path}')
for param_group in opt.param_groups:
param_group['lr'] = param_group['lr'] / 1
print('-----------------------------------------------')
def main(args):
# Check if the output folder is exist
if not os.path.exists(args.folder):
os.mkdir(args.folder)
# Load data
torch.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(datasets.MNIST(
'./data', train=True, download=True, transform=transforms.ToTensor()),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
# Load model
model = CVAE().cuda() if torch.cuda.is_available() else CVAE()
optimizer = optim.Adam(model.parameters(), lr=1e-3)
# Train and generate sample every epoch
loss_list = []
for epoch in range(1, args.epochs + 1):
model.train()
_loss = train(epoch, model, train_loader, optimizer)
loss_list.append(_loss)
model.eval()
sample = torch.randn(100, 20)
label = torch.from_numpy(np.asarray(list(range(10)) * 10))
sample = Variable(
sample).cuda() if torch.cuda.is_available() else Variable(sample)
sample = model.decode(sample, label).cpu()
save_image(sample.view(100, 1, 28, 28).data,
os.path.join(args.folder, 'sample_' + str(epoch) + '.png'),
nrow=10)
plt.plot(range(len(loss_list)), loss_list, '-o')
plt.savefig(os.path.join(args.folder, 'cvae_loss_curve.png'))
torch.save(model.state_dict(), os.path.join(args.folder, 'cvae.pth'))
def main(argv):
manager = DataManager(flags.image_dir)
#manager.load()
sess = tf.Session()
model = CVAE(gamma=flags.gamma,
capacity_limit=flags.capacity_limit,
capacity_change_duration=flags.capacity_change_duration,
learning_rate=flags.learning_rate)
sess.run(tf.global_variables_initializer())
saver = load_checkpoints(sess)
if flags.training:
print("Training")
# Train
train(sess, model, manager, saver)
# train(sess, model, imcrop, saver)
else:
print("jejeje")
def main(args):
# Check if the output folder is exist
if not os.path.exists(args.folder):
os.mkdir(args.folder)
# Load model
model = CVAE().cuda() if torch.cuda.is_available() else CVAE()
model.load_state_dict(torch.load(os.path.join(args.folder, 'cvae.pth')))
# Generate
sample = torch.randn(args.num, 20)
label = torch.from_numpy(np.asarray([args.digits] * args.num))
sample = Variable(
sample).cuda() if torch.cuda.is_available() else Variable(sample)
sample = model.decode(sample, label).cpu()
save_image(sample.view(args.num, 1, 28, 28).data,
os.path.join(args.folder, 'generate.png'),
nrow=10)
train = pd.read_csv("dataset/VAE_Train+.csv")
test = pd.read_csv("dataset/VAE_Test+.csv")
trainx, trainy = np.array(
train[train.columns[train.columns != "class"]]), np.array(
pd.get_dummies(train["class"]))
testx, testy = np.array(
test[train.columns[train.columns != "class"]]), np.array(
pd.get_dummies(test["class"]))
batch_size = 512
max_epoch = 100
train_N = len(train)
test_N = len(test)
gpu = False
device = "cuda" if gpu else "cpu"
model = CVAE()
if gpu:
model = model.cuda()
opt = optim.Adadelta(model.parameters(), lr=1e-3)
def Loss_function(x_hat, x, mu, logsimga):
reconstraction_loss = F.binary_cross_entropy(x_hat, x, size_average=False)
KL_div = -0.5 * th.sum(1 + logsimga - mu.pow(2) - logsimga.exp())
return reconstraction_loss + KL_div
def create_batch(x, y):
a = list(range(len(x)))
np.random.shuffle(a)
_, c_pairs = readLangs("test_c", "./data/test_c.txt")
total_bleu_score = 0.0
for pair, c_pair in zip(pairs, c_pairs):
print('>', pair[0])
print('=', pair[1])
output_words = evaluate(model, c_pair, pair[0])
output_sentence = ''.join(output_words)
print('<', output_sentence)
print('')
total_bleu_score += bleu_score(pair[1], output_sentence)
_bleu_score = total_bleu_score / len(pairs)
print(f"Bleu Score: {_bleu_score}")
return _bleu_score
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tense2index = {'sp': 0, 'tp': 1, 'pg': 2, 'p': 3}
latent_size = 32
hidden_size = 256
input_lang = torch.load("./lang_class.pth")
checkpoint = torch.load("./checkpoint/0.7902374299152759_61208.pth")
model = CVAE(28, hidden_size, latent_size, 28).to(device)
model.load_state_dict(checkpoint['state_dict'])
evaluateByTestData(model)
n_classes = len(i2int)
sos_idx = w2i['SOS']
eos_idx = w2i['EOS']
pad_idx = w2i['<pad>']
unk_idx = w2i['<unk>']
NLL = torch.nn.NLLLoss(reduction='sum', ignore_index=pad_idx)
model = CVAE(vocab_size=len(i2w),
max_sequence_length=args.max_sequence_length,
sos_idx=sos_idx,
eos_idx=eos_idx,
pad_idx=pad_idx,
unk_idx=unk_idx,
embedding_size=args.emb_dim,
rnn_type=args.rnn_type,
hidden_size=args.hidden_size,
word_dropout=args.word_dropout,
embedding_dropout=args.embedding_dropout,
z_size=args.latent_size,
n_classes=n_classes,
num_layers=args.num_layers,
bidirectional=args.bidirectional,
temperature=args.temperature)
if args.load_model is not None:
state_dict = torch.load(args.load_model)
print(state_dict['embedding.weight'].size(),
model.embedding.weight.size())
if state_dict['embedding.weight'].size(
0) != model.embedding.weight.size(0): # vocab changed
state_dict['embedding.weight'] = vocab.vectors
test_size = 10000
random_vector_for_generation = tf.random.normal(
shape=[num_examples_to_generate, latent_dim])
classifier = Classifier(shape=(28, 28, 1))
classifier_path = checkpoint_path = "./checkpoints/classifier"
cls = tf.train.Checkpoint(classifier=classifier)
cls_manager = tf.train.CheckpointManager(cls,
classifier_path,
max_to_keep=5)
inception_model = Inception_score()
if cls_manager.latest_checkpoint:
cls.restore(cls_manager.latest_checkpoint)
print('classifier checkpoint restored!!')
for i in range(10, 0, -1):
epochs = 0
model = CVAE(latent_dim=latent_dim, beta=3)
sample_size = i * 100
train_size = sample_size * 10
train_images = divide_dataset(train_set, train_labels, sample_size)
#train_size = 10000
#train_images = train_set
batch_size = 32
train_dataset = (tf.data.Dataset.from_tensor_slices(
train_images).shuffle(train_size).batch(batch_size))
test_dataset = (tf.data.Dataset.from_tensor_slices(
test_images).shuffle(test_size).batch(batch_size))
date = '3_4/'
str_i = str(i)
file_path = 'sample_test' + str_i
start_train(epochs, model, train_dataset, test_dataset, date,
output_sentence = ''.join(output_words)
# print('<', output_sentence)
# print('')
total_bleu_score += bleu_score(pair[1], output_sentence)
_bleu_score = total_bleu_score / len(pairs)
# print(f"Bleu Score: {_bleu_score}")
return _bleu_score
def evaluateRandomly(encoder, decoder, n=10):
for i in range(n):
pair = random.choice(pairs)
idx = np.random.randint(0, 4)
print('>', pair[idx])
print('=', pair[idx])
output_words = evaluate(encoder, decoder, idx, pair[idx])
output_sentence = ' '.join(output_words)
print('<', output_sentence)
print('')
def save_checkpoint(state, filename):
torch.save(state, filename)
hidden_size = 256
latent_size = 32
model = CVAE(input_lang.n_words, hidden_size, latent_size, input_lang.n_words).to(device)
trainIters(model, 100000, print_every=5000)
evaluateByTestData(model)
def train(run_id=1):
model = CVAE(run_id=run_id)
labelled, unlabelled, validation = get_mnist(
location="./data",
batch_size=args.batch_size,
labels_per_class=args.labels_per_class)
prev_loss = float('inf')
X = 784
Y = 10
Z = 20
H = 400
C = [400, 128]
if args.architecture == 'vae':
model = VAE(X, Y, Z, H)
elif args.architecture == 'cvae':
model = CVAE(X, Y, Z, H, C)
elif args.architecture == 'stackedvae':
vae = VAE(X, Y, Z, H)
vae.load_state_dict(torch.load(args.pretrained_vae))
model = StackedVAE(X, Y, Z, H, C, vae)
elif args.architecture == 'gmvae':
model = GMVAE(X, Y, Z, H, C)
else:
raise ValueError('Model architecture {} is not defined'.format(
args.architecture))
model = model.to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
def train(epoch):
model.train()
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224), Image.LANCZOS),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
pad_idx = vocab.word2idx['<pad>']
sos_idx = vocab.word2idx['<start>']
eos_idx = vocab.word2idx['<end>']
unk_idx = vocab.word2idx['<unk>']
# Build the models
model = CVAE(
vocab_size=len(vocab),
embedding_size=args.embedding_size,
rnn_type=args.rnn_type,
hidden_size=args.hidden_size,
word_dropout=args.word_dropout,
embedding_dropout=args.embedding_dropout,
latent_size=args.latent_size,
max_sequence_length=args.max_sequence_length,
num_layers=args.num_layers,
bidirectional=args.bidirectional,
pad_idx=pad_idx,
sos_idx=sos_idx,
eos_idx=eos_idx,
unk_idx=unk_idx
)
if not os.path.exists(args.load_checkpoint):
raise FileNotFoundError(args.load_checkpoint)
model.load_state_dict(torch.load(args.load_checkpoint))
print("Model loaded from {}".format(args.load_checkpoint))
model.to(device)
model.eval()
# Build data loader
train_data_loader, valid_data_loader = get_loader(args.train_image_dir, args.val_image_dir,
args.train_caption_path, args.val_caption_path, vocab,
args.batch_size,
shuffle=True, num_workers=args.num_workers)
f1 = open('{}/results/generated_captions.txt'.format(dataset_root_dir), 'w')
f2 = open('{}/results/ground_truth_captions.txt'.format(dataset_root_dir), 'w')
for i, (images, captions, lengths) in enumerate(valid_data_loader):
images = images.to(device)
sampled_ids, z = model.inference(n=args.batch_size, c=images)
sampled_ids_batches = sampled_ids.cpu().numpy() # (batch_size, max_seq_length)
captions = captions.cpu().numpy()
# Convert word_ids to words
for j, sampled_ids in enumerate(sampled_ids_batches):
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
generated_sentence = ' '.join(sampled_caption)
generated_sentence = generated_sentence.rstrip()
generated_sentence = generated_sentence.replace("\n", "")
generated_sentence = "{0}\n".format(generated_sentence)
if j == 0:
print("RE: {}".format(generated_sentence))
f1.write(generated_sentence)
for g, ground_truth_ids in enumerate(captions):
ground_truth_caption = []
for word_id in ground_truth_ids:
word = vocab.idx2word[word_id]
ground_truth_caption.append(word)
if word == '<end>':
break
ground_truth_sentence = ' '.join(ground_truth_caption)
ground_truth_sentence = ground_truth_sentence.rstrip()
ground_truth_sentence = ground_truth_sentence.replace("\n", "")
ground_truth_sentence = "{0}\n".format(ground_truth_sentence)
if g == 0:
print("GT: {}".format(ground_truth_sentence))
f2.write(ground_truth_sentence)
if i % 10 == 0:
print("This is the {0}th batch".format(i))
f1.close()
f2.close()
help='name of result filename',
type=str,
default='result.txt')
parser.add_argument('--smiles_data', help='smiles data', type=str)
parser.add_argument('--vocab_from',
help='the file where vocab is extracted from',
type=str)
parser.add_argument('--lr', help='learning rate', type=float, default=0.0001)
args = parser.parse_args()
#extact vocab and char
char, vocab = extract_vocab(args.vocab_from, args.seq_length)
vocab_size = len(char)
#model and restore model parapmeters
model = CVAE(vocab_size, args)
model.restore(args.save_file)
print('Number of parameters : ',
np.sum([np.prod(v.shape) for v in tf.trainable_variables()]))
#target property to numpy array
start_codon = np.array(
[np.array(list(map(vocab.get, 'X'))) for _ in range(args.batch_size)])
#generate smiles
smiles = []
for _ in range(args.num_iteration):
generated = model.sample(start_codon, args.seq_length)
smiles += [
convert_to_smiles(generated[i], char) for i in range(len(generated))
def train(args, train_dataloader, valid_dataloader):
torch.manual_seed(87)
torch.cuda.manual_seed(87)
model = {'autoen': cc(CVAE()), 'discri': cc(Discriminator())}
autoencoder, discriminator = model['autoen'], model['discri']
criterion = torch.nn.MSELoss()
optimizer = {
'autoen_adam': torch.optim.Adam(autoencoder.parameters(), lr=args.lr),
'autoen_sgd': torch.optim.SGD(autoencoder.parameters(), lr=args.lr),
'discri_sgd': torch.optim.SGD(discriminator.parameters(), lr=args.lr)
}
##########################################################################
########## [Stage 1] train autoencoder ##########
##round1
avg_loss = _run_train_autoencoder(model, criterion, optimizer,
train_dataloader)
##round2
avg_loss = _run_train_autoencoder(model, criterion, optimizer,
train_dataloader)
##round3
avg_loss = _run_train_autoencoder(model, criterion, optimizer,
train_dataloader)
##round4
avg_loss = _run_train_autoencoder(model, criterion, optimizer,
train_dataloader)
torch.save({'state_dict': autoencoder.state_dict()},
"{}/stage_1_autoencoder_loss_{:.4f}.pt".format(
args.save_path, avg_loss))
print(
"\t [stage 1] trained autoencoder, avg reconstruct loss:{:.4f}".format(
avg_loss))
print('\t----------------------------------------------------------')
##########################################################################
######### [Stage 2] train discriminator #########
##round1
avg_loss = _run_train_discriminator(model, criterion, optimizer,
train_dataloader)
##round2
avg_loss = _run_train_discriminator(model, criterion, optimizer,
train_dataloader)
##round3
avg_loss = _run_train_discriminator(model, criterion, optimizer,
train_dataloader)
torch.save({'state_dict': discriminator.state_dict()},
"{}/stage_2_discriminat_loss_{:.4f}.pt".format(
args.save_path, avg_loss))
print("\t [stage 2] trained discriminator, avg mse loss:{:.4f}".format(
avg_loss))
print('\t----------------------------------------------------------')
##########################################################################
######## [Stage 3] adverserial training #########
print("\t [stage 3] adversarial training ")
for epoch in range(args.epoch):
print(f' Epoch {epoch}')
avg_auto_loss, avg_disc_loss = _run_train_adversarial(
model, criterion, optimizer, train_dataloader)
print(
"\t [Info] Train, avg autoenc loss:{:.4f}, avg discri loss:{:.4f}".
format(avg_auto_loss, avg_disc_loss))
avg_auto_loss, avg_disc_loss = _run_eval(model, criterion,
valid_dataloader)
print(
"\t [Info] Valid, avg autoenc recons loss:{:.4f},avg discri loss:{:.4f}"
.format(avg_auto_loss, avg_disc_loss))
if True or loss < best_loss:
torch.save({'state_dict': autoencoder.state_dict()},
"{}/epoch_{}_autoencoder_loss_{:.4f}.pt".format(
args.save_path, epoch, avg_auto_loss))
torch.save({'state_dict': discriminator.state_dict()},
"{}/epoch_{}_discriminat_loss_{:.4f}.pt".format(
args.save_path, epoch, avg_disc_loss))
print(
f'\t [Info] save weights at {args.save_path}/epoch_{epoch}_...'
)
print('\t----------------------------------------------------------')
def main():
args = parse_args()
processeddata_dir = \
"../Hanover_Dataset/HannoverDataset/processed_data/juncs_trips_%02.0f.npy"%args.min_trips
rawdata_file = "../Hanover_Dataset/HannoverDataset"
# Process and save the data
if args.process_data:
junctTrajs_dirs = sorted(
glob.glob(os.path.join(rawdata_file, "junctTrajs/*.csv")))
junctions_dir = os.path.join(rawdata_file, "junctions.csv")
juncs_trips = preprocess(junctTrajs_dirs,
junctions_dir,
min_trips=args.min_trips,
upper_threshold=args.upper_threshold,
lower_threshold=args.lower_threshold,
window_size=args.window_size)
np.save(processeddata_dir, juncs_trips)
else:
juncs_trips = np.load(processeddata_dir)
# Load the sequence data using the sliding window scheme
data_loader = Load_data(juncs_trips,
window_size=args.window_size,
stride=args.stride)
# Load the sequence data and get the data index
data = [sequence for sequence in data_loader.sliding_window()]
data = np.reshape(data, (-1, 19)) # data index + features = 10 + 9 = 19
# Note, due to the data imbalance,
# merge tram_rails (-1) and yield_sigh (1) to priority_sign (2)
if args.num_classes == 3:
data[data[:, 2] == -1, 2] = 2
data[data[:, 2] == 1, 2] = 2
# # Filter out -1 and 1
# data = data[data[:, 2]!=-1, :]
# data = data[data[:, 2]!=1, :]
# new target class:
# uncontrolled:0,
# traffic_light:1,
# tram_rails/yield_sigh/priority_sign
data[data[:, 2] == 4, 2] = 1
# Filter out 3:"stop S." and 5:"roundabout"
data = data[data[:, 2] != 3, :]
data = data[data[:, 2] != 5, :]
# Normalize the features
data[:, 10:] = normalization(data[:, 10:])
# Get the class label
label = data[:, 2].astype(int)
assert args.num_classes == len(
np.unique(label)), "The number of classes is not correct"
_label = np.eye(args.num_classes)[label].reshape(-1, args.window_size,
args.num_classes)
# Question: how to do the data partitioning
data = np.reshape(data, (-1, args.window_size, 19))
print(data.shape)
train_val_split = data_partition(data, args)
train_data_index = data[train_val_split,
-1, :10] # the last step of the sliding window
# 10/0: junc_utm_to_center,
# 11/1: utm_east,
# 12/2: utm_north,
# 13/3: utm_east_speed,
# 14/4: utm_east_speed,
# 15/5: speed_1,
# 16/6: speed_2, (old table speed)
# 17/7: delta_time
# 18/8: angle
# =============================================================================
# train_x = data[train_val_split, :, 10:19]
# train_x = np.concatenate((train_x[:, :, 0:6], train_x[:, :, 7:8]), axis=2) # skip the old speed
# train_y = _label[train_val_split, -1, :] # the last step of the sliding window
#
# val_data_index = data[~train_val_split, -1, :10] # the last step of the sliding window
# val_x = data[~train_val_split, :, 10:19]
# val_x = np.concatenate((val_x[:, :, 0:6], val_x[:, :, 7:8]), axis=2)
# val_y = _label[~train_val_split, -1, :] # the last step of the sliding window
# =============================================================================
# ToDo, feature/ablation
# remove delta_t
train_x = data[train_val_split, :, 10:19]
train_x = np.concatenate((train_x[:, :, 0:5], train_x[:, :, 7:8]), axis=2)
# train_x = train_x[:, :, 2:5]
train_y = _label[train_val_split, -1, :]
val_data_index = data[~train_val_split, -1, :10]
val_x = data[~train_val_split, :, 10:19]
val_x = np.concatenate((val_x[:, :, 0:5], val_x[:, :, 7:8]), axis=2)
# val_x = val_x[:, :, 2:5]
val_y = _label[~train_val_split, -1, :]
print(
np.unique(np.argmax(val_y.reshape(-1, args.num_classes), axis=1),
return_counts=True))
print("train_data_index", train_data_index.shape)
print("train_x", train_x.shape)
print("train_y", train_y.shape)
print("val_data_index", val_data_index.shape)
print("val_x", val_x.shape)
print("val_y", val_y.shape)
##########################################################################
## START THE CLASSIFICATION TASK
# Define the callback and early stop
if not os.path.exists("../models"):
os.mkdir("../models")
timestr = time.strftime("%Y%m%d-%H%M%S")
filepath = "../models/cvae_%0.f_%s.hdf5" % (args.epochs, timestr)
## Eraly stop
earlystop = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=args.patience)
checkpoint = ModelCheckpoint(filepath,
monitor='val_loss',
verbose=0,
save_best_only=True,
mode='min')
callbacks_list = [earlystop, checkpoint]
# # Instantiate the model
cvae = CVAE(args)
# Contruct the cave model
train = cvae.training()
train.summary()
# # Start training phase
if args.train_mode:
# train.load_weights("../models/cvae_500_20201008-213111_03_01_90.hdf5")
print("Start training the model...")
train.fit(x=[train_x, train_y],
y=train_y,
shuffle=True,
epochs=args.epochs,
batch_size=args.batch_size,
verbose=1,
callbacks=callbacks_list,
validation_data=([val_x, val_y], val_y))
train.load_weights(filepath)
else:
print('Run pretrained model')
# train.load_weights("../models/cvae_200_20200709-211305.hdf5")
train.load_weights("../models/cvae_500_20201008-213111_03_01_90.hdf5")
# # Start inference phase
x_encoder = cvae.X_encoder()
decoder = cvae.Decoder()
x_encoder.summary()
decoder.summary()
x_latent = x_encoder.predict(val_x, batch_size=args.batch_size)
y_primes = []
for i, x_ in enumerate(x_latent):
# sampling z from a normal distribution
x_ = np.reshape(x_, [1, -1])
z_sample = np.random.rand(1, args.z_dim)
y_p = decoder.predict(np.column_stack([z_sample, x_]))
y_primes.append(y_p)
y_primes = np.reshape(y_primes, (-1, args.num_classes))
## Evaluation
print("Prediction for each sliding window...")
target_names = ['uc', 'tl', 'ps']
eva = Evaluation(val_y.reshape(-1, args.num_classes), y_primes,
target_names)
confusion_matrix = eva.cf_matrix()
classification_report = eva.report()
# Sum up the prediction for each trip and each junction
print("Prediction for each arm...")
junc_classifier = Junction_class(val_data_index, val_y, y_primes)
arm_gt, arm_pd = junc_classifier.avg_classfier()
arm_eva = Evaluation(arm_gt, arm_pd[:, 0], target_names, arg=False)
arm_confusion_matrix = arm_eva.cf_matrix()
arm_classification_report = arm_eva.report()
# ----------------------------------------------------------------------------------------------------------------------
# LOGGING
# ----------------------------------------------------------------------------------------------------------------------
logdir = 'logs/' + datetime.now().strftime("%Y%m%d-%H%M%S")
train_writer = tf.summary.create_file_writer(logdir + '/train')
valid_writer = tf.summary.create_file_writer(logdir + '/valid')
test_writer = tf.summary.create_file_writer(logdir + '/test')
# ----------------------------------------------------------------------------------------------------------------------
# TRAINING
# ----------------------------------------------------------------------------------------------------------------------
# keeping the random vector constant for generation (prediction) so
# it will be easier to see the improvement.
random_vector_for_generation = tf.random.normal(
shape=[num_examples_to_generate, latent_dim])
model = CVAE(latent_dim)
# Create images at epoch 0, i.e. without any training
if generate:
generate_and_save_images(model, 0, random_vector_for_generation)
# First comparison of original and reconstructions before training begins
log_reconstruction_comparison(sample_images=sample_imgs_ds,
model=model,
epoch=0,
summary_writer=test_writer)
# Start training
for epoch in range(1, epochs + 1):
start_time = time.time()
for train_x in train_dataset:
def train(train_A_dir,
train_B_dir,
model_dir,
model_name,
random_seed,
val_A_dir,
val_B_dir,
output_dir,
tensorboard_dir,
load_path,
gen_eval=True):
np.random.seed(random_seed)
# For now, copy hyperparams used in the CycleGAN
num_epochs = 100000
mini_batch_size = 1 # mini_batch_size = 1 is better
learning_rate = 0.0002
learning_rate_decay = learning_rate / 200000
sampling_rate = 16000
num_mcep = 24
frame_period = 5.0
n_frames = 128
lambda_cycle = 10
lambda_identity = 5
device = 'cuda'
# Use the same pre-processing as the CycleGAN
print("Begin Preprocessing")
wavs_A = load_wavs(wav_dir=train_A_dir, sr=sampling_rate)
wavs_B = load_wavs(wav_dir=train_B_dir, sr=sampling_rate)
print("Finished Loading")
f0s_A, timeaxes_A, sps_A, aps_A, coded_sps_A = world_encode_data(
wavs=wavs_A,
fs=sampling_rate,
frame_period=frame_period,
coded_dim=num_mcep)
f0s_B, timeaxes_B, sps_B, aps_B, coded_sps_B = world_encode_data(
wavs=wavs_B,
fs=sampling_rate,
frame_period=frame_period,
coded_dim=num_mcep)
print("Finished Encoding")
log_f0s_mean_A, log_f0s_std_A = logf0_statistics(f0s_A)
log_f0s_mean_B, log_f0s_std_B = logf0_statistics(f0s_B)
print('Log Pitch A')
print('Mean: %f, Std: %f' % (log_f0s_mean_A, log_f0s_std_A))
print('Log Pitch B')
print('Mean: %f, Std: %f' % (log_f0s_mean_B, log_f0s_std_B))
coded_sps_A_transposed = transpose_in_list(lst=coded_sps_A)
coded_sps_B_transposed = transpose_in_list(lst=coded_sps_B)
coded_sps_A_norm, coded_sps_A_mean, coded_sps_A_std = coded_sps_normalization_fit_transoform(
coded_sps=coded_sps_A_transposed)
print("Input data fixed.")
coded_sps_B_norm, coded_sps_B_mean, coded_sps_B_std = coded_sps_normalization_fit_transoform(
coded_sps=coded_sps_B_transposed)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
np.savez(os.path.join(model_dir, 'logf0s_normalization.npz'),
mean_A=log_f0s_mean_A,
std_A=log_f0s_std_A,
mean_B=log_f0s_mean_B,
std_B=log_f0s_std_B)
np.savez(os.path.join(model_dir, 'mcep_normalization.npz'),
mean_A=coded_sps_A_mean,
std_A=coded_sps_A_std,
mean_B=coded_sps_B_mean,
std_B=coded_sps_B_std)
if val_A_dir is not None:
validation_A_output_dir = os.path.join(output_dir, 'converted_A')
if not os.path.exists(validation_A_output_dir):
os.makedirs(validation_A_output_dir)
if val_B_dir is not None:
validation_B_output_dir = os.path.join(output_dir, 'converted_B')
if not os.path.exists(validation_B_output_dir):
os.makedirs(validation_B_output_dir)
print("End Preprocessing")
if load_path is not None:
model = CVAE(num_mcep, 128, num_mcep, 2)
model.load_state_dict(torch.load(load_path))
model.eval()
if device == 'cuda':
model.cuda()
print("Loaded Model from path %s" % load_path)
if val_A_dir is not None and gen_eval:
print("Generating Evaluation Data")
for file in os.listdir(val_A_dir):
filepath = os.path.join(val_A_dir, file)
print(
"Converting {0} from Class 0 to Class 1".format(filepath))
wav, _ = librosa.load(filepath, sr=sampling_rate, mono=True)
wav = wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = pitch_conversion(f0=f0,
mean_log_src=log_f0s_mean_A,
std_log_src=log_f0s_std_A,
mean_log_target=log_f0s_mean_B,
std_log_target=log_f0s_std_B)
coded_sp = world_encode_spectral_envelop(sp=sp,
fs=sampling_rate,
dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
coded_sps_A_mean) / coded_sps_A_std
coded_sp_converted_norm, _, _ = model.convert(
np.array([coded_sp_norm]), 0, 1, device)
coded_sp_converted_norm = coded_sp_converted_norm.cpu().numpy()
coded_sp_converted_norm = np.squeeze(coded_sp_converted_norm)
coded_sp_converted = coded_sp_converted_norm * coded_sps_B_std + coded_sps_B_mean
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
decoded_sp_converted = world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = world_speech_synthesis(
f0=f0_converted,
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period)
librosa.output.write_wav(
os.path.join(validation_A_output_dir,
'eval_' + os.path.basename(file)),
wav_transformed, sampling_rate)
exit(0)
print("Begin Training")
model = CVAE(num_mcep, 128, num_mcep, 2)
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
writer = SummaryWriter(tensorboard_dir)
if device == 'cuda':
model.cuda()
for epoch in tqdm(range(num_epochs)):
dataset_A, dataset_B = sample_train_data(dataset_A=coded_sps_A_norm,
dataset_B=coded_sps_B_norm,
n_frames=n_frames)
dataset_A = torch.tensor(dataset_A).to(torch.float)
dataset_B = torch.tensor(dataset_B).to(torch.float)
n_samples, input_dim, depth = dataset_A.shape
y_A = F.one_hot(torch.zeros(depth).to(torch.int64),
num_classes=2).to(torch.float).T
y_B = F.one_hot(torch.ones(depth).to(torch.int64),
num_classes=2).to(torch.float).T
(y_A, y_B) = (y_A.reshape((1, 2, depth)), y_B.reshape((1, 2, depth)))
y_A = torch.cat([y_A] * n_samples)
y_B = torch.cat([y_B] * n_samples)
# dataset_A = torch.cat((dataset_A, y_A), axis=1)
# dataset_B = torch.cat((dataset_B, y_B), axis=1)
X = torch.cat((dataset_A, dataset_B)).to(device)
Y = torch.cat((y_A, y_B)).to(device)
# out, z_mu, z_var = model(dataset_A, y_A)
# rec_loss = F.binary_cross_entropy(out, dataset_A, size_average=False)
# kl_diver = -0.5 * torch.sum(1 + z_var - z_mu.pow(2) - z_var.exp())
out, z_mu, z_var = model(X, Y)
rec_loss = F.binary_cross_entropy(out, X, size_average=False)
kl_diver = -0.5 * torch.sum(1 + z_var - z_mu.pow(2) - z_var.exp())
loss = rec_loss + kl_diver
writer.add_scalar('Reconstruction Loss', rec_loss, epoch)
writer.add_scalar('KL-Divergence', kl_diver, epoch)
writer.add_scalar('Total Loss', loss, epoch)
# print("loss = {0} || rec = {1} || kl = {2}".format(loss, rec_loss, kl_diver))
loss.backward()
optimizer.step()
if val_A_dir is not None:
if epoch % 1000 == 0:
print('Generating Validation Data...')
for file in os.listdir(val_A_dir):
filepath = os.path.join(val_A_dir, file)
print("Converting {0} from Class 0 to Class 1".format(
filepath))
wav, _ = librosa.load(filepath,
sr=sampling_rate,
mono=True)
wav = wav_padding(wav=wav,
sr=sampling_rate,
frame_period=frame_period,
multiple=4)
f0, timeaxis, sp, ap = world_decompose(
wav=wav, fs=sampling_rate, frame_period=frame_period)
f0_converted = pitch_conversion(
f0=f0,
mean_log_src=log_f0s_mean_A,
std_log_src=log_f0s_std_A,
mean_log_target=log_f0s_mean_B,
std_log_target=log_f0s_std_B)
coded_sp = world_encode_spectral_envelop(sp=sp,
fs=sampling_rate,
dim=num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed -
coded_sps_A_mean) / coded_sps_A_std
coded_sp_converted_norm, _, _ = model.convert(
np.array([coded_sp_norm]), 0, 1, device)
coded_sp_converted_norm = coded_sp_converted_norm.cpu(
).numpy()
coded_sp_converted_norm = np.squeeze(
coded_sp_converted_norm)
coded_sp_converted = coded_sp_converted_norm * coded_sps_B_std + coded_sps_B_mean
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(
coded_sp_converted)
decoded_sp_converted = world_decode_spectral_envelop(
coded_sp=coded_sp_converted, fs=sampling_rate)
wav_transformed = world_speech_synthesis(
f0=f0_converted,
decoded_sp=decoded_sp_converted,
ap=ap,
fs=sampling_rate,
frame_period=frame_period)
librosa.output.write_wav(
os.path.join(validation_A_output_dir,
str(epoch) + '_' +
os.path.basename(file)), wav_transformed,
sampling_rate)
break
if epoch % 1000 == 0:
print('Saving Checkpoint')
filepath = os.path.join(model_dir, model_name)
if not os.path.exists(filepath):
os.makedirs(filepath)
torch.save(model.state_dict(),
os.path.join(filepath, '{0}.ckpt'.format(epoch)))
# tight_layout minimizes the overlap between 2 sub-plots
# plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
plt.show()
epochs = 50
# set the dimensionality of the latent space to a plane for visualization later
latent_dim = 16
num_examples_to_generate = 9
# keeping the random vector constant for generation (prediction) so
# it will be easier to see the improvement.
random_vector_for_generation = tf.random.normal(
shape=[num_examples_to_generate, latent_dim])
model = CVAE(latent_dim)
loss = tf.keras.metrics.Mean()
model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
assert batch_size >= num_examples_to_generate
for test_batch in test_dataset.take(1):
test_sample = test_batch[0:num_examples_to_generate, :, :, :]
for i in range(num_examples_to_generate):
plt.subplot(3, 3, i + 1)
plt.imshow(test_sample[i, :, :, :])
plt.axis('off')
generate_and_save_images(model, 0, test_sample)
for epoch in range(1, epochs + 1):
def load_model(PATH):
model = CVAE(2, 128)
# Restore the weights
model.load_weights(PATH)
return model
def main():
config = Settings(model='GPT',
model_name='9.23_dropout0.1_GPT',
resume='best.pth')
dataset_path = os.path.join(os.getcwd(), config.path)
dataset_filename = config.test_file
device = config.device
model_dir = os.path.join(config.exp_dir, config.model_name)
with open(os.path.join(dataset_path, dataset_filename), 'rb') as f:
dataset = pickle.load(f)
dataset = LightDarkDataset(config, dataset)
data, targets = collect_data(config, dataset)
# with open(os.path.join(dataset_path, 'light_dark_sample_len15.pickle'), 'rb') as f:
# sample = pickle.load(f)
# data, targets = sample['data'], sample['targets']
if config.model == 'GPT':
model = GPT2(config).to(device)
elif config.model == 'RNN':
model = RNN(config).to(device)
elif config.model == 'CVAE':
model = CVAE(config).to(device)
optimizer = th.optim.AdamW(model.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay)
if config.optimizer == 'AdamW':
scheduler = th.optim.lr_scheduler.LambdaLR(
optimizer, lambda step: min((step + 1) / config.warmup_step, 1))
elif config.optimizer == 'AdamWR':
scheduler = CosineAnnealingWarmUpRestarts(optimizer=optimizer,
T_0=config.T_0,
T_mult=config.T_mult,
eta_max=config.lr_max,
T_up=config.warmup_step,
gamma=config.lr_mult)
else:
# |FIXME| using error?exception?logging?
print(
f'"{config.optimizer}" is not support!! You should select "AdamW" or "AdamWR".'
)
return
# load checkpoint for resuming
if config.resume is not None:
filename = os.path.join(model_dir, config.resume)
if os.path.isfile(filename):
start_epoch, best_error, model, optimizer, scheduler = load_checkpoint(
config, filename, model, optimizer, scheduler)
start_epoch += 1
print("Loaded checkpoint '{}' (epoch {})".format(
config.resume, start_epoch))
else:
# |FIXME| using error?exception?logging?
print("No checkpoint found at '{}'".format(config.resume))
return
pred = []
total_time = 0.
for d in data:
for i in range(config.num_output):
tmp_pred, time = predict_action(config, model, d)
pred.append(tmp_pred)
total_time += time
targets = np.asarray(targets).reshape(-1, 2)
pred = np.asarray(pred).reshape(-1, 2)
print(
f'Inference time: {total_time / (config.num_input * config.num_output)}'
)
plt.xlim(-7, 7)
plt.ylim(-7, 7)
plt.scatter(targets[:, 0], targets[:, 1], c='red')
plt.scatter(pred[:, 0], pred[:, 1], c='blue')
plt.show()
torch.backends.cudnn.benchmark = False
# fetch data
data = locate('data.get_%s' % args.dataset)(args)
# make dataloaders
train_loader, val_loader, test_loader = [
CLDataLoader(elem, args, train=t)
for elem, t in zip(data, [True, False, False])
]
model = ResNet18(args.n_classes, nf=20,
input_size=args.input_size).to(args.device)
opt = torch.optim.SGD(model.parameters(), lr=0.1)
gen = CVAE(20, args).cuda() # this is actually an autoencoder
opt_gen = torch.optim.Adam(gen.parameters())
# build buffer
if args.store_latents:
buffer = Buffer(args, input_size=(20 * 4 * 4, ))
else:
buffer = Buffer(args)
buffer.min_per_class = 0
print('multiple heads ', args.multiple_heads)
if run == 0:
print("number of classifier parameters:",
sum([np.prod(p.size()) for p in model.parameters()]))
print("number of generator parameters: ",
def main():
config = Settings()
dataset_filename = config.dataset_file
dataset_path = os.path.join(os.getcwd(), config.path)
if not os.path.exists(config.exp_dir):
os.mkdir(config.exp_dir)
model_dir = os.path.join(config.exp_dir, config.model_name)
logger = SummaryWriter(model_dir)
with open(os.path.join(dataset_path, dataset_filename), 'rb') as f:
dataset = pickle.load(f)
print('#trajectories of dataset:', len(dataset['observation']))
# generate dataloader
data_loader = get_loader_multi_target(config, dataset)
# model
device = th.device(config.device)
if config.model == 'GPT':
model = GPT2(config).to(device)
elif config.model == 'RNN':
model = RNN(config).to(device)
elif config.model == 'LSTM':
model = LSTM(config).to(device)
elif config.model == 'CVAE':
model = CVAE(config).to(device)
else:
raise Exception(
f'"{config.model}" is not support!! You should select "GPT", "RNN", or "LSTM".'
)
# optimizer
optimizer = th.optim.AdamW(model.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay)
# learning rate scheduler
if config.optimizer == 'AdamW':
scheduler = th.optim.lr_scheduler.LambdaLR(
optimizer, lambda step: min((step + 1) / config.warmup_step, 1))
elif config.optimizer == 'AdamWR':
scheduler = CosineAnnealingWarmUpRestarts(optimizer=optimizer,
T_0=config.T_0,
T_mult=config.T_mult,
eta_max=config.lr_max,
T_up=config.warmup_step,
gamma=config.lr_mult)
else:
raise Exception(
f'"{config.optimizer}" is not support!! You should select "AdamW" or "AdamWR".'
)
# Metric
if config.model == 'CVAE':
eval_fn = NNMSE
else:
eval_fn = NMSE
# Trainer & Evaluator
evaluator = MultiTargetEvaluator(config=config,
loader=data_loader,
model=model,
eval_fn=eval_fn)
# load checkpoint for resuming
for ckpt in config.resume:
filename = os.path.join(model_dir, ckpt)
if os.path.isfile(filename):
epoch, best_error, model, optimizer, scheduler = load_checkpoint(
config, filename, model, optimizer, scheduler)
print("Loaded checkpoint '{}' (epoch {})".format(ckpt, epoch))
else:
raise Exception("No checkpoint found at '{}'".format(ckpt))
print(f'===== Evaluate {epoch} epoch =====')
test_val = evaluator.eval(epoch)
# Logging
logger.add_scalar('Eval/Near-Nearest MSE', test_val, epoch)
print(f'===== End {epoch} epoch =====')
import torch as th
import torch.nn.functional as F
import pandas as pd
from sklearn.metrics import classification_report
from model import CVAE
import numpy as np
def Loss_function(x_hat, x, mu, logsimga):
reconstraction_loss = F.binary_cross_entropy(x_hat, x, size_average=False)
KL_div = -0.5 * th.sum(1 + logsimga - mu.pow(2) - logsimga.exp())
return reconstraction_loss + KL_div
model = CVAE()
param = th.load('save_model/vae_adadelta300.pth', map_location=lambda x, y: x)
model.load_state_dict(param)
test = pd.read_csv("dataset/VAE_Test+.csv")
testx, testy = np.array(test[test.columns[test.columns != "class"]]), np.array(
pd.get_dummies(test["class"]))
z_dim = 25
n, m = testx.shape[1], testy.shape[1]
test_label = th.eye(m)
attack_name = ["normal", "Dos", "Probe", "R2L", "U2R"]
pred = []
for x in testx:
each_loss = []
x = th.Tensor(x.reshape(1, n))
for label in test_label:
vocab_size = len(char)
num_train_data = int(len(molecules)*0.75)
train_molecules = molecules[0:num_train_data]
test_molecules = molecules[num_train_data:-1]
train_labels = labels[0:num_train_data]
test_labels = labels[num_train_data:-1]
train_length = length[0:num_train_data]
test_length = length[num_train_data:-1]
model = CVAE(vocab_size,
batch_size = batch_size,
latent_size = latent_size,
stddev = 1.0,
mean = 0.0,
)
num_epochs = 200
save_every = 500
learning_rate = 0.0001
temperature = 1.0
min_temperature = 0.5
decay_rate = 0.95
for epoch in range(num_epochs):
# Learning rate scheduling
st = time.time()
model.assign_lr(learning_rate * (decay_rate ** epoch))
train_loss = []
test_loss = []
(train_images, train_y)).shuffle(train_size).batch(args.batch_size))
test_dataset = (tf.data.Dataset.from_tensor_slices(
(test_images, test_y)).shuffle(test_size).batch(args.batch_size))
optimizer = tf.keras.optimizers.Adam(1e-4)
# # test
# model = CVAE(latent_dim, inter_dim)
# for test_batch in test_dataset.take(1):
# test_sample_x = test_batch[0][0:num_examples_to_generate, :, :, :]
# test_sample_y = test_batch[1][0:num_examples_to_generate]
# generate_and_save_images(model, 0, test_sample_x, test_sample_y)
# reset
model = CVAE(args.latent_dim, args.inter_dim)
for epoch in range(args.EPOCHS):
for train in train_dataset:
train_step(model, train[0], train[1], optimizer)
with train_summary_writer.as_default():
tf.summary.scalar('loss', train_loss.result(), step=epoch)
for test in test_dataset:
test_step(model, test[0], test[1])
with test_summary_writer.as_default():
tf.summary.scalar('loss', test_loss.result(), step=epoch)
template = 'Epoch {}, Loss: {}, Test Loss: {}'
print(
template.format(epoch + 1, train_loss.result(),
def main(**kwargs):
"""
Main function that trains the model
1. Retrieve arguments from kwargs
2. Prepare data
3. Train
4. Display and save first batch of training set (truth and reconstructed) after every epoch
5. If latent dimension is 2, display and save latent variable of first batch of training set after every epoch
Args:
dataset: Which dataset to use
decoder_type: How to model the output pixels, Gaussian or Bernoulli
model_sigma: In case of Gaussian decoder, whether to model the sigmas too
epochs: How many epochs to train model
batch_size: Size of training / testing batch
lr: Learning rate
latent_dim: Dimension of latent variable
print_every: How often to print training progress
resume_path: The path of saved model with which to resume training
resume_epoch: In case of resuming, the number of epochs already done
Notes:
- Saves model to folder 'saved_model/' every 20 epochs and when done
- Capable of training from scratch and resuming (provide saved model location to argument resume_path)
- Schedules learning rate with optim.lr_scheduler.ReduceLROnPlateau
: Decays learning rate by 1/10 when mean loss of all training data does not decrease for 10 epochs
"""
# Retrieve arguments
dataset = kwargs.get('dataset', defaults['dataset'])
decoder_type = kwargs.get('decoder_type', defaults['decoder_type'])
if decoder_type == 'Gaussian':
model_sigma = kwargs.get('model_sigma', defaults['model_sigma'])
epochs = kwargs.get('epochs', defaults['epochs'])
batch_size = kwargs.get('batch_size', defaults['batch_size'])
lr = kwargs.get('learning_rate', defaults['learning_rate'])
latent_dim = kwargs.get('latent_dim', defaults['latent_dim'])
print_every = kwargs.get('print_every', defaults['print_every'])
resume_path = kwargs.get('resume_path', defaults['resume_path'])
resume_epoch = kwargs.get('resume_epoch', defaults['resume_epoch'])
# Specify dataset transform on load
if decoder_type == 'Bernoulli':
trsf = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x: (x >= 0.5).float())
])
elif decoder_type == 'Gaussian':
trsf = transforms.ToTensor()
# Load dataset with transform
if dataset == 'MNIST':
train_data = datasets.MNIST(root='MNIST',
train=True,
transform=trsf,
download=True)
test_data = datasets.MNIST(root='MNIST',
train=False,
transform=trsf,
download=True)
elif dataset == 'CIFAR10':
train_data = datasets.CIFAR10(root='CIFAR10',
train=True,
transform=trsf,
download=True)
test_data = datasets.CIFAR10(root='CIFAR10',
train=False,
transform=trsf,
download=True)
# Instantiate dataloader
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=batch_size,
shuffle=False)
# Instantiate/Load model and optimizer
if resume_path:
autoencoder = torch.load(resume_path, map_location=device)
optimizer = optim.Adam(autoencoder.parameters(), lr=lr)
print('Loaded saved model at ' + resume_path)
else:
if decoder_type == 'Bernoulli':
autoencoder = CVAE(latent_dim, dataset, decoder_type).to(device)
else:
autoencoder = CVAE(latent_dim, dataset, decoder_type,
model_sigma).to(device)
optimizer = optim.Adam(autoencoder.parameters(), lr=lr)
# Instantiate learning rate scheduler
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
verbose=True,
patience=5)
# Announce current mode
print(
f'Start training CVAE with Gaussian encoder and {decoder_type} decoder on {dataset} dataset from epoch {resume_epoch+1}'
)
# Prepare batch to display with plt
first_test_batch, first_test_batch_label = iter(test_loader).next()
first_test_batch, first_test_batch_label = first_test_batch.to(
device), first_test_batch_label.to(device)
# Display latent variable distribution before any training
if latent_dim == 2 and resume_epoch == 0:
autoencoder(first_test_batch, first_test_batch_label)
display_and_save_latent(autoencoder.z, first_test_batch_label,
f'-{decoder_type}-z{latent_dim}-e000')
# Train
autoencoder.train()
for epoch in range(resume_epoch, epochs + resume_epoch):
loss_hist = []
for batch_ind, (input_data, input_label) in enumerate(train_loader):
input_data, input_label = input_data.to(device), input_label.to(
device)
# Forward propagation
if decoder_type == 'Bernoulli':
z_mu, z_sigma, p = autoencoder(input_data, input_label)
elif model_sigma:
z_mu, z_sigma, out_mu, out_sigma = autoencoder(
input_data, input_label)
else:
z_mu, z_sigma, out_mu = autoencoder(input_data, input_label)
# Calculate loss
KL_divergence_i = 0.5 * torch.sum(
z_mu**2 + z_sigma**2 - torch.log(1e-8 + z_sigma**2) - 1.,
dim=1)
if decoder_type == 'Bernoulli':
reconstruction_loss_i = -torch.sum(F.binary_cross_entropy(
p, input_data, reduction='none'),
dim=(1, 2, 3))
elif model_sigma:
reconstruction_loss_i = -0.5 * torch.sum(
torch.log(1e-8 + 6.28 * out_sigma**2) +
((input_data - out_mu)**2) / (out_sigma**2),
dim=(1, 2, 3))
else:
reconstruction_loss_i = -0.5 * torch.sum(
(input_data - out_mu)**2, dim=(1, 2, 3))
ELBO_i = reconstruction_loss_i - KL_divergence_i
loss = -torch.mean(ELBO_i)
loss_hist.append(loss)
# Backward propagation
optimizer.zero_grad()
loss.backward()
# Update parameters
optimizer.step()
# Print progress
if batch_ind % print_every == 0:
train_log = 'Epoch {:03d}/{:03d}\tLoss: {:.6f}\t\tTrain: [{}/{} ({:.0f}%)] '.format(
epoch + 1, epochs + resume_epoch,
loss.cpu().item(), batch_ind + 1, len(train_loader),
100. * batch_ind / len(train_loader))
print(train_log, end='\r')
sys.stdout.flush()
# Learning rate decay
scheduler.step(sum(loss_hist) / len(loss_hist))
# Save model every 20 epochs
if (epoch + 1) % 20 == 0 and epoch + 1 != epochs:
PATH = f'saved_model/{dataset}-{decoder_type}-e{epoch+1}-z{latent_dim}' + datetime.datetime.now(
).strftime("-%b-%d-%H-%M-%p")
torch.save(autoencoder, PATH)
print('\vTemporarily saved model to ' + PATH)
# Display training result with test set
data = f'-{decoder_type}-z{latent_dim}-e{epoch+1:03d}'
with torch.no_grad():
autoencoder.eval()
if decoder_type == 'Bernoulli':
z_mu, z_sigma, p = autoencoder(first_test_batch,
first_test_batch_label)
output = torch.bernoulli(p)
if latent_dim == 2:
display_and_save_latent(autoencoder.z,
first_test_batch_label, data)
display_and_save_batch("Binarized-truth",
first_test_batch,
data,
save=(epoch == 0))
display_and_save_batch("Mean-reconstruction",
p,
data,
save=True)
display_and_save_batch("Sampled-reconstruction",
output,
data,
save=True)
elif model_sigma:
z_mu, z_sigma, out_mu, out_sigma = autoencoder(
first_test_batch, first_test_batch_label)
output = torch.normal(out_mu, out_sigma).clamp(0., 1.)
if latent_dim == 2:
display_and_save_latent(autoencoder.z,
first_test_batch_label, data)
display_and_save_batch("Truth",
first_test_batch,
data,
save=(epoch == 0))
display_and_save_batch("Mean-reconstruction",
out_mu,
data,
save=True)
# display_and_save_batch("Sampled reconstruction", output, data, save=True)
else:
z_mu, z_sigma, out_mu = autoencoder(first_test_batch,
first_test_batch_label)
output = torch.normal(out_mu,
torch.ones_like(out_mu)).clamp(0., 1.)
if latent_dim == 2:
display_and_save_latent(autoencoder.z,
first_test_batch_label, data)
display_and_save_batch("Truth",
first_test_batch,
data,
save=(epoch == 0))
display_and_save_batch("Mean-reconstruction",
out_mu,
data,
save=True)
# display_and_save_batch("Sampled reconstruction", output, data, save=True)
autoencoder.train()
# Save final model
PATH = f'saved_model/{dataset}-{decoder_type}-e{epochs+resume_epoch}-z{latent_dim}' + datetime.datetime.now(
).strftime("-%b-%d-%H-%M-%p")
torch.save(autoencoder, PATH)
print('\vSaved model to ' + PATH)
raw_data = list(filter(lambda x: len(x[0]) <= opt.max_len, raw_data))
print("Load %d datapoints." % len(raw_data))
train_data, val_data, vocab = mydataset.make_dataset(raw_data, opt)
opt.max_words = min(opt.max_words, len(vocab) - 4)
train_iter, val_iter = mydataset.make_iterator((train_data, val_data), opt)
device = T.device(opt.device)
start_time = datetime.now().strftime("%Y%m%d-%H%M%S")
layout = [('model={:s}', 'cvae'), ('z={:02d}', opt.z_dim),
('time={:s}', start_time), ('data={:s}', opt.dataset)]
model_name = '_'.join([t.format(v) for (t, v) in layout])
writer = ut.prepare_writer(model_name, overwrite_existing=False)
model = CVAE(opt).to(device)
model.embedding.weight.data.copy_(vocab.vectors).to(device)
train_gen = mydataset.make_loader(train_iter, opt)
val_gen = mydataset.make_loader(val_iter, opt)
if opt.mode == 'train':
train(model=model,
train_loader=train_gen,
val_loader=val_gen,
tqdm=tqdm.tqdm,
device=device,
writer=writer,
start_time=start_time,
dataset_name=opt.dataset,
iter_max=opt.max_iter,
iter_log=opt.log_iter,
def main():
config = Settings()
# |TODO| go to Setting()
train_filename = config.train_file
# train_filename_1 = config.train_file_1
# train_filename_2 = config.train_file_2
test_filename = config.test_file
dataset_path = os.path.join(os.getcwd(), config.path)
if not os.path.exists(config.exp_dir):
os.mkdir(config.exp_dir)
model_dir = os.path.join(config.exp_dir, config.model_name)
logger = SummaryWriter(model_dir)
if config.data_type == 'success':
# with open(os.path.join(dataset_path, train_filename), 'rb') as f:
# train_dataset = pickle.load(f)
# with open(os.path.join(dataset_path, test_filename), 'rb') as f:
# test_dataset = pickle.load(f)
dataset = glob.glob(f'{dataset_path}/{train_filename}/*.pickle')
# test_dataset = glob.glob(f'{dataset_path}/{test_filename}/*.pickle')
# train_dataset = dataset[:1500000]
# test_dataset = dataset[-200000:]
train_dataset = dataset[:-20000]
test_dataset = dataset[-20000:]
print('#trajectories of train_dataset:', len(train_dataset))
print('#trajectories of test_dataset:', len(test_dataset))
elif config.data_type == 'mcts':
dataset = glob.glob(f'{dataset_path}/{train_filename}/*.pickle')
train_dataset = dataset[:-20000]
test_dataset = dataset[-20000:]
# train_dataset = glob.glob(f'{dataset_path}/{train_filename}/*.pickle')
# test_dataset = glob.glob(f'{dataset_path}/{test_filename}/*.pickle')
if config.filter:
filtered_data_train = []
filtered_data_test = []
total_reward_filt = []
total_reward_not_filt = []
avg_total_reward_not_filt = 0
avg_total_reward_filt = 0
for data in train_dataset:
with open(data, 'rb') as f:
traj = pickle.load(f)
avg_total_reward_not_filt += traj[-1]
total_reward_not_filt.append(traj[-1])
if traj[-1] > config.filter:
filtered_data_train.append(data)
avg_total_reward_filt += traj[-1]
total_reward_filt.append(traj[-1])
for data in test_dataset:
with open(data, 'rb') as f:
traj = pickle.load(f)
if traj[-1] > config.filter:
filtered_data_test.append(data)
total_reward_not_filt_std = np.std(
np.asarray(total_reward_not_filt))
total_reward_filt_std = np.std(np.asarray(total_reward_filt))
print('Average of total reward(not filtered):',
avg_total_reward_not_filt / len(train_dataset))
print('std of total reward(not filtered):',
total_reward_not_filt_std)
print('Average of total reward(filtered):',
avg_total_reward_filt / len(filtered_data_train))
print('std of total reward(filtered):', total_reward_filt_std)
train_dataset = filtered_data_train
test_dataset = filtered_data_test
print('#trajectories of train_dataset:', len(train_dataset))
print('#trajectories of test_dataset:', len(test_dataset))
# # For mixed dataset
# train_dataset_1 = glob.glob(f'{dataset_path}/{train_filename_1}/*.pickle')
# dataset_2 = glob.glob(f'{dataset_path}/{train_filename_2}/*.pickle')
# train_dataset_2 = dataset_2[:100000]
# test_dataset = dataset_2[100000:]
# if config.filter:
# filtered_data_train = []
# filtered_data_test = []
# total_reward_filt = []
# total_reward_not_filt = []
# avg_total_reward_not_filt = 0
# avg_total_reward_filt = 0
# for data in train_dataset_2:
# with open(data, 'rb') as f:
# traj = pickle.load(f)
# avg_total_reward_not_filt += traj[-1]
# total_reward_not_filt.append(traj[-1])
# if traj[-1] > config.filter:
# filtered_data_train.append(data)
# avg_total_reward_filt += traj[-1]
# total_reward_filt.append(traj[-1])
# for data in test_dataset:
# with open(data, 'rb') as f:
# traj = pickle.load(f)
# if traj[-1] > config.filter:
# filtered_data_test.append(data)
# total_reward_not_filt_std = np.std(np.asarray(total_reward_not_filt))
# total_reward_filt_std = np.std(np.asarray(total_reward_filt))
# print('Average of total reward(not filtered):', avg_total_reward_not_filt/len(train_dataset_2))
# print('std of total reward(not filtered):', total_reward_not_filt_std)
# print('Average of total reward(filtered):', avg_total_reward_filt/len(filtered_data_train))
# print('std of total reward(filtered):', total_reward_filt_std)
# train_dataset = train_dataset_1 + filtered_data_train
# test_dataset = filtered_data_test
# print('#trajectories of train_dataset:', len(train_dataset))
# print('#trajectories of test_dataset:', len(test_dataset))
# generate dataloader
train_loader = get_loader(config, train_dataset)
test_loader = get_loader(config, test_dataset)
# model
device = th.device(config.device)
if config.model == 'GPT':
model = GPT2(config).to(device)
elif config.model == 'RNN':
model = RNN(config).to(device)
elif config.model == 'LSTM':
model = LSTM(config).to(device)
elif config.model == 'CVAE' or config.model == 'PolicyValueNet':
model = CVAE(config).to(device)
elif config.model == 'ValueNet':
model = ValueNet(config).to(device)
else:
raise Exception(
f'"{config.model}" is not support!! You should select "GPT", "RNN", "LSTM", "CVAE", "ValueNet", or "PolicyValueNet.'
)
# optimizer
optimizer = th.optim.AdamW(model.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay)
# learning rate scheduler
if config.optimizer == 'AdamW':
scheduler = th.optim.lr_scheduler.LambdaLR(
optimizer, lambda step: min((step + 1) / config.warmup_step, 1))
elif config.optimizer == 'AdamWR':
scheduler = CosineAnnealingWarmUpRestarts(optimizer=optimizer,
T_0=config.T_0,
T_mult=config.T_mult,
eta_max=config.lr_max,
T_up=config.warmup_step,
gamma=config.lr_mult)
else:
raise Exception(
f'"{config.optimizer}" is not support!! You should select "AdamW" or "AdamWR".'
)
# Metric
# |TODO| implement Chamfer distance
if config.model == 'CVAE':
loss_fn = ELBOLoss(config)
eval_fn = ELBOLoss(config)
elif config.model == 'ValueNet':
loss_fn = RegressionLossValue(config)
eval_fn = RegressionLossValue(config)
elif config.model == 'PolicyValueNet':
loss_fn = None
eval_fn = None
else:
loss_fn = RegressionLossPolicy(config)
eval_fn = RegressionLossPolicy(config)
# Trainer & Evaluator
trainer = Trainer(config=config,
loader=train_loader,
model=model,
optimizer=optimizer,
scheduler=scheduler,
loss_fn=loss_fn,
eval_fn=eval_fn)
evaluator = Evaluator(config=config,
loader=test_loader,
model=model,
eval_fn=eval_fn)
# save configuration
config.save(model_dir + '/config.yaml')
# Logging model graph
dummy = next(iter(test_loader))
for k in dummy:
dummy[k].to(device).detach()
logger.add_graph(ModelAsTuple(config, model), dummy)
start_epoch = 1
best_error = 10000.
# load checkpoint for resuming
if config.resume is not None:
filename = os.path.join(model_dir, config.resume)
if os.path.isfile(filename):
start_epoch, best_error, model, optimizer, scheduler = load_checkpoint(
config, filename, model, optimizer, scheduler)
start_epoch += 1
print("Loaded checkpoint '{}' (epoch {})".format(
config.resume, start_epoch))
else:
raise Exception("No checkpoint found at '{}'".format(
config.resume))
# load checkpoint for pre-trained
if config.pre_trained is not None:
pre_trained_path = os.path.join(config.exp_dir, config.pre_trained)
if os.path.isfile(pre_trained_path):
start_epoch, best_error, model, optimizer, scheduler = load_checkpoint(
config, pre_trained_path, model, optimizer, scheduler)
start_epoch = 1
print("Loaded checkpoint '{}'".format(config.pre_trained))
else:
raise Exception("No checkpoint found at '{}'".format(
config.resume))
for epoch in range(start_epoch, config.epochs + 1):
print(f'===== Start {epoch} epoch =====')
# Training one epoch
print("Training...")
train_loss, train_val = trainer.train(epoch)
# Logging
if config.model == 'CVAE':
logger.add_scalar('Loss(total)/train', train_loss['total'], epoch)
logger.add_scalar('Loss(Reconstruction)/train',
train_loss['Recon'], epoch)
logger.add_scalar('Loss(KL_divergence)/train',
train_loss['KL_div'], epoch)
elif config.model == 'ValueNet':
logger.add_scalar('Loss/train', train_loss['total'], epoch)
elif config.model == 'PolicyValueNet':
logger.add_scalar('Loss(total)/train', train_loss['total'], epoch)
logger.add_scalar('Loss(action)/train', train_loss['action'],
epoch)
logger.add_scalar('Loss(accumulated reward)/train',
train_loss['accumulated_reward'], epoch)
# logger.add_scalar('Eval(action)/train', train_val['action'], epoch)
else:
logger.add_scalar('Loss(total)/train', train_loss['total'], epoch)
logger.add_scalar('Loss(action)/train', train_loss['action'],
epoch)
# if config.use_reward:
# logger.add_scalar('Loss(reward)/train', train_loss['reward'], epoch)
# logger.add_scalar('Eval(action)/train', train_val['action'], epoch)
# if config.use_reward:
# logger.add_scalar('Eval(reward)/train', train_val['reward'], epoch)
# |FIXME| debug for eff_grad: "RuntimeError: Boolean value of Tensor with more than one value is ambiguous"
log_gradients(model,
logger,
epoch,
log_grad=config.log_grad,
log_param=config.log_para,
eff_grad=config.eff_grad,
print_num_para=config.print_num_para)
# evaluating
if epoch % config.test_eval_freq == 0:
print("Validating...")
test_val = evaluator.eval(epoch)
# save the best model
# |TODO| change 'action' to 'total' @ trainer.py & evaluator.py -> merge 'CVAE' & others
if config.model == 'CVAE' or config.model == 'ValueNet' or config.model == 'PolicyValueNet':
if test_val['total'] < best_error:
best_error = test_val['total']
save_checkpoint('Saving the best model!',
os.path.join(model_dir, 'best.pth'), epoch,
best_error, model, optimizer, scheduler)
else:
if test_val['action'] < best_error:
best_error = test_val['action']
save_checkpoint('Saving the best model!',
os.path.join(model_dir, 'best.pth'), epoch,
best_error, model, optimizer, scheduler)
# Logging
if config.model == 'CVAE':
logger.add_scalar('Eval(total)/test', test_val['total'], epoch)
logger.add_scalar('Eval(Reconstruction)/test',
test_val['Recon'], epoch)
logger.add_scalar('Eval(KL_divergence)/test',
test_val['KL_div'], epoch)
elif config.model == 'ValueNet':
logger.add_scalar('Eval/test', test_val['total'], epoch)
elif config.model == 'PolicyValueNet':
logger.add_scalar('Eval(total)/test', test_val['total'], epoch)
logger.add_scalar('Eval(action)/test', test_val['action'],
epoch)
logger.add_scalar('Eval(accumulated reward)/test',
test_val['accumulated_reward'], epoch)
else:
logger.add_scalar('Eval(action)/test', test_val['action'],
epoch)
# if config.use_reward:
# logger.add_scalar('Eval(reward)/test', test_val['reward'], epoch)
# save the model
if epoch % config.save_freq == 0:
save_checkpoint('Saving...',
os.path.join(model_dir, f'ckpt_epoch_{epoch}.pth'),
epoch, best_error, model, optimizer, scheduler)
print(f'===== End {epoch} epoch =====')
#divide data into training and test set
num_train_data = int(len(molecules_input) * 0.75)
train_molecules_input = molecules_input[0:num_train_data]
test_molecules_input = molecules_input[num_train_data:-1]
train_molecules_output = molecules_output[0:num_train_data]
test_molecules_output = molecules_output[num_train_data:-1]
train_labels = labels[0:num_train_data]
test_labels = labels[num_train_data:-1]
train_length = length[0:num_train_data]
test_length = length[num_train_data:-1]
model = CVAE(vocab_size, args)
print('Number of parameters : ',
np.sum([np.prod(v.shape) for v in tf.trainable_variables()]))
for epoch in range(args.num_epochs):
st = time.time()
# Learning rate scheduling
#model.assign_lr(learning_rate * (decay_rate ** epoch))
train_loss = []
test_loss = []
st = time.time()
for iteration in range(len(train_molecules_input) // args.batch_size):
n = np.random.randint(len(train_molecules_input), size=args.batch_size)
x = np.array([train_molecules_input[i] for i in n])