def load_model(self,
vgg_path=None,
decoder_path=None,
message_encoder_path=None,
message_decoder_path=None):
self.vgg = model.VGG()
if vgg_path:
self.vgg.load_state_dict(torch.load(vgg_path))
self.vgg = nn.Sequential(*list(self.vgg.children())[:31])
self.decoder = model.Decoder()
if decoder_path:
self.decoder.load_state_dict(torch.load(decoder_path))
self.message_encoder = model.Message_Encoder()
if message_encoder_path:
self.message_encoder.load_state_dict(
torch.load(message_encoder_path))
self.message_decoder = model.Message_Decoder(
input_width=self.image_size,
content_feat_shape=(self.vgg_relu_4_1_dim,
int(self.image_size / self.down_scale),
int(self.image_size / self.down_scale)))
if message_decoder_path:
self.message_decoder.load_state_dict(
torch.load(message_decoder_path))
def main():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
data_device = torch.device("cpu")
print("Device type: %s" % device.type)
encoder = model.Encoder(depth)
decoder = model.Decoder(depth)
net = torch.nn.Sequential(encoder, decoder).to(device)
optimizer = optim.Adadelta(net.parameters(), lr=0.01)
dataset = load.WavDataSet("data/wav/", model.downsample_factor**depth,
data_device)
dataloader = torch.utils.data.dataloader.DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
print("Starting training")
for e in range(epochs):
net.train()
loss_sum = 0
for batch_idx, (data_noise, data) in enumerate(dataloader):
data = data.to(device)
data_noise = data_noise.to(device)
optimizer.zero_grad()
output = net(data_noise)
loss = F.mse_loss(output, data)
loss.backward()
optimizer.step()
loss_sum += loss
print("Epoch: %d\tLoss: %f" % (e, loss_sum))
if e % 50 == 0:
torch.save(net.state_dict(), "checkpoint/model_%d.pth" % e)
def main():
### Create the torch datasets and get the size of the 'on-the-fly' created vocabulary and the length of the longest caption
trainDataset = loadData.FlickrTrainDataset(images_folder, captions_folder,
trans, 'TRAIN')
valDataset = loadData.FlickrValDataset(images_folder, captions_folder,
trans, 'VAL')
voc_size = trainDataset.getVocabSize()
max_capt = trainDataset.getMaxCaptionsLength()
### Create the models
Encoder = model.Encoder()
Decoder = model.Decoder(encoder_dim=2048,
decoder_dim=512,
attention_dim=256,
vocab_size=voc_size)
Embedding = model.Embedding(vocab_size=voc_size, embedding_dim=128)
### Set the optimizer for the decoder(the only component that is actually trained) and the device for the model tensors
decoder_optimizer = torch.optim.Adam(params=filter(
lambda p: p.requires_grad, Decoder.parameters()),
lr=e - 3)
Encoder.to(device)
Decoder.to(device)
Embedding.to(device)
### Create the data loaders for training and evaluation
loader_train = DataLoader(trainDataset,
32,
sampler=sampler.SubsetRandomSampler(
range(30000)))
val_loader = DataLoader(valDataset,
32,
sampler=sampler.SubsetRandomSampler(range(30000)))
best_bleu = 0 #The best blue score by now
for i in range(epochs):
## One epoch's training
train.train(data_loader=loader_train,
encoder=Encoder,
decoder=Decoder,
embedding=Embedding,
max_caption_length=max_capt,
optim=decoder_optimizer)
## One epoch's validation
new_bleu = train.validate(data_loader=val_loader,
encoder=Encoder,
decoder=Decoder,
embedding=Embedding,
max_capt)
if new_bleu > best_bleu:
best_bleu = new_bleu
else:
## We had no improvement since last time,so se don't train more
break
## Save the model for deploying
torch.save(Encoder, 'Encoder')
torch.save(Decoder, 'Decoder')
torch.save(Embedding, 'Embedding')
def __init__(self, model_file1=model_path):
# You should
# 1. create the model object
# 2. load your state_dict
# 3. call cuda()
# self.model = ...
#
self.models_detector = []
for i in range(6):
models = {}
encoder_path = os.path.join(model_file1[i], "encoder.pth")
encoder_dict = torch.load(encoder_path, map_location=device)
feed_height = encoder_dict["height"]
feed_width = encoder_dict["width"]
models["encoder"] = model.Encoder(feed_width, feed_height, False)
filtered_dict_enc = {
k: v
for k, v in encoder_dict.items()
if k in models["encoder"].state_dict()
}
models["encoder"].load_state_dict(filtered_dict_enc)
decoder_path = os.path.join(model_file1[i], "decoder.pth")
models["decoder"] = model.Decoder(models["encoder"].num_ch_enc)
models["decoder"].load_state_dict(
torch.load(decoder_path, map_location=device))
for key in models.keys():
models[key].to(device)
models[key].eval()
self.models_detector.append(models)
def load_model(self, vgg_path, decoder_path=None):
self.vgg = model.VGG()
self.vgg.load_state_dict(torch.load(vgg_path))
self.vgg = nn.Sequential(*list(self.vgg.children())[:31])
self.vgg_encoder = model.VGG_Encoder(self.vgg)
self.decoder = model.Decoder()
if decoder_path:
self.decoder.load_state_dict(torch.load(decoder_path))
def decoder_test(h,score,l_score):
decoder = model.Decoder(3,50,6,10,20)
target = Variable( torch.from_numpy( np.random.rand(3,6,20) ).float() )
first_word = Variable( torch.from_numpy( np.random.rand(3,1,20)/10 ).float() )
training_out = decoder(h,score,training=True,target=target)
pred_out = decoder(h,score,training=False,first_word=first_word)
print("training_out:")
print(training_out)
print("pred_out:")
print(pred_out)
def main():
device = torch.device("cpu")
print("Device type: %s" % device.type)
encoder = model.Encoder(depth)
decoder = model.Decoder(depth)
net = torch.nn.Sequential(encoder, decoder).to(device)
net.load_state_dict(torch.load("checkpoint/model_3150.pth"))
net.eval()
dataset = load.WavDataSet("data/wav/", model.downsample_factor**depth,
device)
dataloader = torch.utils.data.dataloader.DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
for batch_idx, data in enumerate(dataloader):
data = data.to(device)
output = net(data)
scipy.io.wavfile.write("out/%d.wav" % batch_idx, load.sample_rate,
output.data.numpy())
print("Finished %d" % batch_idx)
def load_models(checkpoint_name=None,
encoded_image_size=None,
word_embeddings_dim=None,
attention_dim=None,
decoder_hidden_size=None,
vocab_size=None,
device=None):
'''
:param checkpoint_name: name of checkpoint file
:param encoded_image_size: params to initialize model if there is no checkpoint name
:param word_embeddings_dim: params to initialize model if there is no checkpoint name
:param attention_dim: params to initialize model if there is no checkpoint name
:param decoder_hidden_size: params to initialize model if there is no checkpoint name
:param vocab_size: params to initialize model if there is no checkpoint name
:param device: on this device to store model
:return: start_epoch, end_epoch, loss_fn, enc, dec, optimizer_encoder, optimizer_decoder
'''
loss_fn = nn.CrossEntropyLoss().to(device)
end_epoch = 10_000
if checkpoint_name == None:
start_epoch = 0
enc = model.Encoder(encoded_image_size=encoded_image_size).to(device)
dec = model.Decoder(vocab_size=vocab_size,
word_embeddings_dim=word_embeddings_dim,
attention_dim=attention_dim,
decoder_hidden_size=decoder_hidden_size,
encoded_image_size=encoded_image_size).to(device)
optimizer_decoder = torch.optim.Adam(enc.parameters(), lr=4e-4)
optimizer_encoder = torch.optim.Adam(dec.parameters(), lr=1e-4)
else:
checkpoint = torch.load(checkpoint_name)
start_epoch = checkpoint['epoch']
dec = checkpoint['decoder'].to(device)
optimizer_decoder = checkpoint['decoder_optimizer']
enc = checkpoint['encoder'].to(device)
optimizer_encoder = checkpoint['encoder_optimizer']
return start_epoch, end_epoch, loss_fn, enc, dec, optimizer_encoder, optimizer_decoder
def build_model(hp):
encoder = model.Encoder(num_layers=hp["encoder_num_layers"],
num_units=hp["encoder_num_units"],
dropout=hp["encoder_dropout"],
dropout_prob=hp["encoder_dropout_prob"],
layer_norm=hp["encoder_layer_norm"],
dtype=tf.float32)
decoder = model.Decoder(
attention_unit_num=hp["decoder_attention_unit_num"],
vocab_size=hp["decoder_vocab_size"],
gru_unit_num=hp["decoder_gru_unit_num"],
fc_layer_num=hp["decoder_fc_layer_num"],
fc_unit_num=hp["decoder_fc_unit_num"],
attention_type=hp["decoder_attention_type"],
gru_layer_norm=hp["decoder_gru_layer_norm"],
gru_dropout=hp["decoder_gru_dropout"],
gru_dropout_prob=hp["decoder_gru_dropout_prob"],
fc_activation=hp["decoder_fc_activation"],
dtype=tf.float32)
return encoder, decoder
def make_model(src_vocab,
tgt_vocab,
emb_size=256,
hidden_size=512,
num_layers=1,
dropout=0.1):
"Helper: Construct a model from hyperparameters."
attention = model.BahdanauAttention(hidden_size)
mdl = model.EncoderDecoder(
model.Encoder(emb_size,
hidden_size,
num_layers=num_layers,
dropout=dropout),
model.Decoder(emb_size,
hidden_size,
attention,
num_layers=num_layers,
dropout=dropout), nn.Embedding(src_vocab, emb_size),
nn.Embedding(tgt_vocab, emb_size),
model.Generator(hidden_size, tgt_vocab))
return mdl.cuda() if USE_CUDA else mdl
Enc = model.Encoder(style_dim=4,
latent_dim=latent_dim,
vae_type=args.model_type)
if args.epoch == 0:
Enc.load_state_dict(torch.load(model_dir + "/final_enc.pt"))
else:
Enc.load_state_dict(
torch.load(model_dir + "/parm/" + str(args.epoch) + "_enc.pt"))
Enc.cuda()
Enc.eval()
if args.model_type == "MD":
Dec_dict = dict()
for spk_id in spk_list:
cur_Dec = model.Decoder(style_dim=4,
latent_dim=latent_dim,
vae_type=args.model_type)
cur_Dec.cuda()
cur_Dec.eval()
if args.epoch == 0:
cur_Dec.load_state_dict(
torch.load(model_dir + "/final_VCC2" + spk_id + "_dec.pt"))
else:
cur_Dec.load_state_dict(
torch.load(model_dir + "/parm/" + str(args.epoch) + "_VCC2" +
spk_id + "_dec.pt"))
Dec_dict[spk_id] = cur_Dec
else:
Dec = model.Decoder(style_dim=4,
latent_dim=latent_dim,
vae_type=args.model_type)
print("Config:", config)
train_loader = dataloader.train_loader(args.dataset, args.data_directory,
args.batch_size, args.input_h,
args.input_w, args.cpu_num)
test_loader = dataloader.test_loader(args.dataset, args.data_directory,
args.batch_size, args.input_h,
args.input_w, args.cpu_num)
# encoder = model.Encoder(args.input_h, args.input_w, args.hidden_size, args.latent_size).to(args.device)
# decoder = model.Decoder(args.input_h, args.input_w, args.hidden_size, args.latent_size).to(args.device)
encoder = model.Encoder(args.channel_size, args.filter_size, args.kernel_size,
args.stride_size, args.layer_size,
args.latent_size).to(args.device)
decoder = model.Decoder(args.channel_size, args.filter_size, args.kernel_size,
args.stride_size, args.layer_size,
args.latent_size).to(args.device)
if args.load_model != '000000000000':
encoder.load_state_dict(
torch.load(args.log_directory + args.name + '/' + args.load_model +
'bvae_encoder.pt')).to(args.device)
decoder.load_state_dict(
torch.load(args.log_director + args.name + '/' + args.load_model +
'/bvae_decoder.pt')).to(args.device)
args.time_stamp = args.load_model[:12]
print('Model {} loaded.'.format(args.load_model))
log = args.log_directory + args.name + '/' + args.time_stamp + config + '/'
writer = SummaryWriter(log)
dial_act_dict, dial_acts_data = get_dial_acts('data/dialogue_act_feats.json')
# Create models
encoder = model.Encoder(vocab_size=len(input_w2i),
emb_size=args.emb_size,
hid_size=args.hid_size)
policy = model.Policy(hidden_size=args.hid_size,
db_size=args.db_size,
bs_size=args.bs_size,
da_size=args.da_size)
decoder = model.Decoder(emb_size=args.emb_size,
hid_size=args.hid_size,
vocab_size=len(output_w2i),
use_attn=args.use_attn)
if args.shallow_fusion or args.deep_fusion:
s2s = model.Model(encoder=encoder,
policy=policy,
decoder=decoder,
input_w2i=input_w2i,
output_w2i=output_w2i,
args=args)
lm_decoder = model.Decoder(emb_size=args.emb_size,
hid_size=args.hid_size,
vocab_size=len(output_w2i),
use_attn=False)
lm = model.LanguageModel(decoder=lm_decoder,
input_w2i=input_w2i,
def train():
# 开启tensorflow 动态图功能
tf.enable_eager_execution()
# generate dataset
num_examples = 3000
input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar = utils.load_dataset(
num_examples)
input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(
input_tensor, target_tensor, test_size=0.2)
logging.info('验证集和数据集大小' + str(len(input_tensor_train)) + ' ' +
str(len(input_tensor_val)))
# 创建tf.data.dataset数据集并且设定部分参数
BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = config.BATCH_SIZE
N_BATCH = BUFFER_SIZE / BATCH_SIZE
embedding_dim = config.EMBEDDING_DIM
units = config.UNITS
vocab_inp_size = config.VOCAB_INP_SIZE
vocab_tar_size = config.VOCAB_TAR_SIZE
dataset = tf.data.Dataset.from_tensor_slices(
(input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
# 解码器，编码器
encoder = model.Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
decoder = model.Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)
# 优化器
optimizer = tf.train.AdamOptimizer()
# 损失函数
def loss_function(real, pred):
mask = 1 - np.equal(real, 0)
loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=real, logits=pred) * mask
return tf.reduce_mean(loss_)
# checkpoints(object-based saving)
checkpoint_dir = config.CHECK_POINT_DIR
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
encoder=encoder,
decoder=decoder)
# 训练
EPOCHS = config.EPOCHS
for epoch in range(EPOCHS):
start = time.time()
hidden = encoder.initialize_hidden_state()
total_loss = 0
for (batch, (inp, targ)) in enumerate(dataset):
loss = 0
with tf.GradientTape() as tape:
enc_output, enc_hidden = encoder(inp, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([targ_lang.word2idx['<start>']] *
BATCH_SIZE, 1)
# Teacher forcing - feeding the target as the next input
for t in range(1, targ.shape[1]):
# passing enc_output to the decoder
predictions, dec_hidden, _ = decoder(
dec_input, dec_hidden, enc_output)
loss += loss_function(targ[:, t], predictions)
# using teacher forcing
dec_input = tf.expand_dims(targ[:, t], 1)
batch_loss = (loss / int(targ.shape[1]))
total_loss += batch_loss
variables = encoder.variables + decoder.variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
if batch % 100 == 0:
print('Epoch {} Batch {} Loss {:.4f}'.format(
epoch + 1, batch, batch_loss.numpy()))
if (epoch + 1) % 2 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
print('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss / N_BATCH))
print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
dataset = torch.utils.data.TensorDataset(xs, ys, yphi)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True)
device = torch.device("cuda" if args.cuda else "cpu")
###############################################################################
# Build the model
###############################################################################
ntokens = 850
extractor = model.Extractor(args.model, ntokens, args.emsize, args.nhid,
args.nlayers, args.dropout).to(device)
decoder = model.Decoder(args.nhid, args.nview, args.dropout).to(device)
generator = model.Generator(args.emsize, args.noiseDim,
args.dropout).to(device)
discriminator = model.Discriminator(args.emsize, args.dropout).to(device)
criterionD1 = nn.CrossEntropyLoss() # for D_S
criterionD2 = nn.BCELoss() # for D_N
LocalityConstrains = nn.CosineEmbeddingLoss()
noiseDim = int(args.noiseDim)
real_label = 1
fake_label = 0
lr = args.lr
optimizerExtractor = optim.Adam(extractor.parameters(),
]
if args.sample:
config_list.append('sample')
config = ""
for i in map(str, config_list):
config = config + '_' + i
print("Config:", config)
train_loader = dataloader.train_loader('mnist', args.data_directory,
args.batch_size)
test_loader = dataloader.test_loader('mnist', args.data_directory,
args.batch_size)
encoder = model.Encoder(args.channel_size, args.input_h, args.input_w,
args.filter_num, args.latent_size).to(device)
decoder = model.Decoder(args.channel_size, args.input_h, args.input_w,
args.filter_num, args.latent_size).to(device)
if args.load_model != '000000000000':
encoder.load_state_dict(
torch.load(args.log_directory + args.name + '/' + args.load_model +
'/{}_encoder.pt'.format(args.name)))
decoder.load_state_dict(
torch.load(args.log_directory + args.name + '/' + args.load_model +
'/{}_decoder.pt'.format(args.name)))
args.time_stamp = args.load_model[:12]
log = args.log_directory + args.name + '/' + args.time_stamp + config + '/'
writer = SummaryWriter(log)
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=args.lr)
def main():
print('Loading word embedding')
emb = KeyedVectors.load_word2vec_format(hp.word_embedding,
binary=hp.emb_binary)
print("Loading data")
stereotype_words = {}
gender_words = {}
no_gender_words = make_no_gender_words(open(hp.no_gender_words), emb)
stereotype_words['female'], stereotype_words['male'] = \
make_pair_words(hp.stereotype_words, emb)
gender_words['female'], gender_words['male'] = \
make_pair_words(hp.gender_words, emb)
all_words = no_gender_words \
+ stereotype_words['female'] \
+ stereotype_words['male'] \
+ gender_words['female'] \
+ gender_words['male']
train_words, dev_words = create_train_dev(gender_words, no_gender_words,
stereotype_words)
word2emb = {}
for word in all_words:
word2emb[word] = emb[word]
if hp.pre_train_autoencoder:
print('Pre-training autoencoder')
encoder = model.Encoder(hp.emb_size, hp.hidden_size,
hp.pta_dropout_rate)
decoder = model.Decoder(hp.hidden_size, hp.emb_size,
hp.pta_dropout_rate)
if hp.gpu >= 0:
encoder.cuda()
decoder.cuda()
encoder_optim = make_optim(encoder, hp.pta_optimizer,
hp.pta_learning_rate, hp.pta_lr_decay,
hp.pta_max_grad_norm)
decoder_optim = make_optim(decoder, hp.pta_optimizer,
hp.pta_learning_rate, hp.pta_lr_decay,
hp.pta_max_grad_norm)
if hp.pre_data == 'random':
checkpoint = pre_train_autoencoder(hp, encoder, encoder_optim,
decoder, decoder_optim, emb)
elif hp.pre_data == 'common':
checkpoint = pre_train_autoencoder(hp,
encoder,
encoder_optim,
decoder,
decoder_optim,
emb,
dev_words=dev_words)
encoder = model.Encoder(hp.emb_size, hp.hidden_size, hp.dropout_rate)
decoder = model.Decoder(hp.hidden_size, hp.emb_size, hp.dropout_rate)
if hp.gpu >= 0:
encoder.cuda()
decoder.cuda()
if hp.pre_train_autoencoder:
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
if hp.pre_train_classifier:
female_classifier = model.Classifier(hp.hidden_size)
male_classifier = model.Classifier(hp.hidden_size)
if hp.gpu >= 0:
female_classifier.cuda()
male_classifier.cuda()
female_classifier_optim = make_optim(female_classifier,
hp.cls_optimizer,
hp.cls_learning_rate,
hp.cls_lr_decay,
hp.cls_max_grad_norm)
male_classifier_optim = make_optim(male_classifier, hp.cls_optimizer,
hp.cls_learning_rate,
hp.cls_lr_decay,
hp.cls_max_grad_norm)
encoder.eval()
encoder.zero_grad()
train_females = []
train_males = []
dev_females = []
dev_males = []
train_female_embs = [
encoder(torch.FloatTensor(emb[word[0]]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word[0]])).data
for word in train_words['female & male']
]
encoder.zero_grad()
train_male_embs = [
encoder(torch.FloatTensor(emb[word[1]]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word[1]])).data
for word in train_words['female & male']
]
encoder.zero_grad()
train_stereotype_embs = [
encoder(torch.FloatTensor(emb[word]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word])).data
for word in train_words['no gender']
]
encoder.zero_grad()
dev_female_embs = [
encoder(torch.FloatTensor(emb[word[0]]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word[0]])).data
for word in dev_words['female & male']
]
encoder.zero_grad()
dev_male_embs = [
encoder(torch.FloatTensor(emb[word[1]]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word[1]])).data
for word in dev_words['female & male']
]
encoder.zero_grad()
dev_stereotype_embs = [
encoder(torch.FloatTensor(emb[word]).cuda()).data
if hp.gpu >= 0 else encoder(torch.FloatTensor(emb[word])).data
for word in dev_words['no gender']
]
encoder.zero_grad()
print('Pre-training classifier')
female_checkpoint, male_checkpoint = pre_train_classifier(
hp, female_classifier, male_classifier, female_classifier_optim,
male_classifier_optim, train_female_embs, train_male_embs,
train_stereotype_embs, dev_female_embs, dev_male_embs,
dev_stereotype_embs)
print('Building female & male classifiers')
female_classifier = model.Classifier(hp.hidden_size)
male_classifier = model.Classifier(hp.hidden_size)
if hp.gpu >= 0:
female_classifier.cuda()
male_classifier.cuda()
if hp.pre_train_classifier:
female_classifier.load_state_dict(female_checkpoint['female'])
male_classifier.load_state_dict(male_checkpoint['male'])
print('Setting optimizer')
encoder_optim = make_optim(encoder, hp.optimizer, hp.learning_rate,
hp.lr_decay, hp.max_grad_norm)
female_classifier_optim = make_optim(female_classifier, hp.optimizer,
hp.learning_rate, hp.lr_decay,
hp.max_grad_norm)
male_classifier_optim = make_optim(male_classifier, hp.optimizer,
hp.learning_rate, hp.lr_decay,
hp.max_grad_norm)
decoder_optim = make_optim(decoder, hp.optimizer, hp.learning_rate,
hp.lr_decay, hp.max_grad_norm)
trainModel(encoder, encoder_optim, female_classifier,
female_classifier_optim, male_classifier, male_classifier_optim,
decoder, decoder_optim, train_words, dev_words, word2emb)
config_list = [args.name, args.epochs, args.batch_size, args.lr,
args.input_h, args.input_w,
args.hidden_size, args.latent_size,
args.L, args.binarize, args.mc]
if args.sample:
config_list.append('sample')
config = ""
for i in map(str, config_list):
config = config + '_' + i
print("Config:", config)
train_loader = dataloader.train_loader('mnist', args.data_directory, args.batch_size)
test_loader = dataloader.test_loader('mnist', args.data_directory, args.batch_size)
encoder = model.Encoder(args.input_h, args.input_w, args.hidden_size, args.latent_size).to(device)
decoder = model.Decoder(args.input_h, args.input_w, args.hidden_size, args.latent_size).to(device)
if args.load_model != '000000000000':
encoder.load_state_dict(torch.load(args.log_directory + args.name + '/' + args.load_model+ '/{}_encoder.pt'.format(args.name)))
decoder.load_state_dict(torch.load(args.log_directory + args.name + '/' + args.load_model + '/{}_decoder.pt'.format(args.name)))
args.time_stamp = args.load_model[:12]
log = args.log_directory + args.name + '/' + args.time_stamp + config + '/'
writer = SummaryWriter(log)
optimizer = optim.Adam(list(encoder.parameters())+list(decoder.parameters()), lr = args.lr)
def binarize(data):
data = data > 0.5
return data.float()
def train(epoch):
def run():
Seed = 1234
random.seed(Seed)
np.random.seed(Seed)
torch.manual_seed(Seed)
torch.cuda.manual_seed(Seed)
torch.backends.cudnn.deterministic = True
train, valid, test, SRC, TRG = dataset.create_dataset()
train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
(train, valid, test),
sort_key=lambda x: len(x.source),
batch_size=config.BATCH_SIZE,
device=config.device)
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
ENC_EMB_DIM = config.ENCODER_EMBEDDING_DIMENSION
DEC_EMB_DIM = config.DECODER_EMBEDDING_DIMENSION
HID_DIM = config.LSTM_HIDDEN_DIMENSION
N_LAYERS = config.LSTM_LAYERS
ENC_DROPOUT = config.ENCODER_DROPOUT
DEC_DROPOUT = config.DECODER_DROPOUT
attn = model.Attention(HID_DIM, HID_DIM)
enc = model.Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, HID_DIM, ENC_DROPOUT)
dec = model.Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, HID_DIM, DEC_DROPOUT,
attn)
model_rnn = model.Seq2Seq(enc, dec, config.device).to(config.device)
optimizer = optim.Adam(model_rnn.parameters(), lr=config.LEARNING_RATE)
TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]
criterion = nn.CrossEntropyLoss(ignore_index=TRG_PAD_IDX)
if (args.action == 'train'):
model_rnn.apply(utils.init_weights)
best_valid_loss = float('inf')
for epoch in range(config.N_EPOCHS):
start_time = time.time()
train_loss = engine.train_fn(model_rnn, train_iterator, optimizer,
criterion, config.CLIP)
valid_loss = engine.evaluate_fn(model_rnn, valid_iterator,
criterion)
end_time = time.time()
epoch_mins, epoch_secs = utils.epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model_rnn.state_dict(), config.MODEL_SAVE_FILE)
with open(config.RESULTS_SAVE_FILE, 'a') as f:
print(
f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s',
file=f)
print(
f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}',
file=f)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}',
file=f)
elif (args.action == 'test'):
model_rnn.load_state_dict(torch.load(config.TEST_MODEL))
loss, target, output = engine.test_fn(model_rnn, test_iterator,
criterion, SRC, TRG)
bl = bleu_score(output, target, max_n=1, weights=[1])
met = 0
for z in range(len(output)):
out = ' '.join(output[z][y]
for y in range(min(10, len(output[z]))))
tar = ' '.join(y for y in target[z])
met = met + metric_utils.compute_metric(out, 1.0, tar)
with open(config.TEST_RESULTS_FILE, 'w') as f:
print(f'Test bleu :, {bl*100}, Test PPL: {math.exp(loss):7.3f}',
'Metric:',
met / len(output),
file=f)
elif (args.action == 'save_vocab'):
print('Source Vocab Length', len(SRC.vocab))
print('Target vocab length', len(TRG.vocab))
s1 = '\n'.join(k for k in SRC.vocab.itos)
s2 = '\n'.join(k for k in TRG.vocab.itos)
with open('NL_vocabulary.txt', 'w') as f:
f.write(s1)
with open('Bash_vocabulary.txt', 'w') as f:
f.write(s2)
def __init__(self):
self.e1 = model.Encoder()
self.e_shared = model.Eshared(0.5)
self.d_shared = model.Dshared()
self.d2 = model.Decoder()
self.denoiser = model.Denoiser()
if args.load_model != '000000000000': ce1 = torch.load(args.log_directory + args.load_model + '/content_encoder1.pt') ce2 = torch.load(args.log_directory + args.load_model + '/content_encoder2.pt') se1 = torch.load(args.log_directory + args.load_model + '/style_encoder1.pt') se2 = torch.load(args.log_directory + args.load_model + '/style_encoder2.pt') de1 = torch.load(args.log_directory + args.load_model + '/decoder1.pt') de2 = torch.load(args.log_directory + args.load_model + '/decoder2.pt') dis1 = torch.load(args.log_directory + args.load_model + '/discriminator1.pt') dis2 = torch.load(args.log_directory + args.load_model + '/discriminator2.pt') args.time_stamep = args.load_mode[:12] else: ce1 = model.Content_encoder(args.channel_size, args.content_code_h, args.content_code_w).to(device) ce2 = model.Content_encoder(args.channel_size, args.content_code_h, args.content_code_w).to(device) se1 = model.Style_encoder(args.channel_size, args.style_code_num).to(device) se2 = model.Style_encoder(args.channel_size, args.style_code_num).to(device) de1 = model.Decoder(args.input_h, args.input_w, args.channel_size, args.style_code_num).to(device) de2 = model.Decoder(args.input_h, args.input_w, args.channel_size, args.style_code_num).to(device) dis1 = model.Discriminator(args.channel_size).to(device) dis2 = model.Discriminator(args.channel_size).to(device) log = args.log_directory + 'munit/' + args.time_stamp + config + '/' writer = SummaryWriter(log) gen_optimizer = optim.Adam(list(ce1.parameters()) + list(ce2.parameters()) + list(se1.parameters()) + list(se2.parameters()) + list(de1.parameters()) + list(de2.parameters()), lr = args.lr) dis_optimizer = optim.Adam(list(dis1.parameters()) + list(dis2.parameters()), lr = args.lr) def train(epoch): epoch_start_time = time.time() train_loss = 0
def __init__(self):
self.opt = get_args()
self.models = {}
self.weight = {}
self.weight["dynamic"] = self.opt.dynamic_weight
self.device = "cuda"
self.criterion_d = nn.BCEWithLogitsLoss()
self.parameters_to_train = []
self.parameters_to_train_D = []
# Initializing models
self.models["encoder"] = model.Encoder(self.opt.height, self.opt.width,
True)
self.models["decoder"] = model.Decoder(
self.models["encoder"].num_ch_enc)
self.models["discriminator"] = model.Discriminator()
for key in self.models.keys():
self.models[key].to(self.device)
if "discr" in key:
self.parameters_to_train_D += list(
self.models[key].parameters())
else:
self.parameters_to_train += list(self.models[key].parameters())
# Optimization
self.model_optimizer = optim.Adam(self.parameters_to_train,
self.opt.lr)
self.model_lr_scheduler = optim.lr_scheduler.StepLR(
self.model_optimizer, self.opt.scheduler_step_size, 0.1)
self.model_optimizer_D = optim.Adam(self.parameters_to_train_D,
self.opt.lr)
self.model_lr_scheduler_D = optim.lr_scheduler.StepLR(
self.model_optimizer_D, self.opt.scheduler_step_size, 0.1)
self.patch = (1, self.opt.occ_map_size // 2**4,
self.opt.occ_map_size // 2**4)
self.valid = Variable(torch.Tensor(
np.ones((self.opt.batch_size, *self.patch))),
requires_grad=False).float().cuda()
self.fake = Variable(torch.Tensor(
np.zeros((self.opt.batch_size, *self.patch))),
requires_grad=False).float().cuda()
transform = torchvision.transforms.ToTensor()
labeled_trainset = LabeledDataset(image_folder=image_folder,
annotation_file=annotation_csv,
scene_index=labeled_scene_index,
transform=transform)
self.train_loader = DataLoader(labeled_trainset,
batch_size=self.opt.batch_size,
shuffle=True,
num_workers=self.opt.num_workers,
collate_fn=None,
pin_memory=True,
drop_last=True)
labeled_valset = LabeledDataset(image_folder=image_folder,
annotation_file=annotation_csv,
scene_index=labeled_scene_index_val,
transform=transform)
self.val_loader = DataLoader(labeled_valset,
batch_size=1,
shuffle=True,
num_workers=self.opt.num_workers,
collate_fn=None,
pin_memory=True,
drop_last=True)
print(
"There are {:d} training items and {:d} validation items\n".format(
len(labeled_trainset), len(labeled_valset)))
num_workers=1)
dataset_secret = datasets.ImageFolder(secret_dir, data_transform)
dataloader_secret = torch.utils.data.DataLoader(dataset_secret,
batch_size=batch_size,
shuffle=False,
num_workers=1)
# initialize the model and load the params
encoder = model.Encoder()
encoder = encoder.to(device)
# decoder (discriminator)
decoder = model.Decoder()
decoder = decoder.to(device)
ssim_loss = pytorch_ssim.SSIM()
mssim_loss = pytorch_msssim.MSSSIM()
mse_loss = nn.MSELoss()
# dis_loss=nn.BCELoss()
print('loading params')
path = model_dir + '/' + str(epoch) + '.pth.tar' # load theepoch params
checkpoint = torch.load(path, map_location='cpu')
encoder.load_state_dict(checkpoint['encoder_state_dict'])
decoder.load_state_dict(checkpoint['deocoder_state_dict'])
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
device = device)
#Training the seq2seq model
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
ENC_EMB_DIM = 256
DEC_EMB_DIM = 256
HID_DIM = 512
N_LAYERS = 2
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5
enc = model.Encoder(INPUT_DIM, ENC_EMB_DIM, HID_DIM, N_LAYERS, ENC_DROPOUT)
dec = model.Decoder(OUTPUT_DIM, DEC_EMB_DIM, HID_DIM, N_LAYERS, DEC_DROPOUT)
model = model.Seq2Seq(enc, dec, device).to(device)
def init_weights(m):
for name, param in m.named_parameters():
nn.init.uniform_(param.data, -0.08, 0.08)
model.apply(init_weights)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f'The model has {count_parameters(model):,} trainable parameters')
optimizer = optim.Adam(model.parameters())
def __init__(self):
self.opt = get_args()
self.models = {}
self.weight = {}
self.weight["static"] = self.opt.static_weight
self.weight["dynamic"] = self.opt.dynamic_weight
self.device = "cuda"
self.criterion_d = nn.BCEWithLogitsLoss()
self.parameters_to_train = []
self.parameters_to_train_D = []
# Initializing models
self.models["encoder"] = model.Encoder(18, self.opt.height,
self.opt.width, True)
if self.opt.type == "both":
self.models["static_decoder"] = model.Decoder(
self.models["encoder"].resnet_encoder.num_ch_enc)
self.models["static_discr"] = model.Discriminator()
self.models["dynamic_decoder"] = model.Discriminator()
self.models["dynamic_decoder"] = model.Decoder(
self.models["encoder"].resnet_encoder.num_ch_enc)
else:
self.models["decoder"] = model.Decoder(
self.models["encoder"].resnet_encoder.num_ch_enc)
self.models["discriminator"] = model.Discriminator()
for key in self.models.keys():
self.models[key].to(self.device)
if "discr" in key:
self.parameters_to_train_D += list(
self.models[key].parameters())
else:
self.parameters_to_train += list(self.models[key].parameters())
# Optimization
self.model_optimizer = optim.Adam(self.parameters_to_train,
self.opt.lr)
self.model_lr_scheduler = optim.lr_scheduler.StepLR(
self.model_optimizer, self.opt.scheduler_step_size, 0.1)
self.model_optimizer_D = optim.Adam(self.parameters_to_train_D,
self.opt.lr)
self.model_lr_scheduler_D = optim.lr_scheduler.StepLR(
self.model_optimizer_D, self.opt.scheduler_step_size, 0.1)
self.patch = (1, self.opt.occ_map_size // 2**4,
self.opt.occ_map_size // 2**4)
self.valid = Variable(torch.Tensor(
np.ones((self.opt.batch_size, *self.patch))),
requires_grad=False).float().cuda()
self.fake = Variable(torch.Tensor(
np.zeros((self.opt.batch_size, *self.patch))),
requires_grad=False).float().cuda()
## Data Loaders
dataset_dict = {
"3Dobject": dataloader.KITTIObject,
"odometry": dataloader.KITTIOdometry,
"argo": dataloader.Argoverse
}
self.dataset = dataset_dict[self.opt.split]
fpath = os.path.join(os.path.dirname(__file__), "splits",
self.opt.split, "{}_files.txt")
train_filenames = readlines(fpath.format("train"))
val_filenames = readlines(fpath.format("val"))
self.val_filenames = val_filenames
self.train_filenames = train_filenames
img_ext = '.png' if self.opt.ext == "png" else '.jpg'
train_dataset = self.dataset(self.opt, train_filenames)
val_dataset = self.dataset(self.opt, val_filenames)
self.train_loader = DataLoader(train_dataset,
self.opt.batch_size,
True,
num_workers=self.opt.num_workers,
pin_memory=True,
drop_last=True)
self.val_loader = DataLoader(val_dataset,
1,
True,
num_workers=self.opt.num_workers,
pin_memory=True,
drop_last=True)
print("Using split:\n ", self.opt.split)
print(
"There are {:d} training items and {:d} validation items\n".format(
len(train_dataset), len(val_dataset)))
traindata, en_field, zh_field = data.translate_dataloader("data/en-zh.txt",
args.batch_size,
shuffle=True)
data.save_vocab(en_field.vocab, "models/english.vocab")
data.save_vocab(zh_field.vocab, "models/chinese.vocab")
en_size = len(en_field.vocab)
zh_size = len(zh_field.vocab)
zh_pad = zh_field.vocab.stoi['<pad>']
if os.path.exists(args.model):
seq2seq = torch.load(args.model)
seq2seq = seq2seq.cuda()
else:
encoder = model.Encoder(en_size,
embed_size,
hidden_size,
n_layers=2,
dropout=0.5)
decoder = model.Decoder(embed_size,
hidden_size,
zh_size,
n_layers=1,
dropout=0.5)
seq2seq = model.Seq2Seq(encoder, decoder).cuda()
optimizer = optim.Adam(seq2seq.parameters(), lr=args.lr)
print(seq2seq)
train_model(traindata, args.epochs, seq2seq, optimizer, zh_size, zh_pad)
encoder = model.Encoder(vocab_size=INPUT_DIM,
d_model=config.HID_DIM,
ff_dim=config.PF_DIM,
n_heads=config.N_HEADS,
max_len=config.MAX_LEN,
dropout=config.DROPOUT,
n_layers=config.N_LAYERS,
n_experts=config.N_EXP,
capacity_factor=config.CAPACITY_FACTOR,
device=config.DEVICE).to(config.DEVICE)
decoder = model.Decoder(output_dim=OUTPUT_DIM,
d_model=config.HID_DIM,
ff_dim=config.PF_DIM,
n_heads=config.N_HEADS,
max_len=config.MAX_LEN,
dropout=config.DROPOUT,
n_layers=config.N_LAYERS,
n_experts=config.N_EXP,
capacity_factor=config.CAPACITY_FACTOR,
device=config.DEVICE).to(config.DEVICE)
seq_2_seq = model.Seq2Seq(encoder, decoder, SRC_PAD_IDX, TRG_PAD_IDX,
config.DEVICE).to(config.DEVICE)
optimizer = torch.optim.Adam(seq_2_seq.parameters(), lr=config.LR)
criterion = torch.nn.CrossEntropyLoss(ignore_index=TRG_PAD_IDX)
def initialize_weights(m):
if hasattr(m, 'weight') and m.weight.dim() > 1:
torch.nn.init.xavier_uniform_(m.weight.data)
print('Predicted translation: {}'.format(result))
if __name__ == '__main__':
num_examples = 3000
# 开启tensorflow 动态图功能
tf.enable_eager_execution()
# 解码器，编码器
vocab_inp_size, vocab_tar_size = config.VOCAB_INP_SIZE, config.VOCAB_TAR_SIZE
embedding_dim, units, BATCH_SIZE = config.EMBEDDING_DIM, config.UNITS, config.BATCH_SIZE
_, __, inp_lang, targ_lang, max_length_inp, max_length_tar = utils.load_dataset(
num_examples)
del _, __
gc.collect()
encoder = model.Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
decoder = model.Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)
optimizer = tf.train.AdamOptimizer()
# checkpoints(object-based saving)
checkpoint_dir = config.CHECK_POINT_DIR
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
encoder=encoder,
decoder=decoder)
# restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
last = time.time()
translate(u'hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang,
max_length_inp, max_length_tar)
print(time.time() - last)
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import preprocessing
import model
# Encoder, Decoderの初期化
encoder = model.Encoder(vocab_size, embedding_dim, hidden_dim, batch_size).to(device)
decoder = model.Decoder(vocab_size, embedding_dim, hidden_dim, batch_size).to(device)
criterion = nn.CrossEntropyLoss(ignore_index=word2id["<pad>"], size_average=False)
# 最適化関数の定義
encoder_optimizer = optim.Adam(encoder.parameters(), lr=0.001)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=0.001)
def get_current_time():
return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print("学習開始")
n_epoch = 60
sig_cnt = 0
all_losses = []
for epoch in range(1, n_epoch+1):
title_batch = preprocessing.train2batch(title_train, batch_size)
if epoch > 25:
sig_cnt += 1
img_size = (256, 256)
pix_num = img_size[0] * img_size[1]
batch_size = 32
epoch_num = 200
img_path = '../yumi_data/yumi/_target/' # to model natural cartoon img
test_path = './samples/'
model_dir = './model/'
model_name = '%d_yumi_%s.pth'
model_path = model_dir + model_name
sample_path = './samples/'
conv_dim = 64
enc = model.Encoder(conv_dim=conv_dim)
enc.cuda()
enc_optimizer = torch.optim.Adam(enc.parameters(), lr=6e-4)
dec = model.Decoder(conv_dim=conv_dim)
dec.cuda()
dec_optimizer = torch.optim.Adam(dec.parameters(), lr=2e-4)
data_iterer = data_loader.get_dir_loader(img_path, img_size[0],
batch_size) # TODO: test
data_num = len(data_iterer)
bern_dist = torch.distributions.bernoulli.Bernoulli(probs=0.5 *
torch.ones(batch_size, 31))
print('preparation ready. training...')
for e_idx in range(epoch_num):
for b_idx, img in enumerate(data_iterer):
# Normalize
norm_img = normalize_img(img).cuda()
