def main():
os.chdir('/home/seonhoon/Desktop/workspace/ImageQA/data/')
n_vocab = 12047
y_vocab = 430
dim_word = 1024
dim = 1024
maxlen = 60
train=pd.read_pickle('train_vgg.pkl')
train_x=[ q for q in train['q'] ]
train_y=[ a[0] for a in train['a'] ]
train_y=np.array(train_y)[:,None]
train_y = np_utils.to_categorical(train_y, y_vocab).astype('int32')
train_x , train_x_mask = prepare_data(train_x, maxlen)
train_x_img = np.array([ img.tolist() for img in train['cnn_feature'] ]).astype('float32')
print 'x :', train_x.shape
print 'x_mask:', train_x_mask.shape
print 'x_img:', train_x_img.shape
print 'y : ', train_y.shape
model = RNN(n_vocab, y_vocab, dim_word, dim)
model.train(train_x, train_x_mask, train_x_img, train_y, batch_size=512, epoch=50, save=15)
def main():
os.chdir('/home/seonhoon/Desktop/workspace/ImageQA/data/')
n_vocab = 12047
y_vocab = 430
dim_word = 1024
dim = 1024
maxlen = 60
test=pd.read_pickle('test_vgg.pkl')
test_x=[ q for q in test['q'] ]
test_y=[ a[0] for a in test['a'] ]
test_y_original=test_y
test_y=np.array(test_y)[:,None]
test_y = np_utils.to_categorical(test_y, y_vocab)
test_x , test_x_mask = prepare_data(test_x, maxlen)
test_x_img = [ img.tolist() for img in test['cnn_feature'] ]
print 'x :', test_x.shape
print 'x_mask:', test_x_mask.shape
print 'y : ', test_y.shape
model = RNN(n_vocab, y_vocab, dim_word, dim)
pred_y = model.prediction(test_x, test_x_mask, test_x_img, test_y, batch_size=2048)
print pred_y[:10], len(pred_y)
print test_y_original[:10], len(test_y_original)
correct = 0
for i in range(len(pred_y)):
if pred_y[i]==test_y_original[i] :
correct += 1
print 'accuracy : ', float(correct) / len(pred_y)
print(' vocabulary size: {}'.format(vocab_size))
###############################################################################
#
# MODEL SETUP
#
###############################################################################
# NOTE ==============================================
# This is where your model code will be called. You may modify this code
# if required for your implementation, but it should not typically be necessary,
# and you must let the TAs know if you do so.
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size, hidden_size=args.hidden_size,
seq_len=args.seq_len, batch_size=args.batch_size,
vocab_size=vocab_size, num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'GRU':
model = GRU(emb_size=args.emb_size, hidden_size=args.hidden_size,
seq_len=args.seq_len, batch_size=args.batch_size,
vocab_size=vocab_size, num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'TRANSFORMER':
if args.debug: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size, n_units=16, n_blocks=2)
else:
# Note that we're using num_layers and hidden_size to mean slightly
# different things here than in the RNNs.
# Also, the Transformer also has other hyperparameters
# (such as the number of attention heads) which can change it's behavior.
model = TRANSFORMER(vocab_size=vocab_size, n_units=args.hidden_size,
def main():
parse = argparse.ArgumentParser(
description='Audio Classification Training')
parse.add_argument('class_name',
type=str,
default='Gender',
help='The class name to train you model')
parse.add_argument('model', choices=['conv', 'rnn'])
parse.add_argument('features', choices=['stft', 'mfcc', 'cwt'])
parse.add_argument('lng', choices=['cnh', 'eng'])
parse.add_argument('-optimizer', choices=['sgd', 'adam'], default='adam')
parse.add_argument('-cuda', action='store_true')
# parse.add_argument('-clean', type=bool, default=False, help='To pre processing, remove low frequencies based on
# threshold value')
parse.add_argument('-dropout', type=int, default=0.5)
parse.add_argument('-epochs',
type=int,
default=10,
help='Number of epochs to train for')
parse.add_argument('-threshold',
type=int,
default=0.005,
help='Remove low frequencies below to')
parse.add_argument('-nfilt', type=int, default=26, help='Number of filter')
parse.add_argument('-nfeat', type=int, default=13)
parse.add_argument('-nfft', type=int, default=1103)
parse.add_argument('-rate',
type=int,
default=16000,
help='Down sample rate to')
parse.add_argument('-report', type=bool, default=False, help='Plot report')
opt = parse.parse_args()
# device = torch.device("cuda:0" if torch.cuda.is_available() and opt.cuda else "cpu")
# print(device)
mod = None
path_short = "cnh/"
path_full = "data/cnh/"
csv = "label_cnh.csv"
if opt.lng == "eng":
path_short = "eng/"
path_full = "data/eng/"
csv = "label_eng.csv"
# if opt.clean:
# df_ = read_data()
# clean(df_, opt)
df = read_data(path=path_short, csv=csv)
x, y = build_features(df, opt=opt, path=path_full)
input_shape = (x.shape[1], x.shape[2],
1) if opt.model == 'conv' else (x.shape[1], x.shape[2])
y_flat = np.argmax(y, axis=1)
weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)
class_weight = dict(enumerate(weight.flatten()))
if opt.report:
x_, x__, fft_, fbanks, mfccs, stf_f = features(df=df,
opt=opt,
path=path_full)
distribution_plt(group(df, opt.class_name))
signal_plt(x_, 'Time Series', 'tm')
signal_plt(x__, 'Time Series ENVELOPE', 'tmenv')
if opt.model == 'rnn':
mod = RNN(input_shape, opt.optimizer)
elif opt.model == 'conv':
mod = CONV(input_shape, opt.optimizer)
# if "cuda" in mod.device.type: torch.cuda.empty_cache()
history = mod.fit(x,
y,
validation_split=0.50,
batch_size=4,
epochs=opt.epochs,
class_weight=class_weight)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Loss model')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig('plots/loss_loss.png')
plt.show()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Accuracy model')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.savefig('plots/acc_loss.png')
plt.show()
###############################################################################
#
# MODEL SETUP
#
###############################################################################
# NOTE ==============================================
# This is where your model code will be called. You may modify this code
# if required for your implementation, but it should not typically be necessary,
# and you must let the TAs know if you do so.
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'GRU':
model = GRU(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'TRANSFORMER':
if args.debug: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size, n_units=16, n_blocks=2)
else:
from models import RNN
from models import CNN_emotions
import models
import torch
import torch.nn.functional as F
from torch.utils.model_zoo import load_url
from base64 import b64encode
import age_class
inception_url = 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth'
cnn = getCNN()
cnn.load_state_dict(load_url(inception_url, map_location=torch.device('cpu')))
cnn = cnn.train(False)
rnn = RNN()
rnn.load_state_dict(torch.load('net_param.pt', torch.device('cpu')))
rnn = rnn.train(False)
emotions = CNN_emotions()
emotions.load_state_dict(torch.load('emotions.pth', torch.device('cpu')))
emotions = emotions.train(False)
vocabulary = models.vacabulary
batch_of_captions_into_matrix = models.batch_of_captions_into_matrix
app = Flask(__name__)
tags = {
0: 'Angry',
1: 'Disgust',
model_types=['RNN','GRU']
numLayers=[2,2]
seqLen=[35,35]
seq_len=[35,70]
samples=10
path=['best_params_RNN.pt','best_params_GRU.pt']
for m in range(len(model_types)):
for s in range(len(seq_len)):
print('Processing model: '+model_types[m]+' seq_len: '+str(seq_len[s])+'\n')
if model_types[m]=='RNN':
model = RNN(emb_size=embSize[m], hidden_size=hiddenSize[m],
seq_len=seqLen[m], batch_size=batchSize[m],
vocab_size=vocab_size, num_layers=numLayers[m],
dp_keep_prob=dropOut[m])
else:
model =GRU(emb_size=embSize[m], hidden_size=hiddenSize[m],
seq_len=seqLen[m], batch_size=batchSize[m],
vocab_size=vocab_size, num_layers=numLayers[m],
dp_keep_prob=dropOut[m])
model.load_state_dict(torch.load(path[m]))
model = model.to(device)
hidden = nn.Parameter(torch.zeros(numLayers[m],samples,hiddenSize[m])).to(device)
input=torch.ones(10000)*1/1000
input=torch.multinomial(input,samples).to(device)
model.eval()
output=model.generate(input, hidden, seq_len[s])
print('Saving generated samples')
fid=open(model_types[m]+'_' +str(seq_len[s])+'.txt','w')
embedding)
print(embeddings)
"""
GET THE MODEL
"""
if MODEL == "CNN":
logits = CNN.get_model(X,
W=embeddings,
is_training=is_training,
filters=filters,
n_classes=n_classes)
elif MODEL == "RNN":
logits = RNN.get_model(X,
W=embeddings,
dropout_keep_prob=dropout_keep_prob,
hidden_size=HIDDEN_UNITS,
n_classes=n_classes,
num_layers=NUM_LAYERS)
print(logits)
softmax = tf.nn.softmax(logits)
num_params = np.sum(
[np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])
print("{} params to train".format(num_params))
train_op, loss = train_ops.train_op(logits,
y=y,
learning_rate=lr,
optimizer=OPTIMIZER)
if __name__ == '__main__':
# 1.data load
print('data load start')
train_texts, train_labels = read_imdb_split(train_data_path)
train_data = IMDBDataset(train_texts, train_labels, word2idx)
trainloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, collate_fn=collate_imdb,
shuffle=True)
test_texts, test_labels = read_imdb_split(test_data_path)
test_data = IMDBDataset(test_texts, test_labels, word2idx, attack_label=1)
testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, collate_fn=collate_imdb,
shuffle=False)
print('data load end')
random.seed(11)
np.random.seed(11)
torch.manual_seed(11)
# 2.train or test
criterion = nn.CrossEntropyLoss()
if mode == 1:
model = RNN(vocab_size=vocab_size, embedding_dim=300, hidden_dim=300, output_dim=2).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.0004)
train(model)
else:
baseline_model = RNN(vocab_size=vocab_size, embedding_dim=300, hidden_dim=300, output_dim=2).to(device)
baseline_model.load_state_dict(torch.load(output_model_path))
baseline_model.eval() # 开启eval状态，不再随机dropout
evaluate_model(baseline_model, testloader, criterion)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--vocab_path',
type=str,
default='ckpt_blog_td/vocab.txt',
help='the location of checkpointing files')
parser.add_argument('--ckpt_path',
type=str,
default='ckpt_blog_td2',
help='the location of checkpointing files')
parser.add_argument('--model',
type=str,
default='t-d-s2s',
help='model types: t-d, t-d-s2s or rnn')
parser.add_argument('--beam_size',
type=int,
default=3,
help='beam search size')
parser.add_argument('--prime',
type=str,
default='chia tay me di buc ca minh')
parser.add_argument('--saved_args_path',
type=str,
default="./ckpt_blog_td2/args.pkl")
args = parser.parse_args()
with open(args.saved_args_path, 'rb') as f:
saved_args = cPickle.load(f)
word2idx, idx2word = load_vocab(args.vocab_path)
accent_vocab = dict()
for token, idx in word2idx.items():
raw_token = tone_utils.clear_all_marks(
token) if token[0] != "<" else token
if raw_token not in accent_vocab:
accent_vocab[raw_token] = [token]
else:
curr = accent_vocab[raw_token]
if token not in curr:
curr.append(token)
unk_idx = word2idx["<unk>"]
if args.model == "t-d" or args.model == "t-d-s2s":
# quick fix
model = TransformerDecoder(False, saved_args)
else:
model = RNN(False, saved_args)
with tf.Session(
graph=model.graph if args.model != "rnn" else None) as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(args.ckpt_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
words = basic_tokenizer(args.prime)
if args.model == "t-d":
sos_idx = word2idx["<sos>"]
pad_idx = word2idx["<pad>"]
init_state = np.full(shape=(saved_args.maxlen + 1),
fill_value=pad_idx)
init_state[0] = sos_idx
init_probs = sess.run(
tf.nn.softmax(model.logits),
feed_dict={model.x: np.atleast_2d(init_state)})[0]
paths = beamsearch_transformer(sess, model, words,
args.beam_size,
saved_args.maxlen, init_probs,
accent_vocab, word2idx)
elif args.model == "rnn":
x = np.zeros((1, 1))
words = basic_tokenizer(args.prime)
init_state = sess.run(model.cell.zero_state(1, tf.float32))
if words[0] != "<eos>":
words = ["<eos>"] + words
out_state = init_state
x[0,
0] = word2idx[words[0]] if words[0] in word2idx else unk_idx
# print(x[0,0])
feed = {model.input_data: x, model.initial_state: out_state}
[probs, out_state] = sess.run([model.probs, model.final_state],
feed)
paths = beamsearch_rnn(sess, model, words, args.beam_size,
out_state, probs[0], accent_vocab,
word2idx)
else:
pad_idx = word2idx["<pad>"]
ref = []
for idx, token in idx2word.items():
cleared = clear_all_marks(token)
if cleared not in ref:
ref.append(cleared)
words = basic_tokenizer(args.prime)
feed_x = np.asarray([ref.index(w) for w in words])
feed_x = np.atleast_2d(
np.lib.pad(feed_x, [0, saved_args.maxlen - len(feed_x)],
'constant',
constant_values=pad_idx))
feed = {model.x: feed_x}
paths = [sess.run(model.preds, feed_dict=feed)]
paths[0][len(words):] = pad_idx
result = ""
for path in paths:
for idx, token in enumerate(path):
result += idx2word[token] if token != unk_idx else words[
idx if args.model != "rnn" else idx + 1]
result += " "
result += "\n"
print(result)
def _load_model(emb_size, hidden_size, seq_len, batch_size, vocab_size,
num_layers, dp_keep_prob, PATH, model_type):
# Load model (Change to RNN if you want RNN to predict)
if model_type == 'RNN':
model = RNN(emb_size, hidden_size, seq_len, batch_size, vocab_size,
num_layers, dp_keep_prob)
else:
model = GRU(emb_size, hidden_size, seq_len, batch_size, vocab_size,
num_layers, dp_keep_prob)
if torch.cuda.is_available():
model.load_state_dict(torch.load(PATH)).cuda()
model.eval()
else:
model.load_state_dict(torch.load(PATH, map_location='cpu'))
model.eval()
return model
MAYBE TODO:
1. attention mechanism to the actor
2. attention mechanism in the predictor.
option 1: convert the vector into a feature map
option 2: use directly feature map maybe use feature maps like 5x5x256 and then apply a transformer to it
3. add debugging to see if everything works and how the agent is behaving
'''
vae_sess = tf.Session()
rnn_sess = tf.Session()
actor_sess = tf.Session()
env = EnvWrap(FLAGS.init_frame_skip, FLAGS.frame_skip, FLAGS.env,
FLAGS.renderGame)
vaegan = VAEGAN.VAEGAN(vae_sess)
rnn = RNN.RNN(rnn_sess)
actor = ACTOR.ACTOR(actor_sess)
mcts = MCTS.Tree(rnn, actor)
trainer = Trainer()
#If called, train the VAEGAN AND RNN before the actor
if (FLAGS.preprocessing):
preprocessing.run(env, vaegan, trainer, rnn)
if (FLAGS.playing):
#Make the actor play and train VAEGAN, RNN and actor
playing.run(env, vaegan, rnn, actor, trainer, mcts)
'''
def main():
#Tran alphazero using MCTS
os.rename(tmp_run_path, output_path)
return test_loss
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model_type", type=str, default="linguistic")
args = parser.parse_args()
set_default_tensor()
if args.model_type == "linguistic":
cfg = LinguisticConfig()
test_features, test_labels, val_features, val_labels, train_features, train_labels = load_linguistic_dataset(
)
model = RNN(cfg)
elif args.model_type == "acoustic-lld":
cfg = AcousticLLDConfig()
test_features, test_labels, val_features, val_labels, train_features, train_labels = load_acoustic_features_dataset(
)
model = RNN(cfg)
elif args.model_type == "acoustic-spectrogram":
cfg = AcousticSpectrogramConfig()
test_features, test_labels, val_features, val_labels, train_features, train_labels = load_spectrogram_dataset(
)
model = CNN(cfg)
else:
raise Exception(
"model_type parameter has to be one of [linguistic|acoustic-lld|acoustic-spectrogram]"
)
"""Converting model to specified hardware and format"""
def _load_model(model_type):
emb_size = 200
hidden_size = 1500
seq_len = 35 # 70
batch_size = 20
vocab_size = 10000
num_layers = 2
dp_keep_prob = 0.35
# Load model (Change to RNN if you want RNN to predict)
if model_type == 'RNN':
model = RNN(emb_size, hidden_size, seq_len, batch_size, vocab_size,
num_layers, dp_keep_prob)
PATH = os.path.join("RNN_ADAM_0", "best_params.pt")
else:
model = GRU(emb_size, hidden_size, seq_len, batch_size, vocab_size,
num_layers, dp_keep_prob)
PATH = os.path.join("GRU_SGD_LR_SCHEDULE_0", "best_params.pt")
if torch.cuda.is_available():
model.load_state_dict(torch.load(PATH)).cuda()
model.eval()
else:
model.load_state_dict(torch.load(PATH, map_location='cpu'))
model.eval()
return model
def __init__(self,
layers=[512, 256, 128, 64, 32],
filters=[64, 128, 256, 512],
MODEL="FF",
HIDDEN_UNITS=32,
NUM_LAYERS=2,
do_val=False,
OPTIMIZER='adam',
DEV_SPLIT=0.2,
NUM_EPOCH=50,
min_ngram=1,
up=5,
max_features=None,
dataset="PAN2019",
logger=None,
opts=None,
DEBUG=False,
lang="es"):
"""
Vars
"""
BATCH_SIZE = opts.batch_size
logger = logger or logging.getLogger(__name__)
# MODEL = "RNN"
# MODEL = "CNN"
if not DEBUG:
## PAN
path = opts.tr_data + '/' + lang
path_test = opts.i + '/' + lang
sent = 0
if lang == 'en': sent = 0
if do_val:
txt_train = opts.file_i + "/{}/truth-train.txt".format(lang)
txt_dev = opts.file_i + "/{}/truth-dev.txt".format(lang)
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=sent)
dt_dev = process.PAN2019(path=path,
txt=txt_dev,
join_all=MODEL == "FF",
sentiment_id=sent)
fnames_dev = dt_dev.fnames
y_dev = dt_dev.y
x_dev = dt_dev.X
# sent_dev = dt_dev.sentiment
del dt_dev
else:
txt_train = opts.file_i + "/{}/truth.txt".format(lang)
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=sent)
dt_test = process.PAN2019_Test(path=path_test,
join_all=MODEL == "FF",
sentiment_id=sent)
n_classes = 2 # bot or not bot
# sent_train = dt_train.sentiment
# sent_test = dt_test.sentiment
x_train = dt_train.X
y_train = dt_train.y
y2_train = dt_train.y2
print(len(x_train))
print(len(y_train))
print(len(y2_train))
x_test = dt_test.X
# y_test = dt_test.y
fnames_train = dt_train.fnames
fnames_test = dt_test.fnames
labelencoder = LabelEncoder() #set
y_train_ = np.array(y_train).astype(str)
# y_test_ = np.array(y_test).astype(str)
labelencoder.fit(y_train_)
y_train_ = labelencoder.transform(y_train_)
# y_test_ = labelencoder.transform(y_test_)
n_values = len(np.unique(y_train_))
# To One hot
y_train = to_categorical(y_train_, n_values)
# y_test = to_categorical(y_test_, n_values)
if max_features:
rep = TfidfVectorizer(ngram_range=(min_ngram, up),
max_features=max_features)
else:
rep = TfidfVectorizer(ngram_range=(min_ngram, up))
del dt_train
del dt_test
logger.info("fit_transform tfidf")
texts_rep_train = rep.fit_transform(x_train)
logger.info("To array")
texts_rep_train = texts_rep_train.toarray()
logger.info("transform tfidf")
text_test_rep = rep.transform(x_test)
logger.info("To array")
text_test_rep = text_test_rep.toarray()
if do_val:
text_dev_rep = rep.transform(x_dev)
text_dev_rep = text_dev_rep.toarray()
y_dev_ = np.array(y_dev).astype(str)
y_dev_ = labelencoder.transform(y_dev_)
y_dev = to_categorical(y_dev_, n_values)
if MODEL == "CNN":
num = opts.num_tweets
texts_rep_train = texts_rep_train.reshape(
int(texts_rep_train.shape[0] / num), num,
texts_rep_train.shape[1])
text_test_rep = text_test_rep.reshape(
int(text_test_rep.shape[0] / num), num,
text_test_rep.shape[1])
else:
logger.info(" --------------- DEBUG ON ------------------")
n_classes = 2
n_vcab = 10000
train_data = 128
dev_data = 50
texts_rep_train = np.random.randn(train_data, 100, n_vcab)
text_test_rep = np.random.randn(dev_data, 100, n_vcab)
y_train = np.eye(n_classes)[np.random.choice(
n_classes, train_data)]
y_test = np.eye(n_classes)[np.random.choice(n_classes, dev_data)]
alphabet = list(
'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789'
)
np_alphabet = np.array(alphabet, dtype="|S1")
fnames_train = np.random.choice(np_alphabet, [train_data])
fnames_test = np.random.choice(np_alphabet, [dev_data])
logger.info("Random data created")
# print(len(sent_train))
# print(texts_rep_train.shape)
# texts_rep_train = np.concatenate((texts_rep_train, np.expand_dims(sentiment.append([blob.sentiment.polarity, blob.sentiment.subjectivity]),axis=1)), axis=-1)
# text_test_rep = np.concatenate((text_test_rep, np.expand_dims(sent_test,axis=1)), axis=-1)
# text_dev_rep = np.concatenate((text_dev_rep, np.expand_dims(sent_dev,axis=1)), axis=-1)
# texts_rep_train = np.concatenate((texts_rep_train, sent_train), axis=-1)
logger.info("texts_rep_train: {}".format(texts_rep_train.shape))
logger.info("y_train: {}".format(y_train.shape))
# X_train, X_val, X_test, y_train, y_val, y_test, MAX_SEQUENCE_LENGTH = prepare_data(
# dir_word_embeddings, fname_vocab, train_path, test_path, EMBEDDING_DIM,
# VALIDATION_SPLIT=DEV_SPLIT, MAX_SEQUENCE_LENGTH=MAX_SEQUENCE_LENGTH
# )
"""""" """""" """""" """""" """""" """""
# Tensorflow
""" """""" """""" """""" """""" """""" ""
batch_size = tf.placeholder(tf.int64, name="batch_size")
if MODEL == "CNN":
X = tf.placeholder(tf.float32,
shape=[
None, texts_rep_train.shape[1],
texts_rep_train.shape[2]
],
name="X")
else:
X = tf.placeholder(tf.float32,
shape=[None, len(texts_rep_train[0])],
name="X")
print(X)
y = tf.placeholder(tf.int64, shape=[None, n_classes], name="y")
fnames_plc = tf.placeholder(tf.string, shape=[None], name="fnames_plc")
lr = tf.placeholder(tf.float32, shape=[], name="lr")
is_training = tf.placeholder_with_default(False,
shape=[],
name='is_training')
dropout_keep_prob = tf.placeholder_with_default(1.0,
shape=(),
name="dropout")
"""
GET THE MODEL
"""
if MODEL == "CNN":
logits = CNN.get_model(X,
is_training=is_training,
filters=filters,
n_classes=n_classes,
tf_idf=True,
logger=logger)
elif MODEL == "RNN":
logits = RNN.get_model(X,
dropout_keep_prob,
hidden_size=HIDDEN_UNITS,
n_classes=n_classes,
num_layers=NUM_LAYERS)
elif MODEL == "FF":
logits = FF.get_model(X,
dropout_keep_prob,
is_training=is_training,
layers=layers,
n_classes=n_classes)
logger.info(logits)
softmax = tf.nn.softmax(logits)
num_params = np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
])
logger.info("{} params to train".format(num_params))
train_op, loss = train_ops.train_op(logits,
y,
learning_rate=lr,
optimizer=OPTIMIZER)
""""""
"""Test de embeddings"""
train_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
dev_dataset = tf.data.Dataset.from_tensor_slices(
(X, y, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
test_dataset = tf.data.Dataset.from_tensor_slices(
(X, fnames_plc)).batch(batch_size).shuffle(buffer_size=12)
train_data = (texts_rep_train, y_train, fnames_train)
if do_val:
dev_data = (text_dev_rep, y_dev, fnames_dev)
test_data = (text_test_rep, fnames_test)
print(text_test_rep.shape)
print(len(fnames_test))
# create a iterator of the correct shape and type
iter = tf.data.Iterator.from_structure(train_dataset.output_types,
train_dataset.output_shapes)
iter_test = tf.data.Iterator.from_structure(test_dataset.output_types,
test_dataset.output_shapes)
# create the initialisation operations
train_init_op = iter.make_initializer(train_dataset)
dev_init_op = iter.make_initializer(dev_dataset)
test_init_op = iter_test.make_initializer(test_dataset)
epoch_start = 0
## Train
sess = tf.Session()
init_g = tf.global_variables_initializer()
init_l = tf.local_variables_initializer()
sess.run(init_g)
sess.run(init_l)
best_acc = 0
for epoch in range(epoch_start, NUM_EPOCH + 1):
sess.run(train_init_op,
feed_dict={
X: train_data[0],
y: train_data[1],
fnames_plc: train_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter.get_next()
loss_count = 0
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
_, loss_result = sess.run(
[train_op, loss],
feed_dict={
X: batch_x,
y: batch_tgt,
lr: train_ops.lr_scheduler(epoch),
batch_size: BATCH_SIZE,
is_training: True,
dropout_keep_prob: 0.3,
})
# print("Loss: {}".format(loss_result))
loss_count += loss_result
loss_count = loss_count / current_batch_index
logger.info("Loss on epoch {} : {} - LR: {}".format(
epoch, loss_count, train_ops.lr_scheduler(epoch)))
acc = 0
if do_val:
print("Eval")
## Eval
sess.run(
dev_init_op,
feed_dict={
# sess.run(dev_init_op, feed_dict={
X: dev_data[0],
y: dev_data[1],
fnames_plc: dev_data[2],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter.get_next()
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_tgt, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: batch_tgt,
lr: train_ops.lr_scheduler(epoch),
batch_size: BATCH_SIZE,
is_training: False,
dropout_keep_prob: 1.0
})
results = results[0]
acc_aux = metrics.accuracy(X=results, y=batch_tgt)
acc += acc_aux
acc = acc / current_batch_index
print("Acc Val epoch {} : {}".format(epoch, acc))
print("----------")
if acc > best_acc:
best_acc = acc
logger.info("New acc : {}".format(best_acc))
save_best(sess, opts.work_dir)
if opts.testing and opts.do_val:
logger.info("Model: {}".format(MODEL))
logger.info("layers: {}".format(layers))
logger.info("max_features: {}".format(max_features))
logger.info("Min and max features: {} - {}".format(min_ngram, up))
logger.info("Best acc : {}".format(best_acc))
exit()
"""
----------------- TEST -----------------
"""
logger.info("\n-- TEST --\n")
logger.info("Restoring the best Checkpoint.")
# restore_file = sesions.restore_from_best(sess, save_path)
restore_file = restore_from_best(sess, opts.work_dir)
if restore_file:
logger.info("Best model restored")
else:
logger.info("Cant restore the best model.")
exit()
Y_FALSA = np.random.randint(1, size=(BATCH_SIZE, n_classes))
print(Y_FALSA.shape)
logger.info("\n-- TEST --\n")
sess.run(test_init_op,
feed_dict={
X: test_data[0],
y: Y_FALSA,
fnames_plc: test_data[1],
batch_size: BATCH_SIZE,
})
current_batch_index = 0
next_element = iter_test.get_next()
loss_count = 0
classifieds = []
classifieds_to_write = []
while True:
try:
data = sess.run([next_element])
except tf.errors.OutOfRangeError:
break
current_batch_index += 1
data = data[0]
batch_x, batch_fnames = data
results = sess.run(
[softmax],
feed_dict={
X: batch_x,
y: Y_FALSA,
batch_size: BATCH_SIZE,
dropout_keep_prob: 1.0,
lr: train_ops.lr_scheduler(1)
})
for i in range(len(results[0])):
# to write
hyp = [np.argmax(results[0][i], axis=-1)]
hyp = labelencoder.inverse_transform(hyp)[
0] #real label #set
doc_name = batch_fnames[i].decode("utf-8").split("/")[-1]
classifieds_to_write.append((doc_name, lang, hyp))
logger.info("----------")
logger.info("Writting results in output dir {}".format("{}/{}".format(
opts.o, lang)))
if sent != 2 and lang == 'en':
dt_train = process.PAN2019(path=path,
txt=txt_train,
join_all=MODEL == "FF",
sentiment_id=2)
x_train = dt_train.X
y2_train = dt_train.y2
del dt_train
if max_features:
rep = TfidfVectorizer(ngram_range=(min_ngram, up),
max_features=max_features)
else:
rep = TfidfVectorizer(ngram_range=(min_ngram, up))
texts_rep_train = rep.fit_transform(x_train).toarray()
dt_test = process.PAN2019_Test(path=path_test,
join_all=MODEL == "FF",
sentiment_id=2)
del dt_test
x_test = dt_test.X
text_test_rep = rep.transform(x_test).toarray()
process.write_from_array(classifieds_to_write,
"{}/{}".format(opts.o, lang), x_train,
texts_rep_train, y2_train, x_test,
text_test_rep, fnames_test)
def main():
parser = argparse.ArgumentParser(
description='Spoken Language Idenfication')
parser.add_argument('--hidden_size',
type=int,
default=512,
help='hidden size of model (default: 256)')
parser.add_argument('--layer_size',
type=int,
default=3,
help='number of layers of model (default: 3)')
parser.add_argument('--n_class',
type=int,
default=2,
help='number of classes of data (default: 7)')
parser.add_argument('--dropout',
type=float,
default=0.2,
help='dropout rate in training (default: 0.2')
parser.add_argument('--bidirectional',
default=True,
action='store_true',
help='use bidirectional RNN (default: False')
parser.add_argument('--batch_size',
type=int,
default=2,
help='batch size in training (default: 32')
parser.add_argument(
'--workers',
type=int,
default=4,
help='number of workers in dataset loader (default: 4)')
parser.add_argument('--max_epochs',
type=int,
default=10,
help='number of max epochs in training (default: 10)')
parser.add_argument('--lr',
type=float,
default=1e-04,
help='learning rate (default: 0.0001)')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
help='random seed (default: 1)')
parser.add_argument('--save_name',
type=str,
default='model',
help='the name of model')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--nn_type',
type=str,
default='crnn',
help='type of neural networks')
args = parser.parse_args()
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
feature_size = N_FFT / 2 + 1
cnn = CNN.CNN(feature_size)
rnn = RNN.RNN(cnn.feature_size,
args.hidden_size,
args.n_class,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
n_layers=args.layer_size,
bidirectional=args.bidirectional,
rnn_cell='gru',
variable_lengths=False)
model = CRNN.CRNN(cnn, rnn)
model.flatten_parameters()
model = nn.DataParallel(model).to(device)
optimizer = optim.Adam(model.module.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss(reduction='sum').to(device)
if args.mode != 'train':
return
download_data()
kor_db_list = []
search('dataset/train/train_data', kor_db_list)
train_wav_paths = np.loadtxt("dataset/TRAIN_list.csv",
delimiter=',',
dtype=np.unicode)
valid_wav_paths = np.loadtxt("dataset/TEST_developmentset_list.csv",
delimiter=',',
dtype=np.unicode)
test_wav_paths = np.loadtxt("dataset/TEST_coreset_list.csv",
delimiter=',',
dtype=np.unicode)
train_wav_paths = list(
map(lambda x: "dataset/TIMIT/{}.WAV".format(x), train_wav_paths))
valid_wav_paths = list(
map(lambda x: "dataset/TIMIT/{}.WAV".format(x), valid_wav_paths))
test_wav_paths = list(
map(lambda x: "dataset/TIMIT/{}.WAV".format(x), test_wav_paths))
min_loss = 100000
begin_epoch = 0
loss_acc = [[], [], [], []]
train_batch_num, train_dataset_list, valid_dataset, test_dataset = \
split_dataset(args, train_wav_paths, valid_wav_paths, test_wav_paths, kor_db_list)
logger.info('start')
train_begin = time.time()
for epoch in range(begin_epoch, args.max_epochs):
train_queue = queue.Queue(args.workers * 2)
train_loader = MultiLoader(train_dataset_list, train_queue,
args.batch_size, args.workers, args.nn_type)
train_loader.start()
train_loss, train_acc = train(model, train_batch_num, train_queue,
criterion, optimizer, device,
train_begin, args.workers, 10)
logger.info('Epoch %d (Training) Loss %0.4f Acc %0.4f' %
(epoch, train_loss, train_acc))
train_loader.join()
loss_acc[0].append(train_loss)
loss_acc[1].append(train_acc)
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue,
args.batch_size, 0, args.nn_type)
valid_loader.start()
eval_loss, eval_acc = evaluate(model, valid_loader, valid_queue,
criterion, device)
logger.info('Epoch %d (Evaluate) Loss %0.4f Acc %0.4f' %
(epoch, eval_loss, eval_acc))
valid_loader.join()
loss_acc[2].append(eval_loss)
loss_acc[3].append(eval_acc)
best_model = (eval_loss < min_loss)
if best_model:
min_loss = eval_loss
torch.save(model.state_dict(), './save_model/best_model.pt')
save_epoch = epoch
model.load_state_dict(torch.load('./save_model/best_model.pt'))
test_queue = queue.Queue(args.workers * 2)
test_loader = BaseDataLoader(test_dataset, test_queue, args.batch_size, 0,
args.nn_type)
test_loader.start()
confusion_matrix = torch.zeros((args.n_class, args.n_class))
test_loss, test_acc = evaluate(model, test_loader, test_queue, criterion,
device, confusion_matrix)
logger.info('Epoch %d (Test) Loss %0.4f Acc %0.4f' %
(save_epoch, test_loss, test_acc))
test_loader.join()
save_data(loss_acc, test_loss, test_acc,
confusion_matrix.to('cpu').numpy())
plot_data(loss_acc, test_loss, test_acc)
return 0
###############################################################################
#
# MODEL SETUP
#
###############################################################################
# NOTE ==============================================
# This is where your model code will be called. You may modify this code
# if required for your implementation, but it should not typically be necessary,
# and you must let the TAs know if you do so.
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'GRU':
model = GRU(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'TRANSFORMER':
if args.debug: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size, n_units=16, n_blocks=2)
else:
def __init__(self,
domain,
train_data_file,
validation_data_file,
test_data_file,
minibatch_size,
rng,
device,
behav_policy_file_wDemo,
behav_policy_file,
context_input=False,
context_dim=0,
drop_smaller_than_minibatch=True,
folder_name='/Name',
autoencoder_saving_period=20,
resume=False,
sided_Q='negative',
autoencoder_num_epochs=50,
autoencoder_lr=0.001,
autoencoder='AIS',
hidden_size=16,
ais_gen_model=1,
ais_pred_model=1,
embedding_dim=4,
state_dim=42,
num_actions=25,
corr_coeff_param=10,
dst_hypers={},
cde_hypers={},
odernn_hypers={},
**kwargs):
'''
We assume discrete actions and scalar rewards!
'''
self.rng = rng
self.device = device
self.train_data_file = train_data_file
self.validation_data_file = validation_data_file
self.test_data_file = test_data_file
self.minibatch_size = minibatch_size
self.drop_smaller_than_minibatch = drop_smaller_than_minibatch
self.autoencoder_num_epochs = autoencoder_num_epochs
self.autoencoder = autoencoder
self.autoencoder_lr = autoencoder_lr
self.saving_period = autoencoder_saving_period
self.resume = resume
self.sided_Q = sided_Q
self.num_actions = num_actions
self.state_dim = state_dim
self.corr_coeff_param = corr_coeff_param
self.context_input = context_input # Check to see if we'll one-hot encode the categorical contextual input
self.context_dim = context_dim # Check to see if we'll remove the context from the input and only use it for decoding
self.hidden_size = hidden_size
if self.context_input:
self.input_dim = self.state_dim + self.context_dim + self.num_actions
else:
self.input_dim = self.state_dim + self.num_actions
self.autoencoder_lower = self.autoencoder.lower()
self.data_folder = folder_name + f'/{self.autoencoder_lower}_data'
self.checkpoint_file = folder_name + f'/{self.autoencoder_lower}_checkpoints/checkpoint.pt'
if not os.path.exists(folder_name +
f'/{self.autoencoder_lower}_checkpoints'):
os.mkdir(folder_name + f'/{self.autoencoder_lower}_checkpoints')
if not os.path.exists(folder_name + f'/{self.autoencoder_lower}_data'):
os.mkdir(folder_name + f'/{self.autoencoder_lower}_data')
self.store_path = folder_name
self.gen_file = folder_name + f'/{self.autoencoder_lower}_data/{self.autoencoder_lower}_gen.pt'
self.pred_file = folder_name + f'/{self.autoencoder_lower}_data/{self.autoencoder_lower}_pred.pt'
if self.autoencoder == 'AIS':
self.container = AIS.ModelContainer(device, ais_gen_model,
ais_pred_model)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'AE':
self.container = AE.ModelContainer(device)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'DST':
self.dst_hypers = dst_hypers
self.container = DST.ModelContainer(device)
self.gen = self.container.make_encoder(
self.input_dim,
self.hidden_size,
gru_n_layers=self.dst_hypers['gru_n_layers'],
augment_chs=self.dst_hypers['augment_chs'])
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.dst_hypers['decoder_hidden_units'])
elif self.autoencoder == 'DDM':
self.container = DDM.ModelContainer(device)
self.gen = self.container.make_encoder(
self.state_dim,
self.hidden_size,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.state_dim,
self.hidden_size)
self.dyn = self.container.make_dyn(self.num_actions,
self.hidden_size)
self.all_params = chain(self.gen.parameters(),
self.pred.parameters(),
self.dyn.parameters())
self.inv_loss_coef = 10
self.dec_loss_coef = 0.1
self.max_grad_norm = 50
self.dyn_file = folder_name + '/ddm_data/ddm_dyn.pt'
elif self.autoencoder == 'RNN':
self.container = RNN.ModelContainer(device)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'CDE':
self.cde_hypers = cde_hypers
self.container = CDE.ModelContainer(device)
self.gen = self.container.make_encoder(
self.input_dim + 1,
self.hidden_size,
hidden_hidden_channels=self.
cde_hypers['encoder_hidden_hidden_channels'],
num_hidden_layers=self.cde_hypers['encoder_num_hidden_layers'])
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.cde_hypers['decoder_num_layers'],
self.cde_hypers['decoder_num_units'])
elif self.autoencoder == 'ODERNN':
self.odernn_hypers = odernn_hypers
self.container = ODERNN.ModelContainer(device)
self.gen = self.container.make_encoder(self.input_dim,
self.hidden_size,
self.odernn_hypers)
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.odernn_hypers['decoder_n_layers'],
self.odernn_hypers['decoder_n_units'])
else:
raise NotImplementedError
self.buffer_save_file = self.data_folder + '/ReplayBuffer'
self.next_obs_pred_errors_file = self.data_folder + '/test_next_obs_pred_errors.pt'
self.test_representations_file = self.data_folder + '/test_representations.pt'
self.test_correlations_file = self.data_folder + '/test_correlations.pt'
self.policy_eval_save_file = self.data_folder + '/dBCQ_policy_eval'
self.policy_save_file = self.data_folder + '/dBCQ_policy'
self.behav_policy_file_wDemo = behav_policy_file_wDemo
self.behav_policy_file = behav_policy_file
# Read in the data csv files
assert (domain == 'sepsis')
self.train_demog, self.train_states, self.train_interventions, self.train_lengths, self.train_times, self.acuities, self.rewards = torch.load(
self.train_data_file)
train_idx = torch.arange(self.train_demog.shape[0])
self.train_dataset = TensorDataset(self.train_demog, self.train_states,
self.train_interventions,
self.train_lengths,
self.train_times, self.acuities,
self.rewards, train_idx)
self.train_loader = DataLoader(self.train_dataset,
batch_size=self.minibatch_size,
shuffle=True)
self.val_demog, self.val_states, self.val_interventions, self.val_lengths, self.val_times, self.val_acuities, self.val_rewards = torch.load(
self.validation_data_file)
val_idx = torch.arange(self.val_demog.shape[0])
self.val_dataset = TensorDataset(self.val_demog, self.val_states,
self.val_interventions,
self.val_lengths, self.val_times,
self.val_acuities, self.val_rewards,
val_idx)
self.val_loader = DataLoader(self.val_dataset,
batch_size=self.minibatch_size,
shuffle=False)
self.test_demog, self.test_states, self.test_interventions, self.test_lengths, self.test_times, self.test_acuities, self.test_rewards = torch.load(
self.test_data_file)
test_idx = torch.arange(self.test_demog.shape[0])
self.test_dataset = TensorDataset(self.test_demog, self.test_states,
self.test_interventions,
self.test_lengths, self.test_times,
self.test_acuities,
self.test_rewards, test_idx)
self.test_loader = DataLoader(self.test_dataset,
batch_size=self.minibatch_size,
shuffle=False)
# encode CDE data first to save time
if self.autoencoder == 'CDE':
self.train_coefs = load_cde_data('train', self.train_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
self.val_coefs = load_cde_data('val', self.val_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
self.test_coefs = load_cde_data('test', self.test_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
print("\nPutting log in %s" % experiment_path)
args['save_dir'] = experiment_path
with open(os.path.join(experiment_path, 'exp_config.txt'), 'w') as f:
for key in sorted(args):
f.write(key + ' ' + str(args[key]) + '\n')
###############################################################################
#
# MODEL SETUP
#
###############################################################################
if args["model"] == 'RNN':
model = RNN(emb_size=args["emb_size"],
hidden_size=args["hidden_size"],
seq_len=args["seq_len"],
batch_size=args["batch_size"],
vocab_size=vocab_size,
num_layers=args["num_layers"],
dp_keep_prob=args["dp_keep_prob"])
elif args["model"] == 'GRU':
model = GRU(emb_size=args["emb_size"],
hidden_size=args["hidden_size"],
seq_len=args["seq_len"],
batch_size=args["batch_size"],
vocab_size=vocab_size,
num_layers=args["num_layers"],
dp_keep_prob=args["dp_keep_prob"])
elif args["model"] == 'TRANSFORMER':
if args["debug"]: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size,
n_units=16,
def main(args):
# hyperparameters
batch_size = args.batch_size
num_workers = 2
# Image Preprocessing
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
vocab = load_vocab()
loader = get_basic_loader(dir_path=os.path.join(args.image_path),
transform=transform,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
# Build the models
embed_size = args.embed_size
num_hiddens = args.num_hidden
checkpoint_path = 'checkpoints'
encoder = CNN(embed_size)
decoder = RNN(embed_size,
num_hiddens,
len(vocab),
1,
rec_unit=args.rec_unit)
encoder_state_dict, decoder_state_dict, optimizer, * \
meta = utils.load_models(args.checkpoint_file)
encoder.load_state_dict(encoder_state_dict)
decoder.load_state_dict(decoder_state_dict)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Train the Models
with torch.no_grad():
try:
results = []
for step, (images, image_ids) in enumerate(loader):
images = utils.to_var(images, volatile=True)
features = encoder(images)
captions = decoder.sample(features)
captions = captions.cpu().data.numpy()
captions = [
utils.convert_back_to_text(cap, vocab) for cap in captions
]
captions_formatted = [{
'image_id': int(img_id),
'caption': cap
} for img_id, cap in zip(image_ids, captions)]
results.extend(captions_formatted)
print('Sample:', captions_formatted)
except KeyboardInterrupt:
print('Ok bye!')
finally:
import json
file_name = 'captions_model.json'
with open(file_name, 'w') as f:
json.dump(results, f)
raw_data = ptb_raw_data(data_path=args.data)
train_data, valid_data, test_data, word_to_id, id_2_word = raw_data
vocab_size = len(word_to_id)
print(' vocabulary size: {}'.format(vocab_size))
###############################################################################
#
# MODEL SETUP
#
###############################################################################
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'GRU':
model = GRU(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
batch_size=args.batch_size,
vocab_size=vocab_size,
num_layers=args.num_layers,
dp_keep_prob=args.dp_keep_prob)
elif args.model == 'TRANSFORMER':
if args.debug: # use a very small model
model = TRANSFORMER(vocab_size=vocab_size, n_units=16, n_blocks=2)
else:
vocab_file=args.vocab_file)
# set up batching
loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
collate_fn=SmilesCollate(dataset.vocabulary))
# set up model
if args.embedding_size > 0:
model = RNN(vocabulary=dataset.vocabulary,
rnn_type=args.rnn_type,
embedding_size=args.embedding_size,
hidden_size=args.hidden_size,
n_layers=args.n_layers,
dropout=args.dropout,
bidirectional=args.bidirectional,
tie_weights=args.tie_weights,
nonlinearity=args.nonlinearity)
else:
# no embedding layer (one-hot encoding)
model = OneHotRNN(vocabulary=dataset.vocabulary,
rnn_type=args.rnn_type,
hidden_size=args.hidden_size,
n_layers=args.n_layers,
dropout=args.dropout,
bidirectional=args.bidirectional,
nonlinearity=args.nonlinearity)
# optionally, load model parameters from file
