def evaluate_train():
# Load model
weight_path = 'model/09031925_epoch_0_train_loss_5.9855.h5'
# Load data
Sources, Targets = load_train_data()
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
batch_size = 5
model = TransformerModel(in_vocab_len=len(idx2de),
out_vocab_len=len(idx2en),
max_len=hp.maxlen)
model.load_model(weight_path)
for i in range(5 // batch_size):
x = Sources[i * batch_size:(i + 1) * batch_size]
sources = Sources[i * batch_size:(i + 1) * batch_size]
targets = Targets[i * batch_size:(i + 1) * batch_size]
preds = model.translate_with_ans(sources, targets, idx2en)
# preds = model.translate(x, idx2en)
for source, target, pred in zip(sources, targets, preds):
print('source:', ' '.join(idx2de[idx] for idx in source))
print('expected:', ' '.join(idx2en[idx] for idx in target))
print('pred:', pred)
print()
def train():
current_batches = 0
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
enc_voc = len(de2idx)
dec_voc = len(en2idx)
writer = SummaryWriter()
# Load data
X, Y = load_train_data()
# calc total batch count
num_batch = len(X) // hp.batch_size
model = AttModel(hp, enc_voc, dec_voc)
model.train()
model.cuda()
torch.backends.cudnn.benchmark = True
if not os.path.exists(hp.model_dir):
os.makedirs(hp.model_dir)
if hp.preload is not None and os.path.exists(hp.model_dir + '/history.pkl'):
with open(hp.model_dir + '/history.pkl', 'rb') as in_file:
history = pickle.load(in_file)
else:
history = {'current_batches': 0}
current_batches = history['current_batches']
optimizer = optim.Adam(model.parameters(), lr=hp.lr, betas=[0.9, 0.98], eps=1e-8)
if hp.preload is not None and os.path.exists(hp.model_dir + '/optimizer.pth'):
optimizer.load_state_dict(torch.load(hp.model_dir + '/optimizer.pth'))
if hp.preload is not None and os.path.exists(hp.model_dir + '/model_epoch_%02d.pth' % hp.preload):
model.load_state_dict(torch.load(hp.model_dir + '/model_epoch_%02d.pth' % hp.preload))
startepoch = int(hp.preload) if hp.preload is not None else 1
for epoch in range(startepoch, hp.num_epochs + 1):
current_batch = 0
for index, current_index in get_batch_indices(len(X), hp.batch_size):
tic = time.time()
x_batch = Variable(torch.LongTensor(X[index]).cuda())
y_batch = Variable(torch.LongTensor(Y[index]).cuda())
toc = time.time()
tic_r = time.time()
torch.cuda.synchronize()
optimizer.zero_grad()
loss, _, acc = model(x_batch, y_batch)
loss.backward()
optimizer.step()
torch.cuda.synchronize()
toc_r = time.time()
current_batches += 1
current_batch += 1
if current_batches % 10 == 0:
writer.add_scalar('./loss', loss.data.cpu().numpy(), current_batches)
writer.add_scalar('./acc', acc.data.cpu().numpy(), current_batches)
if current_batches % 5 == 0:
print('epoch %d, batch %d/%d, loss %f, acc %f' % (epoch, current_batch, num_batch, loss.data[0], acc.data[0]))
print('batch loading used time %f, model forward used time %f' % (toc - tic, toc_r - tic_r))
if current_batches % 100 == 0:
writer.export_scalars_to_json(hp.model_dir + '/all_scalars.json')
with open(hp.model_dir + '/history.pkl', 'wb') as out_file:
pickle.dump(history, out_file)
checkpoint_path = hp.model_dir + '/model_epoch_%02d' % epoch + '.pth'
torch.save(model.state_dict(), checkpoint_path)
torch.save(optimizer.state_dict(), hp.model_dir + '/optimizer.pth')
def __init__(self, transformerModel, output_dir):
self.transformerModel = transformerModel
self.output_dir = output_dir
self.Sources, self.Targets = load_train_data()
_, self.idx2de = load_de_vocab()
_, self.idx2en = load_en_vocab()
os.makedirs(self.output_dir, exist_ok=True)
def train():
print('-' * 30)
print('Loading and preprocessing train data...')
print('-' * 30)
# Prepare train and validation data
imgs_scans_train, imgs_mask_train = load_train_data()
imgs_scans_valid, imgs_mask_valid = load_validation_data()
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_scans_train = imgs_scans_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
imgs_scans_train /= 128. # scale input images to [0, 2]
imgs_scans_train = imgs_scans_train - 1. # scale input images to [-1, 1]
imgs_scans_valid = imgs_scans_valid.astype('float32')
imgs_mask_valid = imgs_mask_valid.astype('float32')
imgs_mask_valid /= 255. # scale masks to [0, 1]
imgs_scans_valid /= 128. # scale input images to [0, 2]
imgs_scans_valid = imgs_scans_valid - 1. # scale input images to [-1, 1]
imgs_scans_train = np.repeat(
imgs_scans_train, 3,
axis=4) # repeat three times before multi modality loading
imgs_scans_valid = np.repeat(
imgs_scans_valid, 3,
axis=4) # repeat three times before multi modality loading
print('-' * 30)
print('Creating and compiling model...')
print('-' * 30)
model = Unet_vgg.res_unet_vgg(image_depth=img_depth,
image_rows=img_rows,
image_cols=img_cols,
train_encoder=False)
model.compile(optimizer=Adam(lr=5e-5,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.000000199),
loss=segmentation_loss,
metrics=['accuracy', dice_coef])
model.summary()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model_checkpoint = ModelCheckpoint(os.path.join(weight_dir,
project_name + '.h5'),
monitor='val_dice_coef',
save_best_only=True,
mode='max')
# Load planar 3D encoder
model.load_weights(os.path.join(weight_dir, 'planar_3d_vgg.h5'),
by_name=True)
log_dir = 'logs'
if not os.path.exists(log_dir):
os.mkdir(log_dir)
csv_logger = CSVLogger(os.path.join(log_dir, project_name + '.txt'),
separator=',',
append=False)
print('-' * 30)
print('Fitting model...')
print('-' * 30)
model.fit(
x=imgs_scans_train,
y=imgs_mask_train,
batch_size=3,
epochs=100,
callbacks=[model_checkpoint, csv_logger],
shuffle=True,
validation_data=(imgs_scans_valid, imgs_mask_valid),
)
print('-' * 30)
print('Training finished')
print('-' * 30)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--dim_model", type=int, default=128)
parser.add_argument("--num_iterations", type=int, default=100000)
parser.add_argument("--vocab_path", type=str, default="./corpora/vocab.txt")
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'gpu')
with open(args.vocab_path, "rt") as f:
n_vocab = len(f.readlines())
X, class_weights = load_train_data("./data")
n_class = len(class_weights)
model = make_model(n_vocab, d_model=args.dim_model, batch_size=args.batch_size, n_class=n_class)
model = model.to(device)
print(model)
# epochs = 10
for iter in range(args.num_iterations):
x, y = next_batch(X, args.batch_size, args.dim_model)
# batches = get_batches(in_text, out_text, 10, 200)
# for x, y in batches:
x = torch.tensor(x, dtype=torch.float, device=device)
y = torch.tensor(y, dtype=torch.long, device=device)
y = torch.squeeze(y)
criterion, optimizer = get_criterion(model)
optimizer.zero_grad()
output = model(x, None)
parser.add_argument('--num_batches', type=int, default=30000)
parser.add_argument('--save_every', type=int, default=3000)
parser.add_argument('--maxlen', type=int, default=128)
parser.add_argument('--weight_tying', type=int, default=0)
parser.add_argument('--hidden_units', type=int, default=256)
parser.add_argument('--weighted_loss', type=int, default=1)
parser.add_argument('--dropout_rate', type=float, default=0.4)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--num_blocks', type=int, default=1)
parser.add_argument('--num_heads', type=int, default=8)
parser.add_argument('--num_epochs', type=int, default=9)
parser.add_argument('--sinusoid', type=int, default=0)
args = parser.parse_args()
word2idx, idx2word = load_vocab(args.vocab_path)
X, class_weights = load_train_data(args.train_path)
args.vocab_size = len(word2idx)
if args.weighted_loss:
args.class_weights = class_weights
with open(os.path.join(args.logdir, "args.pkl"), 'wb') as f:
pickle.dump(args, f)
# Construct graph
model = TransformerDecoder(is_training=True, args=args)
print("Graph loaded")
# Start session
with tf.Session(graph=model.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(args.ckpt_path)
def train():
print("Graph loading......Model name:{}".format(hp.modelname))
g = Graph()
print("Data loading...")
_, eng_names, _, kor_names = load_train_data()
_, val_eng_names, _, val_kor_names = load_evaluate_data(
eval_mode="validate")
early_stopping_count = 0
data_list = list(range(len(eng_names)))
with g.graph.as_default():
sv = tf.train.Saver()
with tf.Session() as sess:
# Initialize
sess.run(tf.global_variables_initializer())
best_valid_loss = 100000.
for epoch in range(1, hp.num_epochs + 1):
np.random.shuffle(data_list)
# # Attention Plot per epochs
# al = sess.run(g.alignments, {g.x: eng_names[data_list][:1],
# g.y: kor_names[data_list][:1]})
# plot_alignment(al[0], epoch - 1, eng_names[data_list][:1], kor_names[data_list][:1])
# Train
train_loss = 0
num_batch = len(eng_names) / hp.batch_size
for step in tqdm(range(num_batch),
total=num_batch,
ncols=70,
leave=False,
unit='b'):
name_ids = data_list[step *
hp.batch_size:step * hp.batch_size +
hp.batch_size]
loss, gs = sess.run([g.train_op, g.global_step], {
g.x: eng_names[name_ids],
g.y: kor_names[name_ids]
})
train_loss += loss
if step % 20 == 0:
print('\t step:{} train_loss:{:.3f}'.format(gs, loss))
train_loss /= num_batch
# Validation
valid_loss = 0.
for idx in range(0, len(val_eng_names), hp.batch_size):
v_loss = sess.run(
g.mean_loss, {
g.x: val_eng_names[idx:idx + hp.batch_size],
g.y: val_kor_names[idx:idx + hp.batch_size]
})
valid_loss += v_loss
valid_loss /= len(val_eng_names) / hp.batch_size
print(
"[epoch{}] train_loss={:.3f} validate_loss={:.3f} ".format(
epoch, train_loss, valid_loss))
# Stopping
if valid_loss <= best_valid_loss * 0.999:
best_valid_loss = valid_loss
sv.save(sess, "logdir/" + hp.modelname + '/model.ckpt')
else:
if hp.is_earlystopping:
early_stopping_count += 1
if early_stopping_count == 3:
print("Early Stopping...")
break
if cleared not in ref_:
ref_.append(cleared)
ref.append(ref_.index(cleared))
ref = np.asarray(ref).astype(np.float)
args.target_vocab_size = len(word2idx)
args.vocab_size = ref.shape[0]
else:
args.vocab_size = len(word2idx)
with open(os.path.join(args.logdir, "args.pkl"), 'wb') as f:
pickle.dump(args, f)
# Construct graph
model = TransformerDecoder(is_training=True, args=args)
print("Graph loaded")
X = load_train_data(args.train_path, args.vocab_path, args.maxlen)
pad_idx = word2idx["<pad>"]
num_batch = len(X) // args.batch_size
# Start session
with tf.Session(graph=model.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(args.ckpt_path)
if ckpt:
print("restoring from {}".format(ckpt.model_checkpoint_path))
saver.restore(sess, ckpt.model_checkpoint_path)
for epoch in range(1, args.num_epochs + 1):
gs = sess.run(model.global_step)
for step in range(num_batch - 1):
fig.colorbar(im, ax=ax)
plt.xlabel('Decoder timestep')
plt.ylabel('Encoder timestep')
plt.savefig(hp.logdir + '/alignment_%d' % gs, format='png')
if __name__ == '__main__':
# Load vocabulary
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
# Construct graph
g = Graph("train")
print("Graph loaded")
X, Y = load_train_data()
# calc total batch count
num_batch = len(X) // hp.batch_size
print(X.shape)
g.num_batch = num_batch
# Start session
sv = tf.train.Supervisor(graph=g.graph,
logdir=hp.logdir,
summary_op=None,
save_model_secs=0)
with sv.managed_session() as sess:
i = 0
for epoch in range(1, hp.num_epochs + 1):
if sv.should_stop(): break
for step in tqdm(range(g.num_batch),
parser.add_argument("mode", help="train or eval")
args = parser.parse_args()
en2idx, idx2en = load_de_en_vocab('processed-data/en.vocab.tsv')
de2idx, idx2de = load_de_en_vocab('processed-data/zh.vocab.tsv')
print("读取en,zh字典")
# load train data
en_npy_path = "./processed-data/train_en.npy"
zh_npy_path = "./processed-data/train_zh.npy"
if os.path.exists(en_npy_path) and os.path.exists(zh_npy_path):
print("load training data")
X = np.load(en_npy_path)
Y = np.load(zh_npy_path)
else:
X, Y = load_train_data(de2idx, en2idx)
np.save(en_npy_path, X)
np.save(zh_npy_path, Y)
# load test data
test_en_path = "./processed-data/test_en.npy"
test_s_path = "./processed-data/t_source.npy"
test_t_path = "./processed-data/t_target.npy"
if os.path.exists(test_en_path) and os.path.exists(
test_s_path) and os.path.exists(test_t_path):
print("load testing data")
X_test = np.load(test_en_path)
Source_test = np.load(test_s_path)
Target_test = np.load(test_t_path)
else:
X_test, Source_test, Target_test = load_test_data(de2idx, en2idx)
