def evaluate():
"""
Evaluate the model on the validation set.
"""
val_loss.reset_states()
for inp, tar in val_dataset:
tar_inp = tar[:, :-1]
tar_real = tar[:, 1:]
mask = masks.create_look_ahead_mask(tf.shape(tar_inp)[1])
predictions, _ = model(inp,
tar_inp,
training=False,
look_ahead_mask=mask,
dec_padding_mask=None)
loss = loss_function(tar_real, predictions, val_loss_object)
val_loss(loss)
val_accuracy(tar_real, predictions)
global val_bleu
val_bleu = bleu_score(predictions=predictions, labels=tar_real)
ckpt_manager.save()
def train_step(inp, tar, evaluate_step):
"""
A training step on a batch.
"""
tar_inp = tar[:, :-1]
tar_real = tar[:, 1:]
mask = masks.create_look_ahead_mask(tf.shape(tar_inp)[1])
with tf.GradientTape() as tape:
predictions, _ = model(inp,
tar_inp,
True,
look_ahead_mask=mask,
dec_padding_mask=None)
loss = loss_function(tar_real, predictions, train_loss_object)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
global train_bleu
if evaluate_step:
train_loss(loss)
train_accuracy(tar_real, predictions)
train_bleu = bleu_score(predictions=predictions, labels=tar_real)
def score_helper(image_path):
caption = generate_caption(image_path, vocab, encoder, decoder,
transform)
generated_score, theoratical_score = bleu_score(
image_path, caption, name_caption_frame)
total_generated_score.append(generated_score)
total_theoratical_score.append(theoratical_score)
print(generated_score, theoratical_score)
def train_epochs(self, encoder, decoder, train_data, dev_data=None, \
pred_=None):
encoder.to(self.device)
decoder.to(self.device)
train_dataloader = DataLoader(train_data, self.batch_size, shuffle=True)
encoder_optimizer = optim.Adam(filter(lambda p: p.requires_grad, \
encoder.parameters()), lr=self.learning_rate)
decoder_optimizer = optim.Adam(filter(lambda p: p.requires_grad, \
decoder.parameters()), lr=self.learning_rate)
checkpoint_epoch=0
total_epoch = 0
train_score=[0]
val_score=[0]
if self.snapshot != None:
print('loading model from : {}'.format(self.snapshot))
checkpoint = torch.load(self.snapshot)
total_epoch = checkpoint['epochs']
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
encoder_optimizer.load_state_dict(checkpoint['encoder_optimizer'])
decoder_optimizer.load_state_dict(checkpoint['decoder_optimizer'])
t = tqdm(range(1, self.epochs+1))
try:
for epoch in t:
loss = self.train_batches(encoder, encoder_optimizer, decoder, \
decoder_optimizer, train_dataloader)
if (epoch) % self.eval_frequency == 0:
checkpoint_epoch += self.eval_frequency
train_predict = pred_.predict_dataset(encoder, decoder, train_data)
if dev_data is None:
t.set_description("loss %s" % loss)
print('Please provide a dev set for visualize the plot')
else:
dev_predict = pred_.predict_dataset(encoder, decoder, dev_data)
bleu = bleu_score(dev_predict[1], dev_predict[0])
t.set_description("loss {}, bleu {}".format(loss, bleu))
path = self.path + "/model" + str(total_epoch+epoch) +".pt"
save_model(checkpoint_epoch+total_epoch, encoder, decoder, encoder_optimizer, decoder_optimizer, path)
except KeyboardInterrupt:
# Code to "save"
print('save model')
save_model(checkpoint_epoch+total_epoch, encoder, decoder, encoder_optimizer, decoder_optimizer, path)
def on_batch_begin(self, last_input, last_target, train, **kwargs):
"""
Calculates output sentence using beam search for every batch of
validation examples.
"""
if not train:
batch_size = last_input.size(0)
for in_data in last_input.split(min(batch_size, 16), dim=0):
src_data, tgt_data = in_data.split([self.Ts, self.Tt], dim=1)
out_data = beam_search(self.learn.model, src_data,
self.beam_size, self.Tt - 1)
bleu = 0.0
for b in range(out_data.shape[0]):
out_len = out_data.shape[1]
tgt_len = tgt_data.shape[1]
out_l = []
for i in range(out_data.shape[1]):
if (out_data[b][i].item() == self.eos
or out_data[b][i].item() == self.pad):
out_len = i
break
out_l.append(str(out_data[b][i].item()))
tgt_l = []
for i in range(1, self.Tt):
if (tgt_data[b][i].item() == self.eos
or tgt_data[b][i].item() == self.pad):
tgt_len = i
# The Moses BLEU score script gives 0 for sentences
# of length less than 4, so ignore those BLEU score.
if i < 4:
batch_size -= 1
break
tgt_l.append(str(tgt_data[b][i].item()))
if out_len >= 4 and tgt_len >= 4:
self.count += 1
bleu += bleu_score([' '.join(out_l)],
[[' '.join(tgt_l)]]) * 100
self.bleu += bleu
def main(args):
# Folder setting
if not os.path.isdir('./model'):
os.mkdir('./model')
if not os.path.isdir('./result'):
os.mkdir('./result')
# Hyparameters setting
epochs = args.epochs
batch_size = args.batch_size
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = DataSetWrapper(batch_size,
args.num_workers,
args.valid_size,
input_shape=(224, 224, 3))
train_loader, valid_loader = dataset.get_data_loaders()
vocab = dataset.dataset.vocab
model = make_model(len(vocab), 512, vocab.stoi['<PAD>'],
vocab.stoi['<SOS>'], vocab.stoi['<EOS>'],
device).to(device)
optimizer = torch.optim.RMSprop(params=model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss().to(device)
# torch.set_printoptions(profile='full')
# torch.autograd.set_detect_anomaly(True)
print("Using device: " + str(device))
if args.prev_model != '':
checkpoint = torch.load('./model/' + args.prev_model,
map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
# model.eval()
if not args.inference:
# Train
best_bleu = 0
train_loss_list, valid_loss_list = [], []
bleu_list = []
print('Start training')
for epoch in range(epochs):
train_total_loss, valid_total_loss = 0.0, 0.0
model.train()
for x, tgt, tgt_len, _ in train_loader:
# tgt: (batch_size, tgt_len)
# (batch_size, tgt_len, vocab_size), (batch_size, tgt_len, num pixels)
x = x.to(device)
tgt = tgt.to(device)
tgt_len = tgt_len.to(device)
predictions, tgt_len, alpha = model(x, tgt, tgt_len, True)
predictions = predictions[:, 1:]
packed_predictions = pack_padded_sequence(
predictions, tgt_len, batch_first=True)[0].to(device)
tgt = tgt[:, 1:]
packed_tgt = pack_padded_sequence(
tgt, tgt_len, batch_first=True)[0].to(device)
optimizer.zero_grad()
loss = criterion(packed_predictions, packed_tgt)
loss += ((1 - alpha.sum(dim=1))**2).mean()
loss.backward()
optimizer.step()
_, predictions = torch.max(predictions, dim=2)
train_total_loss += loss
hypotheses = []
references = []
model.eval()
with torch.no_grad():
for x, tgt, tgt_len, all_tgt in valid_loader:
x = x.to(device)
tgt = tgt.to(device)
tgt_len = tgt_len.to(device)
predictions, tgt_len, alpha = model(x, tgt, tgt_len, False)
predictions = predictions[:, 1:]
packed_predictions = pack_padded_sequence(
predictions, tgt_len, batch_first=True)[0].to(device)
tgt = tgt[:, 1:]
packed_tgt = pack_padded_sequence(
tgt, tgt_len, batch_first=True)[0].to(device)
loss = criterion(packed_predictions, packed_tgt)
loss += ((1 - alpha.sum(dim=1))**2).mean()
valid_total_loss += loss
_, predictions = torch.max(predictions, dim=2)
# Calculate BLEU
# TODO: Collect all reference captions, not one
predictions = predictions.cpu().tolist()
all_tgt = all_tgt.tolist()
t_prediciotns = []
t_tgt = []
for i in range(len(tgt)):
t_tgt.append(all_tgt[i])
t_prediciotns.append(predictions[i][:tgt_len[i] - 1])
predictions = t_prediciotns
tgt = t_tgt
hypotheses.extend(predictions)
references.extend(tgt)
assert len(references) == len(hypotheses)
bleus = bleu_score(hypotheses, references)
bleu_list.append(bleus[0])
train_loss_list.append(train_total_loss)
valid_loss_list.append(valid_loss_list)
if (not args.no_save) and best_bleu <= bleus[0]:
best_bleu = bleus[0]
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state': optimizer.state_dict(),
'bleu': best_bleu
}, './model/' + str(epochs) + ".pt")
print('Model saved')
print(
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + "|| [" +
str(epoch) + "/" + str(epochs) + "], train_loss = " +
str(train_total_loss) + ", valid_loss = " +
str(valid_total_loss) + ", BLEU = " + str(bleus[4]) + ', ' +
str(bleus[0]) + '/' + str(bleus[1]) + '/' + str(bleus[2]) +
'/' + str(bleus[3]))
plot_loss_curve(train_loss_list, valid_loss_list, bleus)
else:
print("Start Inference")
"""
Show image with caption
ref: https://github.com/AaronCCWong/Show-Attend-and-Tell
"""
# Load model
assert args.prev_model != '' and args.img_path != ''
checkpoint = torch.load('./model/' + args.prev_model,
map_location=device)
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
model.eval()
img = Image.open(args.img_path).convert('RGB')
img = dataset.dataset.transform(img)
img = torch.FloatTensor(img)
img = img.unsqueeze(0)
img_feature = model.encode(img)
img_feature = img_feature.view(img_feature.shape[0], -1,
img_feature.shape[-1])
tgt_len = [torch.tensor(args.max_length)]
tgt = torch.zeros(1, args.max_length)
sentence, tgt_len, alpha = model.decode(img_feature, tgt, tgt_len,
False, True)
_, sentence = torch.max(sentence, dim=2)
sentence = sentence.squeeze()
sentence = sentence[:tgt_len[0]]
sentence = vocab.interpret(sentence.tolist())
print(sentence)
img = Image.open(args.img_path)
w, h = img.size
if w > h:
w = w * 256 / h
h = 256
else:
h = h * 256 / w
w = 256
left = (w - 224) / 2
top = (h - 224) / 2
resized_img = img.resize((int(w), int(h)), Image.BICUBIC).crop(
(left, top, left + 224, top + 224))
img = np.array(resized_img.convert('RGB').getdata()).reshape(
224, 224, 3)
img = img.astype('float32') / 255
num_words = len(sentence)
w = np.round(np.sqrt(num_words))
h = np.ceil(np.float32(num_words) / w)
alpha = alpha.clone().detach().squeeze()
plot_height = np.ceil((num_words + 3) / 4.0)
ax1 = plt.subplot(4, plot_height, 1)
plt.imshow(img)
plt.axis('off')
for idx in range(num_words):
ax2 = plt.subplot(4, plot_height, idx + 2)
label = sentence[idx]
plt.text(0, 1, label, backgroundcolor='white', fontsize=13)
plt.text(0, 1, label, color='black', fontsize=13)
plt.imshow(img)
shape_size = 14
alpha_img = skimage.transform.pyramid_expand(alpha[idx, :].reshape(
shape_size, shape_size),
upscale=16,
sigma=20)
plt.imshow(alpha_img, alpha=0.8)
plt.set_cmap(cm.Greys_r)
plt.axis('off')
plt.show()
workspace_path = lambda file_path: os.path.join(data_root_dir, file_path)
paras_file, titles_file, embedding_file = workspace_path(paras_file), \
workspace_path(titles_file), workspace_path(embedding_file)
input_data, inputs_for_tf, input_placeholders = input_generator.get_inputs(
paras_file, titles_file, embedding_file, FLAGS)
###########
## Model ##
###########
outputs = RNNModel(inputs_for_tf,
FLAGS,
is_training=IS_TRAINING,
multirnn=False)
blue_score = bleu.bleu_score(predictions=outputs.sample_id,
labels=inputs_for_tf['title_batch'][:, 1:])
##############
## Training ##
##############
sess = tf.Session()
tf.tables_initializer().run(session=sess)
sess.run(tf.global_variables_initializer())
sess.run(
input_placeholders['embedding_init'],
feed_dict={input_placeholders['embedding_ph']: input_data['embedding']})
saver = tf.train.import_meta_graph('checkpoints.meta')
saver.restore(sess, tf.train.latest_checkpoint('.'))
sess.run(inputs_for_tf['iterator'].initializer,
feed_dict={
def model_fn(features, labels, mode):
with tf.variable_scope("transformer_vqvae",
initializer=tf.variance_scaling_initializer(
hparams.initializer_gain,
mode="fan_avg",
distribution="uniform")):
if mode != tf.estimator.ModeKeys.TRAIN:
for key in hparams.keys():
if key.endswith("dropout"):
setattr(hparams, key, 0.0)
with tf.variable_scope("embeddings",
initializer=tf.random_normal_initializer(
0.0, hparams.hidden_size**-0.5)):
source_embeddings = tf.get_variable(
"source_embeddings",
[len(hparams.source_vocab), hparams.hidden_size],
tf.float32)
if hparams.shared_embedding:
target_embeddings = source_embeddings
else:
target_embeddings = tf.get_variable(
"target_embeddings",
[len(hparams.target_vocab), hparams.hidden_size],
tf.float32)
encoder_input_layer = commons.input_layer(source_embeddings,
hparams)
decoder_input_layer = commons.input_layer(target_embeddings,
hparams)
output_layer = tf.layers.Dense(len(hparams.target_vocab),
use_bias=False,
name="output")
# create model
x_enc = encoder_input_layer(features["sources"])
model = TransformerNAT(hparams, mode)
# decode
if mode != tf.estimator.ModeKeys.PREDICT:
x_dec = decoder_input_layer(features["targets"])
decoder_outputs, losses = model.body(features={
'inputs': x_enc,
'targets': x_dec
})
logits = output_layer(decoder_outputs)
predictions = tf.argmax(logits, -1)
tgt_len = commons.shape_list(features["targets"])[1]
losses["cross_entropy"] = commons.compute_loss(
logits[:, :tgt_len], features["targets"])
# losses
loss = 0.
for k, l in losses.items():
tf.summary.scalar(k, l)
loss += l
else:
decoder_outputs = model.infer(features={'inputs': x_enc})
logits = output_layer(decoder_outputs)
predictions = tf.argmax(logits, -1)
loss = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = commons.get_train_op(loss, hparams)
else:
train_op = None
if mode == tf.estimator.ModeKeys.EVAL:
# Names tensors to use in the printing tensor hook.
targets = tf.identity(features["targets"], "targets")
predictions = tf.identity(predictions, "predictions")
bleu_score = bleu.bleu_score(predictions, targets)
eval_metrics = {
"metrics/approx_bleu_score": tf.metrics.mean(bleu_score)
}
# Summaries
eval_summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir=os.path.join(hparams.model_dir, "eval"),
summary_op=tf.summary.merge_all())
eval_summary_hooks = [eval_summary_hook]
else:
eval_metrics = None
eval_summary_hooks = None
return tf.estimator.EstimatorSpec(mode,
predictions=predictions,
loss=loss,
eval_metric_ops=eval_metrics,
evaluation_hooks=eval_summary_hooks,
train_op=train_op)