def __init__(self,
phase,
visualize,
data_path,
data_path_valid,
data_path_test,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
clip_gradients,
max_gradient_norm,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf'),
reg_val=0):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
#print (data_base_dir)
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
self.s_gen_valid = DataGen(data_base_dir,
data_path_valid,
evaluate=True)
self.s_gen_test = DataGen(data_base_dir,
data_path_test,
evaluate=True)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('phase: %s' % phase)
logging.info('model_dir: %s' % (model_dir))
logging.info('load_model: %s' % (load_model))
logging.info('output_dir: %s' % (output_dir))
logging.info('steps_per_checkpoint: %d' % (steps_per_checkpoint))
logging.info('batch_size: %d' % (batch_size))
logging.info('num_epoch: %d' % num_epoch)
logging.info('learning_rate: %d' % initial_learning_rate)
logging.info('reg_val: %d' % (reg_val))
logging.info('max_gradient_norm: %f' % max_gradient_norm)
logging.info('clip_gradients: %s' % clip_gradients)
logging.info('valid_target_length %f' % valid_target_length)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('target_embedding_size: %f' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
logging.info('visualize: %s' % visualize)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 32, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(int(buckets[-1][0] + 1)):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.reg_val = reg_val
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.learning_rate = initial_learning_rate
self.clip_gradients = clip_gradients
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id):
cnn_model = CNN(self.img_data, True) #(not self.forward_only))
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(
axis=1, values=[self.conv_output, self.zero_paddings])
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru)
if not self.forward_only:
self.updates = []
self.summaries_by_bucket = []
with tf.device(gpu_device_id):
params = tf.trainable_variables()
# Gradients and SGD update operation for training the model.
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate)
for b in xrange(len(buckets)):
if self.reg_val > 0:
reg_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
logging.info('Adding %s regularization losses',
len(reg_losses))
logging.debug('REGULARIZATION_LOSSES: %s', reg_losses)
loss_op = self.reg_val * tf.reduce_sum(
reg_losses) + self.attention_decoder_model.losses[b]
else:
loss_op = self.attention_decoder_model.losses[b]
gradients, params = zip(
*opt.compute_gradients(loss_op, params))
if self.clip_gradients:
gradients, _ = tf.clip_by_global_norm(
gradients, max_gradient_norm)
# Add summaries for loss, variables, gradients, gradient norms and total gradient norm.
summaries = []
'''
for gradient, variable in gradients:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
summaries.append(tf.summary.histogram(variable.name, variable))
summaries.append(tf.summary.histogram(variable.name + "/gradients", grad_values))
summaries.append(tf.summary.scalar(variable.name + "/gradient_norm",
tf.global_norm([grad_values])))
'''
summaries.append(tf.summary.scalar("loss", loss_op))
summaries.append(
tf.summary.scalar("total_gradient_norm",
tf.global_norm(gradients)))
all_summaries = tf.summary.merge(summaries)
self.summaries_by_bucket.append(all_summaries)
# update op - apply gradients
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
self.saver_all = tf.train.Saver(tf.all_variables())
ckpt = tf.train.get_checkpoint_state(model_dir)
print(ckpt, load_model)
if ckpt and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
clip_gradients,
max_gradient_norm,
opt_attn,
session,
load_model,
gpu_id,
use_gru,
reg_val,
augmentation,
evaluate=False,
valid_target_length=float('inf')):
# Support two GPUs
gpu_device_id_1 = '/gpu:' + str(gpu_id)
gpu_device_id_2 = '/gpu:' + str(gpu_id)
if gpu_id == 2:
gpu_device_id_1 = '/gpu:' + str(gpu_id - 1)
gpu_device_id_2 = '/gpu:' + str(gpu_id - 2)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('phase: %s' % phase)
logging.info('model_dir: %s' % (model_dir))
logging.info('load_model: %s' % (load_model))
logging.info('output_dir: %s' % (output_dir))
logging.info('steps_per_checkpoint: %d' % (steps_per_checkpoint))
logging.info('batch_size: %d' % (batch_size))
logging.info('num_epoch: %d' % num_epoch)
logging.info('learning_rate: %d' % initial_learning_rate)
logging.info('reg_val: %d' % reg_val)
logging.info('max_gradient_norm: %f' % max_gradient_norm)
logging.info('opt_attn: %s' % opt_attn)
logging.info('clip_gradients: %s' % clip_gradients)
logging.info('valid_target_length %f' % valid_target_length)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('target_embedding_size: %f' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
logging.info('visualize: %s' % visualize)
logging.info('P(data augmentation): %s' % augmentation)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('use GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 64, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(int(buckets[-1][0] + 1)):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.reg_val = reg_val
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.learning_rate = initial_learning_rate
self.clip_gradients = clip_gradients
self.augmentation = augmentation
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id_1):
cnn_model = CNN(self.img_data, True) #(not self.forward_only))
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(
axis=1, values=[self.conv_output, self.zero_paddings])
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id_2):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru,
opt_attn=opt_attn)
if not self.forward_only:
self.updates = []
self.summaries_by_bucket = []
with tf.device(gpu_device_id_2):
params = tf.trainable_variables()
# Gradients and SGD update operation for training the model.
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate)
for b in xrange(len(buckets)):
if self.reg_val > 0:
reg_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
logging.info('Adding %s regularization losses',
len(reg_losses))
logging.debug('REGULARIZATION_LOSSES: %s', reg_losses)
loss_op = self.reg_val * tf.reduce_sum(
reg_losses) + self.attention_decoder_model.losses[b]
else:
loss_op = self.attention_decoder_model.losses[b]
gradients, params = zip(
*opt.compute_gradients(loss_op, params))
if self.clip_gradients:
gradients, _ = tf.clip_by_global_norm(
gradients, max_gradient_norm)
# Add summaries for loss, variables, gradients, gradient norms and total gradient norm.
summaries = []
'''
for gradient, variable in gradients:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
summaries.append(tf.summary.histogram(variable.name, variable))
summaries.append(tf.summary.histogram(variable.name + "/gradients", grad_values))
summaries.append(tf.summary.scalar(variable.name + "/gradient_norm",
tf.global_norm([grad_values])))
'''
summaries.append(tf.summary.scalar("loss", loss_op))
summaries.append(
tf.summary.scalar("total_gradient_norm",
tf.global_norm(gradients)))
all_summaries = tf.summary.merge(summaries)
self.summaries_by_bucket.append(all_summaries)
# update op - apply gradients
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
self.saver_all = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.global_variables_initializer())
#self.sess.run(init_new_vars_op)
# train or test as specified by phase
def launch(self):
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
writer = tf.summary.FileWriter(self.model_dir, self.sess.graph)
if self.phase == 'test':
if not distance_loaded:
logging.info(
'Warning: distance module not installed. Do whole sequence comparison instead.'
)
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
numerator = 0
denominator = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
_, step_loss, step_logits, step_attns = self.step(
encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info(
'step_time: %f, loss: %f, step perplexity: %f' %
(curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn]
for step_attn in step_attns
]).transpose([1, 0, 2])
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
lev = distance.levenshtein(output_valid, ground_valid)
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_incorrect = float(lev) / len(ground_valid)
num_incorrect = min(1.0, num_incorrect)
nchar = len(ground_valid)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_correct += 1. - num_incorrect
numerator += lev
denominator += nchar
logging.info('%f out of %d correct' % (num_correct, num_total))
logging.info('Lev: %f, length of test set: %f' %
(numerator, denominator))
logging.info(
'Global loss: %f, Global perplexity: %f' %
(loss, math.exp(loss) if loss < 300 else float('inf')))
elif self.phase == 'train':
total = (self.s_gen.get_size() // self.batch_size)
with tqdm(desc='Train: ', total=total) as pbar:
st = lambda aug: iaa.Sometimes(self.augmentation, aug)
seq = iaa.Sequential([
st(
iaa.Affine(
scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
}, # scale images to 80-120% of their size, individually per axis
translate_px={
"x": (-16, 16),
"y": (-16, 16)
}, # translate by -16 to +16 pixels (per axis)
rotate=(-45, 45), # rotate by -45 to +45 degrees
shear=(-16, 16), # shear by -16 to +16 degrees
))
])
for epoch in range(self.num_epoch):
logging.info('Generating first batch')
for i, batch in enumerate(self.s_gen.gen(self.batch_size)):
# Get a batch and make a step.
num_total = 0
num_correct = 0
numerator = 0
denominator = 0
start_time = time.time()
batch_len = batch['real_len']
bucket_id = batch['bucket_id']
img_data = batch['data']
img_data = seq.augment_images(
img_data.transpose(0, 2, 3, 1))
img_data = img_data.transpose(0, 3, 1, 2)
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
#logging.info('current_step: %d'%current_step)
#logging.info(np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()[0])
#print (np.array([target_weight.tolist() for target_weight in target_weights]).transpose()[0])
summaries, step_loss, step_logits, _ = self.step(
encoder_masks, img_data, zero_paddings,
decoder_inputs, target_weights, bucket_id,
self.forward_only)
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
lev = distance.levenshtein(
output_valid, ground_valid)
num_incorrect = float(lev) / len(ground_valid)
num_incorrect = min(1.0, num_incorrect)
nchar = len(ground_valid)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
num_correct += 1. - num_incorrect
numerator += lev
denominator += nchar
writer.add_summary(summaries, current_step)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
precision = num_correct / num_total
logging.info(
'step %f - time: %f, loss: %f, perplexity: %f, precision: %f, CER: %f, batch_len: %f'
% (current_step, curr_step_time, step_loss,
math.exp(step_loss)
if step_loss < 300 else float('inf'), precision,
numerator / denominator, batch_len))
loss += step_loss / self.steps_per_checkpoint
pbar.set_description(
'Train, loss={:.8f}'.format(step_loss))
pbar.update()
current_step += 1
# If there is an EOS symbol in outputs, cut them at that point.
#if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(data_utils.EOS_ID)]
#if data_utils.PAD_ID in decoder_inputs:
#decoder_inputs = decoder_inputs[:decoder_inputs.index(data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
logging.info(
"global step %d step-time %.2f loss %f perplexity "
"%.2f" % (self.global_step.eval(), step_time,
loss, math.exp(loss)
if loss < 300 else float('inf')))
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(
self.model_dir, "translate.ckpt")
logging.info("Saving model, current_step: %d" %
current_step)
self.saver_all.save(
self.sess,
checkpoint_path,
global_step=self.global_step)
step_time, loss = 0.0, 0.0
#sys.stdout.flush()
# step, read one batch, generate gradients
def step(self, encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError(
"Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError(
"Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
input_feed[self.img_data.name] = img_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(int(encoder_size)):
try:
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
except Exception as e:
pass
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [
self.updates[bucket_id], # Update Op that does SGD.
#self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id],
self.summaries_by_bucket[bucket_id]
]
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
else:
output_feed = [self.attention_decoder_model.losses[bucket_id]
] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += self.attention_decoder_model.attention_weights_histories[
bucket_id]
outputs = self.sess.run(output_feed, input_feed)
if not forward_only:
return outputs[2], outputs[1], outputs[3:(
3 + self.buckets[bucket_id][1]
)], None # Gradient norm summary, loss, no outputs, no attentions.
else:
return None, outputs[0], outputs[1:(
1 + self.buckets[bucket_id][1])], outputs[(
1 + self.buckets[bucket_id][1]
):] # No gradient norm, loss, outputs, attentions.
def visualize_attention(self, filename, attentions, output_valid,
ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.replace('/', '_'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
gt = ''.join([chr(c - 3 + 33) for c in ground_valid])
ot = ''.join([chr(c - 3 + 33) for c in output_valid])
fword.write(gt + '\n')
fword.write(ot)
with open(filename, 'rb') as img_file:
img = Image.open(img_file)
w, h = img.size
h = 32
img = img.resize((real_len, h), Image.ANTIALIAS)
if img.mode == '1':
img_data = np.asarray(img, dtype=bool) * np.iinfo(
np.uint8).max
img_data = img_data.astype(np.uint8)
else:
img_data = np.asarray(img, dtype=np.uint8)
data = []
for idx in range(len(output_valid)):
output_filename = os.path.join(output_dir,
'image_%d.jpg' % (idx))
# get the first fourth
attention = attentions[idx][:(int(real_len / 4) - 1)]
# repeat each values four times
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i / 4 - 1 and i / 4 - 1 < len(attention):
attention_orig[i] = attention[int(i / 4) - 1]
# do some scaling
attention_orig = np.convolve(
attention_orig,
[0.199547, 0.200226, 0.200454, 0.200226, 0.199547],
mode='same')
attention_orig = np.maximum(attention_orig, 0.3)
# copy values to other heights
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:, i] = attention_orig[i]
if len(img_data.shape) == 3:
attention_out = attention_out[:, :, np.newaxis]
data.append(attention_out[0, :])
img_out_data = img_data * attention_out
img_out = Image.fromarray(img_out_data.astype(np.uint8))
img_out.save(output_filename)
# plot ~ 5% of the time
if np.random.random() < 0.05:
fig = plt.figure(figsize=(2, 6))
gs = matplotlib.gridspec.GridSpec(
2,
1,
height_ratios=[len(ot) * 2, 1],
wspace=0.0,
hspace=0.0,
top=0.95,
bottom=0.05,
left=0.17,
right=0.845)
ax = plt.subplot(gs[0])
ax.imshow(data,
aspect='auto',
interpolation='nearest',
cmap='gray')
ax.set_xticklabels([])
ax.set_yticks(np.arange(len(ot)))
ax.set_yticklabels(ot.replace('|', ' '))
ax.tick_params(axis=u'both', which=u'both', length=0)
ax = plt.subplot(gs[1])
ax.imshow(img,
aspect='auto',
interpolation='nearest',
cmap='gray')
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.tick_params(axis=u'both', which=u'both', length=0)
fig.savefig(os.path.join(output_dir, 'att_mat.png'))
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_path_valid,
data_path_test,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
clip_gradients,
max_gradient_norm,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf'),
reg_val=0):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
#print (data_base_dir)
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
self.s_gen_valid = DataGen(data_base_dir,
data_path_valid,
evaluate=True)
self.s_gen_test = DataGen(data_base_dir,
data_path_test,
evaluate=True)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('phase: %s' % phase)
logging.info('model_dir: %s' % (model_dir))
logging.info('load_model: %s' % (load_model))
logging.info('output_dir: %s' % (output_dir))
logging.info('steps_per_checkpoint: %d' % (steps_per_checkpoint))
logging.info('batch_size: %d' % (batch_size))
logging.info('num_epoch: %d' % num_epoch)
logging.info('learning_rate: %d' % initial_learning_rate)
logging.info('reg_val: %d' % (reg_val))
logging.info('max_gradient_norm: %f' % max_gradient_norm)
logging.info('clip_gradients: %s' % clip_gradients)
logging.info('valid_target_length %f' % valid_target_length)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('target_embedding_size: %f' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
logging.info('visualize: %s' % visualize)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 32, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(int(buckets[-1][0] + 1)):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.reg_val = reg_val
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.learning_rate = initial_learning_rate
self.clip_gradients = clip_gradients
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id):
cnn_model = CNN(self.img_data, True) #(not self.forward_only))
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(
axis=1, values=[self.conv_output, self.zero_paddings])
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru)
if not self.forward_only:
self.updates = []
self.summaries_by_bucket = []
with tf.device(gpu_device_id):
params = tf.trainable_variables()
# Gradients and SGD update operation for training the model.
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate)
for b in xrange(len(buckets)):
if self.reg_val > 0:
reg_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
logging.info('Adding %s regularization losses',
len(reg_losses))
logging.debug('REGULARIZATION_LOSSES: %s', reg_losses)
loss_op = self.reg_val * tf.reduce_sum(
reg_losses) + self.attention_decoder_model.losses[b]
else:
loss_op = self.attention_decoder_model.losses[b]
gradients, params = zip(
*opt.compute_gradients(loss_op, params))
if self.clip_gradients:
gradients, _ = tf.clip_by_global_norm(
gradients, max_gradient_norm)
# Add summaries for loss, variables, gradients, gradient norms and total gradient norm.
summaries = []
'''
for gradient, variable in gradients:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
summaries.append(tf.summary.histogram(variable.name, variable))
summaries.append(tf.summary.histogram(variable.name + "/gradients", grad_values))
summaries.append(tf.summary.scalar(variable.name + "/gradient_norm",
tf.global_norm([grad_values])))
'''
summaries.append(tf.summary.scalar("loss", loss_op))
summaries.append(
tf.summary.scalar("total_gradient_norm",
tf.global_norm(gradients)))
all_summaries = tf.summary.merge(summaries)
self.summaries_by_bucket.append(all_summaries)
# update op - apply gradients
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
self.saver_all = tf.train.Saver(tf.all_variables())
ckpt = tf.train.get_checkpoint_state(model_dir)
print(ckpt, load_model)
if ckpt and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
#self.sess.run(init_new_vars_op)
# train or test as specified by phase
def launch(self):
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
writer = tf.summary.FileWriter(self.model_dir, self.sess.graph)
if self.phase == 'test':
if not distance_loaded:
logging.info(
'Warning: distance module not installed. Do whole sequence comparison instead.'
)
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
_, step_loss, step_logits, step_attns = self.step(
encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info(
'step_time: %f, loss: %f, step perplexity: %f' %
(curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn]
for step_attn in step_attns
]).transpose([1, 0, 2])
#print (step_attns)
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_incorrect = float(num_incorrect) / len(
ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_correct += 1. - num_incorrect
logging.info('%f out of %d correct' % (num_correct, num_total))
elif self.phase == 'train':
total = (self.s_gen.get_size() // self.batch_size)
WER = 1.0
swriter = open("result_log.txt", "a")
with tqdm(desc='Train: ', total=total) as pbar:
for epoch in range(self.num_epoch):
logging.info('Generating first batch)')
n_correct = 0
n_total = 0
for i, batch in enumerate(self.s_gen.gen(self.batch_size)):
# Get a batch and make a step.
num_total = 0
num_correct = 0
start_time = time.time()
batch_len = batch['real_len']
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
#logging.info('current_step: %d'%current_step)
#logging.info(np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()[0])
#print (np.array([target_weight.tolist() for target_weight in target_weights]).transpose()[0])
summaries, step_loss, step_logits, _ = self.step(
encoder_masks, img_data, zero_paddings,
decoder_inputs, target_weights, bucket_id,
self.forward_only)
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
num_incorrect = float(num_incorrect) / len(
ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
num_correct += 1. - num_incorrect
writer.add_summary(summaries, current_step)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / total
precision = num_correct / num_total
n_total += num_total
n_correct += num_correct
#logging.info('step %f - time: %f, loss: %f, perplexity: %f, precision: %f, batch_len: %f'%(current_step, curr_step_time, step_loss, math.exp(step_loss) if step_loss < 300 else float('inf'), precision, batch_len))
loss += step_loss / self.steps_per_checkpoint
pbar.set_description(
'Train, loss={:.8f}'.format(step_loss))
pbar.update()
current_step += 1
# If there is an EOS symbol in outputs, cut them at that point.
#if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(data_utils.EOS_ID)]
#if data_utils.PAD_ID in decoder_inputs:
#decoder_inputs = decoder_inputs[:decoder_inputs.index(data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics, and run evals.
'''if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
logging.info("global step %d step-time %.2f loss %f perplexity "
"%.2f" % (self.global_step.eval(), step_time, loss, perplexity))
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(self.model_dir, "translate.ckpt")
logging.info("Saving model, current_step: %d"%current_step)
self.saver_all.save(self.sess, checkpoint_path, global_step=self.global_step)
step_time, loss = 0.0, 0.0'''
#sys.stdout.flush()
print("Epoch " + str(epoch) + "WER = " +
str(1 - n_correct * 1.0 / n_total))
swriter.write("Epoch " + str(epoch) + " WER = " +
str(1 - n_correct * 1.0 / n_total) + "\n")
print('Run validation...')
valid_error_rate = self.eval(self.s_gen_valid)
print('Finished validation...')
print("WER on validation: %f" % valid_error_rate)
swriter.write("WER on validation: " +
str(valid_error_rate) + "\n")
if WER > valid_error_rate:
WER = valid_error_rate
checkpoint_path = os.path.join(self.model_dir,
"translate.ckpt")
logging.info("Saving model, current_step: %d" %
current_step)
logging.info("best WER on validation: %f" % WER)
self.saver_all.save(self.sess,
checkpoint_path,
global_step=self.global_step)
print('Run testing...')
test_error_rate = self.eval(self.s_gen_test)
print('Finished testing...')
logging.info("best WER on test: %f" % test_error_rate)
swriter.write("best WER on test: %f" +
str(test_error_rate) + "\n")
swriter.close()
# step, read one batch, generate gradients
def step(self, encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError(
"Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError(
"Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
input_feed[self.img_data.name] = img_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(int(encoder_size)):
try:
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
except Exception as e:
pass
#ipdb.set_trace()
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [
self.updates[bucket_id], # Update Op that does SGD.
#self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id],
self.summaries_by_bucket[bucket_id]
]
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
else:
output_feed = [self.attention_decoder_model.losses[bucket_id]
] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += self.attention_decoder_model.attention_weights_histories[
bucket_id]
outputs = self.sess.run(output_feed, input_feed)
if not forward_only:
return outputs[2], outputs[1], outputs[3:(
3 + self.buckets[bucket_id][1]
)], None # Gradient norm summary, loss, no outputs, no attentions.
else:
return None, outputs[0], outputs[1:(
1 + self.buckets[bucket_id][1])], outputs[(
1 + self.buckets[bucket_id][1]
):] # No gradient norm, loss, outputs, attentions.
def visualize_attention(self, filename, attentions, output_valid,
ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.replace('/', '_'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
fword.write(
' '.join([self.s_gen.get_char(c)
for c in ground_valid]) + '\n')
fword.write(' '.join(
[self.s_gen.get_char(c) for c in output_valid]))
with open(filename, 'rb') as img_file:
img = Image.open(img_file)
w, h = img.size
h = 32
img = img.resize((real_len, h), Image.ANTIALIAS)
img_data = np.asarray(img, dtype=np.uint8)
for idx in range(len(output_valid)):
output_filename = os.path.join(output_dir,
'image_%d.png' % (idx))
attention = attentions[idx][:(int(real_len / 4) - 1)]
# I have got the attention_orig here, which is of size 32*len(ground_truth), the only thing left is to visualize it and save it to output_filename
# TODO here
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i / 4 - 1 and i / 4 - 1 < len(attention):
attention_orig[i] = attention[int(i / 4) - 1]
attention_orig = np.convolve(
attention_orig,
[0.199547, 0.200226, 0.200454, 0.200226, 0.199547],
mode='same')
attention_orig = np.maximum(attention_orig, 0.3)
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:, i] = attention_orig[i]
if len(img_data.shape) == 3:
attention_out = attention_out[:, :, np.newaxis]
img_out_data = img_data * attention_out
img_out = Image.fromarray(img_out_data.astype(np.uint8))
img_out.save(output_filename)
#print (output_filename)
#assert False
def eval(self, data_gen):
num_correct = 0
num_total = 0
for batch in data_gen.gen(self.batch_size):
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
grounds = [
a for a in np.array([
decoder_input.tolist() for decoder_input in decoder_inputs
]).transpose()
]
_, step_loss, step_logits, step_attns = self.step(
encoder_masks,
img_data,
zero_paddings,
decoder_inputs,
target_weights,
bucket_id,
forward_only=True)
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist() for logit in step_logits
]).transpose()
]
for idx, output, ground in zip(range(len(grounds)), step_outputs,
grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
if self.visualize:
self.visualize_attention(file_list[idx],
step_attns[idx], output_valid,
ground_valid,
num_incorrect > 0, real_len)
num_incorrect = float(num_incorrect) / len(ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
num_correct += 1. - num_incorrect
return (num_total - num_correct) * 1.0 / num_total
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
evaluate=False,
valid_target_length=float('inf'),
use_lstm=True):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('data_path: %s' % (data_path))
logging.info('phase: %s' % phase)
logging.info('batch_size: %d' % batch_size)
logging.info('num_epoch: %d' % num_epoch)
logging.info('steps_per_checkpoint %d' % steps_per_checkpoint)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('model_dir: %s' % model_dir)
logging.info('target_embedding_size: %d' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 32, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
#with tf.device('/gpu:1'):
with tf.device('/gpu:0'):
cnn_model = CNN(self.img_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(
concat_dim=1, values=[self.conv_output, self.zero_paddings])
#with tf.device('/cpu:0'):
with tf.device('/gpu:0'):
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device('/gpu:0'):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_lstm=use_lstm)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
for param in params_raw:
#if 'running' in param.name or 'conv' in param.name or 'batch' in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
#self.gradient_norms = []
self.updates = []
with tf.device('/gpu:0'):
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate,
rho=0.95,
epsilon=1e-08,
use_locking=False,
name='Adadelta')
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.attention_decoder_model.losses[b], params)
#self.gradient_norms.append(norm)
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
#with tf.device('/gpu:1'):
with tf.device('/gpu:0'):
self.keras_updates = []
for old_value, new_value in cnn_model.model.updates:
self.keras_updates.append(tf.assign(old_value, new_value))
params_raw = tf.all_variables()
params_init = []
params_load = []
for param in params_raw:
#if 'Adadelta' in param.name and ('batch' in param.name or 'conv' in param.name):
# params_add.append(param)
#if not 'BiRNN' in param.name:
# params_load.append(param)
#else:
# params_init.append(param)
if 'running' in param.name or (
('conv' in param.name or 'batch' in param.name) and
('Ada' not in param.name)
) or 'BiRNN' in param.name or 'attention' in param.name:
params_load.append(param)
else:
params_init.append(param)
self.saver_all = tf.train.Saver(tf.all_variables())
#self.saver = tf.train.Saver(list(set(tf.all_variables())-set(params_add)))
self.saver = tf.train.Saver(params_load)
init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
#self.sess.run(init_new_vars_op)
# train or test as specified by phase
def launch(self):
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
if self.phase == 'test':
if not distance_loaded:
logging.info(
'Warning: distance module not installed. Do whole sequence comparison instead.'
)
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
#print (decoder_inputs)
#print (encoder_masks)
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
_, step_loss, step_logits, step_attns = self.step(
encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info(
'step_time: %f, step perplexity: %f' %
(curr_step_time,
math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn]
for step_attn in step_attns
]).transpose([1, 0, 2])
#print (step_attns)
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_incorrect = float(num_incorrect) / len(
ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
if self.visualize:
self.visualize_attention(
file_list[idx], step_attns[idx], output_valid,
ground_valid, num_incorrect > 0, real_len)
num_correct += 1. - num_incorrect
logging.info('%f out of %d correct' % (num_correct, num_total))
elif self.phase == 'train':
for epoch in range(self.num_epoch):
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
logging.info('current_step: %d' % current_step)
#logging.info(np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()[0])
#print (np.array([target_weight.tolist() for target_weight in target_weights]).transpose()[0])
_, step_loss, step_logits, _ = self.step(
encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info('step_time: %f, step perplexity: %f' %
(curr_step_time, math.exp(step_loss)
if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
# If there is an EOS symbol in outputs, cut them at that point.
#if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(data_utils.EOS_ID)]
#if data_utils.PAD_ID in decoder_inputs:
#decoder_inputs = decoder_inputs[:decoder_inputs.index(data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float(
'inf')
logging.info(
"global step %d step-time %.2f perplexity "
"%.2f" %
(self.global_step.eval(), step_time, perplexity))
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(
self.model_dir, "translate.ckpt")
logging.info("Saving model, current_step: %d" %
current_step)
self.saver_all.save(self.sess,
checkpoint_path,
global_step=self.global_step)
step_time, loss = 0.0, 0.0
#sys.stdout.flush()
# step, read one batch, generate gradients
def step(self, encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError(
"Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError(
"Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
if not forward_only:
input_feed[K.learning_phase()] = 0
else:
input_feed[K.learning_phase()] = 0
input_feed[self.img_data.name] = img_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(encoder_size):
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [
self.updates[bucket_id], # Update Op that does SGD.
#self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id]
]
output_feed += self.keras_updates
else:
output_feed = [self.attention_decoder_model.losses[bucket_id]
] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += self.attention_decoder_model.attention_weights_histories[
bucket_id]
outputs = self.sess.run(output_feed, input_feed)
if not forward_only:
return None, outputs[
1], None, None # Gradient norm, loss, no outputs, no attentions.
else:
return None, outputs[0], outputs[1:(
1 + self.buckets[bucket_id][1])], outputs[(
1 + self.buckets[bucket_id][1]
):] # No gradient norm, loss, outputs, attentions.
def visualize_attention(self, filename, attentions, output_valid,
ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.replace('/', '_'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
fword.write(' '.join([
chr(c - 13 + 97) if c - 13 + 97 > 96 else chr(c - 3 + 48)
for c in ground_valid
]) + '\n')
fword.write(' '.join([
chr(c - 13 + 97) if c - 13 + 97 > 96 else chr(c - 3 + 48)
for c in output_valid
]))
with open(filename, 'rb') as img_file:
img = Image.open(img_file)
w, h = img.size
h = 32
img = img.resize((real_len, h), Image.ANTIALIAS)
img_data = np.asarray(img, dtype=np.uint8)
for idx in range(len(output_valid)):
output_filename = os.path.join(output_dir,
'image_%d.jpg' % (idx))
attention = attentions[idx][:(real_len / 4 - 1)]
# I have got the attention_orig here, which is of size 32*len(ground_truth), the only thing left is to visualize it and save it to output_filename
# TODO here
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i / 4 - 1 and i / 4 - 1 < len(attention):
attention_orig[i] = attention[i / 4 - 1]
attention_orig = np.convolve(
attention_orig,
[0.199547, 0.200226, 0.200454, 0.200226, 0.199547],
mode='same')
attention_orig = np.maximum(attention_orig, 0.3)
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:, i] = attention_orig[i]
if len(img_data.shape) == 3:
attention_out = attention_out[:, :, np.newaxis]
img_out_data = img_data * attention_out
img_out = Image.fromarray(img_out_data.astype(np.uint8))
img_out.save(output_filename)
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
evaluate=False,
valid_target_length=float('inf'),
use_lstm=True):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('data_path: %s' % (data_path))
logging.info('phase: %s' % phase)
logging.info('batch_size: %d' % batch_size)
logging.info('num_epoch: %d' % num_epoch)
logging.info('steps_per_checkpoint %d' % steps_per_checkpoint)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('model_dir: %s' % model_dir)
logging.info('target_embedding_size: %d' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 32, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
#with tf.device('/gpu:1'):
with tf.device('/gpu:0'):
cnn_model = CNN(self.img_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(
concat_dim=1, values=[self.conv_output, self.zero_paddings])
#with tf.device('/cpu:0'):
with tf.device('/gpu:0'):
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device('/gpu:0'):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_lstm=use_lstm)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
for param in params_raw:
#if 'running' in param.name or 'conv' in param.name or 'batch' in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
#self.gradient_norms = []
self.updates = []
with tf.device('/gpu:0'):
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate,
rho=0.95,
epsilon=1e-08,
use_locking=False,
name='Adadelta')
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.attention_decoder_model.losses[b], params)
#self.gradient_norms.append(norm)
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
#with tf.device('/gpu:1'):
with tf.device('/gpu:0'):
self.keras_updates = []
for old_value, new_value in cnn_model.model.updates:
self.keras_updates.append(tf.assign(old_value, new_value))
params_raw = tf.all_variables()
params_init = []
params_load = []
for param in params_raw:
#if 'Adadelta' in param.name and ('batch' in param.name or 'conv' in param.name):
# params_add.append(param)
#if not 'BiRNN' in param.name:
# params_load.append(param)
#else:
# params_init.append(param)
if 'running' in param.name or (
('conv' in param.name or 'batch' in param.name) and
('Ada' not in param.name)
) or 'BiRNN' in param.name or 'attention' in param.name:
params_load.append(param)
else:
params_init.append(param)
self.saver_all = tf.train.Saver(tf.all_variables())
#self.saver = tf.train.Saver(list(set(tf.all_variables())-set(params_add)))
self.saver = tf.train.Saver(params_load)
init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
tb_logs,
tb_log_every,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf'),
old_model_version=False):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
if phase == 'train':
self.s_gen = DataGen(
data_base_dir, data_path, valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(
data_base_dir, data_path, evaluate=True)
logging.info('data_path: %s' % (data_path))
logging.info('dTensorboard_logging_path: %s' % (tb_logs))
logging.info('phase: %s' % phase)
logging.info('batch_size: %d' % batch_size)
logging.info('num_epoch: %d' % num_epoch)
logging.info('steps_per_checkpoint %d' % steps_per_checkpoint)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('model_dir: %s' % model_dir)
logging.info('target_embedding_size: %d' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.seq_data = tf.placeholder(tf.float32, shape=(None, 1, 10, None),
name='seq_data')
self.zero_paddings = tf.placeholder(tf.float32, shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(
tf.placeholder(tf.float32, shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(
i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(
i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.tb_logs = tb_logs
self.tb_log_every = tb_log_every
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id):
cnn_model = CNN(self.seq_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(concat_dim=1,
values=[self.conv_output,
self.zero_paddings])
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
params_run = []
for param in params_raw:
# if 'running' in param.name or 'conv' in param.name or 'batch'
# in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
# for old keras conversion
if 'running_std' in param.name:
params_run.append(param)
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
# self.gradient_norms = []
self.updates = []
with tf.device(gpu_device_id):
#opt = tf.train.AdadeltaOptimizer(
# learning_rate=initial_learning_rate, rho=0.95,
# epsilon=1e-08, use_locking=False, name='Adadelta')
opt = tf.train.AdamOptimizer(learning_rate=initial_learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.attention_decoder_model.losses[b], params)
# self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(gradients, params), global_step=self.global_step))
with tf.device(gpu_device_id):
self.keras_updates = []
for i in xrange(int(len(cnn_model.model.updates) / 2)):
old_value = cnn_model.model.updates[i * 2]
new_value = cnn_model.model.updates[i * 2 + 1]
self.keras_updates.append(tf.assign(old_value, new_value))
params_dict = {}
params_raw = tf.all_variables()
# params_init = []
# params_load = []
for param in params_raw:
name = param.name
# to be compatible with old version saved model
if 'BiRNN' in name:
name = name.replace('BiRNN/', 'BiRNN_')
if ':0' in name:
name = name.replace(':0', '')
params_dict[name] = param
self._activation_summary(param)
# if 'Adadelta' in param.name and ('batch' in param.name or
# 'conv' in param.name):
# params_add.append(param)
# if not 'BiRNN' in param.name:
# params_load.append(param)
# else:
# params_init.append(param)
# if 'BiRNN/' in param.name:
# param.name = param.name.replace('BiRNN/', 'BiRNN_')
# if 'running' in param.name or (('conv' in param.name or 'batch'
# in param.name) and ('Ada' not in param.name)) or 'BiRNN' in
# param.name or 'attention' in param.name:
# params_load.append(param)
# else:
# params_init.append(param)
#for b_id, loss in enumerate(self.attention_decoder_model.losses):
# print(loss)
# tf.scalar_summary("Bucket loss/" + str(buckets[b_id]), loss)
# tf.scalar_summary("Bucket perplexity/" + str(buckets[b_id]),
# tf.exp(loss))
self.summary_op = tf.merge_all_summaries()
self.saver_all = tf.train.Saver(params_dict)
# self.saver = tf.train.Saver(list(set(tf.all_variables())-set(
# params_add)))
# self.saver = tf.train.Saver(params_load)
# init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
self.model_loaded = True
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
# self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
if old_model_version:
for param in params_run:
self.sess.run([param.assign(tf.square(param))])
else:
self.model_loaded = False
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
tb_logs,
tb_log_every,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf'),
old_model_version=False):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
if phase == 'train':
self.s_gen = DataGen(
data_base_dir, data_path, valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(
data_base_dir, data_path, evaluate=True)
logging.info('data_path: %s' % (data_path))
logging.info('dTensorboard_logging_path: %s' % (tb_logs))
logging.info('phase: %s' % phase)
logging.info('batch_size: %d' % batch_size)
logging.info('num_epoch: %d' % num_epoch)
logging.info('steps_per_checkpoint %d' % steps_per_checkpoint)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('model_dir: %s' % model_dir)
logging.info('target_embedding_size: %d' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.seq_data = tf.placeholder(tf.float32, shape=(None, 1, 10, None),
name='seq_data')
self.zero_paddings = tf.placeholder(tf.float32, shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(
tf.placeholder(tf.float32, shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(
i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(
i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.tb_logs = tb_logs
self.tb_log_every = tb_log_every
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id):
cnn_model = CNN(self.seq_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(concat_dim=1,
values=[self.conv_output,
self.zero_paddings])
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
params_run = []
for param in params_raw:
# if 'running' in param.name or 'conv' in param.name or 'batch'
# in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
# for old keras conversion
if 'running_std' in param.name:
params_run.append(param)
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
# self.gradient_norms = []
self.updates = []
with tf.device(gpu_device_id):
#opt = tf.train.AdadeltaOptimizer(
# learning_rate=initial_learning_rate, rho=0.95,
# epsilon=1e-08, use_locking=False, name='Adadelta')
opt = tf.train.AdamOptimizer(learning_rate=initial_learning_rate)
for b in xrange(len(buckets)):
gradients = tf.gradients(
self.attention_decoder_model.losses[b], params)
# self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(gradients, params), global_step=self.global_step))
with tf.device(gpu_device_id):
self.keras_updates = []
for i in xrange(int(len(cnn_model.model.updates) / 2)):
old_value = cnn_model.model.updates[i * 2]
new_value = cnn_model.model.updates[i * 2 + 1]
self.keras_updates.append(tf.assign(old_value, new_value))
params_dict = {}
params_raw = tf.all_variables()
# params_init = []
# params_load = []
for param in params_raw:
name = param.name
# to be compatible with old version saved model
if 'BiRNN' in name:
name = name.replace('BiRNN/', 'BiRNN_')
if ':0' in name:
name = name.replace(':0', '')
params_dict[name] = param
self._activation_summary(param)
# if 'Adadelta' in param.name and ('batch' in param.name or
# 'conv' in param.name):
# params_add.append(param)
# if not 'BiRNN' in param.name:
# params_load.append(param)
# else:
# params_init.append(param)
# if 'BiRNN/' in param.name:
# param.name = param.name.replace('BiRNN/', 'BiRNN_')
# if 'running' in param.name or (('conv' in param.name or 'batch'
# in param.name) and ('Ada' not in param.name)) or 'BiRNN' in
# param.name or 'attention' in param.name:
# params_load.append(param)
# else:
# params_init.append(param)
#for b_id, loss in enumerate(self.attention_decoder_model.losses):
# print(loss)
# tf.scalar_summary("Bucket loss/" + str(buckets[b_id]), loss)
# tf.scalar_summary("Bucket perplexity/" + str(buckets[b_id]),
# tf.exp(loss))
self.summary_op = tf.merge_all_summaries()
self.saver_all = tf.train.Saver(params_dict)
# self.saver = tf.train.Saver(list(set(tf.all_variables())-set(
# params_add)))
# self.saver = tf.train.Saver(params_load)
# init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
self.model_loaded = True
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
# self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
if old_model_version:
for param in params_run:
self.sess.run([param.assign(tf.square(param))])
else:
self.model_loaded = False
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
# train or test as specified by phase
def launch(self):
step_time, loss = 0.0, 0.0
current_step = 0
if not os.path.exists(self.tb_logs):
os.makedirs(self.tb_logs)
summary_writer = tf.train.SummaryWriter(self.tb_logs, self.sess.graph)
previous_losses = []
if self.phase == 'test':
if not distance_loaded:
logging.info(
'Warning: distance module not installed. Do whole '
'sequence comparison instead.')
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
seq_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
grounds = [a for a in np.array(
[decoder_input.tolist() for decoder_input in
decoder_inputs]).transpose()]
_, step_loss, step_logits, step_attns = self.step(encoder_masks,
seq_data,
zero_paddings,
decoder_inputs,
target_weights,
bucket_id,
self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info('step_time: %f, loss: %f, step perplexity: %f' % (
curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [b for b in np.array(
[np.argmax(logit, axis=1).tolist() for logit in
step_logits]).transpose()]
if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn]
for step_attn in
step_attns]).transpose([1, 0, 2])
# print (step_attns)
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(output_valid,
ground_valid)
if self.visualize:
self.visualize_attention(file_list[idx],
step_attns[idx],
output_valid, ground_valid,
num_incorrect > 0,
real_len)
num_incorrect = float(num_incorrect) / len(ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
if self.visualize:
self.visualize_attention(file_list[idx],
step_attns[idx],
output_valid, ground_valid,
num_incorrect > 0,
real_len)
num_correct += 1. - num_incorrect
logging.info('%f out of %d correct' % (num_correct, num_total))
elif self.phase == 'train':
for epoch in range(self.num_epoch):
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
seq_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
logging.info('current_step: %d' % current_step)
# logging.info(np.array([decoder_input.tolist() for
# decoder_input in decoder_inputs]).transpose()[0])
# print (np.array([target_weight.tolist() for
# target_weight in target_weights]).transpose()[0])
_, step_loss, step_logits, _ = self.step(encoder_masks,
seq_data,
zero_paddings,
decoder_inputs,
target_weights,
bucket_id,
self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info(
'step_time: %f, step_loss: %f, step perplexity: %f' % (
curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else float(
'inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
# If there is an EOS symbol in outputs, cut them at that
# point.
# if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(
# data_utils.EOS_ID)]
# if data_utils.PAD_ID in decoder_inputs:
# decoder_inputs = decoder_inputs[:decoder_inputs.index(
# data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics,
# and run evals.
loss_dumps_path = os.path.join(self.tb_logs,
'loss_perp_log.tsv')
if current_step % self.tb_log_every == 0:
summary_str = self.sess.run(self.summary_op)
summary_writer.add_summary(summary_str, current_step)
perplexity = math.exp(step_loss) if loss < 300 else float('inf')
if self.model_loaded:
with open(loss_dumps_path, "a") as myfile :
myfile.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
time.time(),
epoch,
current_step,
step_loss,
perplexity,
curr_step_time))
else:
with open(loss_dumps_path, "w") as myfile :
myfile.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
time.time(),
epoch,
current_step,
step_loss,
perplexity,
curr_step_time))
self.model_loaded = True
if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float(
'inf')
logging.info(
"global step %d step-time %.2f loss %f perplexity "
"%.2f" % (self.global_step.eval(), step_time, loss,
perplexity))
previous_losses.append(loss)
print(
"epoch: {}, step: {}, loss: {}, perplexity: {}, "
"step_time: {}".format(
epoch, current_step, loss, perplexity,
curr_step_time))
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(self.model_dir,
"attn_seq.ckpt")
logging.info(
"Saving model, current_step: %d" % current_step)
self.saver_all.save(self.sess, checkpoint_path,
global_step=self.global_step)
step_time, loss = 0.0, 0.0
# sys.stdout.flush()
# step, read one batch, generate gradients
def step(self, encoder_masks, seq_data, zero_paddings, decoder_inputs,
target_weights,
bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError(
"Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError(
"Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights,
# as provided.
input_feed = {}
if not forward_only:
input_feed[K.learning_phase()] = 1
else:
input_feed[K.learning_phase()] = 0
input_feed[self.seq_data.name] = seq_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(encoder_size):
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [self.updates[bucket_id], # Update Op that does SGD.
# self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id]]
output_feed += self.keras_updates
else:
output_feed = [self.attention_decoder_model.losses[
bucket_id]] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += \
self.attention_decoder_model.attention_weights_histories[
bucket_id]
outputs = self.sess.run(output_feed, input_feed)
if not forward_only:
return None, outputs[
1], None, None # Gradient norm, loss, no outputs,
# no attentions.
else:
return None, outputs[0], outputs[1:(
1 + self.buckets[bucket_id][1])], outputs[(
1 + self.buckets[bucket_id][
1]):] # No gradient norm, loss, outputs, attentions.
def _activation_summary(self, x) :
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Args:
x: Tensor
Returns:
nothing
"""
tensor_name = x.name
tf.histogram_summary(tensor_name + '/activations', x)
def visualize_attention(self, filename, attentions, output_valid,
ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.split("/")[-1].split(
".")[0] + filename.split("/")[-1].split(".")[1])
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
gt_str = ' '.join([chr(c - 13 + 97) if c - 13 + 97 > 96 else chr(c - 3 + 48)
for c in ground_valid])
fword.write(gt_str + '\n')
output_str = ' '.join(
[chr(c - 13 + 97) if c - 13 + 97 > 96 else chr(c - 3 + 48) for c
in output_valid])
fword.write(output_str)
# with open(filename, 'rb') as seq_file:
seq = np.load(filename)
w, h = seq.shape
h = 10
seq = signal.resample(seq, real_len)
#seq = seq.transpose()
seq_data = np.asarray(seq, dtype=np.float32)
out_attns = []
output_filename = os.path.join(output_dir,'image.png')
for idx in range(len(output_valid)):
attention = attentions[idx][:(int(real_len / 16) - 1)]
# I have got the attention_orig here, which is of size 32*len(ground_truth),
# the only thing left is to visualize it and save it to output_filename
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i / 16 - 1 and i / 16 - 1 < len(attention):
attention_orig[i] = attention[int(i / 16) - 1]
attention_orig = np.convolve(attention_orig,
[0.199547, 0.200226, 0.200454,
0.200226, 0.199547], mode='same')
#attention_orig = np.maximum(attention_orig, 0.3)
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:, i] = attention_orig[i]
out_attns.append(attention_orig)
out_attns = np.vstack(out_attns)
out_attns = out_attns.transpose()
seq_np = np.array(seq_data)
rows, cols = seq_np.shape[0], seq_np.shape[1]
f1 = plt.figure()
#f2 = plt.figure()
ax1 = f1.add_subplot(121)
ax2 = f1.add_subplot(122)
y_axis_ticks = np.arange(0, rows, 1)
#x_axis_ticks = np.arange(1, rows, 1)
for i in range(cols):
dat = seq_np[:, i]
ax1.plot(dat, y_axis_ticks)
#ax1.set_title('Sharing Y axis')
ax2.imshow(out_attns, interpolation='nearest', aspect='auto', cmap=cm.jet)
#ax2.set_xticks(output_str.split(' '))
#plt.show()
plt.savefig(output_filename, bbox_inches='tight', dpi=750)
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf')):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(
data_base_dir, data_path, valid_target_len=valid_target_length, evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(
data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('data_path: %s' %(data_path))
logging.info('phase: %s' %phase)
logging.info('batch_size: %d' %batch_size)
logging.info('num_epoch: %d' %num_epoch)
logging.info('steps_per_checkpoint %d' %steps_per_checkpoint)
logging.info('target_vocab_size: %d' %target_vocab_size)
logging.info('model_dir: %s' %model_dir)
logging.info('target_embedding_size: %d' %target_embedding_size)
logging.info('attn_num_hidden: %d' %attn_num_hidden)
logging.info('attn_num_layers: %d' %attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32, shape=(None, 1, 32, None), name='img_data')
self.zero_paddings = tf.placeholder(tf.float32, shape=(None, None, 512), name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(tf.placeholder(tf.float32, shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
cnn_model = CNN(self.img_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(concat_dim=1, values=[self.conv_output, self.zero_paddings])
#with tf.device('/cpu:0'):
with tf.device(gpu_device_id):
self.perm_conv_output = tf.transpose(self.concat_conv_output, perm=[1, 0, 2])
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks = self.encoder_masks,
encoder_inputs_tensor = self.perm_conv_output,
decoder_inputs = self.decoder_inputs,
target_weights = self.target_weights,
target_vocab_size = target_vocab_size,
buckets = buckets,
target_embedding_size = target_embedding_size,
attn_num_layers = attn_num_layers,
attn_num_hidden = attn_num_hidden,
forward_only = self.forward_only,
use_gru = use_gru)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
for param in params_raw:
#if 'running' in param.name or 'conv' in param.name or 'batch' in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
#self.gradient_norms = []
self.updates = []
with tf.device(gpu_device_id):
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.AdadeltaOptimizer(learning_rate=initial_learning_rate, rho=0.95, epsilon=1e-08, use_locking=False, name='Adadelta')
for b in xrange(len(buckets)):
gradients = tf.gradients(self.attention_decoder_model.losses[b], params)
#self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(gradients, params), global_step=self.global_step))
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
self.keras_updates = []
for old_value, new_value in cnn_model.model.updates:
self.keras_updates.append(tf.assign(old_value, new_value))
params_raw = tf.all_variables()
params_init = []
params_load = []
for param in params_raw:
#if 'Adadelta' in param.name and ('batch' in param.name or 'conv' in param.name):
# params_add.append(param)
#if not 'BiRNN' in param.name:
# params_load.append(param)
#else:
# params_init.append(param)
if 'running' in param.name or (('conv' in param.name or 'batch' in param.name) and ('Ada' not in param.name)) or 'BiRNN' in param.name or 'attention' in param.name:
params_load.append(param)
else:
params_init.append(param)
self.saver_all = tf.train.Saver(tf.all_variables())
#self.saver = tf.train.Saver(list(set(tf.all_variables())-set(params_add)))
self.saver = tf.train.Saver(params_load)
init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
logging.info("Reading model parameters from %s" % ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf')):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(
data_base_dir, data_path, valid_target_len=valid_target_length, evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(
data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('data_path: %s' %(data_path))
logging.info('phase: %s' %phase)
logging.info('batch_size: %d' %batch_size)
logging.info('num_epoch: %d' %num_epoch)
logging.info('steps_per_checkpoint %d' %steps_per_checkpoint)
logging.info('target_vocab_size: %d' %target_vocab_size)
logging.info('model_dir: %s' %model_dir)
logging.info('target_embedding_size: %d' %target_embedding_size)
logging.info('attn_num_hidden: %d' %attn_num_hidden)
logging.info('attn_num_layers: %d' %attn_num_layers)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32, shape=(None, 1, 32, None), name='img_data')
self.zero_paddings = tf.placeholder(tf.float32, shape=(None, None, 512), name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(buckets[-1][0] + 1):
self.encoder_masks.append(tf.placeholder(tf.float32, shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}".format(i)))
self.sess = session
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
cnn_model = CNN(self.img_data)
self.conv_output = cnn_model.tf_output()
self.concat_conv_output = tf.concat(concat_dim=1, values=[self.conv_output, self.zero_paddings])
#with tf.device('/cpu:0'):
with tf.device(gpu_device_id):
self.perm_conv_output = tf.transpose(self.concat_conv_output, perm=[1, 0, 2])
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks = self.encoder_masks,
encoder_inputs_tensor = self.perm_conv_output,
decoder_inputs = self.decoder_inputs,
target_weights = self.target_weights,
target_vocab_size = target_vocab_size,
buckets = buckets,
target_embedding_size = target_embedding_size,
attn_num_layers = attn_num_layers,
attn_num_hidden = attn_num_hidden,
forward_only = self.forward_only,
use_gru = use_gru)
# Gradients and SGD update operation for training the model.
params_raw = tf.trainable_variables()
params = []
params_add = []
for param in params_raw:
#if 'running' in param.name or 'conv' in param.name or 'batch' in param.name:
if 'running' in param.name:
logging.info('parameter {0} NOT trainable'.format(param.name))
else:
logging.info('parameter {0} trainable'.format(param.name))
params.append(param)
logging.info(param.get_shape())
if not self.forward_only:
#self.gradient_norms = []
self.updates = []
with tf.device(gpu_device_id):
#opt = tf.train.GradientDescentOptimizer(self.learning_rate)
opt = tf.train.AdadeltaOptimizer(learning_rate=initial_learning_rate, rho=0.95, epsilon=1e-08, use_locking=False, name='Adadelta')
for b in xrange(len(buckets)):
gradients = tf.gradients(self.attention_decoder_model.losses[b], params)
#self.gradient_norms.append(norm)
self.updates.append(opt.apply_gradients(
zip(gradients, params), global_step=self.global_step))
#with tf.device('/gpu:1'):
with tf.device(gpu_device_id):
self.keras_updates = []
for old_value, new_value in cnn_model.model.updates:
self.keras_updates.append(tf.assign(old_value, new_value))
params_raw = tf.all_variables()
params_init = []
params_load = []
for param in params_raw:
#if 'Adadelta' in param.name and ('batch' in param.name or 'conv' in param.name):
# params_add.append(param)
#if not 'BiRNN' in param.name:
# params_load.append(param)
#else:
# params_init.append(param)
if 'running' in param.name or (('conv' in param.name or 'batch' in param.name) and ('Ada' not in param.name)) or 'BiRNN' in param.name or 'attention' in param.name:
params_load.append(param)
else:
params_init.append(param)
self.saver_all = tf.train.Saver(tf.all_variables())
#self.saver = tf.train.Saver(list(set(tf.all_variables())-set(params_add)))
self.saver = tf.train.Saver(params_load)
init_new_vars_op = tf.initialize_variables(params_init)
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path) and load_model:
logging.info("Reading model parameters from %s" % ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
#self.sess.run(init_new_vars_op)
# train or test as specified by phase
def launch(self):
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
if self.phase == 'test':
if not distance_loaded:
logging.info('Warning: distance module not installed. Do whole sequence comparison instead.')
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
#print (decoder_inputs)
#print (encoder_masks)
grounds = [a for a in np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()]
_, step_loss, step_logits, step_attns = self.step(encoder_masks, img_data, zero_paddings, decoder_inputs, target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info('step_time: %f, step perplexity: %f'%(curr_step_time, math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [b for b in np.array([np.argmax(logit, axis=1).tolist() for logit in step_logits]).transpose()]
if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn] for step_attn in step_attns]).transpose([1, 0, 2])
#print (step_attns)
for idx, output, ground in zip(range(len(grounds)), step_outputs, grounds):
flag_ground,flag_out = True,True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1,len(ground)):
s1 = output[j-1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(output_valid, ground_valid)
if self.visualize:
self.visualize_attention(file_list[idx], step_attns[idx], output_valid, ground_valid, num_incorrect>0, real_len)
num_incorrect = float(num_incorrect) / len(ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
if self.visualize:
self.visualize_attention(file_list[idx], step_attns[idx], output_valid, ground_valid, num_incorrect>0, real_len)
num_correct += 1. - num_incorrect
logging.info('%f out of %d correct' %(num_correct, num_total))
elif self.phase == 'train':
for epoch in range(self.num_epoch):
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
logging.info('current_step: %d'%current_step)
#logging.info(np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()[0])
#print (np.array([target_weight.tolist() for target_weight in target_weights]).transpose()[0])
_, step_loss, step_logits, _ = self.step(encoder_masks, img_data, zero_paddings, decoder_inputs, target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info('step_time: %f, step perplexity: %f'%(curr_step_time, math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
# If there is an EOS symbol in outputs, cut them at that point.
#if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(data_utils.EOS_ID)]
#if data_utils.PAD_ID in decoder_inputs:
#decoder_inputs = decoder_inputs[:decoder_inputs.index(data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(loss) if loss < 300 else float('inf')
logging.info("global step %d step-time %.2f perplexity "
"%.2f" % (self.global_step.eval(),
step_time, perplexity))
previous_losses.append(loss)
print("epoch: {}, step: {}, loss: {}, perplexity: {}, step_time: {}".format(epoch, current_step, loss, perplexity, curr_step_time))
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(self.model_dir, "translate.ckpt")
logging.info("Saving model, current_step: %d"%current_step)
self.saver_all.save(self.sess, checkpoint_path, global_step=self.global_step)
step_time, loss = 0.0, 0.0
#sys.stdout.flush()
# step, read one batch, generate gradients
def step(self, encoder_masks, img_data, zero_paddings, decoder_inputs, target_weights,
bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError("Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError("Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
if not forward_only:
input_feed[K.learning_phase()] = 0
else:
input_feed[K.learning_phase()] = 0
input_feed[self.img_data.name] = img_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(encoder_size):
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [self.updates[bucket_id], # Update Op that does SGD.
#self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id]]
output_feed += self.keras_updates
else:
output_feed = [self.attention_decoder_model.losses[bucket_id]] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += self.attention_decoder_model.attention_weights_histories[bucket_id]
outputs = self.sess.run(output_feed, input_feed)
if not forward_only:
return None, outputs[1], None, None # Gradient norm, loss, no outputs, no attentions.
else:
return None, outputs[0], outputs[1:(1+self.buckets[bucket_id][1])], outputs[(1+self.buckets[bucket_id][1]):] # No gradient norm, loss, outputs, attentions.
def visualize_attention(self, filename, attentions, output_valid, ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.replace('/', '_'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
fword.write(' '.join([chr(c-13+97) if c-13+97>96 else chr(c-3+48) for c in ground_valid])+'\n')
fword.write(' '.join([chr(c-13+97) if c-13+97>96 else chr(c-3+48) for c in output_valid]))
with open(filename, 'rb') as img_file:
img = Image.open(img_file)
w, h = img.size
h = 32
img = img.resize(
(real_len, h),
Image.ANTIALIAS)
img_data = np.asarray(img, dtype=np.uint8)
for idx in range(len(output_valid)):
output_filename = os.path.join(output_dir, 'image_%d.jpg'%(idx))
attention = attentions[idx][:(real_len/4-1)]
# I have got the attention_orig here, which is of size 32*len(ground_truth), the only thing left is to visualize it and save it to output_filename
# TODO here
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i/4-1 and i/4-1 < len(attention):
attention_orig[i] = attention[i/4-1]
attention_orig = np.convolve(attention_orig, [0.199547,0.200226,0.200454,0.200226,0.199547], mode='same')
attention_orig = np.maximum(attention_orig, 0.3)
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:,i] = attention_orig[i]
if len(img_data.shape) == 3:
attention_out = attention_out[:,:,np.newaxis]
img_out_data = img_data * attention_out
img_out = Image.fromarray(img_out_data.astype(np.uint8))
img_out.save(output_filename)
class Model(object):
def __init__(self,
phase,
visualize,
data_path,
data_base_dir,
output_dir,
batch_size,
initial_learning_rate,
num_epoch,
steps_per_checkpoint,
target_vocab_size,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
clip_gradients,
max_gradient_norm,
session,
load_model,
gpu_id,
use_gru,
evaluate=False,
valid_target_length=float('inf'),
reg_val=0):
gpu_device_id = '/gpu:' + str(gpu_id)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
logging.info('loading data')
# load data
if phase == 'train':
self.s_gen = DataGen(data_base_dir,
data_path,
valid_target_len=valid_target_length,
evaluate=False)
else:
batch_size = 1
self.s_gen = DataGen(data_base_dir, data_path, evaluate=True)
#logging.info('valid_target_length: %s' %(str(valid_target_length)))
logging.info('phase: %s' % phase)
logging.info('model_dir: %s' % (model_dir))
logging.info('load_model: %s' % (load_model))
logging.info('output_dir: %s' % (output_dir))
logging.info('steps_per_checkpoint: %d' % (steps_per_checkpoint))
logging.info('batch_size: %d' % (batch_size))
logging.info('num_epoch: %d' % num_epoch)
logging.info('learning_rate: %d' % initial_learning_rate)
logging.info('reg_val: %d' % (reg_val))
logging.info('max_gradient_norm: %f' % max_gradient_norm)
logging.info('clip_gradients: %s' % clip_gradients)
logging.info('valid_target_length %f' % valid_target_length)
logging.info('target_vocab_size: %d' % target_vocab_size)
logging.info('target_embedding_size: %f' % target_embedding_size)
logging.info('attn_num_hidden: %d' % attn_num_hidden)
logging.info('attn_num_layers: %d' % attn_num_layers)
logging.info('visualize: %s' % visualize)
buckets = self.s_gen.bucket_specs
logging.info('buckets')
logging.info(buckets)
if use_gru:
logging.info('ues GRU in the decoder.')
# variables
self.img_data = tf.placeholder(tf.float32,
shape=(None, 1, 32, None),
name='img_data')
self.zero_paddings = tf.placeholder(tf.float32,
shape=(None, None, 512),
name='zero_paddings')
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
for i in xrange(int(buckets[-1][0] + 1)):
self.encoder_masks.append(
tf.placeholder(tf.float32,
shape=[None, 1],
name="encoder_mask{0}".format(i)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs.append(
tf.placeholder(tf.int32,
shape=[None],
name="decoder{0}".format(i)))
self.target_weights.append(
tf.placeholder(tf.float32,
shape=[None],
name="weight{0}".format(i)))
self.reg_val = reg_val
self.sess = session #会话，客户端使用会话来和TF系统交互，一般的模式是，建立会话，此时会生成一张空图；在会话中添加节点和边，形成一张图，然后执行。
self.evaluate = evaluate
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.buckets = buckets
self.batch_size = batch_size
self.num_epoch = num_epoch
self.global_step = tf.Variable(0, trainable=False)
self.valid_target_length = valid_target_length
self.phase = phase
self.visualize = visualize
self.learning_rate = initial_learning_rate
self.clip_gradients = clip_gradients
self.target_vocab_size = target_vocab_size
if phase == 'train':
self.forward_only = False
elif phase == 'test':
self.forward_only = True
else:
assert False, phase
with tf.device(gpu_device_id):
cnn_model = CNN(self.img_data, not self.forward_only) #(True))
self.conv_output = cnn_model.tf_output()
#print ("self.conv_output1:{}".format(self.conv_output.get_shape()))
self.concat_conv_output = tf.concat(
axis=1, values=[self.conv_output, self.zero_paddings])
#print ("self.conv_output2:{}".format(self.concat_conv_output.get_shape()))
self.perm_conv_output = tf.transpose(self.concat_conv_output,
perm=[1, 0, 2])
with tf.device(gpu_device_id):
self.attention_decoder_model = Seq2SeqModel(
encoder_masks=self.encoder_masks,
encoder_inputs_tensor=self.perm_conv_output,
decoder_inputs=self.decoder_inputs,
target_weights=self.target_weights,
target_vocab_size=target_vocab_size,
buckets=buckets,
target_embedding_size=target_embedding_size,
attn_num_layers=attn_num_layers,
attn_num_hidden=attn_num_hidden,
forward_only=self.forward_only,
use_gru=use_gru)
if not self.forward_only:
self.updates = []
self.summaries_by_bucket = []
with tf.device(gpu_device_id):
params = tf.trainable_variables() #返回的是需要训练的变量列表
# Gradients and SGD update operation for training the model.
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate
) #learning_rate=0.001, rho=0.95, epsilon=1e-08
for b in xrange(len(buckets)):
if self.reg_val > 0:
reg_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES
) #从一个结合中取出全部变量，是一个列表;损失函数上加上正则项是防止过拟合的
logging.info('Adding %s regularization losses',
len(reg_losses))
logging.debug('REGULARIZATION_LOSSES: %s', reg_losses)
loss_op = self.reg_val * tf.reduce_sum(
reg_losses) + self.attention_decoder_model.losses[
b] #tf.reduce_sum 沿着指定轴求和
else:
loss_op = self.attention_decoder_model.losses[b]
gradients, params = zip(
*opt.compute_gradients(loss_op, params))
if self.clip_gradients:
gradients, _ = tf.clip_by_global_norm(
gradients, max_gradient_norm) #函数控制梯度的大小,避免梯度膨胀的问题
# Add summaries for loss, variables, gradients, gradient norms and total gradient norm.
summaries = []
'''
for gradient, variable in gradients:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
summaries.append(tf.summary.histogram(variable.name, variable))
summaries.append(tf.summary.histogram(variable.name + "/gradients", grad_values))
summaries.append(tf.summary.scalar(variable.name + "/gradient_norm",
tf.global_norm([grad_values])))
'''
summaries.append(tf.summary.scalar("loss", loss_op))
summaries.append(
tf.summary.scalar("total_gradient_norm",
tf.global_norm(gradients)))
all_summaries = tf.summary.merge(summaries)
self.summaries_by_bucket.append(all_summaries)
# update op - apply gradients
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
self.updates.append(
opt.apply_gradients(zip(gradients, params),
global_step=self.global_step))
#
if self.phase == 'train':
self.sess.run(tf.initialize_all_variables())
variables_to_restore = []
for v in tf.global_variables():
if not (v.name.startswith(
"embedding_attention_decoder/attention_decoder/AttnOutputProjection"
) or (v.name.startswith(
"embedding_attention_decoder/embedding"))):
variables_to_restore.append(v)
self.saver_all = tf.train.Saver(variables_to_restore)
else:
self.saver_all = tf.train.Saver(tf.all_variables())
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and load_model:
logging.info("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
#self.saver.restore(self.sess, ckpt.model_checkpoint_path)
self.saver_all.restore(self.sess, ckpt.model_checkpoint_path)
else:
logging.info("Created model with fresh parameters.")
self.sess.run(tf.initialize_all_variables())
#self.sess.run(init_new_vars_op)
self.saver_all = tf.train.Saver(tf.all_variables())
def chinese_display(self, filename, output, fw, flag):
lst = []
#print (len(output))
if flag == 1: #标签
fw.write(filename[0] + " ")
for c in output:
if c > 2 and c < self.target_vocab_size + 1: #4450:#24:#951:
#print ("label type:{}".format(type(c))) #<type 'numpy.int64'>
c = self.s_gen.label_word(c)
#print ("word type:{}".format(type(c))) #<type 'unicode'>
#print ("c word:{}".format(c.encode("utf-8")))
lst.append(c.encode(
"utf-8")) #c.decode("raw_unicode_escape").encode("utf-8")
fw.write(c)
#print ("11111111111")
#print (lst)
s = "".join(lst)
if flag == 1: #标签
fw.write(" ")
else:
fw.write("\n")
return s
def label_to_word(self, labels):
for c in labels:
if c > 2 and c < self.target_vocab_size + 1: #4450:#24:#951:
#print ("label type:{}".format(type(c))) #<type 'numpy.int64'>
c = self.s_gen.label_word(c)
#print ("word type:{}".format(type(c))) #<type 'unicode'>
#print ("c word:{}".format(c.encode("utf-8")))
lst.append(c.encode(
"utf-8")) #c.decode("raw_unicode_escape").encode("utf-8")
#words = "".join(lst)
return lst
def write_result(self, words, fw, flag):
for c in words:
fw.write(c)
if flag == 1: #标签
fw.write(" ")
else:
fw.write('\n')
# train or test as specified by phase
def launch(self):
#加载字典
#trieTree.trie = trieTree.construction_trietree("dict.txt", "lm.binary") #("userdic.txt")
step_time, loss = 0.0, 0.0
current_step = 0
previous_losses = []
writer = tf.summary.FileWriter(self.model_dir, self.sess.graph)
if self.phase == 'test':
if not distance_loaded:
logging.info(
'Warning: distance module not installed. Do whole sequence comparison instead.'
)
else:
logging.info('Compare word based on edit distance.')
num_correct = 0
num_total = 0
num_correct1 = 0
for batch in self.s_gen.gen(self.batch_size):
# Get a batch and make a step.
start_time = time.time()
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
file_list = batch['filenames']
real_len = batch['real_len']
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
_, step_loss, step_logits, step_attns = self.step(
encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, self.forward_only)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
logging.info(
'step_time: %f, loss: %f, step perplexity: %f' %
(curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else float('inf')))
loss += step_loss / self.steps_per_checkpoint
current_step += 1
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
'''if self.visualize:
step_attns = np.array([[a.tolist() for a in step_attn] for step_attn in step_attns]).transpose([1, 0, 2])
#print (step_attns)'''
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
'''
# 将标签转换成字
output_words = self.label_to_word(output_valid)
#ground_words = self.label_to_word(ground_valid)
# 添加字典矫正，英文准确率可提供10%
s = ''.join(output_words)#s = ''.join([chr(c-13+97) if c-13+97>96 else chr(c-3+48) for c in output_valid])
#print ("recongition word:{}".format(s)) #如果是汉字这样显示是有问题的
output_valid[:] = []
#输出矫正后的word
if trieTree.trie is not None:
word = trieTree.correct_word(s, 2, trieTree.trie)
#print ("correct word:{}".format(word))
for c in word:
output_valid.append( self.s_gen.word_label(c) )#ord(c) - 97 + 13 if ord(c) > 96 else ord(c) - 48 + 3 )
#print ("output_valid:{}".format(output_valid))'''
#print (output) #[14 13 31 31 35 27 27 16 2 2 2 2 2 2 2 12 2 2 2 2 2 2 23 2 2 2 2 2 2 2 2 2]
#print (output_valid) #[14, 13, 31, 31, 35, 27, 27, 16]
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
#print ("num_incorrect:{}\n".format(num_incorrect))
'''if self.visualize:
self.visualize_attention(file_list[idx], step_attns[idx], output_valid, ground_valid, num_incorrect>0, real_len)'''
num_incorrect = float(num_incorrect) / len(
ground_valid) #
num_incorrect1 = min(1.0, num_incorrect)
#print (num_incorrect1)
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
'''if self.visualize:
self.visualize_attention(file_list[idx], step_attns[idx], output_valid, ground_valid, num_incorrect>0, real_len)'''
#print ("num_incorrect --- :{}\n".format(num_incorrect))
num_correct1 += 1. - num_incorrect1
#print (num_incorrect)
#display chinese
#print ("grounds:{}".format(grounds))
#print ("ground_valid:{}".format(ground_valid))
#print ("output_valid:{}".format(output_valid))
if num_incorrect < 1:
self.chinese_display(file_list, ground_valid,
fw_correct, 1)
self.chinese_display(file_list, output_valid,
fw_correct, 0)
else:
self.chinese_display(file_list, ground_valid,
fw_imcorrect, 1)
self.chinese_display(file_list, output_valid,
fw_imcorrect, 0)
if num_incorrect < 1:
num_correct = num_correct + 1
logging.info('%f out of %d correct, precision=%f, %f, %f' %
(num_correct, num_total, num_correct /
(num_total + 1), num_correct1, num_correct1 /
(num_total + 1)))
elif self.phase == 'train':
total = (self.s_gen.get_size() // self.batch_size)
with tqdm(desc='Train: ', total=total) as pbar:
for epoch in range(self.num_epoch):
logging.info('Generating first batch)')
precision_ave = 0.0
for i, batch in enumerate(self.s_gen.gen(self.batch_size)):
# Get a batch and make a step.
num_total = 0
num_correct = 0
start_time = time.time()
batch_len = batch['real_len'] #图片的宽度
bucket_id = batch['bucket_id']
img_data = batch['data']
zero_paddings = batch['zero_paddings']
decoder_inputs = batch['decoder_inputs']
target_weights = batch['target_weights']
encoder_masks = batch['encoder_mask']
#logging.info('current_step: %d'%current_step)
#logging.info(np.array([decoder_input.tolist() for decoder_input in decoder_inputs]).transpose()[0])
#print (np.array([target_weight.tolist() for target_weight in target_weights]).transpose()[0])
summaries, step_loss, step_logits, _ = self.step(
encoder_masks, img_data, zero_paddings,
decoder_inputs, target_weights, bucket_id,
self.forward_only)
grounds = [
a for a in np.array([
decoder_input.tolist()
for decoder_input in decoder_inputs
]).transpose()
]
step_outputs = [
b for b in np.array([
np.argmax(logit, axis=1).tolist()
for logit in step_logits
]).transpose()
]
for idx, output, ground in zip(range(len(grounds)),
step_outputs, grounds):
flag_ground, flag_out = True, True
num_total += 1
output_valid = []
ground_valid = []
for j in range(1, len(ground)):
s1 = output[j - 1]
s2 = ground[j]
if s2 != 2 and flag_ground:
ground_valid.append(s2)
else:
flag_ground = False
if s1 != 2 and flag_out:
output_valid.append(s1)
else:
flag_out = False
if distance_loaded:
num_incorrect = distance.levenshtein(
output_valid, ground_valid)
num_incorrect = float(num_incorrect) / len(
ground_valid)
num_incorrect = min(1.0, num_incorrect)
else:
if output_valid == ground_valid:
num_incorrect = 0
else:
num_incorrect = 1
num_correct += 1. - num_incorrect
writer.add_summary(summaries, current_step)
curr_step_time = (time.time() - start_time)
step_time += curr_step_time / self.steps_per_checkpoint
precision = num_correct / num_total
precision_ave = precision_ave + precision #2000次后统计平均准确率
logging.info(
'step %f - time: %f, loss: %f, perplexity: %f, precision: %f, batch_len: %f'
% (current_step, curr_step_time, step_loss,
math.exp(step_loss) if step_loss < 300 else
float('inf'), precision, batch_len))
loss += step_loss / self.steps_per_checkpoint
#pbar.set_description('Train, loss={:.8f}'.format(step_loss))
#pbar.update()
current_step += 1
#print (epoch, current_step)
# If there is an EOS symbol in outputs, cut them at that point.
#if data_utils.EOS_ID in step_outputs:
# step_outputs = step_outputs[:step_outputs.index(data_utils.EOS_ID)]
#if data_utils.PAD_ID in decoder_inputs:
#decoder_inputs = decoder_inputs[:decoder_inputs.index(data_utils.PAD_ID)]
# print (step_outputs[0])
# Once in a while, we save checkpoint, print statistics, and run evals.
if current_step % self.steps_per_checkpoint == 0:
# Print statistics for the previous epoch.
perplexity = math.exp(
loss) if loss < 300 else float('inf')
logging.info(
"global step %d step-time %.2f loss %f perplexity %.2f precision_ave %.4f"
% (self.global_step.eval(), step_time, loss,
perplexity,
precision_ave / self.steps_per_checkpoint))
precision_ave = 0.0
previous_losses.append(loss)
# Save checkpoint and zero timer and loss.
if not self.forward_only:
checkpoint_path = os.path.join(
self.model_dir, "translate.ckpt")
logging.info("Saving model, current_step: %d" %
current_step)
#self.saver_all.save(self.sess, checkpoint_path, global_step=self.global_step)
self.saver_all.save(
self.sess,
checkpoint_path,
global_step=self.global_step)
step_time, loss = 0.0, 0.0
#sys.stdout.flush()
# step, read one batch, generate gradients
def step(self, encoder_masks, img_data, zero_paddings, decoder_inputs,
target_weights, bucket_id, forward_only):
# Check if the sizes match.
encoder_size, decoder_size = self.buckets[bucket_id]
if len(decoder_inputs) != decoder_size:
raise ValueError(
"Decoder length must be equal to the one in bucket,"
" %d != %d." % (len(decoder_inputs), decoder_size))
if len(target_weights) != decoder_size:
raise ValueError(
"Weights length must be equal to the one in bucket,"
" %d != %d." % (len(target_weights), decoder_size))
# Input feed: encoder inputs, decoder inputs, target_weights, as provided.
input_feed = {}
input_feed[self.img_data.name] = img_data
input_feed[self.zero_paddings.name] = zero_paddings
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[l].name] = decoder_inputs[l]
input_feed[self.target_weights[l].name] = target_weights[l]
for l in xrange(int(encoder_size)):
try:
input_feed[self.encoder_masks[l].name] = encoder_masks[l]
except Exception as e:
pass
#ipdb.set_trace()
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only: #train
output_feed = [
self.updates[bucket_id], # Update Op that does SGD.
#self.gradient_norms[bucket_id], # Gradient norm.
self.attention_decoder_model.losses[bucket_id],
self.summaries_by_bucket[bucket_id]
]
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
else:
output_feed = [self.attention_decoder_model.losses[bucket_id]
] # Loss for this batch.
#output_feed.append(self.conv_output1)#perm_conv_output)# img_data)
for l in xrange(decoder_size): # Output logits.
output_feed.append(
self.attention_decoder_model.outputs[bucket_id][l])
if self.visualize:
output_feed += self.attention_decoder_model.attention_weights_histories[
bucket_id]
outputs = self.sess.run(output_feed, input_feed) #(dict，list)
#print ("output_feed:{}\n".format(len(output_feed))) #list, w=96, len(output)=20
#print ("img_data size:{}\n".format(img_data.shape)) #img_data size:(1, 1, 32, 100)
#print ("cnn:{}\n".format(outputs[1][0][0][:10])) #list [27,?,512]
'''size1 = outputs[1].shape
print ("cnn:{}\n".format(size1))
for i in range(size1[3]):
fw_data.write(str(outputs[1][0][0][0][i])[:5] + " ")
fw_data.write("\n")'''
'''
for i in range(32):
for j in range(100):
fw_data.write(str(img_data[0][0][i][j]) + " ")
fw_data.write("\n")'''
#for i in range():
#print ("cnn:{}\n".format(outputs[1][:10][0][0]))
if not forward_only:
return outputs[2], outputs[1], outputs[3:(
3 + self.buckets[bucket_id][1]
)], None # Gradient norm summary, loss, no outputs, no attentions.
else:
return None, outputs[0], outputs[1:(
1 + self.buckets[bucket_id][1])], outputs[(
1 + self.buckets[bucket_id][1]
):] # No gradient norm, loss, outputs, attentions.
def visualize_attention(self, filename, attentions, output_valid,
ground_valid, flag_incorrect, real_len):
if flag_incorrect:
output_dir = os.path.join(self.output_dir, 'incorrect')
else:
output_dir = os.path.join(self.output_dir, 'correct')
output_dir = os.path.join(output_dir, filename.replace('/', '_'))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'word.txt'), 'w') as fword:
#fword.write(' '.join([chr(c-13+97) if c-13+97>96 else chr(c-3+48) for c in ground_valid])+'\n')
#fword.write(' '.join([chr(c-13+97) if c-13+97>96 else chr(c-3+48) for c in output_valid]))
#fword.write(' '.join([chinese(c).decode("raw_unicode_escape").encode("utf-8") for c in ground_valid])+'\n')
#fword.write(' '.join([chinese(c).decode("raw_unicode_escape").encode("utf-8") for c in output_valid]))
with open(filename, 'rb') as img_file:
img = Image.open(img_file)
w, h = img.size
h = 32
img = img.resize((real_len, h), Image.ANTIALIAS)
img_data = np.asarray(img, dtype=np.uint8)
for idx in range(len(output_valid)):
output_filename = os.path.join(output_dir,
'image_%d.jpg' % (idx))
attention = attentions[idx][:(int(real_len / 4) - 1)]
# I have got the attention_orig here, which is of size 32*len(ground_truth), the only thing left is to visualize it and save it to output_filename
# TODO here
attention_orig = np.zeros(real_len)
for i in range(real_len):
if 0 < i / 4 - 1 and i / 4 - 1 < len(attention):
attention_orig[i] = attention[int(i / 4) - 1]
attention_orig = np.convolve(
attention_orig,
[0.199547, 0.200226, 0.200454, 0.200226, 0.199547],
mode='same')
attention_orig = np.maximum(attention_orig, 0.3)
attention_out = np.zeros((h, real_len))
for i in range(real_len):
attention_out[:, i] = attention_orig[i]
if len(img_data.shape) == 3:
attention_out = attention_out[:, :, np.newaxis]
img_out_data = img_data * attention_out
img_out = Image.fromarray(img_out_data.astype(np.uint8))
img_out.save(output_filename)
