def __init__(self):
img_width_range = cfg.img_width_range
word_len = cfg.word_len
self.batch_size = cfg.batch_size
self.visualize = cfg.visualize
gpu_device_id = '/gpu:' + str(cfg.gpu_id)
if cfg.gpu_id == -1:
gpu_device_id = '/cpu:0'
print("Using CPU model!")
with tf.device(gpu_device_id):
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.bucket_specs = [(int(math.floor(64 / 4)), int(word_len + 2)),
(int(math.floor(108 / 4)), int(word_len + 2)),
(int(math.floor(140 / 4)), int(word_len + 2)),
(int(math.floor(256 / 4)), int(word_len + 2)),
(int(math.floor(img_width_range[1] / 4)),
int(word_len + 2))]
buckets = self.buckets = self.bucket_specs
self.decoder_inputs = []
self.encoder_masks = []
self.target_weights = []
with tf.device(gpu_device_id):
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.bucket_min_width, self.bucket_max_width = img_width_range
self.image_height = cfg.img_height
self.valid_target_len = cfg.valid_target_len
self.forward_only = True
self.bucket_data = {
i: BucketData()
for i in range(self.bucket_max_width + 1)
}
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=cfg.target_vocab_size,
buckets=self.buckets,
target_embedding_size=cfg.target_embedding_size,
attn_num_layers=cfg.attn_num_layers,
attn_num_hidden=cfg.attn_num_hidden,
forward_only=self.forward_only,
use_gru=cfg.use_gru)
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)
self.sess = tf.Session(config=tf.ConfigProto(
allow_soft_placement=True))
self.saver_all = tf.train.Saver(tf.global_variables())
self.saver_all.restore(self.sess, cfg.ocr_model_path)
def __init__(self,
phase,
visualize,
output_dir,
batch_size,
initial_learning_rate,
steps_per_checkpoint,
model_dir,
target_embedding_size,
attn_num_hidden,
attn_num_layers,
clip_gradients,
max_gradient_norm,
session,
load_model,
gpu_id,
use_gru,
use_distance=True,
max_image_width=160,
max_image_height=60,
max_prediction_length=8,
channels=1,
reg_val=0):
self.use_distance = use_distance
# We need resized width, not the actual width
max_resized_width = 1. * max_image_width / max_image_height * DataGen.IMAGE_HEIGHT
self.max_original_width = max_image_width
self.max_width = int(math.ceil(max_resized_width))
self.encoder_size = int(math.ceil(1. * self.max_width / 4))
self.decoder_size = max_prediction_length + 2
self.buckets = [(self.encoder_size, self.decoder_size)]
if gpu_id >= 0:
device_id = '/gpu:' + str(gpu_id)
else:
device_id = '/cpu:0'
self.device_id = device_id
if not os.path.exists(model_dir):
os.makedirs(model_dir)
if phase == 'test':
batch_size = 1
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('learning_rate: %f', 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('max_image_width %f', max_image_width)
logging.info('max_prediction_length %f', max_prediction_length)
logging.info('channels: %d', channels)
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)
if use_gru:
logging.info('using GRU in the decoder.')
self.reg_val = reg_val
self.sess = session
self.steps_per_checkpoint = steps_per_checkpoint
self.model_dir = model_dir
self.output_dir = output_dir
self.batch_size = batch_size
self.global_step = tf.Variable(0, trainable=False)
self.phase = phase
self.visualize = visualize
self.learning_rate = initial_learning_rate
self.clip_gradients = clip_gradients
self.channels = channels
if phase == 'train':
self.forward_only = False
else:
self.forward_only = True
with tf.device(device_id):
self.height = tf.constant(DataGen.IMAGE_HEIGHT, dtype=tf.int32)
self.height_float = tf.constant(DataGen.IMAGE_HEIGHT,
dtype=tf.float64)
self.img_pl = tf.placeholder(tf.string,
name='input_image_as_bytes')
self.img_data = tf.cond(tf.less(tf.rank(self.img_pl), 1),
lambda: tf.expand_dims(self.img_pl, 0),
lambda: self.img_pl)
self.img_data = tf.map_fn(self._prepare_image,
self.img_data,
dtype=tf.float32)
num_images = tf.shape(self.img_data)[0]
# TODO: create a mask depending on the image/batch size
self.encoder_masks = []
for i in xrange(self.encoder_size + 1):
self.encoder_masks.append(tf.tile([[1.]], [num_images, 1]))
self.decoder_inputs = []
self.target_weights = []
for i in xrange(self.decoder_size + 1):
self.decoder_inputs.append(tf.tile([1], [num_images]))
if i < self.decoder_size:
self.target_weights.append(tf.tile([1.], [num_images]))
else:
self.target_weights.append(tf.tile([0.], [num_images]))
cnn_model = CNN(self.img_data, not self.forward_only)
self.conv_output = cnn_model.tf_output()
self.perm_conv_output = tf.transpose(self.conv_output,
perm=[1, 0, 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=len(DataGen.CHARMAP),
buckets=self.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)
table = tf.contrib.lookup.MutableHashTable(
key_dtype=tf.int64,
value_dtype=tf.string,
default_value="",
checkpoint=True,
)
insert = table.insert(
tf.constant(list(range(len(DataGen.CHARMAP))), dtype=tf.int64),
tf.constant(DataGen.CHARMAP),
)
with tf.control_dependencies([insert]):
num_feed = []
prb_feed = []
for line in xrange(len(self.attention_decoder_model.output)):
guess = tf.argmax(
self.attention_decoder_model.output[line], axis=1)
proba = tf.reduce_max(tf.nn.softmax(
self.attention_decoder_model.output[line]),
axis=1)
num_feed.append(guess)
prb_feed.append(proba)
# Join the predictions into a single output string.
trans_output = tf.transpose(num_feed)
trans_output = tf.map_fn(
lambda m: tf.foldr(
lambda a, x: tf.cond(
tf.equal(x, DataGen.EOS_ID),
lambda: '',
lambda: table.lookup(x) + a # pylint: disable=undefined-variable
),
m,
initializer=''),
trans_output,
dtype=tf.string)
# Calculate the total probability of the output string.
trans_outprb = tf.transpose(prb_feed)
trans_outprb = tf.gather(trans_outprb,
tf.range(tf.size(trans_output)))
trans_outprb = tf.map_fn(lambda m: tf.foldr(
lambda a, x: tf.multiply(tf.cast(x, tf.float64), a),
m,
initializer=tf.cast(1, tf.float64)),
trans_outprb,
dtype=tf.float64)
self.prediction = tf.cond(
tf.equal(tf.shape(trans_output)[0], 1),
lambda: trans_output[0],
lambda: trans_output,
)
self.probability = tf.cond(
tf.equal(tf.shape(trans_outprb)[0], 1),
lambda: trans_outprb[0],
lambda: trans_outprb,
)
self.prediction = tf.identity(self.prediction,
name='prediction')
self.probability = tf.identity(self.probability,
name='probability')
if not self.forward_only: # train
self.updates = []
self.summaries_by_bucket = []
params = tf.trainable_variables()
opt = tf.train.AdadeltaOptimizer(
learning_rate=initial_learning_rate)
loss_op = self.attention_decoder_model.loss
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) + loss_op
gradients, params = list(
zip(*opt.compute_gradients(loss_op, params)))
if self.clip_gradients:
gradients, _ = tf.clip_by_global_norm(
gradients, max_gradient_norm)
# Summaries for loss, variables, gradients, gradient norms and total gradient norm.
summaries = [
tf.summary.scalar("loss", loss_op),
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(list(zip(gradients, params)),
global_step=self.global_step))
self.saver_all = tf.train.Saver(tf.all_variables())
self.checkpoint_path = os.path.join(self.model_dir, "model.ckpt")
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt and load_model:
# pylint: disable=no-member
logging.info("Reading model parameters from %s",
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())
