def crnn_net(is_training, feature, label, batch_size, l_size):
seq_len = l_size
if is_training:
shadownet = crnn_model.ShadowNet(
phase='Train',
hidden_nums=256,
layers_nums=2,
seq_length=seq_len,
num_classes=config.cfg.TRAIN.CLASSES_NUMS,
rnn_cell_type='lstm')
imgs = tf.image.resize_images(feature, (32, l_size * 4), method=0)
input_imgs = tf.cast(x=imgs, dtype=tf.float32)
with tf.variable_scope('shadow', reuse=False):
net_out, tensor_dict = shadownet.build_shadownet(
inputdata=input_imgs)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=label,
inputs=net_out,
sequence_length=seq_len * np.ones(batch_size)))
# lstm l2
lstm_tv = tf.trainable_variables(scope='LSTMLayers')
r_lambda = 0.001
regularization_cost = r_lambda * tf.reduce_sum(
[tf.nn.l2_loss(v) for v in lstm_tv])
cost = cost + regularization_cost
model_params = tf.trainable_variables()
tower_grad = tf.gradients(cost, model_params)
return cost, zip(tower_grad,
model_params), net_out, tensor_dict, seq_len
else:
shadownet = crnn_model.ShadowNet(
phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=seq_len,
num_classes=config.cfg.TRAIN.CLASSES_NUMS,
rnn_cell_type='lstm')
imgs = tf.image.resize_images(feature, (32, l_size * 4), method=0)
input_imgs = tf.cast(x=imgs, dtype=tf.float32)
with tf.variable_scope('shadow', reuse=False):
net_out, tensor_dict = shadownet.build_shadownet(
inputdata=input_imgs)
cost = None
model_params = None
tower_grad = None
return cost, None, net_out, tensor_dict, seq_len
def recognize(image_path, weights_path, is_vis=True):
"""
:param image_path:
:param weights_path:
:param is_vis:
:return:
"""
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.resize(image, (100, 32))
image = np.expand_dims(image, axis=0).astype(np.float32)
inputdata = tf.placeholder(dtype=tf.float32,
shape=[1, 32, 100, 3],
name='input')
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=inputdata)
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=net_out,
sequence_length=25 * np.ones(1),
merge_repeated=False)
decoder = data_utils.TextFeatureIO()
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
preds = sess.run(decodes, feed_dict={inputdata: image})
preds = decoder.writer.sparse_tensor_to_str(preds[0])
logger.info('Predict image {:s} label {:s}'.format(
ops.split(image_path)[1], preds[0]))
if is_vis:
plt.figure('CRNN Model Demo')
plt.imshow(
cv2.imread(image_path, cv2.IMREAD_COLOR)[:, :, (2, 1, 0)])
plt.show()
sess.close()
return
def load_model(self):
sess = tf.Session()
x = tf.placeholder(dtype=tf.float32,
shape=[1, 32, config.cfg.TRAIN.width, 3],
name='input')
phase_tensor = tf.constant('test', tf.string)
net = crnn_model.ShadowNet(phase=phase_tensor,
hidden_nums=256,
layers_nums=2,
seq_length=15,
num_classes=config.cfg.TRAIN.CLASSES_NUMS,
rnn_cell_type='lstm')
with tf.variable_scope('shadow'):
net_out, tensor_dict = net.build_shadownet(inputdata=x)
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=net_out,
sequence_length=20 *
np.ones(1),
merge_repeated=False)
saver = tf.train.Saver()
params_file = tf.train.latest_checkpoint(self.model_dir)
saver.restore(sess=sess, save_path=params_file)
self.output['sess'] = sess
self.output['x'] = x
self.output['y_'] = decodes
def load_model(self):
sess = tf.Session()
x = tf.placeholder(dtype=tf.float32,
shape=[1, 32, 100, 3],
name='input')
#define model
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=x)
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=net_out,
sequence_length=25 *
np.ones(1),
merge_repeated=False)
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
saver = tf.train.Saver()
params_file = tf.train.latest_checkpoint(self.model_dir)
saver.restore(sess=sess, save_path=params_file)
self.output['sess'] = sess
self.output['x'] = x
self.output['y_'] = decodes
def recognize(image_path, weights_path, is_vis=True):
image = cv2.imread(image_path, cv2.IMREAD_COLOR) #读取图片
image = cv2.resize(image, (100, 32)) #调整图片分辨率
image = np.expand_dims(image, axis=0).astype(np.float32) #将图片格式转为浮点型
inputdata = tf.placeholder(dtype=tf.float32,
shape=[1, 32, 100, 3],
name='input') #为输入数据占位
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37) #声明网络的类
with tf.variable_scope('shadow'): #通过tf.variable_scope生成一个上下文管理器
net_out = net.build_shadownet(inputdata=inputdata) #创建网络，指定输入数据
decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=net_out,
sequence_length=25 * np.ones(1),
merge_repeated=False) #对数据解码
# 设置session配置参数
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
# 初始化保存数据
saver = tf.train.Saver()
sess = tf.Session(config=sess_config) #创建图运算
with sess.as_default(): #创建一个上下文管理器
saver.restore(sess=sess, save_path=weights_path) #载入训练好的网络权重
preds = sess.run(decodes, feed_dict={inputdata: image}) #网络计算
preds = data_utils.sparse_tensor_to_str(preds[0]) #得到的结果保存为字符串型
print('预测的图像为 %s 结果为 %s' % (ops.split(image_path)[1], preds[0])) #打印结果
if is_vis: #如果在recognize()中，将is_vis=True，则显示图片
plt.figure('CRNN 图片')
plt.imshow(
cv2.imread(image_path, cv2.IMREAD_COLOR)[:, :, (2, 1, 0)])
plt.show()
sess.close()
return
def _tower_fn(feature):
'''
The l_size should be compatable with the train_shadownet_multi.py --l_size param
This is also related with the
'''
l_size = 10
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=256,
layers_nums=2,
seq_length=l_size,
num_classes=config.cfg.TRAIN.CLASSES_NUMS,
rnn_cell_type='lstm')
imgs = tf.image.resize_images(feature, (32, l_size * 4), method=0)
input_imgs = tf.cast(x=imgs, dtype=tf.float32)
with tf.variable_scope('shadow', reuse=False):
net_out, tensor_dict = shadownet.build_shadownet(inputdata=input_imgs)
return net_out, tensor_dict, l_size
def train_shadownet_multi_gpu(dataset_dir_train, dataset_dir_val, weights_path,
char_dict_path, ord_map_dict_path,
model_save_dir):
"""
:param dataset_dir:
:param weights_path:
:param char_dict_path:
:param ord_map_dict_path:
:return:
"""
# prepare dataset information
NUM_CLASSES = get_num_class(char_dict_path)
print(" dataset_dir_train ", dataset_dir_train)
"""
train_dataset = shadownet_data_feed_pipline.CrnnDataFeeder(
dataset_dir=dataset_dir_train,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='train'
)
val_dataset = shadownet_data_feed_pipline.CrnnDataFeeder(
dataset_dir=dataset_dir_train,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='valid'
)
"""
# FIXME: 以下的代码会出现问题
train_dataset = read_tfrecord.CrnnDataFeeder(
dataset_dir=dataset_dir_train,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='train')
val_dataset = read_tfrecord.CrnnDataFeeder(
dataset_dir=dataset_dir_train,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='valid')
train_images, train_labels, train_images_paths = train_dataset.inputs(
batch_size=CFG.TRAIN.BATCH_SIZE)
val_images, val_labels, val_images_paths = val_dataset.inputs(
batch_size=CFG.TRAIN.BATCH_SIZE)
# set crnn net
shadownet = crnn_model.ShadowNet(phase='train',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=NUM_CLASSES)
shadownet_val = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=NUM_CLASSES)
# set average container
tower_grads = []
train_tower_loss = []
val_tower_loss = []
batchnorm_updates = None
train_summary_op_updates = None
# set lr
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
learning_rate=CFG.TRAIN.LEARNING_RATE,
global_step=global_step,
decay_steps=CFG.TRAIN.LR_DECAY_STEPS,
decay_rate=CFG.TRAIN.LR_DECAY_RATE,
staircase=CFG.TRAIN.LR_STAIRCASE)
# set up optimizer
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.9)
# set distributed train op
with tf.variable_scope(tf.get_variable_scope()):
is_network_initialized = False
for i in range(CFG.TRAIN.GPU_NUM):
with tf.device('/gpu:{:d}'.format(i)):
with tf.name_scope('tower_{:d}'.format(i)) as _:
train_loss, grads = compute_net_gradients(
train_images,
train_labels,
shadownet,
optimizer,
is_net_first_initialized=is_network_initialized)
is_network_initialized = True
# Only use the mean and var in the first gpu tower to update the parameter
if i == 0:
batchnorm_updates = tf.get_collection(
tf.GraphKeys.UPDATE_OPS)
train_summary_op_updates = tf.get_collection(
tf.GraphKeys.SUMMARIES)
tower_grads.append(grads)
train_tower_loss.append(train_loss)
with tf.name_scope('validation_{:d}'.format(i)) as _:
val_loss, _ = compute_net_gradients(
val_images,
val_labels,
shadownet_val,
optimizer,
is_net_first_initialized=is_network_initialized)
val_tower_loss.append(val_loss)
grads = average_gradients(tower_grads)
avg_train_loss = tf.reduce_mean(train_tower_loss)
avg_val_loss = tf.reduce_mean(val_tower_loss)
# Track the moving averages of all trainable variables
variable_averages = tf.train.ExponentialMovingAverage(
CFG.TRAIN.MOVING_AVERAGE_DECAY, num_updates=global_step)
variables_to_average = tf.trainable_variables(
) + tf.moving_average_variables()
variables_averages_op = variable_averages.apply(variables_to_average)
# Group all the op needed for training
batchnorm_updates_op = tf.group(*batchnorm_updates)
apply_gradient_op = optimizer.apply_gradients(grads,
global_step=global_step)
train_op = tf.group(apply_gradient_op, variables_averages_op,
batchnorm_updates_op)
# set tensorflow summary
tboard_save_path = model_save_dir
os.makedirs(tboard_save_path, exist_ok=True)
summary_writer = tf.summary.FileWriter(tboard_save_path)
avg_train_loss_scalar = tf.summary.scalar(name='average_train_loss',
tensor=avg_train_loss)
avg_val_loss_scalar = tf.summary.scalar(name='average_val_loss',
tensor=avg_val_loss)
learning_rate_scalar = tf.summary.scalar(name='learning_rate_scalar',
tensor=learning_rate)
train_merge_summary_op = tf.summary.merge(
[avg_train_loss_scalar, learning_rate_scalar] +
train_summary_op_updates)
val_merge_summary_op = tf.summary.merge([avg_val_loss_scalar])
# set tensorflow saver
saver = tf.train.Saver()
os.makedirs(model_save_dir, exist_ok=True)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# set sess config
sess_config = tf.ConfigProto(device_count={'GPU': CFG.TRAIN.GPU_NUM},
allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
# Set the training parameters
train_epochs = CFG.TRAIN.EPOCHS
logger.info('Global configuration is as follows:')
logger.info(CFG)
sess = tf.Session(config=sess_config)
summary_writer.add_graph(sess.graph)
with sess.as_default():
epoch = 0
tf.train.write_graph(
graph_or_graph_def=sess.graph,
logdir='',
name='{:s}/shadownet_model.pb'.format(model_save_dir))
if weights_path is None or not os.path.exists(weights_path) or len(
os.listdir(weights_path)) < 5:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
weights_path = tf.train.latest_checkpoint(weights_path)
logger.info('Restore model from last model checkpoint {:s}'.format(
weights_path))
saver.restore(sess=sess, save_path=weights_path)
epoch = sess.run(tf.train.get_global_step())
train_cost_time_mean = []
val_cost_time_mean = []
while epoch < train_epochs:
epoch += 1
# training part
t_start = time.time()
_, train_loss_value, train_summary, lr = \
sess.run(fetches=[train_op,
avg_train_loss,
train_merge_summary_op,
learning_rate])
if math.isnan(train_loss_value):
raise ValueError('Train loss is nan')
summary_writer.add_summary(summary=train_summary,
global_step=epoch)
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
logger.info(
'lr={:.5f} epoch:{:6d} total_loss={:.5f} '.format(
lr,
epoch + 1,
train_loss_value,
))
if epoch % CFG.TRAIN.VAL_DISPLAY_STEP == 0:
# validation part
val_loss_value, val_summary = \
sess.run(fetches=[avg_val_loss,
val_merge_summary_op])
summary_writer.add_summary(val_summary, global_step=epoch)
logger.info(
'Valid----- epoch:{:6d} total_loss={:.5f} '.format(
epoch + 1, val_loss_value))
if epoch % CFG.TRAIN.VAL_DISPLAY_STEP == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
sess.close()
return
def build_saved_model(ckpt_path, export_dir):
"""
Convert source ckpt weights file into tensorflow saved model
:param ckpt_path:
:param export_dir:
:return:
"""
if ops.exists(export_dir):
raise ValueError('Export dir must be a dir path that does not exist')
assert ops.exists(ops.split(ckpt_path)[0])
# build inference tensorflow graph
image_size = tuple(CFG.ARCH.INPUT_SIZE)
image_tensor = tf.placeholder(dtype=tf.float32,
shape=[1, image_size[1], image_size[0], 3],
name='input_tensor')
# set crnn net
net = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=CFG.ARCH.NUM_CLASSES)
# compute inference logits
inference_ret = net.inference(inputdata=image_tensor,
name='shadow_net',
reuse=False)
# beam search decode
decodes, _ = tf.nn.ctc_beam_search_decoder(
inputs=inference_ret,
sequence_length=CFG.ARCH.SEQ_LENGTH * np.ones(1),
merge_repeated=False)
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=ckpt_path)
# set model save builder
saved_builder = sm.builder.SavedModelBuilder(export_dir)
# add tensor need to be saved
saved_input_tensor = sm.utils.build_tensor_info(image_tensor)
saved_prediction_tensor = sm.utils.build_tensor_info(decodes[0])
# build SignatureDef protobuf
signatur_def = sm.signature_def_utils.build_signature_def(
inputs={'input_tensor': saved_input_tensor},
outputs={'prediction': saved_prediction_tensor},
method_name=sm.signature_constants.PREDICT_METHOD_NAME)
# add graph into MetaGraphDef protobuf
saved_builder.add_meta_graph_and_variables(
sess,
tags=[sm.tag_constants.SERVING],
signature_def_map={
sm.signature_constants.PREDICT_OUTPUTS: signatur_def
})
# save model
saved_builder.save()
return
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Restoring and running multiple tensorflow network models needs some workaround->
https://stackoverflow.com/questions/41607144/loading-two-models-from-saver-in-the-same-tensorflow-session
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
os.chdir(CRNN_DIR)
import tools.demo_shadownet as crnn
import crnn_model.crnn_model as crnn_model
crnn_weights_path = 'model/shadownet/shadownet_2017-10-17-11-47-46.ckpt-199999'
crnn_graph = tf.Graph()
with crnn_graph.as_default():
crnn_net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'):
crnn_inputdata = tf.placeholder(dtype=tf.float32,
shape=[1, 32, 100, 3],
name='input')
crnn_net_out = crnn_net.build_shadownet(inputdata=crnn_inputdata)
crnn_decodes, _ = tf.nn.ctc_beam_search_decoder(inputs=crnn_net_out,
sequence_length=25 *
np.ones(1),
merge_repeated=False)
crnn_decoder = crnn.data_utils.TextFeatureIO()
# config tf session
def train_shadownet(cfg: EasyDict,
weights_path: str = None,
decode: bool = False,
num_threads: int = 4) -> np.array:
"""
:param cfg: configuration EasyDict (e.g. global_config.config.cfg)
:param weights_path: Path to stored weights
:param decode: Whether to perform CTC decoding to report progress during training
:param num_threads: Number of threads to use in tf.train.shuffle_batch
:return History of values of the cost function
"""
# decode the tf records to get the training data
decoder = data_utils.TextFeatureIO(
char_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR, 'char_dict.json'),
ord_map_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR,
'ord_map.json')).reader
input_images, input_labels, input_image_names = decoder.read_features(
cfg, cfg.TRAIN.BATCH_SIZE, num_threads)
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=cfg.ARCH.HIDDEN_UNITS,
layers_nums=cfg.ARCH.HIDDEN_LAYERS,
num_classes=len(decoder.char_dict) + 1)
with tf.variable_scope('shadow', reuse=False):
net_out = shadownet.build_shadownet(inputdata=input_images)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=cfg.ARCH.SEQ_LENGTH *
np.ones(cfg.TRAIN.BATCH_SIZE)))
decoded, log_prob = tf.nn.ctc_beam_search_decoder(
net_out,
cfg.ARCH.SEQ_LENGTH * np.ones(cfg.TRAIN.BATCH_SIZE),
merge_repeated=False)
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
global_step = tf.Variable(0, name='global_step', trainable=False)
starter_learning_rate = cfg.TRAIN.LEARNING_RATE
learning_rate = tf.train.exponential_decay(
starter_learning_rate,
global_step,
cfg.TRAIN.LR_DECAY_STEPS,
cfg.TRAIN.LR_DECAY_RATE,
staircase=cfg.TRAIN.LR_STAIRCASE)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# Set tf summary
os.makedirs(cfg.PATH.TBOARD_SAVE_DIR, exist_ok=True)
tf.summary.scalar(name='Cost', tensor=cost)
tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
if decode:
tf.summary.scalar(name='Seq_Dist', tensor=sequence_dist)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
os.makedirs(cfg.PATH.TBOARD_SAVE_DIR, exist_ok=True)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(cfg.PATH.MODEL_SAVE_DIR, model_name)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(cfg.PATH.TBOARD_SAVE_DIR)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = cfg.TRAIN.EPOCHS
with sess.as_default():
if weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
logger.info('Restore model from {:s}'.format(weights_path))
saver.restore(sess=sess, save_path=weights_path)
patience_counter = 1
cost_history = [np.inf]
for epoch in range(train_epochs):
if epoch > 1 and cfg.TRAIN.EARLY_STOPPING:
# We always compare to the first point where cost didn't improve
if cost_history[-1 - patience_counter] - cost_history[
-1] > cfg.TRAIN.PATIENCE_DELTA:
patience_counter = 1
else:
patience_counter += 1
if patience_counter > cfg.TRAIN.PATIENCE_EPOCHS:
logger.info(
"Cost didn't improve beyond {:f} for {:d} epochs, stopping early."
.format(cfg.TRAIN.PATIENCE_DELTA, patience_counter))
break
if decode:
_, c, seq_distance, predictions, labels, summary = sess.run([
optimizer, cost, sequence_dist, decoded, input_labels,
merge_summary_op
])
labels = decoder.sparse_tensor_to_str(labels)
predictions = decoder.sparse_tensor_to_str(predictions[0])
accuracy = compute_accuracy(labels, predictions)
if epoch % cfg.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, c, seq_distance, accuracy))
else:
_, c, summary = sess.run([optimizer, cost, merge_summary_op])
if epoch % cfg.TRAIN.DISPLAY_STEP == 0:
logger.info('Epoch: {:d} cost= {:9f}'.format(epoch + 1, c))
cost_history.append(c)
summary_writer.add_summary(summary=summary, global_step=epoch)
saver.save(sess=sess, save_path=model_save_path, global_step=epoch)
return np.array(cost_history[1:]) # Don't return the first np.inf
def test_shadownet(dataset_dir, weights_path, is_vis=False, is_recursive=True):
images_t, labels_t, imagenames_t = data_utils.read_features(
dataset_dir, num_epochs=None) #读取.tfrecords文件
if not is_recursive:
#如果设置is_recursive为flase，则创建一个乱序的数据序列。
#capacity读取数据范围；min_after_dequeue越大，数据越乱
images_sh, labels_sh, imagenames_sh = tf.train.shuffle_batch(
tensors=[images_t, labels_t, imagenames_t],
batch_size=32,
capacity=1000 + 32 * 2,
min_after_dequeue=2,
num_threads=4)
else:
#如果设置is_recursive为True，则不打乱数据顺序
images_sh, labels_sh, imagenames_sh = tf.train.batch(
tensors=[images_t, labels_t, imagenames_t],
batch_size=32,
capacity=1000 + 32 * 2,
num_threads=4)
images_sh = tf.cast(x=images_sh, dtype=tf.float32) #将图像数据类型转为float32
# 在这里声明了创建网络的类
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'): #通过tf.variable_scope生成一个上下文管理器
net_out = net.build_shadownet(inputdata=images_sh) #创建网络，指定输入数据
decoded, _ = tf.nn.ctc_beam_search_decoder(net_out,
25 * np.ones(32),
merge_repeated=False) #对数据解码
# 设置session配置参数
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
# 初始化保存数据
saver = tf.train.Saver()
#创建图运算
sess = tf.Session(config=sess_config)
test_sample_count = 0
for record in tf.python_io.tf_record_iterator(dataset_dir):
test_sample_count += 1
loops_nums = int(math.ceil(test_sample_count / 32))
with sess.as_default(): #创建图计算的默认会话，当上下文管理器关闭时，这个对话不会关闭
# 加载网络权重
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator() #创建一个协调器，管理线程
threads = tf.train.start_queue_runners(
sess=sess, coord=coord) #启动QueueRunner, 此时文件名队列已经进队
print('开始预测文字......')
if not is_recursive: #如果设置is_recursive为flase，则创建一个乱序的数据序列。，和最开始创建数据系列方式保持一致
predictions, images, labels, imagenames = sess.run(
[decoded, images_sh, labels_sh, imagenames_sh]) #运行图计算
imagenames = np.reshape(imagenames, newshape=imagenames.shape[0])
imagenames = [tmp.decode('utf-8') for tmp in imagenames]
preds_res = data_utils.sparse_tensor_to_str(
predictions[0]) #获取的预测文字结果
gt_res = data_utils.sparse_tensor_to_str(labels) #真实的结果
accuracy = [] #用来保存准确率
for index, gt_label in enumerate(
gt_res): #enumerate方式同时获取来一个list的索引和对应元素
pred = preds_res[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(
gt_label): #这里逐项对比预测结果和真实结果，记录准确结果个数
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count /
totol_count) #错误的/全部的几位准确率
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
print(' test accuracy 为 %f' % (accuracy))
for index, image in enumerate(images):
print('预测图片 %s 准确的label为: %s **** 预测的 label: %s' %
(imagenames[index], gt_res[index], preds_res[index]))
if is_vis:
plt.imshow(image[:, :, (2, 1, 0)])
plt.show()
else: #这里是非乱序获取数据序列的，和上面的if对应
accuracy = []
for epoch in range(loops_nums):
predictions, images, labels, imagenames = sess.run(
[decoded, images_sh, labels_sh, imagenames_sh])
imagenames = np.reshape(imagenames,
newshape=imagenames.shape[0])
imagenames = [tmp.decode('utf-8') for tmp in imagenames]
preds_res = data_utils.sparse_tensor_to_str(predictions[0])
gt_res = data_utils.sparse_tensor_to_str(labels)
for index, gt_label in enumerate(gt_res):
pred = preds_res[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
for index, image in enumerate(images):
print('预测图片 %s 准确的label为: %s **** 预测的label: %s' %
(imagenames[index], gt_res[index], preds_res[index]))
if is_vis: #如果在recognize()中，将is_vis=True，则显示图片
plt.imshow(image[:, :, (2, 1, 0)])
plt.show()
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
print('Test accuracy is %f' % (accuracy))
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def train_shadownet(dataset_dir, weights_path=None):
print("读取训练数据")
images, labels, imagenames = data_utils.read_features(
dataset_dir, num_epochs=None) #读取.tfrecords文件
#创建一个乱序序列用于训练
inputdata, input_labels, input_imagenames = tf.train.shuffle_batch(
tensors=[images, labels, imagenames],
batch_size=32,
capacity=1000 + 2 * 32,
min_after_dequeue=100,
num_threads=1)
inputdata = tf.cast(x=inputdata, dtype=tf.float32) #占位
print("初始化网络") # 在这里声明了创建网络的类
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow',
reuse=False): #通过tf.variable_scope生成一个上下文管理器
net_out = shadownet.build_shadownet(inputdata=inputdata) #创建网络，指定输入数据
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=25 * np.ones(32))) #按照设定的维度求张量平均值
decoded, log_prob = tf.nn.ctc_beam_search_decoder(
net_out, 25 * np.ones(32), merge_repeated=False) #对数据解码
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32),
input_labels)) #按照设定的维度求张量平均值
global_step = tf.Variable(0, name='global_step', trainable=False) #初始化图变量
starter_learning_rate = config.cfg.TRAIN.LEARNING_RATE #设定初始学习速率
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
config.cfg.TRAIN.LR_DECAY_STEPS,
config.cfg.TRAIN.LR_DECAY_RATE,
staircase=True) #按照指数衰减方式改变学习速率
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) #设定为全局变量
with tf.control_dependencies(update_ops): #Adadelta算法的优化器
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# 设置tensorflow的模型管理模式
tboard_save_path = 'tboard/shadownet'
if not ops.exists(tboard_save_path):
os.makedirs(tboard_save_path)
tf.summary.scalar(name='Cost', tensor=cost)
tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
tf.summary.scalar(name='Seq_Dist', tensor=sequence_dist)
merge_summary_op = tf.summary.merge_all() #自动管理模式，导入之前已经保存的模型继续训练
# 设置模型保存路径
saver = tf.train.Saver()
model_save_dir = 'model/shadownet'
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# gpu参数
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config) #创建图运算
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# 迭代次数
train_epochs = config.cfg.TRAIN.EPOCHS
print("开始训练")
with sess.as_default():
if weights_path is None:
print('完全重新开始训练')
init = tf.global_variables_initializer()
sess.run(init)
else:
print('在之前的模型:' + 'weights_path' + '上继续训练')
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(train_epochs):
_, c, seq_distance, preds, gt_labels, summary = sess.run([
optimizer, cost, sequence_dist, decoded, input_labels,
merge_summary_op
])
# calculate the precision
preds = data_utils.sparse_tensor_to_str(preds[0])
gt_labels = data_utils.sparse_tensor_to_str(gt_labels)
accuracy = []
for index, gt_label in enumerate(gt_labels):
pred = preds[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
#
if epoch % config.cfg.TRAIN.DISPLAY_STEP == 0:
print(
'Epoch: %d cost= %f seq distance= %f train accuracy= %f' %
(epoch + 1, c, seq_distance, accuracy))
summary_writer.add_summary(summary=summary, global_step=epoch)
saver.save(sess=sess, save_path=model_save_path, global_step=epoch)
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def recognize_jmz(image_path, weights_path, char_dict_path, txt_file_path,
test_number):
"""
识别函数
:param image_path: 图片所在路径
:param weights_path: 模型保存路径
:param char_dict_path: 字典文件存放位置
:param txt_file_path: 包含图片名的txt文件
:return: None
"""
char_map_dict = json.load(open(char_dict_path, 'r', encoding='utf-8'))
num_classes = len(char_map_dict) + 1
print('num_classes: ', num_classes)
with open(txt_file_path, 'r') as f1:
linelist = f1.readlines()
image_list = []
for i in range(test_number):
image_path_temp = image_path + linelist[i].split(' ')[0]
image_list.append((image_path_temp, linelist[i].split(' ')[1].replace(
'\r', '').replace('\n', '').replace('\t', '')))
global reg_result
tf.reset_default_graph()
inputdata = tf.placeholder(
dtype=tf.float32,
shape=[1, CFG.ARCH.INPUT_SIZE[1], None,
CFG.ARCH.INPUT_CHANNELS], # 宽度可变
name='input')
input_sequence_length = tf.placeholder(tf.int32,
shape=[1],
name='input_sequence_length')
net = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=num_classes)
inference_ret = net.inference(inputdata=inputdata,
name='shadow_net',
reuse=False)
decodes, _ = tf.nn.ctc_beam_search_decoder(
inputs=inference_ret,
sequence_length=input_sequence_length, # 序列宽度可变
merge_repeated=False,
beam_width=1)
# config tf saver
saver = tf.train.Saver()
# config tf session
sess_config = tf.ConfigProto(allow_soft_placement=True)
# sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
# sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
weights_path = tf.train.latest_checkpoint(weights_path)
print('Restore model from last model checkpoint {:s}'.format(weights_path))
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
for image_name, label in image_list:
image = cv2.imread(image_name, cv2.IMREAD_COLOR)
if image is None:
print(image_name + 'is not exist')
continue
# image = _resize_image(image)
image = cv2.resize(image,
dsize=tuple(CFG.ARCH.INPUT_SIZE),
interpolation=cv2.INTER_LINEAR)
image = np.array(image, np.float32) / 127.5 - 1.0
seq_len = np.array([image.shape[1] / 4], dtype=np.int32)
preds = sess.run(decodes,
feed_dict={
inputdata: [image],
input_sequence_length: seq_len
})
preds = _sparse_matrix_to_list(preds[0], char_map_dict)
print('Label: [{:20s}]'.format(label))
print('Pred : [{}]\n'.format(preds[0]))
sess.close()
return
def train_shadownet(dataset_dir_train, dataset_dir_val, weights_path,
char_dict_path, model_save_dir):
"""
训练网络，参考：
https://github.com/MaybeShewill-CV/CRNN_Tensorflow
:param dataset_dir: tfrecord文件路径
:param weights_path: 要加载的预训练模型路径
:param char_dict_path: 字典文件路径
:param save_path: 模型保存路径
:return: None
"""
# prepare dataset
train_dataset = read_tfrecord.CrnnDataFeeder(dataset_dir=dataset_dir_train,
char_dict_path=char_dict_path,
flags='train')
train_images, train_labels, train_images_paths = train_dataset.inputs(
batch_size=CFG.TRAIN.BATCH_SIZE)
####################添加数据增强##############################
# train_images = tf.multiply(tf.add(train_images, 1.0), 128.0) # removed since read_tfrecord.py is changed
tf.summary.image('original_image', train_images) # 保存到log，方便测试观察
images = apply_with_random_selector(
train_images,
lambda x, ordering: distort_color(x, ordering),
num_cases=2) #
images = tf.subtract(tf.divide(images, 127.5),
1.0) # 转化到【-1，1】 changed 128.0 to 127.5
train_images = tf.clip_by_value(images, -1.0, 1.0)
tf.summary.image('distord_turned_image', train_images)
################################################################
NUM_CLASSES = get_num_class(char_dict_path)
# declare crnn net
shadownet = crnn_model.ShadowNet(phase='train',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=NUM_CLASSES)
# set up training graph
with tf.device('/gpu:0'):
# compute loss and seq distance
train_inference_ret, train_ctc_loss = shadownet.compute_loss(
inputdata=train_images,
labels=train_labels,
name='shadow_net',
reuse=False)
# set learning rate
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
learning_rate=CFG.TRAIN.LEARNING_RATE,
global_step=global_step,
decay_steps=CFG.TRAIN.LR_DECAY_STEPS,
decay_rate=CFG.TRAIN.LR_DECAY_RATE,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
#optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
# momentum=0.9).minimize(loss=train_ctc_loss, global_step=global_step)
optimizer = tf.train.AdadeltaOptimizer(learning_rate=\
learning_rate).minimize(loss=train_ctc_loss, global_step=global_step)
# 源代码优化器是momentum，改成adadelta，与CRNN论文一致
# Set tf summary
os.makedirs(save_path, exist_ok=True)
tf.summary.scalar(name='train_ctc_loss', tensor=train_ctc_loss)
tf.summary.scalar(name='learning_rate', tensor=learning_rate)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# Set sess configuration
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(model_save_dir)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = CFG.TRAIN.EPOCHS
with sess.as_default():
epoch = 0
if weights_path is None:
print('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
weights_path = tf.train.latest_checkpoint(weights_path)
print('Restore model from last model checkpoint {:s}'.format(
weights_path))
saver.restore(sess=sess, save_path=weights_path)
epoch = sess.run(tf.train.get_global_step())
cost_history = [np.inf]
while epoch < train_epochs:
epoch += 1
_, train_ctc_loss_value, merge_summary_value, learning_rate_value = sess.run(
[optimizer, train_ctc_loss, merge_summary_op, learning_rate])
if (epoch + 1) % CFG.TRAIN.DISPLAY_STEP == 0:
current_time = time.strftime('%m-%d-%H-%M-%S',
time.localtime(time.time()))
print('{} lr={:.5f} step:{:6d} train_loss={:.4f}'.format(\
current_time, learning_rate_value, epoch+1, train_ctc_loss_value))
# record history train ctc loss
cost_history.append(train_ctc_loss_value)
# add training sumary
summary_writer.add_summary(summary=merge_summary_value,
global_step=epoch)
if (epoch + 1) % CFG.TRAIN.SAVE_STEPS == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
return np.array(cost_history[1:]) # Don't return the first np.inf
def train_shadownet(dataset_dir, weights_path=None):
"""
:param dataset_dir:
:param weights_path:
:return:
"""
# decode the tf records to get the training data
# initializa the net model
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
inputdata = tf.placeholder(dtype=tf.float32, shape=(32, 32, 100, 3))
input_labels = tf.sparse_placeholder(tf.int32, shape=(None, -1))
with tf.variable_scope('shadow', reuse=False):
net_out = shadownet.build_shadownet(inputdata=inputdata)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=25 * np.ones(32)))
decoded, log_prob = tf.nn.ctc_beam_search_decoder(net_out,
25 * np.ones(32),
merge_repeated=False)
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
global_step = tf.Variable(0, name='global_step', trainable=False)
starter_learning_rate = config.cfg.TRAIN.LEARNING_RATE
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
config.cfg.TRAIN.LR_DECAY_STEPS,
config.cfg.TRAIN.LR_DECAY_RATE,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# Set tf summary
tboard_save_path = 'tboard/shadownet'
if not ops.exists(tboard_save_path):
os.makedirs(tboard_save_path)
tf.summary.scalar(name='Cost', tensor=cost)
tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
tf.summary.scalar(name='Seq_Dist', tensor=sequence_dist)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
model_save_dir = 'model/shadownet'
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = config.cfg.TRAIN.EPOCHS
with sess.as_default():
if weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
logger.info('Restore model from {:s}'.format(weights_path))
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(train_epochs):
try:
training_img, training_label = get_training_data()
feed_dict = {
inputdata: training_img,
input_labels: training_label
}
_, c, seq_distance, preds, gt_labels, summary = sess.run(
[
optimizer, cost, sequence_dist, decoded, input_labels,
merge_summary_op
],
feed_dict=feed_dict)
# calculate the precision
preds_sequence = preds[0].values.tolist()
gt_value = gt_labels.values.tolist()
pre_count = len(preds_sequence)
accu_num = 0
gt_count = len(gt_value)
for index in range(gt_count):
if index < pre_count:
if gt_value[index] is not None and preds_sequence[
index] is not None:
if gt_value[index] == preds_sequence[index]:
accu_num += 1
accuracy = accu_num * 1.0 / pre_count
if epoch % config.cfg.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, c, seq_distance, accuracy))
if epoch % 1000 == 0 and epoch != 0:
summary_writer.add_summary(summary=summary,
global_step=epoch)
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
logger.info(
'save_model!!!!!!!!!!!!!!!!!!!___________________')
except Exception as e:
print(e)
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def test_shadownet(dataset_dir, weights_path, is_vis=True):
"""
:param dataset_dir:
:param weights_path:
:param is_vis:
:return:
"""
# Initialize the record decoder
decoder = data_utils.TextFeatureIO().reader
images_t, labels_t, imagenames_t = decoder.read_features(ops.join(
dataset_dir, 'test_feature.tfrecords'),
num_epochs=None)
images_sh, labels_sh, imagenames_sh = tf.train.shuffle_batch(
tensors=[images_t, labels_t, imagenames_t],
batch_size=32,
capacity=1000 + 32 * 2,
min_after_dequeue=2,
num_threads=4)
images_sh = tf.cast(x=images_sh, dtype=tf.float32)
# build shadownet
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=images_sh)
decoded, _ = tf.nn.ctc_beam_search_decoder(net_out,
25 * np.ones(32),
merge_repeated=False)
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
with sess.as_default():
# restore the model weights
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print('Start predicting ......')
predictions, images, labels, imagenames = sess.run(
[decoded, images_sh, labels_sh, imagenames_sh])
imagenames = np.reshape(imagenames, newshape=imagenames.shape[0])
imagenames = [tmp.decode('utf-8') for tmp in imagenames]
preds_res = decoder.sparse_tensor_to_str(predictions[0])
gt_res = decoder.sparse_tensor_to_str(labels)
for index, image in enumerate(images):
print(
'Predict {:s} image with gt label: {:s} **** predict label: {:s}'
.format(imagenames[index], gt_res[index], preds_res[index]))
if is_vis:
plt.imshow(image[:, :, (2, 1, 0)])
plt.show()
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def evaluate_shadownet(dataset_dir,
weights_path,
char_dict_path,
ord_map_dict_path,
is_visualize=False,
is_process_all_data=False):
"""
:param dataset_dir:
:param weights_path:
:param char_dict_path:
:param ord_map_dict_path:
:param is_visualize:
:param is_process_all_data:
:return:
"""
# prepare dataset
test_dataset = shadownet_data_feed_pipline.CrnnDataFeeder(
dataset_dir=dataset_dir,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='test')
test_images, test_labels, test_images_paths = test_dataset.inputs(
batch_size=CFG.TEST.BATCH_SIZE, num_epochs=1)
# set up test sample count
if is_process_all_data:
log.info('Start computing test dataset sample counts')
t_start = time.time()
test_sample_count = test_dataset.sample_counts()
log.info(
'Computing test dataset sample counts finished, cost time: {:.5f}'.
format(time.time() - t_start))
num_iterations = int(math.ceil(test_sample_count /
CFG.TEST.BATCH_SIZE))
else:
num_iterations = 1
# declare crnn net
shadownet = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=CFG.ARCH.NUM_CLASSES)
# set up decoder
decoder = tf_io_pipline_tools.TextFeatureIO(
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path).reader
# compute inference result
test_inference_ret = shadownet.inference(inputdata=test_images,
name='shadow_net',
reuse=False)
test_decoded, test_log_prob = tf.nn.ctc_beam_search_decoder(
test_inference_ret,
CFG.ARCH.SEQ_LENGTH * np.ones(CFG.TEST.BATCH_SIZE),
beam_width=1,
merge_repeated=False)
# recover image from [-1.0, 1.0] ---> [0.0, 255.0]
test_images = tf.multiply(tf.add(test_images, 1.0),
127.5,
name='recoverd_test_images')
# Set saver configuration
saver = tf.train.Saver()
# Set sess configuration
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
log.info('Start predicting...')
per_char_accuracy = 0.0
full_sequence_accuracy = 0.0
total_labels_char_list = []
total_predictions_char_list = []
while True:
try:
for epoch in range(num_iterations):
test_predictions_value, test_images_value, test_labels_value, \
test_images_paths_value = sess.run(
[test_decoded, test_images, test_labels, test_images_paths]
)
test_images_paths_value = np.reshape(
test_images_paths_value,
newshape=test_images_paths_value.shape[0])
test_images_paths_value = [
tmp.decode('utf-8') for tmp in test_images_paths_value
]
test_images_names_value = [
ops.split(tmp)[1] for tmp in test_images_paths_value
]
test_labels_value = decoder.sparse_tensor_to_str(
test_labels_value)
test_predictions_value = decoder.sparse_tensor_to_str(
test_predictions_value[0])
per_char_accuracy += evaluation_tools.compute_accuracy(
test_labels_value,
test_predictions_value,
display=False,
mode='per_char')
full_sequence_accuracy += evaluation_tools.compute_accuracy(
test_labels_value,
test_predictions_value,
display=False,
mode='full_sequence')
for index, test_image in enumerate(test_images_value):
log.info(
'Predict {:s} image with gt label: {:s} **** predicted label: {:s}'
.format(test_images_names_value[index],
test_labels_value[index],
test_predictions_value[index]))
if is_visualize:
plt.imshow(
np.array(test_image, np.uint8)[:, :,
(2, 1, 0)])
plt.show()
test_labels_char_list_value = [
s for s in test_labels_value[index]
]
test_predictions_char_list_value = [
s for s in test_predictions_value[index]
]
if not test_labels_char_list_value or not test_predictions_char_list_value:
continue
if len(test_labels_char_list_value) != len(
test_predictions_char_list_value):
min_length = min(
len(test_labels_char_list_value),
len(test_predictions_char_list_value))
test_labels_char_list_value = test_labels_char_list_value[:
min_length
-
1]
test_predictions_char_list_value = test_predictions_char_list_value[:
min_length
-
1]
assert len(test_labels_char_list_value) == len(test_predictions_char_list_value), \
log.error('{}, {}'.format(test_labels_char_list_value, test_predictions_char_list_value))
total_labels_char_list.extend(
test_labels_char_list_value)
total_predictions_char_list.extend(
test_predictions_char_list_value)
if is_visualize:
plt.imshow(
np.array(test_image, np.uint8)[:, :,
(2, 1, 0)])
except tf.errors.OutOfRangeError:
log.error('End of tfrecords sequence')
break
except Exception as err:
log.error(err)
break
avg_per_char_accuracy = per_char_accuracy / num_iterations
avg_full_sequence_accuracy = full_sequence_accuracy / num_iterations
log.info('Mean test per char accuracy is {:5f}'.format(
avg_per_char_accuracy))
log.info('Mean test full sequence accuracy is {:5f}'.format(
avg_full_sequence_accuracy))
# compute confusion matrix
cnf_matrix = confusion_matrix(total_labels_char_list,
total_predictions_char_list)
np.set_printoptions(precision=2)
evaluation_tools.plot_confusion_matrix(cm=cnf_matrix, normalize=True)
plt.show()
def train_shadownet(cfg: EasyDict,
weights_path: str = None,
decode: bool = False,
num_threads: int = 4):
"""
:param cfg: configuration EasyDict (e.g. global_config.config.cfg)
:param weights_path: Path to stored weights
:param decode: Whether to perform CTC decoding to report progress during training
:param num_threads: Number of threads to use in tf.train.shuffle_batch
"""
# decode the tf records to get the training data
decoder = data_utils.TextFeatureIO(
char_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR, 'char_dict.json'),
ord_map_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR,
'ord_map.json')).reader
images, labels, imagenames = decoder.read_features(
ops.join(cfg.PATH.TFRECORDS_DIR, 'train_feature.tfrecords'),
num_epochs=None,
input_size=cfg.ARCH.INPUT_SIZE,
input_channels=cfg.ARCH.INPUT_CHANNELS)
inputdata, input_labels, input_imagenames = tf.train.shuffle_batch(
tensors=[images, labels, imagenames],
batch_size=cfg.TRAIN.BATCH_SIZE,
capacity=1000 + 2 * cfg.TRAIN.BATCH_SIZE,
min_after_dequeue=100,
num_threads=num_threads)
inputdata = tf.cast(x=inputdata, dtype=tf.float32)
# initialise the net model
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=cfg.ARCH.HIDDEN_UNITS,
layers_nums=cfg.ARCH.HIDDEN_LAYERS,
num_classes=len(decoder.char_dict) + 1)
with tf.variable_scope('shadow', reuse=False):
net_out = shadownet.build_shadownet(inputdata=inputdata)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=cfg.ARCH.SEQ_LENGTH *
np.ones(cfg.TRAIN.BATCH_SIZE)))
decoded, log_prob = tf.nn.ctc_beam_search_decoder(
net_out,
cfg.ARCH.SEQ_LENGTH * np.ones(cfg.TRAIN.BATCH_SIZE),
merge_repeated=False)
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
global_step = tf.Variable(0, name='global_step', trainable=False)
starter_learning_rate = cfg.TRAIN.LEARNING_RATE
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
cfg.TRAIN.LR_DECAY_STEPS,
cfg.TRAIN.LR_DECAY_RATE,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# Set tf summary
os.makedirs(cfg.PATH.TBOARD_SAVE_DIR, exist_ok=True)
tf.summary.scalar(name='Cost', tensor=cost)
tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
tf.summary.scalar(name='Seq_Dist', tensor=sequence_dist)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
os.makedirs(cfg.PATH.TBOARD_SAVE_DIR, exist_ok=True)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(cfg.PATH.MODEL_SAVE_DIR, model_name)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(cfg.PATH.TBOARD_SAVE_DIR)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = cfg.TRAIN.EPOCHS
with sess.as_default():
if weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
logger.info('Restore model from {:s}'.format(weights_path))
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(train_epochs):
if decode:
_, c, seq_distance, predictions, labels, summary = sess.run([
optimizer, cost, sequence_dist, decoded, input_labels,
merge_summary_op
])
labels = decoder.sparse_tensor_to_str(labels)
predictions = decoder.sparse_tensor_to_str(predictions[0])
accuracy = compute_accuracy(labels, predictions)
if epoch % cfg.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, c, seq_distance, accuracy))
else:
_, c, summary = sess.run([optimizer, cost, merge_summary_op])
if epoch % cfg.TRAIN.DISPLAY_STEP == 0:
logger.info('Epoch: {:d} cost= {:9f}'.format(epoch + 1, c))
summary_writer.add_summary(summary=summary, global_step=epoch)
saver.save(sess=sess, save_path=model_save_path, global_step=epoch)
coord.request_stop()
coord.join(threads=threads)
def test_shadownet(weights_path: str,
cfg: EasyDict,
visualize: bool,
process_all_data: bool = True,
num_threads: int = 4,
num_classes: int = 0):
"""
:param tfrecords_dir: Directory with test_feature.tfrecords
:param charset_dir: Path to char_dict.json and ord_map.json (generated with write_text_features.py)
:param weights_path: Path to stored weights
:param cfg: configuration EasyDict (e.g. global_config.config.cfg)
:param visualize: whether to display the images
:param process_all_data:
:param num_threads: Number of threads for tf.train.(shuffle_)batch
:param num_classes: Number of different characters in the dataset
"""
decoder = data_utils.TextFeatureIO(
char_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR, 'char_dict.json'),
ord_map_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR,
'ord_map.json')).reader
input_images, input_labels, input_image_names = decoder.read_features(
cfg, cfg.TEST.BATCH_SIZE, num_threads, False)
num_classes = len(
decoder.char_dict) + 1 if num_classes == 0 else num_classes
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=cfg.ARCH.HIDDEN_UNITS,
layers_nums=cfg.ARCH.HIDDEN_LAYERS,
num_classes=num_classes)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=input_images)
decoded, _ = tf.nn.ctc_beam_search_decoder(net_out,
cfg.ARCH.SEQ_LENGTH *
np.ones(cfg.TEST.BATCH_SIZE),
merge_repeated=False)
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = cfg.TRAIN.TF_ALLOW_GROWTH
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
test_sample_count = sum(1 for _ in tf.python_io.tf_record_iterator(
ops.join(cfg.PATH.TFRECORDS_DIR, 'test_feature.tfrecords')))
num_iterations = int(math.ceil(test_sample_count / cfg.TEST.BATCH_SIZE)) if process_all_data \
else 1
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
print('Start predicting...')
accuracy = 0
for epoch in range(num_iterations):
predictions, images, labels, image_names = sess.run(
[decoded, input_images, input_labels, input_image_names])
image_names = np.reshape(image_names,
newshape=image_names.shape[0])
image_names = [tmp.decode('utf-8') for tmp in image_names]
labels = decoder.sparse_tensor_to_str(labels)
predictions = decoder.sparse_tensor_to_str(predictions[0])
accuracy += compute_accuracy(labels, predictions, display=False)
for index, image in enumerate(images):
print(
'Predict {:s} image with gt label: {:s} **** predicted label: {:s}'
.format(image_names[index], labels[index],
predictions[index]))
# avoid accidentally displaying for the whole dataset
if visualize and not process_all_data:
plt.imshow(image[:, :, (2, 1, 0)])
plt.show()
# We compute a mean of means, so we need the sample sizes to be constant
# (BATCH_SIZE) for this to equal the actual mean
accuracy /= num_iterations
print('Mean test accuracy is {:5f}'.format(accuracy))
def train_shadownet(dataset_dir, weights_path=None):
"""
:param dataset_dir:
:param weights_path:
:return:
"""
# decode the tf records to get the training data
decoder = data_utils.TextFeatureIO().reader
images, labels, imagenames = decoder.read_features(ops.join(
dataset_dir, 'train_feature.tfrecords'),
num_epochs=None)
inputdata, input_labels, input_imagenames = tf.train.shuffle_batch(
tensors=[images, labels, imagenames],
batch_size=32,
capacity=1000 + 2 * 32,
min_after_dequeue=100,
num_threads=1)
inputdata = tf.cast(x=inputdata, dtype=tf.float32)
# initializa the net model
shadownet = crnn_model.ShadowNet(phase='Train',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow', reuse=False):
net_out = shadownet.build_shadownet(inputdata=inputdata)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=25 * np.ones(32)))
decoded, log_prob = tf.nn.ctc_beam_search_decoder(net_out,
25 * np.ones(32),
merge_repeated=False)
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
global_step = tf.Variable(0, name='global_step', trainable=False)
starter_learning_rate = config.cfg.TRAIN.LEARNING_RATE
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
config.cfg.TRAIN.LR_DECAY_STEPS,
config.cfg.TRAIN.LR_DECAY_RATE,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# Set tf summary
tboard_save_path = 'tboard/shadownet'
if not ops.exists(tboard_save_path):
os.makedirs(tboard_save_path)
tf.summary.scalar(name='Cost', tensor=cost)
tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
tf.summary.scalar(name='Seq_Dist', tensor=sequence_dist)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
model_save_dir = 'model/shadownet'
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = config.cfg.TRAIN.EPOCHS
with sess.as_default():
if weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
logger.info('Restore model from {:s}'.format(weights_path))
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(train_epochs):
_, c, seq_distance, preds, gt_labels, summary = sess.run([
optimizer, cost, sequence_dist, decoded, input_labels,
merge_summary_op
])
# calculate the precision
preds = decoder.sparse_tensor_to_str(preds[0])
gt_labels = decoder.sparse_tensor_to_str(gt_labels)
accuracy = []
for index, gt_label in enumerate(gt_labels):
pred = preds[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
#
if epoch % config.cfg.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, c, seq_distance, accuracy))
summary_writer.add_summary(summary=summary, global_step=epoch)
saver.save(sess=sess, save_path=model_save_path, global_step=epoch)
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def train_shadownet():
"""
:param dataset_dir:
:param weights_path:
:return:
"""
# input_tensor = tf.placeholder(dtype=tf.float32, shape=[config.cfg.TRAIN.BATCH_SIZE, 32, 100, 3],
# name='input_tensor')
# decode the tf records to get the training data
decoder = data_utils.TextFeatureIO().reader
images, labels, imagenames = decoder.read_features(FLAGS.dataset_dir,
num_epochs=None,
flag='Train')
# images_val, labels_val, imagenames_val = decoder.read_features(dataset_dir, num_epochs=None,
# flag='Validation')
inputdata, input_labels, input_imagenames = tf.train.shuffle_batch(
tensors=[images, labels, imagenames],
batch_size=config.cfg.TRAIN.BATCH_SIZE,
capacity=1000 + 2 * config.cfg.TRAIN.BATCH_SIZE,
min_after_dequeue=100,
num_threads=1)
# inputdata_val, input_labels_val, input_imagenames_val = tf.train.shuffle_batch(
# tensors=[images_val, labels_val, imagenames_val], batch_size=config.TRAIN.BATCH_SIZE,
# capacity=1000 + 2 * config.TRAIN.BATCH_SIZE,
# min_after_dequeue=100, num_threads=1)
inputdata = tf.cast(x=inputdata, dtype=tf.float32)
phase_tensor = tf.placeholder(dtype=tf.string, shape=None, name='phase')
accuracy_tensor = tf.placeholder(dtype=tf.float32,
shape=None,
name='accuracy_tensor')
# initialize the net model
shadownet = crnn_model.ShadowNet(phase=phase_tensor,
hidden_nums=256,
layers_nums=2,
seq_length=15,
num_classes=config.cfg.TRAIN.CLASSES_NUMS,
rnn_cell_type='lstm')
with tf.variable_scope('shadow', reuse=False):
net_out, tensor_dict = shadownet.build_shadownet(inputdata=inputdata)
cost = tf.reduce_mean(
tf.nn.ctc_loss(labels=input_labels,
inputs=net_out,
sequence_length=20 *
np.ones(config.cfg.TRAIN.BATCH_SIZE)))
decoded, log_prob = tf.nn.ctc_beam_search_decoder(
net_out,
20 * np.ones(config.cfg.TRAIN.BATCH_SIZE),
merge_repeated=False)
sequence_dist = tf.reduce_mean(
tf.edit_distance(tf.cast(decoded[0], tf.int32), input_labels))
global_step = tf.Variable(0, name='global_step', trainable=False)
starter_learning_rate = config.cfg.TRAIN.LEARNING_RATE
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
config.cfg.TRAIN.LR_DECAY_STEPS,
config.cfg.TRAIN.LR_DECAY_RATE,
staircase=True)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=learning_rate).minimize(loss=cost,
global_step=global_step)
# optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate, momentum=0.9).minimize(
# loss=cost, global_step=global_step)
# Set tf summary
tboard_save_path = '/data/output/'
if not ops.exists(tboard_save_path):
os.makedirs(tboard_save_path)
visualizor = tensorboard_vis_summary.CNNVisualizer()
# training过程summary
train_cost_scalar = tf.summary.scalar(name='train_cost', tensor=cost)
train_accuracy_scalar = tf.summary.scalar(name='train_accuray',
tensor=accuracy_tensor)
train_seq_scalar = tf.summary.scalar(name='train_seq_dist',
tensor=sequence_dist)
train_conv1_image = visualizor.merge_conv_image(
feature_map=tensor_dict['conv1'], scope='conv1_image')
train_conv2_image = visualizor.merge_conv_image(
feature_map=tensor_dict['conv2'], scope='conv2_image')
train_conv3_image = visualizor.merge_conv_image(
feature_map=tensor_dict['conv3'], scope='conv3_image')
train_conv7_image = visualizor.merge_conv_image(
feature_map=tensor_dict['conv7'], scope='conv7_image')
lr_scalar = tf.summary.scalar(name='Learning_Rate', tensor=learning_rate)
weights_tensor_dict = dict()
for vv in tf.trainable_variables():
if 'conv' in vv.name:
weights_tensor_dict[vv.name[:-2]] = vv
train_weights_hist_dict = visualizor.merge_weights_hist(
weights_tensor_dict=weights_tensor_dict,
scope='weights_histogram',
is_merge=False)
train_summary_merge_list = [
train_cost_scalar, train_accuracy_scalar, train_seq_scalar, lr_scalar,
train_conv1_image, train_conv2_image, train_conv3_image
]
for _, weights_hist in train_weights_hist_dict.items():
train_summary_merge_list.append(weights_hist)
train_summary_op_merge = tf.summary.merge(inputs=train_summary_merge_list)
# validation过程summary
# val_cost_scalar = tf.summary.scalar(name='val_cost', tensor=cost)
# val_seq_scalar = tf.summary.scalar(name='val_seq_dist', tensor=sequence_dist)
# val_accuracy_scalar = tf.summary.scalar(name='val_accuracy', tensor=accuracy_tensor)
# test_summary_op_merge = tf.summary.merge(inputs=[val_cost_scalar, val_accuracy_scalar,
# val_seq_scalar])
# Set saver configuration
restore_variable_list = [tmp.name for tmp in tf.trainable_variables()]
saver = tf.train.Saver()
model_save_dir = '/data/output'
if not ops.exists(model_save_dir):
os.makedirs(model_save_dir)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# Set sess configuration
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
sess_config.gpu_options.allocator_type = 'BFC'
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_path)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = config.cfg.TRAIN.EPOCHS
print('Global configuration is as follows:')
pprint.pprint(config.cfg)
with sess.as_default():
if FLAGS.weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
# logger.info('Restore model from last crnn check point{:s}'.format(weights_path))
# init = tf.global_variables_initializer()
# sess.run(init)
# restore_saver = tf.train.Saver(var_list=restore_variable_list)
# restore_saver.restore(sess=sess, save_path=weights_path)
logger.info('Restore model from last crnn check point{:s}'.format(
FLAGS.weights_path))
saver.restore(sess=sess, save_path=FLAGS.weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for epoch in range(train_epochs):
_, c, seq_distance, preds, gt_labels = sess.run(
[optimizer, cost, sequence_dist, decoded, input_labels],
feed_dict={phase_tensor: 'train'})
# calculate the precision
preds = decoder.sparse_tensor_to_str(preds[0])
gt_labels = decoder.sparse_tensor_to_str(gt_labels)
accuracy = []
for index, gt_label in enumerate(gt_labels):
pred = preds[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
train_summary = sess.run(train_summary_op_merge,
feed_dict={
accuracy_tensor: accuracy,
phase_tensor: 'train'
})
summary_writer.add_summary(summary=train_summary,
global_step=epoch)
if epoch % config.cfg.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, c, seq_distance, accuracy))
# if epoch % config.cfg.TRAIN.VAL_STEP == 0:
# inputdata_value = sess.run(inputdata_val)
# val_c, val_seq, val_preds, val_gt_labels = sess.run([
# cost, sequence_dist, decoded, input_labels_val],
# feed_dict={phase_tensor: 'test',
# input_tensor: inputdata_value})
#
# preds_val = decoder.sparse_tensor_to_str(val_preds[0])
# gt_labels_val = decoder.sparse_tensor_to_str(val_gt_labels)
#
# accuracy_val = []
#
# for index, gt_label in enumerate(gt_labels_val):
# pred = preds_val[index]
# totol_count = len(gt_label)
# correct_count = 0
# try:
# for i, tmp in enumerate(gt_label):
# if tmp == pred[i]:
# correct_count += 1
# except IndexError:
# continue
# finally:
# try:
# accuracy_val.append(correct_count / totol_count)
# except ZeroDivisionError:
# if len(pred) == 0:
# accuracy_val.append(1)
# else:
# accuracy_val.append(0)
#
# accuracy_val = np.mean(np.array(accuracy_val).astype(np.float32), axis=0)
#
# test_summary = sess.run(test_summary_op_merge,
# feed_dict={accuracy_tensor: accuracy_val,
# phase_tensor: 'test',
# input_tensor: inputdata_value})
# summary_writer.add_summary(summary=test_summary, global_step=epoch)
#
# logger.info('Epoch: {:d} val_cost= {:9f} val_seq_distance= {:9f} val_accuracy= {:9f}'.format(
# epoch + 1, val_c, val_seq, accuracy_val))
if epoch % 500 == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def recognize(image_path, weights_path, char_dict_path, ord_map_dict_path,
is_vis):
"""
:param image_path:
:param weights_path:
:param char_dict_path:
:param ord_map_dict_path:
:param is_vis:
:return:
"""
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.resize(image,
tuple(CFG.ARCH.INPUT_SIZE),
interpolation=cv2.INTER_LINEAR)
image_vis = image
image = np.array(image, np.float32) / 127.5 - 1.0
[IMAGE_WIDTH, IMAGE_HEIGHT] = tuple(CFG.ARCH.INPUT_SIZE)
inputdata = tf.placeholder(
dtype=tf.float32,
shape=[1, IMAGE_HEIGHT, IMAGE_WIDTH, CFG.ARCH.INPUT_CHANNELS],
name='input')
codec = tf_io_pipline_tools.TextFeatureIO(
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path).reader
net = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=CFG.ARCH.NUM_CLASSES)
inference_ret = net.inference(inputdata=inputdata,
name='shadow_net',
reuse=False)
decodes, _ = tf.nn.ctc_beam_search_decoder(
inputs=inference_ret,
sequence_length=CFG.ARCH.SEQ_LENGTH * np.ones(1),
merge_repeated=False)
# config tf saver
saver = tf.train.Saver()
# config tf session
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TEST.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TEST.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
preds = sess.run(decodes, feed_dict={inputdata: [image]})
preds = codec.sparse_tensor_to_str(preds[0])
logger.info('Predict image {:s} result {:s}'.format(
ops.split(image_path)[1], preds[0]))
if is_vis:
plt.figure('CRNN Model Demo')
plt.imshow(image_vis[:, :, (2, 1, 0)])
plt.show()
sess.close()
return
def recognize(image_path: str,
weights_path: str,
cfg: EasyDict,
is_vis: bool = True,
num_classes: int = 0):
"""
:param image_path:
:param weights_path: Path to stored weights
:param cfg:
:param is_vis:
:param num_classes:
"""
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
image = cv2.resize(image, tuple(cfg.ARCH.INPUT_SIZE))
image = np.expand_dims(image, axis=0).astype(np.float32)
w, h = cfg.ARCH.INPUT_SIZE
inputdata = tf.placeholder(dtype=tf.float32,
shape=[1, h, w, cfg.ARCH.INPUT_CHANNELS],
name='input')
codec = data_utils.TextFeatureIO(
char_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR, 'char_dict.json'),
ord_map_dict_path=ops.join(cfg.PATH.CHAR_DICT_DIR, 'ord_map.json'))
num_classes = len(
codec.reader.char_dict) + 1 if num_classes == 0 else num_classes
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=cfg.ARCH.HIDDEN_UNITS,
layers_nums=cfg.ARCH.HIDDEN_LAYERS,
num_classes=num_classes)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=inputdata)
decodes, _ = tf.nn.ctc_beam_search_decoder(
inputs=net_out,
sequence_length=cfg.ARCH.SEQ_LENGTH * np.ones(1),
merge_repeated=False)
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = cfg.TRAIN.TF_ALLOW_GROWTH
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
with sess.as_default():
saver.restore(sess=sess, save_path=weights_path)
preds = sess.run(decodes, feed_dict={inputdata: image})
preds = codec.writer.sparse_tensor_to_str(preds[0])
logger.info('Predict image {:s} label {:s}'.format(
ops.split(image_path)[1], preds[0]))
if is_vis:
plt.figure('CRNN Model Demo')
plt.imshow(
cv2.imread(image_path, cv2.IMREAD_COLOR)[:, :, (2, 1, 0)])
plt.show()
sess.close()
def test_shadownet(dataset_dir, weights_path, is_vis=False, is_recursive=True):
"""
:param dataset_dir:
:param weights_path:
:param is_vis:
:param is_recursive:
:return:
"""
# Initialize the record decoder
decoder = data_utils.TextFeatureIO().reader
images_t, labels_t, imagenames_t = decoder.read_features(ops.join(
dataset_dir, 'test_feature.tfrecords'),
num_epochs=None)
if not is_recursive:
images_sh, labels_sh, imagenames_sh = tf.train.shuffle_batch(
tensors=[images_t, labels_t, imagenames_t],
batch_size=32,
capacity=1000 + 32 * 2,
min_after_dequeue=2,
num_threads=4)
else:
images_sh, labels_sh, imagenames_sh = tf.train.batch(
tensors=[images_t, labels_t, imagenames_t],
batch_size=32,
capacity=1000 + 32 * 2,
num_threads=4)
images_sh = tf.cast(x=images_sh, dtype=tf.float32)
# build shadownet
net = crnn_model.ShadowNet(phase='Test',
hidden_nums=256,
layers_nums=2,
seq_length=25,
num_classes=37)
with tf.variable_scope('shadow'):
net_out = net.build_shadownet(inputdata=images_sh)
decoded, _ = tf.nn.ctc_beam_search_decoder(net_out,
25 * np.ones(32),
merge_repeated=False)
# config tf session
sess_config = tf.ConfigProto()
sess_config.gpu_options.per_process_gpu_memory_fraction = config.cfg.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = config.cfg.TRAIN.TF_ALLOW_GROWTH
# config tf saver
saver = tf.train.Saver()
sess = tf.Session(config=sess_config)
test_sample_count = 0
for record in tf.python_io.tf_record_iterator(
ops.join(dataset_dir, 'test_feature.tfrecords')):
test_sample_count += 1
loops_nums = int(math.ceil(test_sample_count / 32))
# loops_nums = 100
with sess.as_default():
# restore the model weights
saver.restore(sess=sess, save_path=weights_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
print('Start predicting ......')
if not is_recursive:
predictions, images, labels, imagenames = sess.run(
[decoded, images_sh, labels_sh, imagenames_sh])
imagenames = np.reshape(imagenames, newshape=imagenames.shape[0])
imagenames = [tmp.decode('utf-8') for tmp in imagenames]
preds_res = decoder.sparse_tensor_to_str(predictions[0])
gt_res = decoder.sparse_tensor_to_str(labels)
accuracy = []
for index, gt_label in enumerate(gt_res):
pred = preds_res[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
print('Mean test accuracy is {:5f}'.format(accuracy))
for index, image in enumerate(images):
print(
'Predict {:s} image with gt label: {:s} **** predict label: {:s}'
.format(imagenames[index], gt_res[index],
preds_res[index]))
if is_vis:
plt.imshow(image[:, :, (2, 1, 0)])
plt.show()
else:
accuracy = []
for epoch in range(loops_nums):
predictions, images, labels, imagenames = sess.run(
[decoded, images_sh, labels_sh, imagenames_sh])
imagenames = np.reshape(imagenames,
newshape=imagenames.shape[0])
imagenames = [tmp.decode('utf-8') for tmp in imagenames]
preds_res = decoder.sparse_tensor_to_str(predictions[0])
gt_res = decoder.sparse_tensor_to_str(labels)
for index, gt_label in enumerate(gt_res):
pred = preds_res[index]
totol_count = len(gt_label)
correct_count = 0
try:
for i, tmp in enumerate(gt_label):
if tmp == pred[i]:
correct_count += 1
except IndexError:
continue
finally:
try:
accuracy.append(correct_count / totol_count)
except ZeroDivisionError:
if len(pred) == 0:
accuracy.append(1)
else:
accuracy.append(0)
for index, image in enumerate(images):
print(
'Predict {:s} image with gt label: {:s} **** predict label: {:s}'
.format(imagenames[index], gt_res[index],
preds_res[index]))
# if is_vis:
# plt.imshow(image[:, :, (2, 1, 0)])
# plt.show()
accuracy = np.mean(np.array(accuracy).astype(np.float32), axis=0)
print('Test accuracy is {:5f}'.format(accuracy))
coord.request_stop()
coord.join(threads=threads)
sess.close()
return
def train_shadownet(dataset_dir,
weights_path,
char_dict_path,
ord_map_dict_path,
need_decode=False):
"""
:param dataset_dir:
:param weights_path:
:param char_dict_path:
:param ord_map_dict_path:
:param need_decode:
:return:
"""
# prepare dataset
train_dataset = shadownet_data_feed_pipline.CrnnDataFeeder(
dataset_dir=dataset_dir,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='train')
val_dataset = shadownet_data_feed_pipline.CrnnDataFeeder(
dataset_dir=dataset_dir,
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path,
flags='val')
train_images, train_labels, train_images_paths = train_dataset.inputs(
batch_size=CFG.TRAIN.BATCH_SIZE, num_epochs=1)
val_images, val_labels, val_images_paths = val_dataset.inputs(
batch_size=CFG.TRAIN.BATCH_SIZE, num_epochs=1)
# declare crnn net
shadownet = crnn_model.ShadowNet(phase='train',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=CFG.ARCH.NUM_CLASSES)
shadownet_val = crnn_model.ShadowNet(phase='test',
hidden_nums=CFG.ARCH.HIDDEN_UNITS,
layers_nums=CFG.ARCH.HIDDEN_LAYERS,
num_classes=CFG.ARCH.NUM_CLASSES)
# set up decoder
decoder = tf_io_pipline_tools.TextFeatureIO(
char_dict_path=char_dict_path,
ord_map_dict_path=ord_map_dict_path).reader
# set up training graph
with tf.device('/gpu:1'):
# compute loss and seq distance
train_inference_ret, train_ctc_loss = shadownet.compute_loss(
inputdata=train_images,
labels=train_labels,
name='shadow_net',
reuse=False)
val_inference_ret, val_ctc_loss = shadownet_val.compute_loss(
inputdata=val_images,
labels=val_labels,
name='shadow_net',
reuse=True)
train_decoded, train_log_prob = tf.nn.ctc_beam_search_decoder(
train_inference_ret,
CFG.ARCH.SEQ_LENGTH * np.ones(CFG.TRAIN.BATCH_SIZE),
merge_repeated=False)
val_decoded, val_log_prob = tf.nn.ctc_beam_search_decoder(
val_inference_ret,
CFG.ARCH.SEQ_LENGTH * np.ones(CFG.TRAIN.BATCH_SIZE),
merge_repeated=False)
train_sequence_dist = tf.reduce_mean(tf.edit_distance(
tf.cast(train_decoded[0], tf.int32), train_labels),
name='train_edit_distance')
val_sequence_dist = tf.reduce_mean(tf.edit_distance(
tf.cast(val_decoded[0], tf.int32), val_labels),
name='val_edit_distance')
# set learning rate
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
learning_rate=CFG.TRAIN.LEARNING_RATE,
global_step=global_step,
decay_steps=CFG.TRAIN.LR_DECAY_STEPS,
decay_rate=CFG.TRAIN.LR_DECAY_RATE,
staircase=CFG.TRAIN.LR_STAIRCASE)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.9).minimize(
loss=train_ctc_loss,
global_step=global_step)
# Set tf summary
tboard_save_dir = 'tboard/crnn_syn90k'
os.makedirs(tboard_save_dir, exist_ok=True)
tf.summary.scalar(name='train_ctc_loss', tensor=train_ctc_loss)
tf.summary.scalar(name='val_ctc_loss', tensor=val_ctc_loss)
tf.summary.scalar(name='learning_rate', tensor=learning_rate)
if need_decode:
tf.summary.scalar(name='train_seq_distance',
tensor=train_sequence_dist)
tf.summary.scalar(name='val_seq_distance', tensor=val_sequence_dist)
merge_summary_op = tf.summary.merge_all()
# Set saver configuration
saver = tf.train.Saver()
model_save_dir = 'model/crnn_syn90k'
os.makedirs(model_save_dir, exist_ok=True)
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'shadownet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = ops.join(model_save_dir, model_name)
# Set sess configuration
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.per_process_gpu_memory_fraction = CFG.TRAIN.GPU_MEMORY_FRACTION
sess_config.gpu_options.allow_growth = CFG.TRAIN.TF_ALLOW_GROWTH
sess = tf.Session(config=sess_config)
summary_writer = tf.summary.FileWriter(tboard_save_dir)
summary_writer.add_graph(sess.graph)
# Set the training parameters
train_epochs = CFG.TRAIN.EPOCHS
with sess.as_default():
if weights_path is None:
logger.info('Training from scratch')
init = tf.global_variables_initializer()
sess.run(init)
else:
logger.info('Restore model from {:s}'.format(weights_path))
saver.restore(sess=sess, save_path=weights_path)
patience_counter = 1
cost_history = [np.inf]
for epoch in range(train_epochs):
# setup early stopping
if epoch > 1 and CFG.TRAIN.EARLY_STOPPING:
# We always compare to the first point where cost didn't improve
if cost_history[-1 - patience_counter] - cost_history[
-1] > CFG.TRAIN.PATIENCE_DELTA:
patience_counter = 1
else:
patience_counter += 1
if patience_counter > CFG.TRAIN.PATIENCE_EPOCHS:
logger.info(
"Cost didn't improve beyond {:f} for {:d} epochs, stopping early."
.format(CFG.TRAIN.PATIENCE_DELTA, patience_counter))
break
if need_decode and epoch % 500 == 0:
# train part
_, train_ctc_loss_value, train_seq_dist_value, \
train_predictions, train_labels, merge_summary_value = sess.run(
[optimizer, train_ctc_loss, train_sequence_dist,
train_decoded, train_labels, merge_summary_op])
train_labels = decoder.sparse_tensor_to_str(train_labels)
train_predictions = decoder.sparse_tensor_to_str(
train_predictions[0])
avg_train_accuracy = evaluation_tools.compute_accuracy(
train_labels, train_predictions)
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch_Train: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, train_ctc_loss_value,
train_seq_dist_value, avg_train_accuracy))
# validation part
val_ctc_loss_value, val_seq_dist_value, \
val_predictions, val_labels = sess.run(
[val_ctc_loss, val_sequence_dist, val_decoded, val_labels])
val_labels = decoder.sparse_tensor_to_str(val_labels)
val_predictions = decoder.sparse_tensor_to_str(
val_predictions[0])
avg_val_accuracy = evaluation_tools.compute_accuracy(
val_labels, val_predictions)
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
logger.info(
'Epoch_Val: {:d} cost= {:9f} seq distance= {:9f} train accuracy= {:9f}'
.format(epoch + 1, val_ctc_loss_value,
val_seq_dist_value, avg_val_accuracy))
else:
_, train_ctc_loss_value, merge_summary_value = sess.run(
[optimizer, train_ctc_loss, merge_summary_op])
if epoch % CFG.TRAIN.DISPLAY_STEP == 0:
logger.info('Epoch_Train: {:d} cost= {:9f}'.format(
epoch + 1, train_ctc_loss_value))
# record history train ctc loss
cost_history.append(train_ctc_loss_value)
# add training sumary
summary_writer.add_summary(summary=merge_summary_value,
global_step=epoch)
if epoch % 2000 == 0:
saver.save(sess=sess,
save_path=model_save_path,
global_step=epoch)
return np.array(cost_history[1:]) # Don't return the first np.inf
