def acc():
acc_all = []
g = model_transfer.Graph(pb_file_path = pb_file_path)
with tf.Session(graph = g.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(),max_to_keep=100)
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess,ckpt)
print('restore from ckpt{}'.format(ckpt))
else:
print('cannot restore')
test_feeder=utils.DataIterator2(data_dir=data_dir,text_dir=text_dir)
print("total data:",test_feeder.size)
print("total image in folder", test_feeder.total_pic_read)
total_epoch = int(test_feeder.size / FLAGS.batch_size) + 1
for cur_batch in range(total_epoch):
print("cur_epoch/total_epoch",cur_batch,"/",total_epoch)
indexs=[]
cur_batch_num = FLAGS.batch_size
if cur_batch == int(test_feeder.size / FLAGS.batch_size):
cur_batch_num = test_feeder.size - cur_batch * FLAGS.batch_size
for i in range(cur_batch_num):
indexs.append(cur_batch * FLAGS.batch_size + i)
test_inputs,test_seq_len,test_labels=test_feeder.input_index_generate_batch(indexs)
cur_labels = [test_feeder.labels[i] for i in indexs]
test_feed={g.original_pic: test_inputs,
g.labels: test_labels,
g.seq_len: np.array([Flage_width]*test_inputs.shape[0])}
dense_decoded= sess.run(g.dense_decoded, test_feed)
acc = utils.accuracy_calculation(cur_labels,dense_decoded,ignore_value=-1,isPrint=False)
acc_all.append(acc)
print("$$$$$$$$$$$$$$$$$ ACC is :",acc_all,"$$$$$$$$$$$$$$$$$")
print("avg_acc:",np.array(acc_all).mean())
def acc():
g = model.Graph()
with tf.Session(graph=g.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from ckpt{}'.format(ckpt))
else:
print('cannot restore')
val_feeder = utils.DataIterator(data_dir=inferFolder)
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(
)
val_feed = {
g.inputs: val_inputs,
g.labels: val_labels,
g.seq_len: np.array([27] * val_inputs.shape[0])
}
dense_decoded = sess.run(g.dense_decoded, val_feed)
# print the decode result
acc = utils.accuracy_calculation(val_feeder.labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
print(acc)
def train(train_dir=None, val_dir=None):
#载入训练、测试数据
print('Loading training data...')
train_feeder = utils.DataIterator(data_dir=train_dir)
print('Get images: ', train_feeder.size)
print('Loading validate data...')
val_feeder=utils.DataIterator(data_dir=val_dir)
print('Get images: ', val_feeder.size)
#定义网络结构
g = Graph()
#训练样本总数
num_train_samples = train_feeder.size
#每一轮(epoch)样本可以跑多少个batch
num_batches_per_epoch = int(num_train_samples / batch_size)
with tf.Session(graph = g.graph) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=10)
# restore = True 加载模型
if restore:
ckpt = tf.train.latest_checkpoint(checkpoint_dir)
if ckpt:
# global_step也会被加载
saver.restore(sess, ckpt);
print('restore from the checkpoint{0}'.format(ckpt))
print('============begin training============')
# 获取一个batch的验证数据，制作成placeholder的输入格式
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch()
val_feed = {g.inputs: val_inputs, g.labels: val_labels, g.seq_len: val_seq_len}
start_time = time.time();
for cur_epoch in range(num_epochs): #按照epoch进行循环
shuffle_idx = np.random.permutation(num_train_samples) #将训练样本的index打乱
train_cost = 0;
for cur_batch in range(num_batches_per_epoch): #对于当前epoch中的每个bacth进行训练
# 获取一个batch的训练样本，制作成placeholder的输入格式
indexs = [shuffle_idx[i % num_train_samples] for i in range(cur_batch * batch_size, (cur_batch+1) * batch_size)];
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(indexs);
feed = {g.inputs: batch_inputs, g.labels:batch_labels, g.seq_len:batch_seq_len};
# 训练run
summary_str, batch_cost, step, _ = sess.run([g.merged_summay, g.cost, g.global_step, g.optimizer], feed)
# 计算损失
train_cost += batch_cost;
# 打印
if step % 50 == 1:
end_time = time.time();
print('No. %5d batches, loss: %5.2f, time: %3.1fs' % (step, batch_cost, end_time-start_time));
start_time = time.time();
#验证集验证、保存checkpoint：
if step % validation_steps == 1:
if not os.path.isdir(checkpoint_dir): os.mkdir(checkpoint_dir);
saver.save(sess,os.path.join(checkpoint_dir, 'ocr-model'), global_step=step)
#解码的结果：
dense_decoded, lastbatch_err, lr = sess.run([g.dense_decoded, g.lerr, g.learning_rate], val_feed)
acc = utils.accuracy_calculation(val_feeder.labels, dense_decoded, ignore_value=-1, isPrint=False)
print('-After %5d steps, Val accu: %4.2f%%' % (step, acc));
def train():
g = Graph()
with g.graph.as_default():
print('loading train data, please wait---------------------', end=' ')
train_feeder = utils.DataIterator(data_dir='../train/')
print('get image: ', train_feeder.size)
print('loading validation data, please wait---------------------',
end=' ')
val_feeder = utils.DataIterator(data_dir='../test/')
print('get image: ', val_feeder.size)
num_train_samples = train_feeder.size # 12800
num_batches_per_epoch = int(num_train_samples /
FLAGS.batch_size) # example: 12800/64
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)
with tf.Session(graph=g.graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print(
'=============================begin training============================='
)
# the cuda trace
#run_metadata = tf.RunMetadata()
#trace_file = open('timeline.ctf.json','w')
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(
)
val_feed = {
g.inputs: val_inputs,
g.labels: val_labels,
g.seq_len: val_seq_len
}
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = train_err = 0
start = time.time()
batch_time = time.time()
#the tracing part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time',
time.time() - batch_time)
batch_time = time.time()
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(
indexs)
feed = {
g.inputs: batch_inputs,
g.labels: batch_labels,
g.seq_len: batch_seq_len
}
#_,batch_cost, the_err,d,lr,train_summary,step = sess.run([optimizer,cost,lerr,decoded[0],learning_rate,merged_summay,global_step],feed)
#_,batch_cost, the_err,d,lr,step = sess.run([optimizer,cost,lerr,decoded[0],learning_rate,global_step],feed)
#the_err,d,lr = sess.run([lerr,decoded[0],learning_rate])
# if summary is needed
#batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
batch_cost, step, _ = sess.run(
[g.cost, g.global_step, g.optimizer], feed)
#calculate the cost
train_cost += batch_cost * FLAGS.batch_size
## the tracing part
#_,batch_cost,the_err,step,lr,d = sess.run([optimizer,cost,lerr,
# global_step,learning_rate,decoded[0]],feed)
#options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
#run_metadata=run_metadata)
#trace = timeline.Timeline(step_stats=run_metadata.step_stats)
#race_file.write(trace.generate_chrome_trace_format())
#trace_file.close()
#train_writer.add_summary(train_summary,step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
#train_err+=the_err*FLAGS.batch_size
d, lastbatch_err = sess.run([g.decoded[0], g.lerr], val_feed)
dense_decoded = tf.sparse_tensor_to_dense(
d, default_value=-1).eval(session=sess)
# print the decode result
acc = utils.accuracy_calculation(val_feeder.labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
train_cost /= num_train_samples
#train_err/=num_train_samples
now = datetime.datetime.now()
log = "{}-{} {}:{}:{} Epoch {}/{}, accuracy = {:.3f},train_cost = {:.3f}, lastbatch_err = {:.3f}, time = {:.3f}"
print(
log.format(now.month, now.day, now.hour, now.minute,
now.second, cur_epoch + 1, FLAGS.num_epochs, acc,
train_cost, lastbatch_err,
time.time() - start))
def train(train_dir=None, val_dir=None, mode='train'):
#加载模型类
model = orcmodel.LSTMOCR(mode)
#创建模型，这一步，运算图和训练操作等都已经具备
model.build_graph()
print('loading train data, please wait---------------------')
train_feeder = utils.DataIterator(data_dir=train_dir) # 准备训练数据
print('get image: ', train_feeder.size)
print('loading validation data, please wait---------------------')
val_feeder = utils.DataIterator(data_dir=val_dir) # 准备验证数据
print('get image: ', val_feeder.size)
num_train_samples = train_feeder.size # 训练样本个数
num_batches_per_epoch = int(
num_train_samples / FLAGS.batch_size) # 一轮有多少批次 example: 100000/100
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(
num_val_samples / FLAGS.batch_size) # 一轮有多少批次 example: 10000/100
shuffle_idx_val = np.random.permutation(num_val_samples)
with tf.device('/cpu:0'):
# ConfigProto 用于配置Session
# 如果你指定的设备不存在，允许TF自动分配设备
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
# max_to_keep 用于指定保存最近的N个checkpoint
saver = tf.train.Saver(tf.global_variables(), max_to_keep=50)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph) # summary 可视化
# 根据配置是否恢复权重
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# 恢复的时候global_step也会被恢复
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print(
'=============================begin training============================='
)
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(
num_train_samples) # 乱序训练样本的index，达到SGD
train_cost = 0
start_time = time.time()
batch_time = time.time()
# 开始一轮的N批训练
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time',
time.time() - batch_time)
batch_time = time.time()
# 生成训练批次的index
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, batch_seq_len, batch_labels = \
train_feeder.input_index_generate_batch(indexs)
# 填充placeholder
feed = {
model.inputs: batch_inputs,
model.labels: batch_labels,
model.seq_len: batch_seq_len
}
# 开始run，记录可视化数据，计算成本值，获取global_step，并训练
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step,
model.train_op], feed)
# batch_cost是一个均值，这里计算一个batch的cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(
summary_str,
step) # run merge_all的到一个summery信息，然后写入，用于可视化
# 保存checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir,
'ocr-model'),
global_step=step)
# 验证
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
# 按SGD验证一个最小批
indexs_val = [
shuffle_idx_val[i % num_val_samples]
for i in range(j * FLAGS.batch_size, (j + 1) *
FLAGS.batch_size)
]
val_inputs, val_seq_len, val_labels = \
val_feeder.input_index_generate_batch(indexs_val)
val_feed = {
model.inputs: val_inputs,
model.labels: val_labels,
model.seq_len: val_seq_len
}
# 解码，并获得当前学习率
dense_decoded, lr = sess.run(
[model.dense_decoded, model.lrn_rate],
val_feed)
# print the decode result
ori_labels = val_feeder.the_label(indexs_val)
# 计算一个批次正确率
acc = utils.accuracy_calculation(ori_labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total * FLAGS.batch_size
) / num_val_samples # 求一轮的平均正确率
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size) # 整一轮的当前平均损失值
# 输出训练最新信息
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_cost = {:f}, time = {:.3f},lr={:.8f}"
print(
log.format(now.month, now.day, now.hour,
now.minute, now.second, cur_epoch + 1,
FLAGS.num_epochs, accuracy,
avg_train_cost, batch_cost,
time.time() - start_time, lr))
def train(train_dir=None, mode='train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
model.build_graph()
label_files = [os.pat.join(train_dir, e) for e in os.listdir(train_dir) if e.endswith('.txt') and os.path.exists(os.path.join(train_dir, e.replace('.txt', '.jpg')))]
train_num = int(len(label_files) * 0.8)
test_num = len(label_files) - train_num
print('total num', len(label_files), 'train num', train_num, 'test num', test_num)
train_imgs = label_files[0:train_num]
test_imgs = label_files[train_num:]
print('loading train data')
train_feeder = utils.DataIterator(data_dir=train_imgs)
print('loading validation data')
val_feeder = utils.DataIterator(data_dir=test_imgs)
num_batches_per_epoch_train = int(train_num / FLAGS.batch_size) # example: 100000/100
num_batches_per_epoch_val = int(test_num / FLAGS.batch_size) # example: 10000/100
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
for cur_epoch in range(FLAGS.num_epochs):
train_cost = 0
start_time = time.time()
batch_time = time.time()
if cur_epoch == 0:
random.shuffle(train_feeder.train_data)
# the training part
for cur_batch in range(num_batches_per_epoch_train):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time', time.time() - batch_time)
batch_time = time.time()
batch_inputs, result_img_length, batch_labels = \
train_feeder.get_batchsize_data(cur_batch)
feed = {model.inputs: batch_inputs,
model.labels: batch_labels,
model.seq_len: result_img_length}
# if summary is needed
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step, model.train_op], feed)
# calculate the cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save checkpoint at step {0}', format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'ocr-model'), global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for val_j in range(num_batches_per_epoch_val):
result_img_val, seq_len_input_val, batch_label_val = \
val_feeder.get_batchsize_data(val_j)
val_feed = {model.inputs: result_img_val,
model.labels: batch_label_val,
model.seq_len: seq_len_input_val}
dense_decoded, lastbatch_err, lr = \
sess.run([model.dense_decoded, model.cost, model.lrn_rate],
val_feed)
# print the decode result
val_pre_list = []
for decode_code in dense_decoded:
pred_strings = utils.label2text(decode_code)
val_pre_list.append(pred_strings)
ori_labels = val_feeder.get_val_label(val_j)
acc = utils.accuracy_calculation(ori_labels, val_pre_list,
ignore_value=-1, isPrint=True)
acc_batch_total += acc
accuracy = acc_batch_total / num_batches_per_epoch_val
avg_train_cost = train_cost / ((cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}"
print(log.format(now.month, now.day, now.hour, now.minute, now.second,
cur_epoch + 1, FLAGS.num_epochs, accuracy, avg_train_cost,
lastbatch_err, time.time() - start_time, lr))
def train(train_dir=None, val_dir=None, mode='train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
model.build_graph()
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
num_batches_per_epoch = int(TRAIN_SET_NUM /
FLAGS.batch_size) # example: 100000/100
num_batches_per_epoch_val = int(TRAIN_SET_NUM /
FLAGS.batch_size) # example: 10000/100
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from checkpoint{0}'.format(ckpt))
print(
'=============================begin training============================='
)
for epoch in range(1000):
train_feeder = sess.run(train_batch)
batch_inputs, batch_labels = \
train_feeder[0],read_labels(train_feeder[1])
feed = {model.inputs: batch_inputs, model.labels: batch_labels}
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step, model.train_op], feed)
train_writer.add_summary(summary_str, step)
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save checkpoint at step {0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
val_inputs, val_labels = \
train_feeder[0], read_labels(train_feeder[1])
val_feed = {
model.inputs: val_inputs,
model.labels: val_labels
}
dense_decoded, lastbatch_err, lr = \
sess.run([model.dense_decoded, model.cost, model.lrn_rate],
val_feed)
# print the decode result
print(dense_decoded)
print(val_labels)
acc = utils.accuracy_calculation(val_labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total * FLAGS.batch_size) / 2
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}"
print(
log.format(now.month, now.day, now.hour, now.minute,
now.second, epoch + 1, FLAGS.num_epochs,
accuracy, epoch, lastbatch_err,
time.time() - epoch, lr))
def train(train_dir=None,
val_dir=None,
train_text_dir=None,
val_text_dir=None,
pb_file_path=None):
g = model_transfer.Graph(is_training=True, pb_file_path=pb_file_path)
print('loading train data, please wait---------------------', 'end= ')
train_feeder = utils.DataIterator2(data_dir=train_dir,
text_dir=train_text_dir)
print('***************get image: ', train_feeder.size)
print('loading validation data, please wait---------------------', 'end= ')
val_feeder = utils.DataIterator2(data_dir=val_dir, text_dir=val_text_dir)
print('***************get image: ', val_feeder.size)
'''
print('loading train data, please wait---------------------','end= ')
train_feeder=utils.DataIterator(data_dir=train_dir)
print('get image: ',train_feeder.size)
print('loading validation data, please wait---------------------','end= ')
val_feeder=utils.DataIterator(data_dir=val_dir)
print('get image: ',val_feeder.size)
'''
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples / FLAGS.batch_size)
num_val_samples = val_feeder.size
num_val_per_epoch = int(num_val_samples / FLAGS.batch_size)
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)
config.gpu_options.allow_growth = True
with tf.Session(graph=g.graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
if FLAGS.restore:
print("restore is true")
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
g.graph.finalize()
print(
'=============================begin training============================='
)
cur_training_step = 0
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(
)
val_feed = {
g.original_pic: val_inputs,
g.labels: val_labels,
g.seq_len: np.array([Flage_width] * val_inputs.shape[0])
}
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
for cur_batch in range(num_batches_per_epoch):
cur_training_step += 1
if cur_training_step % Flage_print_frequency == 0:
print("epochs", cur_epoch, cur_batch)
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(
indexs)
transfer_train_batch_feed = {
g.original_pic: batch_inputs,
g.seq_len: np.array([Flage_width] * batch_inputs.shape[0]),
g.labels: batch_labels
}
summary_str, batch_cost, all_step, _ = sess.run(
[g.merged_summay, g.cost, g.global_step, g.optimizer],
transfer_train_batch_feed)
train_cost += batch_cost * FLAGS.batch_size
if all_step % FLAGS.save_steps == 0:
print("**********save checkpoint********** all_step:",
all_step)
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=all_step)
if all_step % FLAGS.validation_steps == 0:
print("**********CrossValidation********** all_step:",
all_step)
dense_decoded, lastbatch_err, lr = sess.run(
[g.dense_decoded, g.lerr, g.learning_rate], val_feed)
acc = utils.accuracy_calculation(val_feeder.labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size)
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} all_step==={}, Epoch {}/{}, accuracy = {:.3f},avg_train_cost = {:.3f}, lastbatch_err = {:.3f},lr={:.8f}\n"
print(
log.format(now.month, now.day, now.hour, now.minute,
now.second, all_step, cur_epoch + 1,
FLAGS.num_epochs, acc, avg_train_cost,
lastbatch_err, lr))
if Flag_Isserver:
f = open('../log/acc/acc.txt', mode="a")
f.write(
log.format(now.month, now.day, now.hour,
now.minute, now.second, all_step,
cur_epoch + 1, FLAGS.num_epochs, acc,
avg_train_cost, lastbatch_err, lr))
f.close()
def train(train_dir=None,
val_dir=None,
train_text_dir=None,
val_text_dir=None):
g = model.Graph(is_training=True)
print('loading train data, please wait---------------------', 'end= ')
train_feeder = utils.DataIterator2(data_dir=train_dir,
text_dir=train_text_dir)
print('get image: ', train_feeder.size)
print('loading validation data, please wait---------------------', 'end= ')
val_feeder = utils.DataIterator2(data_dir=val_dir, text_dir=val_text_dir)
print('get image: ', val_feeder.size)
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples / FLAGS.batch_size)
num_val_samples = val_feeder.size
num_val_per_epoch = int(num_val_samples / FLAGS.batch_size)
config = tf.ConfigProto(log_device_placement=False,
allow_soft_placement=False)
config.gpu_options.allow_growth = True
#config.gpu_options.per_process_gpu_memory_fraction = 0.6
with tf.Session(graph=g.graph, config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
g.graph.finalize()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
if FLAGS.restore:
print("restore is true")
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print(
'=============================begin training============================='
)
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(
)
#print(len(val_inputs))
val_feed = {
g.inputs: val_inputs,
g.labels: val_labels,
g.seq_len: np.array([g.cnn_time] * val_inputs.shape[0])
}
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
#the tracing part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time',
time.time() - batch_time)
batch_time = time.time()
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(
indexs)
#batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {
g.inputs: batch_inputs,
g.labels: batch_labels,
g.seq_len: np.array([g.cnn_time] * batch_inputs.shape[0])
}
# if summary is needed
#batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
summary_str, batch_cost, step, _ = sess.run(
[g.merged_summay, g.cost, g.global_step, g.optimizer],
feed)
#calculate the cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 0:
print("save checkpoint", step)
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
#train_err+=the_err*FLAGS.batch_size
#do validation
if step % FLAGS.validation_steps == 0:
dense_decoded, lastbatch_err, lr = sess.run(
[g.dense_decoded, g.lerr, g.learning_rate], val_feed)
# print the decode result
acc = utils.accuracy_calculation(val_feeder.labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size)
#train_err/=num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} step==={}, Epoch {}/{}, accuracy = {:.3f},avg_train_cost = {:.3f}, lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}\n"
print(
log.format(now.month, now.day, now.hour, now.minute,
now.second, step, cur_epoch + 1,
FLAGS.num_epochs, acc, avg_train_cost,
lastbatch_err,
time.time() - start_time, lr))
if Flag_Isserver:
f = open('../log/acc/acc.txt', mode="a")
f.write(
log.format(now.month, now.day, now.hour,
now.minute, now.second, step,
cur_epoch + 1, FLAGS.num_epochs, acc,
avg_train_cost, lastbatch_err,
time.time() - start_time, lr))
f.close()
def train(train_dir=None, val_dir=None, mode='train'):
if FLAGS.model == 'lstm':
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
else:
print("no such model")
sys.exit()
#开始构建图
model.build_graph()
#########################read train data###############################
print('loading train data, please wait---------------------')
train_feeder = utils.DataIterator(data_dir=FLAGS.train_dir, istrain=True)
print('get image data size: ', train_feeder.size)
filename = train_feeder.image
label = train_feeder.labels
print(len(filename))
train_data = ReadData.ReadData(filename, label)
##################################read test data######################################
print('loading validation data, please wait---------------------')
val_feeder = utils.DataIterator(data_dir=FLAGS.val_dir, istrain=False)
filename1 = val_feeder.image
label1 = val_feeder.labels
test_data = ReadData.ReadData(filename1, label1)
print('val get image: ', val_feeder.size)
##################计算batch 数
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples /
FLAGS.batch_size) # 训练集一次epoch需要的batch数
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(num_val_samples /
FLAGS.batch_size) # 验证集一次epoch需要的batch数
###########################data################################################
with tf.device('/cpu:0'):
config = tf.ConfigProto(allow_soft_placement=True)
#######################read data###################################
with tf.Session(config=config) as sess:
#初始化data迭代器
train_data.init_itetator(sess)
test_data.init_itetator(sess)
train_data = train_data.get_nex_batch()
test_data = test_data.get_nex_batch()
#全局变量初始化
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=100) #存储模型
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
#导入预训练模型
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
else:
print("No checkpoint")
print(
'=============================begin training============================='
)
accuracy_res = []
epoch_res = []
tmp_max = 0
tmp_epoch = 0
for cur_epoch in range(FLAGS.num_epochs):
train_cost = 0
batch_time = time.time()
for cur_batch in range(num_batches_per_epoch):
#获得这一轮batch数据的标号##############################
#read_data_start = time.time()
batch_inputs, batch_labels = sess.run(train_data)
#print('read data timr',time.time()-read_data_start)
process_data_start = time.time()
#print('233333333333333',type(batch_labels))
new_batch_labels = utils.sparse_tuple_from_label(
batch_labels.tolist()) # 对了
batch_seq_len = np.asarray(
[FLAGS.max_stepsize for _ in batch_inputs],
dtype=np.int64)
#print('process data timr', time.time() - process_data_start)
#train_data_start = time.time()
#print('2444444',batch_inputs.shape())
feed = {
model.inputs: batch_inputs,
model.labels: new_batch_labels,
model.seq_len: batch_seq_len
}
# if summary is needed
# batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step,
model.train_op], feed)
# calculate the cost
train_cost += batch_cost * FLAGS.batch_size
#print train_cost
#train_writer.add_summary(summary_str, step)
#print('train data timr', time.time() - train_data_start)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir,
'ocr-model'),
global_step=step)
if (cur_batch) % 100 == 1:
print('batch', cur_batch, ': time',
time.time() - batch_time, 'loss', batch_cost)
batch_time = time.time()
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
validation_start_time = time.time()
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
batch_inputs, batch_labels = sess.run(test_data)
new_batch_labels = utils.sparse_tuple_from_label(
batch_labels.tolist()) # 对了
batch_seq_len = np.asarray(
[FLAGS.max_stepsize for _ in batch_inputs],
dtype=np.int64)
val_feed = {
model.inputs: batch_inputs,
model.labels: new_batch_labels,
model.seq_len: batch_seq_len
}
dense_decoded, lr = \
sess.run([model.dense_decoded, model.lrn_rate],
val_feed)
acc = utils.accuracy_calculation(
batch_labels.tolist(),
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total *
FLAGS.batch_size) / num_val_samples
accuracy_res.append(accuracy)
epoch_res.append(cur_epoch)
if accuracy > tmp_max:
tmp_max = accuracy
tmp_epoch = cur_epoch
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"max_accuracy = {:.3f},max_Epoch {},accuracy = {:.3f},acc_batch_total = {:.3f},avg_train_cost = {:.3f}, " \
" time = {:.3f},lr={:.8f}"
print(
log.format(now.month, now.day, now.hour,
now.minute, now.second, cur_epoch + 1,
FLAGS.num_epochs, tmp_max, tmp_epoch,
accuracy, acc_batch_total,
avg_train_cost,
time.time() - validation_start_time,
lr))
def train(train_dir=None, val_dir=None, mode='train'):
if FLAGS.model == 'lstm':
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
else:
print("no such model")
sys.exit()
#开始构建图
model.build_graph()
print('loading train data, please wait---------------------')
train_feeder = utils.DataIterator(data_dir=train_dir, num=4000000)
print('get image: ', train_feeder.size)
print('loading validation data, please wait---------------------')
val_feeder = utils.DataIterator(data_dir=val_dir, num=40000)
print('get image: ', val_feeder.size)
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples /
FLAGS.batch_size) # 训练集一次epoch需要的batch数
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(num_val_samples /
FLAGS.batch_size) # 验证集一次epoch需要的batch数
shuffle_idx_val = np.random.permutation(num_val_samples)
with tf.device('/cpu:0'):
config = tf.ConfigProto(allow_soft_placement=True)
with tf.Session(config=config) as sess:
#全局变量初始化
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(),
max_to_keep=100) #存储模型
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
#导入预训练模型
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
else:
print("No checkpoint")
print(
'=============================begin training============================='
)
accuracy_res = []
accuracy_per_res = []
epoch_res = []
tmp_max = 0
tmp_epoch = 0
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
# the tracing part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time',
time.time() - batch_time)
batch_time = time.time()
#获得这一轮batch数据的标号
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, batch_seq_len, batch_labels = \
train_feeder.input_index_generate_batch(indexs)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {
model.inputs: batch_inputs,
model.labels: batch_labels,
model.seq_len: batch_seq_len
}
# if summary is needed
# batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step,
model.train_op], feed)
# calculate the cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir,
'ocr-model'),
global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
acc_per_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
indexs_val = [
shuffle_idx_val[i % num_val_samples]
for i in range(j * FLAGS.batch_size, (j + 1) *
FLAGS.batch_size)
]
val_inputs, val_seq_len, val_labels = \
val_feeder.input_index_generate_batch(indexs_val)
val_feed = {
model.inputs: val_inputs,
model.labels: val_labels,
model.seq_len: val_seq_len
}
dense_decoded, lr = \
sess.run([model.dense_decoded, model.lrn_rate],
val_feed)
# print the decode result
ori_labels = val_feeder.the_label(indexs_val)
acc = utils.accuracy_calculation(ori_labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_per = utils.accuracy_calculation_single(
ori_labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_per_batch_total += acc_per
acc_batch_total += acc
accuracy_per = (acc_per_batch_total *
FLAGS.batch_size) / num_val_samples
accuracy = (acc_batch_total *
FLAGS.batch_size) / num_val_samples
accuracy_per_res.append(accuracy_per)
accuracy_res.append(accuracy)
epoch_res.append(cur_epoch)
if accuracy_per > tmp_max:
tmp_max = accuracy
tmp_epoch = cur_epoch
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"max_accuracy = {:.3f},max_Epoch {},accuracy = {:.3f},acc_batch_total = {:.3f},avg_train_cost = {:.3f}, " \
" time = {:.3f},lr={:.8f}"
print(
log.format(now.month, now.day, now.hour,
now.minute, now.second, cur_epoch + 1,
FLAGS.num_epochs, tmp_max, tmp_epoch,
accuracy_per, acc_batch_total,
avg_train_cost,
time.time() - start_time, lr))
def train(train_dir=None, val_dir=None, mode='train'):
model = mdlstm_ctc_ocr.LSTMOCR(mode)
model.build_graph()
print('loading train data, please wait---------------------')
train_feeder = utils.DataIterator(data_dir=train_dir)
print('get image: ', train_feeder.size)
print('loading validation data, please wait---------------------')
val_feeder = utils.DataIterator(data_dir=val_dir)
print('get image: ', val_feeder.size)
num_train_samples = train_feeder.size # 100000
num_batches_per_epoch = int(num_train_samples / FLAGS.batch_size) # example: 100000/100
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(num_val_samples / FLAGS.batch_size) # example: 10000/100
shuffle_idx_val = np.random.permutation(num_val_samples)
# define batch_size
batch_size = FLAGS.batch_size
config = tf.ConfigProto( allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
# the tracing part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time', time.time() - batch_time)
batch_time = time.time()
indexs = [shuffle_idx[i % num_train_samples] for i in range(cur_batch * FLAGS.batch_size, (cur_batch + 1) * FLAGS.batch_size)]
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(indexs)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {model.inputs: batch_inputs,
model.labels: batch_labels,
model.seq_len: batch_seq_len}
model.is_training = True
# if summary is needed
# batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
summary_str, batch_cost, step, _ = sess.run([model.merged_summay, model.cost, model.global_step,model.train_op], feed)
# calculate the cost
delta_batch_cost = batch_cost * FLAGS.batch_size
train_cost += delta_batch_cost
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lr = 0
for j in range(num_batches_per_epoch_val):
indexs_val = [shuffle_idx_val[i % num_val_samples] for i in
range(j * FLAGS.batch_size, (j + 1) * FLAGS.batch_size)]
val_inputs, val_seq_len, val_labels = \
val_feeder.input_index_generate_batch(indexs_val)
val_feed = {model.inputs: val_inputs,
model.labels: val_labels,
model.seq_len: val_seq_len}
model.is_training = False
dense_decoded, lr = \
sess.run([model.dense_decoded, model.lrn_rate],
val_feed)
# print the decode result
ori_labels = val_feeder.the_label(indexs_val)
acc = utils.accuracy_calculation(ori_labels, dense_decoded,
ignore_value=-1, isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total * FLAGS.batch_size) / num_val_samples
avg_train_cost = train_cost / ((cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"time = {:.3f},lr={:.8f}"
print(log.format(now.month, now.day, now.hour, now.minute, now.second,
cur_epoch + 1, FLAGS.num_epochs, accuracy, avg_train_cost,
time.time() - start_time, lr))
def train(train_dir=None,val_dir=None,initial_learning_rate=None,num_hidden=None,num_classes=None,hparam=None):
g = Graph()
print('loading train data, please wait---------------------')
train_feeder=utils.DataIterator(data_dir=train_dir)
print('get image: ',train_feeder.size)
print('loading validation data, please wait---------------------')
val_feeder=utils.DataIterator(data_dir=val_dir)
print('get image: ',val_feeder.size)
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples/FLAGS.batch_size)
config=tf.ConfigProto(log_device_placement=False,allow_soft_placement=False)
with tf.Session(graph=g.graph,config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(),max_to_keep=100) #持久化
g.graph.finalize() #???
train_writer=tf.summary.FileWriter(FLAGS.log_dir+hparam,sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess,ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
val_inputs,val_seq_len,val_labels,_=val_feeder.input_index_generate_batch() # seq_len 时序长度120
val_feed={g.inputs: val_inputs,
g.labels: val_labels,
g.seq_len: val_seq_len}
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx=np.random.permutation(num_train_samples)
train_cost = train_err=0
start_time = time.time()
batch_time = time.time()
for cur_batch in range(num_batches_per_epoch):
batch_time = time.time()
indexs = [shuffle_idx[i%num_train_samples] for i in range(cur_batch*FLAGS.batch_size,(cur_batch+1)*FLAGS.batch_size)]
batch_inputs,batch_seq_len,batch_labels,label_batch=train_feeder.input_index_generate_batch(indexs)
feed={g.inputs: batch_inputs,
g.labels:batch_labels,
g.seq_len:batch_seq_len}
train_dense_decoded,summary_str, batch_cost,step,_ = sess.run([g.dense_decoded,g.merged_summay,g.CTCloss,g.global_step,g.optimizer],feed)
#check computing cell propeties
# print "||||||||||||||||||||"
# print(sess.run(g.dense_decoded,feed))
# print(np.shape(sess.run(g.pool1,feed)))
train_cost+=batch_cost*FLAGS.batch_size
train_writer.add_summary(summary_str,step)
# save the checkpoint
if step%FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
saver.save(sess,os.path.join(FLAGS.checkpoint_dir,'ocr-model'),global_step=step)
if step%FLAGS.validation_steps == 0:
dense_decoded,validation_length_err,learningRate = sess.run([g.dense_decoded,g.lengthOferr,
g.learning_rate],val_feed)
valid_acc = utils.accuracy_calculation(val_feeder.labels,dense_decoded,ignore_value=-1,isPrint=True)
train_acc = utils.accuracy_calculation(label_batch,train_dense_decoded,ignore_value=-1,isPrint=True)
avg_train_cost=train_cost/((cur_batch+1)*FLAGS.batch_size)
now = datetime.datetime.now()
log = "*{}/{} {}:{}:{} Epoch {}/{}, accOfvalidation = {:.3f},train_accuracy={:.3f}, time = {:.3f},learningRate={:.8f}"
print(log.format(now.month,now.day,now.hour,now.minute,now.second,cur_epoch+1,FLAGS.num_epochs,valid_acc,train_acc,time.time()-start_time,learningRate))
def train(train_dir=None, val_dir=None, mode='train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
model.build_graph()
print('loading train data, please wait---------------------')
train_feeder, num_train_samples = data_prep.input_batch_generator('train', is_training=True, batch_size = FLAGS.batch_size)
print('get image: ', num_train_samples)
print('loading validation data, please wait---------------------')
val_feeder, num_val_samples = data_prep.input_batch_generator('val', is_training=False, batch_size = FLAGS.batch_size * 2)
print('get image: ', num_val_samples)
num_batches_per_epoch = int(math.ceil(num_train_samples / float(FLAGS.batch_size)))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
for cur_epoch in range(FLAGS.num_epochs):
start_time = time.time()
batch_time = time.time()
# the tracing part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time', time.time() - batch_time)
batch_time = time.time()
batch_inputs, batch_labels, _ = next(train_feeder)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {model.inputs: batch_inputs,
model.labels: batch_labels}
# if summary is needed
# batch_cost,step,train_summary,_ = sess.run([cost,global_step,merged_summay,optimizer],feed)
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step,
model.train_op], feed)
# calculate the cost
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
val_inputs, val_labels, ori_labels = next(val_feeder)
val_feed = {model.inputs: val_inputs,
model.labels: val_labels}
dense_decoded, lr = \
sess.run([model.dense_decoded, model.lrn_rate],
val_feed)
# print the decode result
accuracy = utils.accuracy_calculation(ori_labels, dense_decoded,
ignore_value=-1, isPrint=True)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.5f},train_cost = {:.5f}, " \
", time = {:.3f},lr={:.8f}"
print(log.format(now.month, now.day, now.hour, now.minute, now.second,
cur_epoch + 1, FLAGS.num_epochs, accuracy, batch_cost,
time.time() - start_time, lr))
def train(train_dir=None, val_dir=None, mode='train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
model.build_graph()
print('loading train data')
train_feeder = utils.DataIterator(data_dir=train_dir)
print('size: ', train_feeder.size)
print('loading validation data')
val_feeder = utils.DataIterator(data_dir=val_dir)
print('size: {}\n'.format(val_feeder.size))
num_train_samples = train_feeder.size # 100000
num_batches_per_epoch = int(num_train_samples / FLAGS.batch_size) # example: 100000/100
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(num_val_samples / FLAGS.batch_size) # example: 10000/100
shuffle_idx_val = np.random.permutation(num_val_samples)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
# the training part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time', time.time() - batch_time)
batch_time = time.time()
indexs = [shuffle_idx[i % num_train_samples] for i in
range(cur_batch * FLAGS.batch_size, (cur_batch + 1) * FLAGS.batch_size)]
batch_inputs, _, batch_labels = \
train_feeder.input_index_generate_batch(indexs)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {model.inputs: batch_inputs,
model.labels: batch_labels}
# if summary is needed
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step, model.train_op], feed)
# calculate the cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save checkpoint at step {0}', format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'ocr-model'), global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
indexs_val = [shuffle_idx_val[i % num_val_samples] for i in
range(j * FLAGS.batch_size, (j + 1) * FLAGS.batch_size)]
val_inputs, _, val_labels = \
val_feeder.input_index_generate_batch(indexs_val)
val_feed = {model.inputs: val_inputs,
model.labels: val_labels}
dense_decoded, lastbatch_err, lr = \
sess.run([model.dense_decoded, model.cost, model.lrn_rate],
val_feed)
# print the decode result
ori_labels = val_feeder.the_label(indexs_val)
acc = utils.accuracy_calculation(ori_labels, dense_decoded,
ignore_value=-1, isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total * FLAGS.batch_size) / num_val_samples
avg_train_cost = train_cost / ((cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}"
print(log.format(now.month, now.day, now.hour, now.minute, now.second,
cur_epoch + 1, FLAGS.num_epochs, accuracy, avg_train_cost,
lastbatch_err, time.time() - start_time, lr))
def train_process(mode=RunMode.Trains):
model = framework.GraphOCR(mode, NETWORK_MAP[NEU_CNN],
NETWORK_MAP[NEU_RECURRENT])
model.build_graph()
print('Loading Trains DataSet...')
train_feeder = utils.DataIterator(mode=RunMode.Trains)
if TRAINS_USE_TFRECORDS:
train_feeder.read_sample_from_tfrecords(TRAINS_PATH)
print('Loading Test DataSet...')
test_feeder = utils.DataIterator(mode=RunMode.Test)
test_feeder.read_sample_from_tfrecords(TEST_PATH)
else:
if isinstance(TRAINS_PATH, list):
origin_list = []
for trains_path in TRAINS_PATH:
origin_list += [
os.path.join(trains_path, trains)
for trains in os.listdir(trains_path)
]
else:
origin_list = [
os.path.join(TRAINS_PATH, trains)
for trains in os.listdir(TRAINS_PATH)
]
random.shuffle(origin_list)
if not HAS_TEST_SET:
test_list = origin_list[:TEST_SET_NUM]
trains_list = origin_list[TEST_SET_NUM:]
else:
if isinstance(TEST_PATH, list):
test_list = []
for test_path in TEST_PATH:
test_list += [
os.path.join(test_path, test)
for test in os.listdir(test_path)
]
else:
test_list = [
os.path.join(TEST_PATH, test)
for test in os.listdir(TEST_PATH)
]
random.shuffle(test_list)
trains_list = origin_list
train_feeder.read_sample_from_files(trains_list)
print('Loading Test DataSet...')
test_feeder = utils.DataIterator(mode=RunMode.Test)
test_feeder.read_sample_from_files(test_list)
print('Total {} Trains DataSets'.format(train_feeder.size))
print('Total {} Test DataSets'.format(test_feeder.size))
if test_feeder.size >= train_feeder.size:
exception(
"The number of training sets cannot be less than the test set.", )
num_train_samples = train_feeder.size
num_test_samples = test_feeder.size
if num_test_samples < TEST_BATCH_SIZE:
exception(
"The number of test sets cannot be less than the test batch size.",
ConfigException.INSUFFICIENT_SAMPLE)
num_batches_per_epoch = int(num_train_samples / BATCH_SIZE)
config = tf.ConfigProto(
# allow_soft_placement=True,
log_device_placement=False,
gpu_options=tf.GPUOptions(
allocator_type='BFC',
allow_growth=True, # it will cause fragmentation.
per_process_gpu_memory_fraction=GPU_USAGE))
accuracy = 0
epoch_count = 1
with tf.Session(config=config) as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
train_writer = tf.summary.FileWriter('logs', sess.graph)
try:
saver.restore(sess, tf.train.latest_checkpoint(MODEL_PATH))
except ValueError:
pass
print('Start training...')
while 1:
shuffle_trains_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
_avg_train_cost = 0
for cur_batch in range(num_batches_per_epoch):
batch_time = time.time()
index_list = [
shuffle_trains_idx[i % num_train_samples]
for i in range(cur_batch * BATCH_SIZE, (cur_batch + 1) *
BATCH_SIZE)
]
if TRAINS_USE_TFRECORDS:
batch_inputs, batch_seq_len, batch_labels = train_feeder.generate_batch_by_tfrecords(
sess)
else:
batch_inputs, batch_seq_len, batch_labels = train_feeder.generate_batch_by_files(
index_list)
feed = {
model.inputs: batch_inputs,
model.labels: batch_labels,
}
summary_str, batch_cost, step, _ = sess.run([
model.merged_summary, model.cost, model.global_step,
model.train_op
],
feed_dict=feed)
train_cost += batch_cost * BATCH_SIZE
avg_train_cost = train_cost / ((cur_batch + 1) * BATCH_SIZE)
_avg_train_cost = avg_train_cost
train_writer.add_summary(summary_str, step)
if step % 100 == 0 and step != 0:
print('Step: {} Time: {:.3f}, Cost = {:.5f}'.format(
step,
time.time() - batch_time, avg_train_cost))
if step % TRAINS_SAVE_STEPS == 0 and step != 0:
saver.save(sess, SAVE_MODEL, global_step=step)
logger.info('save checkpoint at step {0}', format(step))
if step % TRAINS_VALIDATION_STEPS == 0 and step != 0:
shuffle_test_idx = np.random.permutation(num_test_samples)
batch_time = time.time()
index_test = [
shuffle_test_idx[i % num_test_samples]
for i in range(cur_batch *
TEST_BATCH_SIZE, (cur_batch + 1) *
TEST_BATCH_SIZE)
]
if TRAINS_USE_TFRECORDS:
test_inputs, batch_seq_len, test_labels = test_feeder.generate_batch_by_tfrecords(
sess)
else:
test_inputs, batch_seq_len, test_labels = test_feeder.generate_batch_by_files(
index_test)
val_feed = {
model.inputs: test_inputs,
model.labels: test_labels
}
dense_decoded, lr = sess.run(
[model.dense_decoded, model.lrn_rate],
feed_dict=val_feed)
accuracy = utils.accuracy_calculation(
test_feeder.labels(
None if TRAINS_USE_TFRECORDS else index_test),
dense_decoded,
ignore_value=[0, -1],
)
log = "Epoch: {}, Step: {}, Accuracy = {:.4f}, Cost = {:.5f}, " \
"Time = {:.3f}, LearningRate: {}"
print(
log.format(epoch_count, step, accuracy, avg_train_cost,
time.time() - batch_time, lr))
if accuracy >= TRAINS_END_ACC and epoch_count >= TRAINS_END_EPOCHS and avg_train_cost <= TRAINS_END_COST:
break
if accuracy >= TRAINS_END_ACC and epoch_count >= TRAINS_END_EPOCHS and _avg_train_cost <= TRAINS_END_COST:
compile_graph(accuracy)
print('Total Time: {}'.format(time.time() - start_time))
break
epoch_count += 1
def train(train_dir = None, val_dir = None, mode = 'train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
# 创建图
model.build_graph()
print('loading train data, please wait---------------------')
# 训练数据构造器
train_feeder = utils.DataIterator(data_dir = train_dir)
print('get image:', train_feeder.size)
print('loading validation data, please wait---------------------')
# 验证数据构造器
val_feeder = utils.DataIterator(data_dir = val_dir)
print('get image:', val_feeder.size)
num_train_samples = train_feeder.size # 100000
num_batches_per_epoch = int(num_train_samples / FLAGS.batch_size) # example: 100000 / 100
num_val_samples = val_feeder.size # 100000
num_batches_per_epoch_val = int(num_val_samples / FLAGS.batch_size) # example: 100000 / 100
# 随机打乱验证集样本
shuffle_idx_val = np.random.permutation(num_val_samples)
with tf.device('/gpu:0'):
# tf.ConfigProto一般用在创建session的时候。用来对session进行参数配置
# allow_soft_placement = True
# 如果你指定的设备不存在，允许TF自动分配设备
config = tf.ConfigProto(allow_soft_placement = True)
with tf.Session(config = config) as sess:
sess.run(tf.global_variables_initializer())
# 创建saver对象，用来保存和恢复模型的参数
saver = tf.train.Saver(tf.global_variables(), max_to_keep = 100)
# 将sess里的graph放到日志文件中
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
# 如果之前有保存的模型参数，将之恢复到现在的sess中
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
saver.restore(sess, ckpt)
print('restore from the checkpoint{0}'.format(ckpt))
print('=============================begin training=============================')
# 开始训练
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ':time', time.time() - batch_time)
batch_time = time.time()
# 构造当前batch的样本indexs
# 在训练样本空间中随机选取batch_size数量的的样本
indexs = [shuffle_idx[i % num_train_samples] for i in range(cur_batch * FLAGS.batch_size, (cur_batch + 1) * FLAGS.batch_size)]
batch_inputs, batch_seq_len, batch_labels = train_feeder.input_index_generate_batch(indexs)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
# 构造模型feed参数
feed = {model.inputs:batch_inputs, model.labels: batch_labels, model.seq_len: batch_seq_len}
# 执行图
# fetch操作取回tensors
summar_str, batch_cost, step, _ = sess.run([model.merged_summay, model.cost, model.global_step, model.train_op], feed)
# 计算损失值
# 这里的batch_cost是一个batch里的均值
train_cost += batch_cost * FLAGS.batch_size
# 可视化
train_writer.add_summary(summar_str, step)
# 保存模型文件checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save the checkpoint of{0}', format(step))
saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'ocr-model'), global_step = step)
# 每个batch验证集上得到解码结果
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
# 得到验证集的输入
# 每个batch做迭代验证
for j in xrange(num_batches_per_epoch_val):
indexs_val = [shuffle_idx_val[i % num_val_samples] for i in range(j * FLAGS.batch_size, (j + 1) * FLAGS.batch_size)]
val_inputs, val_seq_len, val_labels = val_feeder.input_index_generate_batch(indexs_val)
val_feed = {model.inputs: val_inputs, modell.labels: val_labels, model.seq_len: val_seq_len}
dense_decoded, lastbatch_err, lr = sess.run([model.dense_decoded, model.lrn_rate], val_feed)
# 打印在验证集上返回的结果
ori_labels = val_feeder.the_label(indexs_val)
acc = utils.accuracy_calculation(ori_labels, dense_decoded, ignore_value = -1, isPrint = True)
acc_batch_total += acc
accuracy = (acc_batch_total * FLAGS.batch_size) / num_val_samples
avg_train_cost = train_cost / ((cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_smaples
now = datetime.datetime.time()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}"
print(log.format(now.month, now.day, now.hour, now.minute, now.second, cur_epoch + 1, FLAGS.num_epochs, accuracy, avg_train_cost, lastbatch_err, time.time() - start_time, lr))
def train(train_dir=None, val_dir=None, mode='train'):
model = cnn_lstm_otc_ocr.LSTMOCR(mode)
model.build_graph()
print(FLAGS.image_channel)
print('loading train data')
train_feeder = utils.DataIterator(data_dir=train_dir)
print('size: ', train_feeder.size)
print('loading validation data')
val_feeder = utils.DataIterator(data_dir=val_dir)
print('size: {}\n'.format(val_feeder.size))
num_train_samples = train_feeder.size # 100000
num_batches_per_epoch = int(num_train_samples /
FLAGS.batch_size) # example: 100000/100
num_val_samples = val_feeder.size
num_batches_per_epoch_val = int(num_val_samples /
FLAGS.batch_size) # example: 10000/100
shuffle_idx_val = np.random.permutation(num_val_samples)
os.environ["CUDA_VISIBLE_DEVICES"] = '2'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
if FLAGS.restore:
ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if ckpt:
# the global_step will restore sa well
saver.restore(sess, ckpt)
print('restore from checkpoint{0}'.format(ckpt))
print(
'=============================begin training============================='
)
sess.graph.finalize()
for cur_epoch in range(FLAGS.num_epochs):
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
batch_time = time.time()
# the training part
for cur_batch in range(num_batches_per_epoch):
if (cur_batch + 1) % 100 == 0:
print('batch', cur_batch, ': time',
time.time() - batch_time)
batch_time = time.time()
indexs = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch *
FLAGS.batch_size, (cur_batch + 1) *
FLAGS.batch_size)
]
batch_inputs, _, batch_labels = \
train_feeder.input_index_generate_batch(indexs)
# batch_inputs,batch_seq_len,batch_labels=utils.gen_batch(FLAGS.batch_size)
feed = {model.inputs: batch_inputs, model.labels: batch_labels}
# if summary is needed
summary_str, batch_cost, step, _ = \
sess.run([model.merged_summay, model.cost, model.global_step, model.train_op], feed)
# calculate the cost
train_cost += batch_cost * FLAGS.batch_size
train_writer.add_summary(summary_str, step)
# save the checkpoint
if step % FLAGS.save_steps == 1:
if not os.path.isdir(FLAGS.checkpoint_dir):
os.mkdir(FLAGS.checkpoint_dir)
logger.info('save checkpoint at step {0}', format(step))
saver.save(sess,
os.path.join(FLAGS.checkpoint_dir, 'ocr-model'),
global_step=step)
# train_err += the_err * FLAGS.batch_size
# do validation
if step % FLAGS.validation_steps == 0:
acc_batch_total = 0
lastbatch_err = 0
lr = 0
for j in range(num_batches_per_epoch_val):
indexs_val = [
shuffle_idx_val[i % num_val_samples]
for i in range(j * FLAGS.batch_size, (j + 1) *
FLAGS.batch_size)
]
val_inputs, _, val_labels = \
val_feeder.input_index_generate_batch(indexs_val)
val_feed = {
model.inputs: val_inputs,
model.labels: val_labels
}
dense_decoded, lastbatch_err, lr = \
sess.run([model.dense_decoded, model.cost, model.lrn_rate],
val_feed)
# print the decode result
ori_labels = val_feeder.the_label(indexs_val)
acc = utils.accuracy_calculation(ori_labels,
dense_decoded,
ignore_value=-1,
isPrint=True)
acc_batch_total += acc
accuracy = (acc_batch_total *
FLAGS.batch_size) / num_val_samples
avg_train_cost = train_cost / (
(cur_batch + 1) * FLAGS.batch_size)
# train_err /= num_train_samples
now = datetime.datetime.now()
log = "{}/{} {}:{}:{} Epoch {}/{}, " \
"accuracy = {:.3f},avg_train_cost = {:.3f}, " \
"lastbatch_err = {:.3f}, time = {:.3f},lr={:.8f}"
with open('test_acc.txt', 'a') as f:
f.write(
str(
log.format(now.month, now.day, now.hour,
now.minute, now.second, cur_epoch +
1, FLAGS.num_epochs, accuracy,
avg_train_cost, lastbatch_err,
time.time() - start_time, lr)) +
"\n")
print(
log.format(now.month, now.day, now.hour, now.minute,
now.second, cur_epoch + 1, FLAGS.num_epochs,
accuracy, avg_train_cost, lastbatch_err,
time.time() - start_time, lr))
def train_process(mode=RunMode.Trains):
model = framework_lstm.LSTM(mode)
model.build_graph()
test_list, trains_list = None, None
if not HAS_TEST_SET:
trains_list = os.listdir(TRAINS_PATH)
random.shuffle(trains_list)
origin_list = [
os.path.join(TRAINS_PATH, trains)
for i, trains in enumerate(trains_list)
]
test_list = origin_list[:TEST_SET_NUM]
trains_list = origin_list[TEST_SET_NUM:]
print('Loading Trains DataSet...')
train_feeder = utils.DataIterator(mode=RunMode.Trains)
if TRAINS_USE_TFRECORDS:
train_feeder.read_sample_from_tfrecords()
else:
train_feeder.read_sample_from_files(trains_list)
print('Total {} Trains DataSets'.format(train_feeder.size))
print('Loading Test DataSet...')
test_feeder = utils.DataIterator(mode=RunMode.Test)
if TEST_USE_TFRECORDS:
test_feeder.read_sample_from_tfrecords()
else:
test_feeder.read_sample_from_files(test_list)
print('Total {} Test DataSets'.format(test_feeder.size))
num_train_samples = train_feeder.size
num_batches_per_epoch = int(num_train_samples / BATCH_SIZE)
num_val_samples = test_feeder.size
num_batches_per_epoch_val = int(num_val_samples / BATCH_SIZE)
shuffle_idx_val = np.random.permutation(num_val_samples)
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=False,
gpu_options=tf.GPUOptions(
allow_growth=True, # it will cause fragmentation.
per_process_gpu_memory_fraction=GPU_USAGE))
accuracy = 0
epoch_count = 1
with tf.Session(config=config) as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
saver = tf.train.Saver(tf.global_variables(), max_to_keep=2)
train_writer = tf.summary.FileWriter('logs', sess.graph)
try:
saver.restore(sess, tf.train.latest_checkpoint(MODEL_PATH))
except ValueError:
pass
print('Start training...')
while 1:
shuffle_idx = np.random.permutation(num_train_samples)
train_cost = 0
start_time = time.time()
for cur_batch in range(num_batches_per_epoch):
index_list = [
shuffle_idx[i % num_train_samples]
for i in range(cur_batch * BATCH_SIZE, (cur_batch + 1) *
BATCH_SIZE)
]
if TRAINS_USE_TFRECORDS:
batch_inputs, _, batch_labels = train_feeder.generate_batch_by_tfrecords(
sess)
else:
batch_inputs, _, batch_labels = train_feeder.generate_batch_by_index(
index_list)
feed = {
model.batch_size: BATCH_SIZE,
model.inputs: batch_inputs,
model.labels: batch_labels,
}
summary_str, batch_cost, step, _ = sess.run([
model.merged_summary, model.cost, model.global_step,
model.train_op
], feed)
train_cost += batch_cost * BATCH_SIZE
train_writer.add_summary(summary_str, step)
if step % TRAINS_SAVE_STEPS == 0:
saver.save(sess, SAVE_MODEL, global_step=step)
logger.info('save checkpoint at step {0}', format(step))
if step % TRAINS_VALIDATION_STEPS == 0:
acc_batch_total = 0
lr = 0
batch_time = time.time()
for j in range(num_batches_per_epoch_val):
index_val = [
shuffle_idx_val[i % num_val_samples]
for i in range(j * BATCH_SIZE, (j + 1) *
BATCH_SIZE)
]
if TRAINS_USE_TFRECORDS:
val_inputs, _, val_labels = test_feeder.generate_batch_by_tfrecords(
sess)
else:
val_inputs, _, val_labels = test_feeder.generate_batch_by_index(
index_val)
val_feed = {
model.batch_size: BATCH_SIZE,
model.inputs: val_inputs,
model.labels: val_labels,
}
dense_decoded, last_batch_err, lr = sess.run(
[model.dense_decoded, model.cost, model.lrn_rate],
val_feed)
if TRAINS_USE_TFRECORDS:
ori_labels = test_feeder.label_by_tfrecords()
else:
ori_labels = test_feeder.label_by_index(index_val)
acc = utils.accuracy_calculation(
ori_labels,
dense_decoded,
ignore_value=-1,
)
acc_batch_total += acc
accuracy = (acc_batch_total * BATCH_SIZE) / num_val_samples
avg_train_cost = train_cost / (
(cur_batch + 1) * BATCH_SIZE)
log = "Epoch: {}, Step: {} Accuracy = {:.3f}, Cost = {:.3f}, Time = {:.3f}, LearningRate: {}"
print(
log.format(epoch_count, step, accuracy, avg_train_cost,
time.time() - batch_time, lr))
if accuracy >= TRAINS_END_ACC and epoch_count >= TRAINS_END_EPOCHS:
break
if accuracy >= TRAINS_END_ACC and epoch_count >= TRAINS_END_EPOCHS:
compile_graph(sess, accuracy)
print('Total Time: {}'.format(time.time() - start_time))
break
epoch_count += 1
coord.request_stop()
coord.join(threads)
