def _build_model(self):
"""选择采用哪种卷积网络"""
if self.network == CNNNetwork.CNN5:
x = CNN5(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.CNNX:
x = CNNX(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNetTiny:
x = ResNetTiny(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNet50:
x = ResNet50(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.DenseNet:
x = DenseNet(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
else:
raise ValueError('This cnn neural network is not supported at this time.')
"""选择采用哪种循环网络"""
# time_major = True: [max_time_step, batch_size, num_classes]
tf.compat.v1.logging.info("CNN Output: {}".format(x.get_shape()))
self.seq_len = tf.fill([tf.shape(x)[0]], tf.shape(x)[1], name="seq_len")
# self.labels_len = tf.fill([BATCH_SIZE], 12, name="labels_len")
if self.recurrent == RecurrentNetwork.NoRecurrent:
self.recurrent_network_builder = None
elif self.recurrent == RecurrentNetwork.LSTM:
self.recurrent_network_builder = LSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTM:
self.recurrent_network_builder = BiLSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRU:
self.recurrent_network_builder = GRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiGRU:
self.recurrent_network_builder = BiGRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.LSTMcuDNN:
self.recurrent_network_builder = LSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTMcuDNN:
self.recurrent_network_builder = BiLSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRUcuDNN:
self.recurrent_network_builder = GRUcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
else:
raise ValueError('This recurrent neural network is not supported at this time.')
logits = self.recurrent_network_builder.build() if self.recurrent_network_builder else x
if self.recurrent_network_builder and self.model_conf.loss_func != LossFunction.CTC:
raise ValueError('CTC loss must use recurrent neural network.')
"""输出层，根据Loss函数区分"""
with tf.keras.backend.name_scope('output'):
if self.model_conf.loss_func == LossFunction.CTC:
self.outputs = FullConnectedRNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.outputs = FullConnectedCNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
return self.outputs
class NeuralNetwork(object):
"""
神经网络构建类
"""
def __init__(self, model_conf: ModelConfig, mode: RunMode,
backbone: CNNNetwork, recurrent: RecurrentNetwork):
"""
:param model_conf: 模型配置
:param mode: 运行模式 (Trains/Validation/Predict)
:param backbone:
:param recurrent:
"""
self.model_conf = model_conf
self.decoder = Decoder(self.model_conf)
self.mode = mode
self.network = backbone
self.recurrent = recurrent
self.inputs = tf.keras.Input(dtype=tf.float32,
shape=self.input_shape,
name='input')
self.labels = tf.keras.Input(dtype=tf.int32,
shape=[None],
sparse=True,
name='labels')
self.utils = NetworkUtils(mode)
self.merged_summary = None
self.optimizer = None
self.dataset_size = None
@property
def input_shape(self):
"""
:return: tuple/list 类型，输入的 Shape
"""
return RESIZE_MAP[self.model_conf.loss_func](
*self.model_conf.resize) + [self.model_conf.image_channel]
def build_graph(self):
"""
在当前Session中构建网络计算图
"""
self._build_model()
def build_train_op(self, dataset_size=None):
self.dataset_size = dataset_size
self._build_train_op()
self.merged_summary = tf.compat.v1.summary.merge_all()
def _build_model(self):
"""选择采用哪种卷积网络"""
if self.network == CNNNetwork.CNN5:
x = CNN5(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
elif self.network == CNNNetwork.CNNX:
x = CNNX(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
elif self.network == CNNNetwork.ResNetTiny:
x = ResNetTiny(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
elif self.network == CNNNetwork.ResNet50:
x = ResNet50(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
elif self.network == CNNNetwork.DenseNet:
x = DenseNet(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
elif self.network == CNNNetwork.MobileNetV2:
x = MobileNetV2(model_conf=self.model_conf,
inputs=self.inputs,
utils=self.utils).build()
else:
raise ValueError(
'This cnn neural network is not supported at this time.')
"""选择采用哪种循环网络"""
# time_major = True: [max_time_step, batch_size, num_classes]
tf.compat.v1.logging.info("CNN Output: {}".format(x.get_shape()))
self.seq_len = tf.fill([tf.shape(x)[0]],
tf.shape(x)[1],
name="seq_len")
if self.recurrent == RecurrentNetwork.NoRecurrent:
self.recurrent_network_builder = None
elif self.recurrent == RecurrentNetwork.LSTM:
self.recurrent_network_builder = LSTM(model_conf=self.model_conf,
inputs=x,
utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTM:
self.recurrent_network_builder = BiLSTM(model_conf=self.model_conf,
inputs=x,
utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRU:
self.recurrent_network_builder = GRU(model_conf=self.model_conf,
inputs=x,
utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiGRU:
self.recurrent_network_builder = BiGRU(model_conf=self.model_conf,
inputs=x,
utils=self.utils)
elif self.recurrent == RecurrentNetwork.LSTMcuDNN:
self.recurrent_network_builder = LSTMcuDNN(
model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTMcuDNN:
self.recurrent_network_builder = BiLSTMcuDNN(
model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRUcuDNN:
self.recurrent_network_builder = GRUcuDNN(
model_conf=self.model_conf, inputs=x, utils=self.utils)
else:
raise ValueError(
'This recurrent neural network is not supported at this time.')
logits = self.recurrent_network_builder.build(
) if self.recurrent_network_builder else x
if self.recurrent_network_builder and self.model_conf.loss_func != LossFunction.CTC:
raise ValueError('CTC loss must use recurrent neural network.')
"""输出层，根据Loss函数区分"""
with tf.keras.backend.name_scope('output'):
if self.model_conf.loss_func == LossFunction.CTC:
self.outputs = FullConnectedRNN(model_conf=self.model_conf,
outputs=logits).build()
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.outputs = FullConnectedCNN(model_conf=self.model_conf,
outputs=logits).build()
return self.outputs
@property
def decay_steps(self):
if not self.dataset_size:
return 10000
# return 10000
epoch_step = int(self.dataset_size / self.model_conf.batch_size)
return int(epoch_step / 4)
def _build_train_op(self):
"""构建训练操作符"""
# 步数
self.global_step = tf.train.get_or_create_global_step()
# Loss函数
if self.model_conf.loss_func == LossFunction.CTC:
self.loss = Loss.ctc(labels=self.labels,
logits=self.outputs,
sequence_length=self.seq_len)
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.loss = Loss.cross_entropy(labels=self.labels,
logits=self.outputs)
self.cost = tf.reduce_mean(self.loss)
tf.compat.v1.summary.scalar('cost', self.cost)
# 学习率 指数衰减法
self.lrn_rate = tf.compat.v1.train.exponential_decay(
self.model_conf.trains_learning_rate,
self.global_step,
staircase=True,
decay_steps=self.decay_steps,
decay_rate=0.98,
)
tf.compat.v1.summary.scalar('learning_rate', self.lrn_rate)
if self.model_conf.neu_optimizer == Optimizer.AdaBound:
self.optimizer = AdaBoundOptimizer(learning_rate=self.lrn_rate,
final_lr=0.001,
beta1=0.9,
beta2=0.999,
amsbound=True)
elif self.model_conf.neu_optimizer == Optimizer.Adam:
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.lrn_rate)
elif self.model_conf.neu_optimizer == Optimizer.RAdam:
self.optimizer = RAdamOptimizer(learning_rate=self.lrn_rate,
warmup_proportion=0.1,
min_lr=1e-6)
elif self.model_conf.neu_optimizer == Optimizer.Momentum:
self.optimizer = tf.train.MomentumOptimizer(
learning_rate=self.lrn_rate,
use_nesterov=True,
momentum=0.9,
)
elif self.model_conf.neu_optimizer == Optimizer.SGD:
self.optimizer = tf.train.GradientDescentOptimizer(
learning_rate=self.lrn_rate, )
elif self.model_conf.neu_optimizer == Optimizer.AdaGrad:
self.optimizer = tf.train.AdagradOptimizer(
learning_rate=self.lrn_rate, )
elif self.model_conf.neu_optimizer == Optimizer.RMSProp:
self.optimizer = tf.train.RMSPropOptimizer(
learning_rate=self.lrn_rate, )
# BN 操作符更新(moving_mean, moving_variance)
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
# 将 train_op 和 update_ops 融合
with tf.control_dependencies(update_ops):
self.train_op = self.optimizer.minimize(
loss=self.cost,
global_step=self.global_step,
)
# 转录层-Loss函数
if self.model_conf.loss_func == LossFunction.CTC:
self.dense_decoded = self.decoder.ctc(inputs=self.outputs,
sequence_length=self.seq_len)
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.dense_decoded = self.decoder.cross_entropy(
inputs=self.outputs)
class NeuralNetwork(object):
"""
神经网络构建类
"""
def __init__(self, model_conf: ModelConfig, mode: RunMode, cnn: CNNNetwork, recurrent: RecurrentNetwork):
self.model_conf = model_conf
self.mode = mode
self.decoder = Decoder(self.model_conf)
self.utils = NetworkUtils(mode)
self.network = cnn
self.recurrent = recurrent
self.inputs = tf.keras.Input(dtype=tf.float32, shape=self.input_shape, name='input')
self.labels = tf.keras.Input(dtype=tf.int32, shape=[None], sparse=True, name='labels')
self.merged_summary = None
@property
def input_shape(self):
"""
:return: tuple/list 类型，输入的Shape
"""
return RESIZE_MAP[self.model_conf.loss_func](*self.model_conf.resize) + [self.model_conf.image_channel]
def build_graph(self):
"""在当前Session中构建网络计算图，无返回"""
self._build_model()
self._build_train_op()
self.merged_summary = tf.compat.v1.summary.merge_all()
def _build_model(self):
"""选择采用哪种卷积网络"""
if self.network == CNNNetwork.CNN5:
x = CNN5(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.CNNX:
x = CNNX(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNetTiny:
x = ResNetTiny(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.ResNet50:
x = ResNet50(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
elif self.network == CNNNetwork.DenseNet:
x = DenseNet(model_conf=self.model_conf, inputs=self.inputs, utils=self.utils).build()
else:
raise ValueError('This cnn neural network is not supported at this time.')
"""选择采用哪种循环网络"""
# time_major = True: [max_time_step, batch_size, num_classes]
tf.compat.v1.logging.info("CNN Output: {}".format(x.get_shape()))
self.seq_len = tf.fill([tf.shape(x)[0]], tf.shape(x)[1], name="seq_len")
# self.labels_len = tf.fill([BATCH_SIZE], 12, name="labels_len")
if self.recurrent == RecurrentNetwork.NoRecurrent:
self.recurrent_network_builder = None
elif self.recurrent == RecurrentNetwork.LSTM:
self.recurrent_network_builder = LSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTM:
self.recurrent_network_builder = BiLSTM(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRU:
self.recurrent_network_builder = GRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiGRU:
self.recurrent_network_builder = BiGRU(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.LSTMcuDNN:
self.recurrent_network_builder = LSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.BiLSTMcuDNN:
self.recurrent_network_builder = BiLSTMcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
elif self.recurrent == RecurrentNetwork.GRUcuDNN:
self.recurrent_network_builder = GRUcuDNN(model_conf=self.model_conf, inputs=x, utils=self.utils)
else:
raise ValueError('This recurrent neural network is not supported at this time.')
logits = self.recurrent_network_builder.build() if self.recurrent_network_builder else x
if self.recurrent_network_builder and self.model_conf.loss_func != LossFunction.CTC:
raise ValueError('CTC loss must use recurrent neural network.')
"""输出层，根据Loss函数区分"""
with tf.keras.backend.name_scope('output'):
if self.model_conf.loss_func == LossFunction.CTC:
self.outputs = FullConnectedRNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.outputs = FullConnectedCNN(model_conf=self.model_conf, mode=self.mode, outputs=logits).build()
return self.outputs
def _build_train_op(self):
"""操作符生成器"""
# 步数
self.global_step = tf.train.get_or_create_global_step()
# Loss函数
if self.model_conf.loss_func == LossFunction.CTC:
self.loss = Loss.ctc(
labels=self.labels,
logits=self.outputs,
sequence_length=self.seq_len
)
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.loss = Loss.cross_entropy(
labels=self.labels,
logits=self.outputs
)
self.cost = tf.reduce_mean(self.loss)
tf.compat.v1.summary.scalar('cost', self.cost)
# 学习率
self.lrn_rate = tf.compat.v1.train.exponential_decay(
self.model_conf.trains_learning_rate,
self.global_step,
staircase=True,
decay_steps=10000,
decay_rate=0.98,
)
tf.compat.v1.summary.scalar('learning_rate', self.lrn_rate)
# 训练参数更新
update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
# Storing adjusted smoothed mean and smoothed variance operations
with tf.control_dependencies(update_ops):
# TODO 这种if-else结构感觉很蠢，优化器选择器
if self.model_conf.neu_optimizer == Optimizer.AdaBound:
self.train_op = AdaBoundOptimizer(
learning_rate=self.lrn_rate,
final_lr=0.001,
beta1=0.9,
beta2=0.999,
amsbound=True
).minimize(
loss=self.cost,
global_step=self.global_step
)
elif self.model_conf.neu_optimizer == Optimizer.Adam:
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.lrn_rate
).minimize(
self.cost,
global_step=self.global_step
)
elif self.model_conf.neu_optimizer == Optimizer.Momentum:
self.train_op = tf.train.MomentumOptimizer(
learning_rate=self.lrn_rate,
use_nesterov=True,
momentum=0.9,
).minimize(
self.cost,
global_step=self.global_step
)
elif self.model_conf.neu_optimizer == Optimizer.SGD:
self.train_op = tf.train.GradientDescentOptimizer(
learning_rate=self.lrn_rate,
).minimize(
self.cost,
global_step=self.global_step
)
elif self.model_conf.neu_optimizer == Optimizer.AdaGrad:
self.train_op = tf.train.AdagradOptimizer(
learning_rate=self.lrn_rate,
).minimize(
self.cost,
global_step=self.global_step
)
elif self.model_conf.neu_optimizer == Optimizer.RMSProp:
self.train_op = tf.train.RMSPropOptimizer(
learning_rate=self.lrn_rate,
).minimize(
self.cost,
global_step=self.global_step
)
# 转录层-Loss函数
if self.model_conf.loss_func == LossFunction.CTC:
self.dense_decoded = self.decoder.ctc(
inputs=self.outputs,
sequence_length=self.seq_len
)
elif self.model_conf.loss_func == LossFunction.CrossEntropy:
self.dense_decoded = self.decoder.cross_entropy(
inputs=self.outputs
)