def init_test(self, testresult):
'''测试用例初始化
'''
super(iTestCase, self).init_test(testresult)
if not os.path.exists(self.attachments_path):
os.makedirs(self.attachments_path)
self._device_manager = DeviceManager()
def __init__(self):
self.bytes_free = 0
self.fail = 0
config = ConfigParser()
config.read(os.path.join(os.path.dirname(__file__), './config/settings_dev.cfg'))
for s in config.sections():
self.__dict__ = dict(self.__dict__.items() + {i[0]: i[1] for i in config.items(s)}.items())
self.device = DeviceManager(hostname=self.device_hostname, username=self.ssh_username)
self.music_path = os.path.join(self.music_base_path, self.music_folder)
if not os.path.exists(self.music_path):
logger.error("Music path %s does not exist." %(self.music_path))
exit(1)
def __init__(self,
mode,
iterator,
params,
rev_vocab_table=None,
scope=None,
log_trainables=True):
print_out("# creating %s graph ..." % mode)
self.dtype = tf.float32
self.mode = mode
self.embedding_size = params.embedding_size
self.num_layers = params.num_layers
self.iterator = iterator
# self.scheduled_sampling_prob = scheduled_sampling_prob
# self.num_samples_for_loss = num_samples_for_loss
self.device_manager = DeviceManager()
self.round_robin = RoundRobin(self.device_manager)
self.num_gpus = self.device_manager.num_available_gpus()
print_out("# number of gpus %d" % self.num_gpus)
with tf.variable_scope(scope or 'ta_seq2seq_graph', dtype=self.dtype):
self.init_embeddings(params.vocab_file,
params.embedding_type,
self.embedding_size,
scope=scope)
with tf.variable_scope(scope or "build_network"):
with tf.variable_scope("output_projection") as output_scope:
if params.boost_topic_gen_prob:
self.output_layer = taware_layer.JointDenseLayer(
params.vocab_size,
params.topic_vocab_size,
scope=output_scope,
name="output_projection")
else:
self.output_layer = layers_core.Dense(
params.vocab_size,
# activation=tf.nn.tanh,
use_bias=False,
name="output_projection")
encoder_keep_prob, decoder_keep_prob = self.get_keep_probs(
mode, params)
self.batch_size = tf.size(self.iterator.source_sequence_lengths)
encoder_outputs, encoder_state = self.__build_encoder(
params, encoder_keep_prob)
logits, sample_id, final_decoder_state = self.__build_decoder(
params, encoder_outputs, encoder_state, decoder_keep_prob)
if mode != tf.contrib.learn.ModeKeys.INFER:
with tf.device(self.device_manager.tail_gpu()):
loss = self.__compute_loss(logits)
else:
loss = None
if mode == tf.contrib.learn.ModeKeys.TRAIN:
self.train_loss = loss
self.word_count = tf.reduce_sum(
self.iterator.source_sequence_lengths) + tf.reduce_sum(
self.iterator.target_sequence_length)
elif mode == tf.contrib.learn.ModeKeys.EVAL:
self.eval_loss = loss
elif mode == tf.contrib.learn.ModeKeys.INFER:
self.sample_words = rev_vocab_table.lookup(
tf.to_int64(sample_id))
if mode != tf.contrib.learn.ModeKeys.INFER:
## Count the number of predicted words for compute ppl.
self.predict_count = tf.reduce_sum(
self.iterator.target_sequence_length)
self.global_step = tf.Variable(0, trainable=False)
trainables = tf.trainable_variables()
# Gradients and SGD update operation for training the model.
# Arrage for the embedding vars to appear at the beginning.
if mode == tf.contrib.learn.ModeKeys.TRAIN:
self.learning_rate = tf.constant(params.learning_rate)
# decay
self.learning_rate = self._get_learning_rate_decay(
params, self.global_step, self.learning_rate)
# Optimizer
if params.optimizer.lower() == "sgd":
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
tf.summary.scalar("lr", self.learning_rate)
elif params.optimizer.lower() == "adam":
opt = tf.train.AdamOptimizer(self.learning_rate)
tf.summary.scalar("lr", self.learning_rate)
else:
raise ValueError('Unknown optimizer: ' + params.optimizer)
# Gradients
gradients = tf.gradients(self.train_loss,
trainables,
colocate_gradients_with_ops=True)
clipped_grads, grad_norm = tf.clip_by_global_norm(
gradients, params.max_gradient_norm)
grad_norm_summary = [tf.summary.scalar("grad_norm", grad_norm)]
grad_norm_summary.append(
tf.summary.scalar("clipped_gradient",
tf.global_norm(clipped_grads)))
self.grad_norm = grad_norm
self.update = opt.apply_gradients(zip(clipped_grads,
trainables),
global_step=self.global_step)
# Summary
self.train_summary = tf.summary.merge([
tf.summary.scalar("lr", self.learning_rate),
tf.summary.scalar("train_loss", self.train_loss),
] + grad_norm_summary)
if mode == tf.contrib.learn.ModeKeys.INFER:
self.infer_logits, self.sample_id = logits, sample_id
self.infer_summary = tf.no_op()
# Saver
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)
# Print trainable variables
if log_trainables:
print_out("# Trainable variables")
for trainable in trainables:
print_out(" %s, %s, %s" %
(trainable.name, str(
trainable.get_shape()), trainable.op.device))
