def __init__(self, checkpoint_path):
layers = 50
num_blocks = [3, 4, 6, 3]
self.inference = lambda images, is_train : inference(images,
is_training=is_train,
num_classes=NUM_AGES*2,
num_blocks=num_blocks,
bottleneck=True)
self.x = tf.placeholder(tf.uint8, shape=(256,256,3), name='input_image')
self.crops = fixed_crops(self.x)
self.logits = self.inference(self.crops, is_train=False)
self.pred = tf.nn.softmax(self.logits, name='prediction')
# Restore saved weights
restore_variables = tf.trainable_variables() \
+ tf.moving_average_variables()
self.saver = tf.train.Saver(restore_variables)
self.sess = tf.Session()
self.saver.restore(self.sess, checkpoint_path)
#self.sess.run(tf.initialize_variables([var for var \
# in tf.all_variables() if var not in restore_variables]))
python类moving_average_variables()的实例源码
def variables_to_restore(self, moving_avg_variables=None):
""""""
name_map = {}
if moving_avg_variables is None:
moving_avg_variables = tf.trainable_variables()
moving_avg_variables += tf.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving average as well.
for v in list(set(tf.all_variables()) - moving_avg_variables):
if v.op.name not in name_map:
name_map[v.op.name] = v
return name_map
#===============================================================
def variables_to_restore(self, moving_avg_variables=None):
""""""
name_map = {}
if moving_avg_variables is None:
moving_avg_variables = tf.trainable_variables()
moving_avg_variables += tf.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving average as well.
for v in list(set(tf.all_variables()) - moving_avg_variables):
if v.op.name not in name_map:
name_map[v.op.name] = v
return name_map
#===============================================================
def variables_to_restore(self, moving_avg_variables=None):
""""""
name_map = {}
if moving_avg_variables is None:
moving_avg_variables = tf.trainable_variables()
moving_avg_variables += tf.moving_average_variables()
# Remove duplicates
moving_avg_variables = set(moving_avg_variables)
# Collect all the variables with moving average,
for v in moving_avg_variables:
name_map[self.average_name(v)] = v
# Make sure we restore variables without moving average as well.
for v in list(set(tf.all_variables()) - moving_avg_variables):
if v.op.name not in name_map:
name_map[v.op.name] = v
return name_map
#===============================================================
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def _setup_model_loss(self, inputs, labels, validation_inputs, validation_labels, is_chief, task_id, num_workers, is_training, scope, initial_lr=0.1, reuse=None, global_step=None, num_replicas_to_aggregate=-1):
validation_metric = []
validation_metric_tmp = [[] for _, _ in self.validation_metrics_def]
self.learning_rate = tf.placeholder(
tf.float32, shape=[], name="learning_rate_placeholder")
losses, total_loss = self._tower_loss(
scope, self.model, inputs, labels, is_training, reuse, is_classification=True)
val_total_loss = self._tower_loss(
scope, self.model, validation_inputs, validation_labels, False, True, is_classification=True)
for i, (_, metric_function) in enumerate(self.validation_metrics_def):
metric_score = metric_function(
validation_labels, tf.argmax(self.validation_predictions, 1))
validation_metric_tmp[i].append(metric_score)
for i, (_, _) in enumerate(self.validation_metrics_def):
validation_metric.append(sum(validation_metric_tmp[i]))
validation_metric.append(val_total_loss)
if is_chief:
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
loss_averages_op = loss_averages.apply(losses + [total_loss])
with tf.control_dependencies([loss_averages_op]):
total_loss = tf.identity(total_loss)
exp_moving_averager = tf.train.ExponentialMovingAverage(
self.cnf.get('mv_decay', 0.9), global_step)
variables_to_average = (
tf.trainable_variables() + tf.moving_average_variables())
# Create synchronous replica optimizer.
learning_rate = self.lr_policy.batch_update(initial_lr, 0)
opt = self._optimizer(learning_rate, optname=self.cnf.get(
'optname', 'momentum'), **self.cnf.get('opt_kwargs', {'decay': 0.9}))
opt = tf.train.SyncReplicasOptimizer(opt, replicas_to_aggregate=num_replicas_to_aggregate,
total_num_replicas=num_workers, variable_averages=exp_moving_averager, variables_to_average=variables_to_average)
return total_loss, opt, validation_metric
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images)
beta = variables.get_variables_by_name('beta')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
beta = variables.get_variables_by_name('beta')[0]
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(beta.op.name, 'BatchNorm/beta')
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=True)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')[0]
self.assertEquals(gamma.op.name, 'BatchNorm/gamma')
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testCreateVariablesWithoutCenterWithoutScale(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, center=False, scale=False)
beta = variables.get_variables_by_name('beta')
self.assertEquals(beta, [])
gamma = variables.get_variables_by_name('gamma')
self.assertEquals(gamma, [])
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
def testMovingAverageVariables(self):
height, width = 3, 3
with self.test_session():
images = tf.random_uniform((5, height, width, 3), seed=1)
ops.batch_norm(images, scale=True)
moving_mean = tf.moving_average_variables()[0]
moving_variance = tf.moving_average_variables()[1]
self.assertEquals(moving_mean.op.name, 'BatchNorm/moving_mean')
self.assertEquals(moving_variance.op.name, 'BatchNorm/moving_variance')
