本文整理汇总了Python中tensorflow.contrib.slim.get_variables方法的典型用法代码示例。如果您正苦于以下问题:Python slim.get_variables方法的具体用法?Python slim.get_variables怎么用?Python slim.get_variables使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.slim
的用法示例。
在下文中一共展示了slim.get_variables方法的9个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: add_save_vars
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def add_save_vars(self, prefixes):
"""Prepares the list of variables that should be saved based on
their name prefix.
Args:
prefixes: Variable name prefixes to find and save.
"""
for pre in prefixes:
pre_vars = slim.get_variables(pre)
self.save_vars.update(pre_vars)
var_list = ''
for var in self.save_vars:
var_list = var_list + var.name + ' '
print ('Saving these variables: {}'.format(var_list))
示例2: try_load_weights
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def try_load_weights(self):
fn = None
if self.load != "":
fn = self.load.replace(".index", "")
else:
files = sorted(glob.glob(self.model_dir + self.model + "-*.index"))
if len(files) > 0:
fn = files[-1].replace(".index", "")
if fn is not None:
print("loading model from", fn)
# vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='ReID_net')
varlist = slim.get_variables()
# print (np.unique(np.array([var.name.split('/')[0] for var in varlist])))
good_list = ['conv0','conv1','fc1','fc2','outputTriplet', 'res0', 'res1', 'res10', 'res11','res12', 'res13',
'res14', 'res15', 'res16', 'res2', 'res3', 'res4', 'res5','res6', 'res7', 'res8', 'res9']
varlist = [var for var in varlist if var.name.split('/')[0] in good_list]
self.saver = tf.train.Saver(pad_step_number=True, var_list=varlist)
self.saver.restore(self.session, fn)
if self.model == fn.split("/")[-2]:
self.start_epoch = int(fn.split("-")[-1])
print("starting from epoch", self.start_epoch + 1)
else:
if self.load_init_savers is None:
self.load_init_savers = [self._create_load_init_saver(x) for x in self.load_init]
assert len(self.load_init) == len(self.load_init_savers)
for fn, load_init_saver in zip(self.load_init, self.load_init_savers):
if fn.endswith(".pickle"):
print("trying to initialize model from wider-or-deeper mxnet model", fn)
load_wider_or_deeper_mxnet_model(fn, self.session)
else:
print("initializing model from", fn)
assert load_init_saver is not None
load_init_saver.restore(self.session, fn)
示例3: _initialize_saver
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def _initialize_saver(self, prefixes=None, force=False, max_to_keep=5):
"""Initializes the saver object.
Args:
prefixes: The prefixes that the saver should take care of.
force (optional): Even if saver is set, reconstruct the saver
object.
max_to_keep (optional):
"""
if self.saver is not None and not force:
return
else:
if prefixes is None or not (
type(prefixes) != list or type(prefixes) != tuple):
raise ValueError(
'Prefix of variables that needs saving are not defined')
prefixes_str = ''
for pref in prefixes:
prefixes_str = prefixes_str + pref + ' '
print('[#] Initializing it with variable prefixes: {}'.format(
prefixes_str))
saved_vars = []
for pref in prefixes:
saved_vars.extend(slim.get_variables(pref))
self.saver = tf.train.Saver(saved_vars, max_to_keep=max_to_keep)
示例4: _build
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def _build(self):
"""Builds the computation graph."""
assert (self.batch_size % self.rec_rr) == 0, 'Batch size ' \
'should be ' \
'divisable by ' \
'random restart'
self.test_batch_size = self.batch_size
# Defining batch_size in input placeholders is inevitable at least
# for now, because the z vectors are Tensorflow variables.
self.real_data_pl = tf.placeholder(
tf.float32, shape=[self.batch_size] + self.image_dim,
)
self.real_data_test_pl = tf.placeholder(
tf.float32, shape=[self.test_batch_size] + self.image_dim,
)
self.input_pl_transform()
self._build_generator_discriminator()
self.fake_data = self.generator_fn()
self.disc_real = self.discriminator_fn(self.real_data)
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
sc = tf.get_variable_scope()
sc.reuse_variables()
self.disc_fake = self.discriminator_fn(self.fake_data)
self.generator_vars = slim.get_variables('Generator')
self.discriminator_vars = slim.get_variables('Discriminator')
self.fixed_noise = tf.constant(
np.random.normal(size=(128, self.latent_dim)).astype(
'float32'))
self.fixed_noise_samples = self.generator_fn(self.fixed_noise,
is_training=False)
示例5: train
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def train(image_dir, label_dir):
# load data & gen data
all_objs = parse_annotation(label_dir, image_dir)
all_exams = len(all_objs)
np.random.shuffle(all_objs)
train_gen = data_gen(image_dir, all_objs, BATCH_SIZE)
train_num = int(np.ceil(all_exams/BATCH_SIZE))
### buile graph
dimension, orientation, confidence, loss, optimizer, loss_d, loss_o, loss_c = build_model()
### GPU config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfconfig)
# create a folder for saving model
if os.path.isdir(save_path) == False:
os.mkdir(save_path)
variables_to_restore = slim.get_variables()[:26] ## vgg16-conv5
saver = tf.train.Saver(max_to_keep=100)
#Load pretrain VGG model
ckpt_list = tf.contrib.framework.list_variables('./vgg_16.ckpt')[1:-7]
new_ckpt_list = []
for name in range(1,len(ckpt_list),2):
tf.contrib.framework.init_from_checkpoint('./vgg_16.ckpt', {ckpt_list[name-1][0]: variables_to_restore[name]})
tf.contrib.framework.init_from_checkpoint('./vgg_16.ckpt', {ckpt_list[name][0]: variables_to_restore[name-1]})
# Initializing the variables
init = tf.global_variables_initializer()
print(sess.run(init))
# Start to train model
for epoch in range(epochs):
epoch_loss = np.zeros((train_num,1),dtype = float)
tStart_epoch = time.time()
batch_loss = 0.0
for num_iters in tqdm(range(train_num),ascii=True,desc='Epoch '+str(epoch+1)+' : Loss:'+str(batch_loss)):
train_img, train_label = train_gen.next()
_,batch_loss = sess.run([optimizer,loss], feed_dict={inputs: train_img, d_label: train_label[0], o_label: train_label[1], c_label: train_label[2]})
epoch_loss[num_iters] = batch_loss
# save model
if (epoch+1) % 5 == 0:
saver.save(sess,save_path+"model", global_step = epoch+1)
# Print some information
print("Epoch:", epoch+1, " done. Loss:", np.mean(epoch_loss))
tStop_epoch = time.time()
print("Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s")
sys.stdout.flush()
示例6: train
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def train(image_dir, box2d_loc, label_dir):
# load data & gen data
all_objs = parse_annotation(label_dir, image_dir)
all_exams = len(all_objs)
np.random.shuffle(all_objs)
train_gen = data_gen(image_dir, all_objs, BATCH_SIZE)
train_num = int(np.ceil(all_exams/BATCH_SIZE))
### buile graph
dimension, orientation, confidence, loss, optimizer, loss_d, loss_o, loss_c = build_model()
### GPU config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
sess = tf.Session(config=tfconfig)
# create a folder for saving model
if os.path.isdir(save_path) == False:
os.mkdir(save_path)
variables_to_restore = slim.get_variables()[:26] ## vgg16-conv5
saver = tf.train.Saver(max_to_keep=100)
#Load pretrain VGG model
ckpt_list = tf.contrib.framework.list_variables('./vgg_16.ckpt')[1:-7]
new_ckpt_list = []
for name in range(1,len(ckpt_list),2):
tf.contrib.framework.init_from_checkpoint('./vgg_16.ckpt', {ckpt_list[name-1][0]: variables_to_restore[name]})
tf.contrib.framework.init_from_checkpoint('./vgg_16.ckpt', {ckpt_list[name][0]: variables_to_restore[name-1]})
# Initializing the variables
init = tf.global_variables_initializer()
sess.run(init)
# Start to train model
for epoch in range(epochs):
epoch_loss = np.zeros((train_num,1),dtype = float)
tStart_epoch = time.time()
batch_loss = 0.0
for num_iters in tqdm(range(train_num),ascii=True,desc='Epoch '+str(epoch+1)+' : Loss:'+str(batch_loss)):
train_img, train_label = train_gen.next()
_,batch_loss = sess.run([optimizer,loss], feed_dict={inputs: train_img, d_label: train_label[0], o_label: train_label[1], c_label: train_label[2]})
epoch_loss[num_iters] = batch_loss
# save model
if (epoch+1) % 5 == 0:
saver.save(sess,save_path+"model", global_step = epoch+1)
# Print some information
print "Epoch:", epoch+1, " done. Loss:", np.mean(epoch_loss)
tStop_epoch = time.time()
print "Epoch Time Cost:", round(tStop_epoch - tStart_epoch,2), "s"
sys.stdout.flush()
示例7: build_pairwise_tower_loss
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def build_pairwise_tower_loss(fvecs_i, fvecs_j, scope=None,
lambda_weight=LAMBDA_WEIGHT):
"""
Builds an adversarial regressor from fvecs_j to fvecs_i.
Args:
fvecs_i: the target embedding (i.e. the smaller embedding)
fvecs_j: the source embedding (i.e. the begger embedding)
scope: scope name of the regressor.
lambda_weight: the regularization parameter for the weights.
Returns:
An adversarial regressor loss function.
"""
# build a regressor from fvecs_j to fvecs_i
fvecs_i = flip_gradient.flip_gradient(fvecs_i)
fvecs_j = flip_gradient.flip_gradient(fvecs_j)
net = fvecs_j
bias_loss = 0.0
weight_loss = 0.0
adversarial_loss = 0.0
with tf.variable_scope(scope):
for i in xrange(NUM_HIDDENS_ADVERSARIAL):
if i < NUM_HIDDENS_ADVERSARIAL - 1:
net = slim.fully_connected(
net, HIDDEN_ADVERSARIAL_SIZE, scope='fc_{}'.format(i),
activation_fn=tf.nn.relu)
else:
net = slim.fully_connected(net, fvecs_i.get_shape().as_list(
)[-1], scope='fc_{}'.format(i), activation_fn=None)
b = slim.get_variables(
scope=tf.get_variable_scope().name + '/fc_{}/biases'.format(i)
)[0]
W = slim.get_variables(
scope=tf.get_variable_scope().name + '/fc_{}/weights'.format(i)
)[0]
weight_loss += tf.reduce_mean(
tf.square(tf.reduce_sum(W * W, axis=1) - 1)) * lambda_weight
if b is not None:
bias_loss += tf.maximum(
0.0,
tf.reduce_sum(b * b) - 1.0) * lambda_weight
adversarial_loss += -tf.reduce_mean(tf.square(fvecs_i * net))
tf.summary.scalar('adversarial loss', adversarial_loss)
tf.summary.scalar('weight loss', weight_loss)
tf.summary.scalar('bias loss', bias_loss)
return adversarial_loss + weight_loss + bias_loss
示例8: build_train_op
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def build_train_op( self, global_step ):
'''
Builds two train ops, one for the Generator and one for the Discriminator. These can
be run independently any number of times, and each time will increase the global_step.
Args:
global_step: A Tensor to be incremented
Returns:
[ g_train_op, d_train_op ]
'''
if not self.model_built or not self.losses_built :
raise RuntimeError( "Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run".format(
self.model_built, self.losses_built ) )
self.global_step = global_step
self.global_step_copy = tf.identity( global_step, name='global_step_copy' )
t_vars = tf.trainable_variables()
# Create the optimizer train_op for the generator
self.g_optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=self.cfg )
self.g_vars = slim.get_variables( scope='encoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.g_vars += slim.get_variables( scope='decoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.g_train_op = optimize.create_train_op( self.loss_g_total, self.g_optimizer,
variables_to_train=self.g_vars, update_global_step=True )
self.g_lnorm_op = optimize.create_train_op( self.softmax_loss, self.g_optimizer,
variables_to_train=self.g_vars, update_global_step=True )
# Create a train_op for the discriminator
if 'discriminator_learning_args' in self.cfg: # use these
discriminator_learning_args = self.cfg[ 'discriminator_learning_args' ]
else:
discriminator_learning_args = self.cfg
self.d_optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=discriminator_learning_args )
self.d_vars = slim.get_variables( scope='discriminator', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.d_vars += slim.get_variables( scope='discriminator_1', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.d_train_op = slim.learning.create_train_op( self.loss_d_total, self.d_optimizer,
variables_to_train=self.d_vars )
self.train_op = [ self.g_train_op, self.d_train_op, self.g_lnorm_op, self.accuracy]
self.train_op_built = True
return self.train_op
示例9: build_train_op
# 需要导入模块: from tensorflow.contrib import slim [as 别名]
# 或者: from tensorflow.contrib.slim import get_variables [as 别名]
def build_train_op( self, global_step ):
'''
Builds two train ops, one for the Generator and one for the Discriminator. These can
be run independently any number of times, and each time will increase the global_step.
Args:
global_step: A Tensor to be incremented
Returns:
[ g_train_op, d_train_op ]
'''
if not self.model_built or not self.losses_built :
raise RuntimeError( "Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run".format(
self.model_built, self.losses_built ) )
self.global_step = global_step
self.global_step_copy = tf.identity( global_step, name='global_step_copy' )
t_vars = tf.trainable_variables()
# Create the optimizer train_op for the generator
self.g_optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=self.cfg )
self.g_vars = slim.get_variables( scope='encoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.g_vars += slim.get_variables( scope='decoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.g_train_op = optimize.create_train_op( self.loss_g_total, self.g_optimizer,
variables_to_train=self.g_vars, update_global_step=True )
self.g_lnorm_op = optimize.create_train_op( self.l1_loss, self.g_optimizer,
variables_to_train=self.g_vars, update_global_step=True )
# Create a train_op for the discriminator
if 'discriminator_learning_args' in self.cfg: # use these
discriminator_learning_args = self.cfg[ 'discriminator_learning_args' ]
else:
discriminator_learning_args = self.cfg
self.d_optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=discriminator_learning_args )
self.d_vars = slim.get_variables( scope='discriminator', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.d_vars += slim.get_variables( scope='discriminator_1', collection=tf.GraphKeys.TRAINABLE_VARIABLES )
self.d_train_op = slim.learning.create_train_op( self.loss_d_total, self.d_optimizer,
variables_to_train=self.d_vars )
self.train_op = [ self.g_train_op, self.d_train_op, self.g_lnorm_op ]
self.train_op_built = True
return self.train_op