def testSaveByDict(self):
with ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
v2 = resource_variable_ops.ResourceVariable(1.0, name='v2')
def model():
return array_ops.constant(2.0) * v1 * v2
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
# Save the variables under different names.
_ = model()
saver = _saver.Saver({'ckpt/v1': v1, 'ckpt/v2': v2})
saver.save(ckpt_prefix)
v1.assign(2.0)
v2.assign(2.0)
self.assertEqual(v1.read_value().numpy(), 2.0)
self.assertEqual(v2.read_value().numpy(), 2.0)
# Can still restore it.
saver.restore(ckpt_prefix)
self.assertEqual(v1.read_value().numpy(), 1.0)
self.assertEqual(v1.read_value().numpy(), 1.0)
# However, cannot restore it with default name.
with self.assertRaisesOpError('not found in checkpoint'):
saver = _saver.Saver([v1, v2]).restore(ckpt_prefix)
# Can specify which variable in ckpt to restore to which variable.
def map_func(x):
return {'v3': 'ckpt/v1', 'v4': 'ckpt/v2'}.get(x, x)
with _saver.restore_variables_on_create(ckpt_prefix, map_func):
v3 = resource_variable_ops.ResourceVariable(2.0, name='v3')
v4 = resource_variable_ops.ResourceVariable(2.0, name='v4')
self.assertEqual(v3.read_value().numpy(), 1.0)
self.assertEqual(v4.read_value().numpy(), 1.0)
def testSaveRestoreGraphCallable(self):
with ops.device(self._dev()):
@graph_callable.graph_callable(
[graph_callable.ShapeAndDtype(shape=(), dtype=dtypes.float32)])
def model(x):
v = variable_scope.get_variable(
'v', initializer=init_ops.zeros_initializer(), shape=())
return v + x
# Default 2 + 0 = 2
self.assertEqual(
2,
model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# Save the variable value 0.
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_saver.Saver(model.variables).save(ckpt_prefix)
# update variable to 1, so that 2 + 1 = 3
model.variables[0].assign(1.)
self.assertEqual(
3,
model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# load the variable value 0, so that 2 + 0 = 2
_saver.Saver(model.variables).restore(ckpt_prefix)
self.assertEqual(
2,
model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# update checkpoint variable to 1 and memory value to 2.
model.variables[0].assign(1.)
_saver.Saver(model.variables).save(ckpt_prefix)
model.variables[0].assign(2.)
self.assertEqual(
4,
model(array_ops.constant(2, dtype=dtypes.float32)).numpy())
# reset the graph and reload on create, so that 1 + 2 = 3
with ops.Graph().as_default():
with _saver.restore_variables_on_create(ckpt_prefix):
@graph_callable.graph_callable([
graph_callable.ShapeAndDtype(shape=(),
dtype=dtypes.float32)
])
def model2(x):
v = variable_scope.get_variable(
'v',
initializer=init_ops.zeros_initializer(),
shape=())
return v + x
self.assertEqual(
3,
model2(array_ops.constant(
2, dtype=dtypes.float32)).numpy())
def train(training_dataset, preprocessing_type, base_model, optimizer, summary_dir,
ckpt_dir, logging_every_n_steps, summary_every_n_steps, save_every_n_steps,
restore_ckpt_file_path):
# 获取 pretained model
variables = base_model.variables + [tf.train.get_or_create_global_step()]
saver = model_saver.Saver(variables)
if restore_ckpt_file_path is not None:
saver.restore(restore_ckpt_file_path)
if tf.train.latest_checkpoint(ckpt_dir) is not None:
saver.restore(tf.train.latest_checkpoint(ckpt_dir))
# writer = tf.summary.FileWriter(train_dir, flush_secs=100)
writer = summary.create_file_writer(summary_dir, flush_millis=100000)
for i in range(configs['epoches']):
tf.compat.v1.logging.info('epoch %d starting...' % (i + 1))
start_time = time.time()
with writer.as_default(), summary.always_record_summaries():
train_one_epoch(dataset=training_dataset, base_model=base_model, optimizer=optimizer, preprocessing_type=preprocessing_type,
logging_every_n_steps=logging_every_n_steps, summary_every_n_steps=summary_every_n_steps,
save_path=ckpt_dir, saver=saver, save_every_n_steps=save_every_n_steps)
tf.set_random_seed(1)
end_time = time.time()
tf.compat.v1.logging.info('epoch %d training finished, costing %d seconds...' % (i, end_time - start_time))
def _load_from_ckpt_file(model, ckpt_file_path):
saver = eager_saver.Saver(model.variables)
for var in model.variables:
tf.logging.info('restore var {}'.format(var.name))
if tf.train.latest_checkpoint(ckpt_file_path) is not None:
saver.restore(tf.train.latest_checkpoint(ckpt_file_path))
else:
raise ValueError('unknown ckpt file {}'.format(ckpt_file_path))
def testSameObjectOK(self):
with context.eager_mode(), ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
# While different objects with the same shared_name are not good, passing
# in the same object multiple times is fine.
saver = _saver.Saver([v1, v1])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
saver.save(ckpt_prefix)
def testRestoreOnCreate(self):
with ops.device(self._dev()):
def model(init_val):
v1 = resource_variable_ops.ResourceVariable(init_val, name='v1')
return array_ops.constant(1.0) * v1, v1
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_, v1 = model(1.0)
saver = _saver.Saver([v1])
saver.save(ckpt_prefix)
with ops.Graph().as_default():
saver = _saver.Saver([v1])
with _saver.restore_variables_on_create(ckpt_prefix):
# Value is from checkpoint, but not from argument.
ret, _ = model(2.0)
self.assertEqual(ret.numpy(), 1.0)
def testSameNameNoClobbering(self):
with ops.device(self._dev()):
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
v2 = resource_variable_ops.ResourceVariable(2.0, name='v1')
saver = _saver.Saver([v1, v2])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
with self.assertRaisesRegexp(ValueError, 'v1'):
saver.save(ckpt_prefix)
def testDifferentGraphError(self):
with context.eager_mode(), ops.device(self._dev()):
with ops.Graph().as_default():
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
with ops.Graph().as_default():
saver = _saver.Saver([v1])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
with self.assertRaisesRegexp(ValueError, 'Graph'):
saver.save(ckpt_prefix)
def testRestoreOnCreate(self):
with context.eager_mode():
def model(init_val):
v1 = resource_variable_ops.ResourceVariable(init_val, name='v1')
return array_ops.constant(1.0) * v1, v1
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_, v1 = model(1.0)
saver = _saver.Saver([v1])
saver.save(ckpt_prefix)
with ops.Graph().as_default():
saver = _saver.Saver([v1])
with saver.maybe_restore_on_create(ckpt_prefix):
# Value is from checkpoint, but not from argument.
ret, _ = model(2.0)
self.assertEqual(ret.numpy(), 1.0)
# Create it a second time won't re-assign the checkpoint value.
v1_2 = resource_variable_ops.ResourceVariable(3.0, name='v1')
self.assertEqual(v1_2.read_value().numpy(), 3.0)
def testSameNameNoClobbering(self):
with ops.device(self._dev()):
# Note that this test purposefully uses Graphs rather than
# IsolateTest. Users are more likely to accidentally create the same
# variable name this way.
first_graph = ops.Graph()
with first_graph.as_default():
v1_first_graph = resource_variable_ops.ResourceVariable(1.0, name='v1')
with ops.Graph().as_default():
v1_second_graph = resource_variable_ops.ResourceVariable(2.0, name='v1')
saver = _saver.Saver([v1_first_graph, v1_second_graph])
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
with self.assertRaisesRegexp(ValueError, 'v1'):
saver.save(ckpt_prefix)
def testRestoreNotFound(self):
with context.eager_mode():
def model(v):
return array_ops.constant(1.0) * v
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
v = resource_variable_ops.ResourceVariable(1.0, name='v1')
_ = model(v)
saver = _saver.Saver([v])
saver.save(ckpt_prefix)
with self.assertRaisesRegexp(errors.NotFoundError,
'v2 not found in checkpoint'):
with saver.maybe_restore_on_create(ckpt_prefix):
_ = model(resource_variable_ops.ResourceVariable(1.0, name='v2'))
def _optimizer_test_template(self, optimizer):
"""Checks save and restore. Returns the optimizer variables."""
v = resource_variable_ops.ResourceVariable([[2., 3.]], name='v')
loss_fn = lambda: v[0, 0]**2 + v[0, 1]**2
optimizer.minimize(loss_fn)
optimizer_variables = _saver.get_optimizer_variables(optimizer)
saver = _saver.Saver(optimizer_variables + [v])
checkpoint_path = saver.save(self.get_temp_dir())
optimizer.minimize(loss_fn)
after_first_minimize = v.numpy()
# After we restore, the next step should be exactly the same as the one we
# just did.
saver.restore(checkpoint_path)
optimizer.minimize(loss_fn)
self.assertAllEqual(after_first_minimize, v.numpy())
return optimizer_variables
def testBasics(self):
with context.eager_mode():
v1 = resource_variable_ops.ResourceVariable(1.0, name='v1')
def model():
return array_ops.constant(2.0) * v1
ckpt_prefix = os.path.join(test.get_temp_dir(), 'ckpt')
_ = model()
saver = _saver.Saver([v1])
saver.save(ckpt_prefix)
v1.assign(2.0)
self.assertEqual(v1.read_value().numpy(), 2.0)
saver.restore(ckpt_prefix)
self.assertEqual(v1.read_value().numpy(), 1.0)
def train(
training_dataset,
preprocessing_type,
base_model,
optimizer,
logging_every_n_steps,
save_every_n_steps,
summary_every_n_steps,
train_dir,
ckpt_dir,
restore_ckpt_file_path,
):
# 获取 pretrained model
variables = base_model.variables + [tf.train.get_or_create_global_step()]
saver = eager_saver.Saver(variables)
# 命令行指定 ckpt file
if restore_ckpt_file_path is not None:
saver.restore(restore_ckpt_file_path)
# 当前 logs_dir 中的预训练模型，用于继续训练
if tf.train.latest_checkpoint(ckpt_dir) is not None:
saver.restore(tf.train.latest_checkpoint(ckpt_dir))
train_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=100000)
for i in range(CONFIG['epochs']):
tf_logging.info('epoch %d starting...' % (i + 1))
start = time.time()
with train_writer.as_default(), summary.always_record_summaries():
train_one_epoch(
dataset=training_dataset,
base_model=base_model,
optimizer=optimizer,
preprocessing_type=preprocessing_type,
logging_every_n_steps=logging_every_n_steps,
summary_every_n_steps=summary_every_n_steps,
saver=saver,
save_every_n_steps=save_every_n_steps,
save_path=ckpt_dir,
)
tf.set_random_seed(1)
train_end = time.time()
tf_logging.info('epoch %d training finished, costing %d seconds...' %
(i + 1, train_end - start))
