def testWarmStartVarBothVarsPartitioned(self):
_, weights = self._create_prev_run_var(
"old_scope/fruit_weights",
shape=[4, 1],
initializer=[[0.5], [1.], [1.5], [2.]],
partitioner=lambda shape, dtype: [2, 1])
prev_val = np.concatenate([weights[0], weights[1]], axis=0)
# New session and new graph.
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"new_scope/fruit_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
self.assertTrue(
isinstance(fruit_weights, variables.PartitionedVariable))
ws_util._warmstart_var(
fruit_weights,
self.get_temp_dir(),
prev_tensor_name="old_scope/fruit_weights")
sess.run(variables.global_variables_initializer())
fruit_weights = fruit_weights._get_variable_list()
new_val = np.concatenate(
[fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0)
self.assertAllEqual(prev_val, new_val)
def testWarmStartVarMultipleVarsBothPartitioned(self):
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights"],
shapes=[[4, 1], [4, 1]],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15], [.2]]],
partitioners=[lambda shape, dtype: [2, 1], lambda shape, dtype: [2, 1]])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
other_weights = variable_scope.get_variable(
"other_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
ws_util._warmstart_var([fruit_weights, other_weights],
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
fruit_weights = fruit_weights._get_variable_list()
new_fruit_weights_val = np.concatenate(
[fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)], axis=0)
other_weights = other_weights._get_variable_list()
new_other_weights_val = np.concatenate(
[other_weights[0].eval(sess), other_weights[1].eval(sess)], axis=0)
self.assertAllEqual(
np.concatenate(prev_vals[0], axis=0), new_fruit_weights_val)
self.assertAllEqual(
np.concatenate(prev_vals[1], axis=0), new_other_weights_val)
def testWarmStartVar(self):
_, prev_val = self._create_prev_run_var(
"fruit_weights", initializer=[[0.5], [1.], [1.5], [2.]])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
ws_util._warmstart_var(fruit_weights, self.get_temp_dir())
sess.run(variables.global_variables_initializer())
self.assertAllEqual(prev_val, fruit_weights.eval(sess))
def testWarmStartVarPrevVarPartitioned(self):
_, weights = self._create_prev_run_var(
"fruit_weights",
shape=[4, 1],
initializer=[[0.5], [1.], [1.5], [2.]],
partitioner=lambda shape, dtype: [2, 1])
prev_val = np.concatenate([weights[0], weights[1]], axis=0)
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
ws_util._warmstart_var(fruit_weights, self.get_temp_dir())
sess.run(variables.global_variables_initializer())
self.assertAllEqual(prev_val, fruit_weights.eval(sess))
def testWarmStartVarMultipleVarsMixOfPartitions(self):
# First is not partitioned, but the second two are.
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights", "veggie_weights"],
shapes=[None, [4, 1], [4, 1]],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15],
[.2]],
[[5.], [10.], [15.], [20.]]],
partitioners=[
None, lambda shape, dtype: [2, 1], lambda shape, dtype: [2, 1]
])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable("fruit_weights",
initializer=[[0.],
[0.],
[0.],
[0.]])
other_weights = variable_scope.get_variable(
"other_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
veggie_weights = variable_scope.get_variable(
"veggie_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
# Flatten one of the partitioned variables.
ws_util._warmstart_var([fruit_weights, other_weights] +
veggie_weights._get_variable_list(),
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
veggie_weights = veggie_weights._get_variable_list()
new_veggie_weights_val = np.concatenate([
veggie_weights[0].eval(sess), veggie_weights[1].eval(sess)
],
axis=0)
other_weights = other_weights._get_variable_list()
new_other_weights_val = np.concatenate(
[other_weights[0].eval(sess), other_weights[1].eval(sess)],
axis=0)
self.assertAllEqual(prev_vals[0], fruit_weights.eval(sess))
self.assertAllEqual(np.concatenate(prev_vals[1], axis=0),
new_other_weights_val)
self.assertAllEqual(np.concatenate(prev_vals[2], axis=0),
new_veggie_weights_val)
def testWarmStartVarMultipleVars(self):
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights"],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15], [.2]]])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
other_weights = variable_scope.get_variable(
"other_weights", initializer=[[0.], [0.], [0.], [0.]])
ws_util._warmstart_var([fruit_weights, other_weights],
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
self.assertAllEqual(prev_vals[0], fruit_weights.eval(sess))
self.assertAllEqual(prev_vals[1], other_weights.eval(sess))
def testWarmStartVarMultipleVarsMixOfPartitions(self):
# First is not partitioned, but the second two are.
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights", "veggie_weights"],
shapes=[None, [4, 1], [4, 1]],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15], [.2]],
[[5.], [10.], [15.], [20.]]],
partitioners=[
None, lambda shape, dtype: [2, 1], lambda shape, dtype: [2, 1]
])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights", initializer=[[0.], [0.], [0.], [0.]])
other_weights = variable_scope.get_variable(
"other_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
veggie_weights = variable_scope.get_variable(
"veggie_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
# Flatten one of the partitioned variables.
ws_util._warmstart_var([fruit_weights, other_weights] +
veggie_weights._get_variable_list(),
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
veggie_weights = veggie_weights._get_variable_list()
new_veggie_weights_val = np.concatenate(
[veggie_weights[0].eval(sess), veggie_weights[1].eval(sess)],
axis=0)
other_weights = other_weights._get_variable_list()
new_other_weights_val = np.concatenate(
[other_weights[0].eval(sess), other_weights[1].eval(sess)], axis=0)
self.assertAllEqual(prev_vals[0], fruit_weights.eval(sess))
self.assertAllEqual(
np.concatenate(prev_vals[1], axis=0), new_other_weights_val)
self.assertAllEqual(
np.concatenate(prev_vals[2], axis=0), new_veggie_weights_val)
def testWarmStartVarMultipleVars(self):
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights"],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15],
[.2]]])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable("fruit_weights",
initializer=[[0.],
[0.],
[0.],
[0.]])
other_weights = variable_scope.get_variable("other_weights",
initializer=[[0.],
[0.],
[0.],
[0.]])
ws_util._warmstart_var([fruit_weights, other_weights],
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
self.assertAllEqual(prev_vals[0], fruit_weights.eval(sess))
self.assertAllEqual(prev_vals[1], other_weights.eval(sess))
def testWarmStartVarMultipleVarsBothPartitioned(self):
_, prev_vals = self._create_prev_run_multiple_vars(
var_names=["fruit_weights", "other_weights"],
shapes=[[4, 1], [4, 1]],
initializers=[[[0.5], [1.], [1.5], [2.]], [[.05], [.1], [.15],
[.2]]],
partitioners=[
lambda shape, dtype: [2, 1], lambda shape, dtype: [2, 1]
])
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
fruit_weights = variable_scope.get_variable(
"fruit_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
other_weights = variable_scope.get_variable(
"other_weights",
shape=[4, 1],
initializer=[[0.], [0.], [0.], [0.]],
partitioner=lambda shape, dtype: [2, 1])
ws_util._warmstart_var([fruit_weights, other_weights],
self.get_temp_dir())
sess.run(variables.global_variables_initializer())
fruit_weights = fruit_weights._get_variable_list()
new_fruit_weights_val = np.concatenate(
[fruit_weights[0].eval(sess), fruit_weights[1].eval(sess)],
axis=0)
other_weights = other_weights._get_variable_list()
new_other_weights_val = np.concatenate(
[other_weights[0].eval(sess), other_weights[1].eval(sess)],
axis=0)
self.assertAllEqual(np.concatenate(prev_vals[0], axis=0),
new_fruit_weights_val)
self.assertAllEqual(np.concatenate(prev_vals[1], axis=0),
new_other_weights_val)
