def SplitInputBatch(self, num_splits):
"""Splits the current InputBatch into num_splits ways.
Args:
num_splits: The number of splits.
Returns:
A list of `.NestedMap`. Each `.NestedMap` represents the input
tensors in one split.
"""
assert num_splits >= 1
batch = self.GetPreprocessedInputBatch()
if num_splits == 1:
# Special case. No split is needed.
return [batch]
assert not py_utils.use_tpu()
field_split = ig_helper.SplitTensors(batch.Flatten(), num_splits)
num_fields = len(field_split)
ret = []
for j in range(num_splits):
split_flatten = [field_split[i][j] for i in range(num_fields)]
split = batch.Pack(split_flatten)
ret += [split]
return ret
def testSplitTensorsAssert(self):
t1 = tf.constant([[1], [7], [8]])
t2 = tf.constant([[5], [9], [10]])
t3 = tf.constant([[13], [14]])
tensor_tuple = (t1, t2, t3)
num_splits = 2
with self.assertRaisesRegexp(
ValueError, 'can\'t split axis of size 2 into pieces of size \[2,1\]'):
splits = input_generator_helper.SplitTensors(tensor_tuple, num_splits)
def testSplitTensorsLessThanNumSplits(self):
t1 = tf.constant([[1, 2, 3, 4]])
t2 = tf.constant([[5, 6, 7, 8]])
t3 = tf.constant([[13, 14, 15, 16]])
tensor_tuple = (t1, t2, t3)
num_splits = 2
splits = input_generator_helper.SplitTensors(tensor_tuple, num_splits)
with self.session(use_gpu=False) as sess:
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
'first dim of tensors in xs must be greater '
'than num_splits'):
sess.run(splits)
def testSplitTensorsUneven(self):
with self.session(use_gpu=False) as sess:
t1 = tf.constant([[1], [4], [8]])
t2 = tf.constant([[5], [9], [10]])
t3 = tf.constant([[13], [14], [11]])
tensor_tuple = (t1, t2, t3)
num_splits = 2
splits = input_generator_helper.SplitTensors(tensor_tuple, num_splits)
expected = ([np.array([[1], [4]]), np.array([[8]])],
[np.array([[5], [9]]), np.array([[10]])],
[np.array([[13], [14]]), np.array([[11]])])
actual = sess.run(splits)
self._assertTupleOfListsEqual(actual, expected)
def testSplitTensorsEven(self):
with self.session(use_gpu=False) as sess:
t1 = tf.constant([[1, 2, 3, 4], [4, 5, 6, 7]])
t2 = tf.constant([[5, 6, 7, 8], [9, 10, 11, 12]])
t3 = tf.constant([[13, 14, 15, 16], [14, 15, 16, 17]])
tensor_tuple = (t1, t2, t3)
num_splits = 2
splits = input_generator_helper.SplitTensors(tensor_tuple, num_splits)
expected = ([np.array([[1, 2, 3, 4]]), np.array([[4, 5, 6, 7]])],
[np.array([[5, 6, 7, 8]]), np.array([[9, 10, 11, 12]])],
[np.array([[13, 14, 15, 16]]), np.array([[14, 15, 16, 17]])])
actual = sess.run(splits)
self._assertTupleOfListsEqual(actual, expected)
def SplitInputBatch(self, num_splits):
"""Splits the current InputBatch into num_splits ways.
Args:
num_splits: The number of splits.
Returns:
A list of `.NestedMap`. Each `.NestedMap` represents the input
tensors in one split.
"""
assert num_splits >= 1
print("num_splits " + str(num_splits))
batch = self.GetPreprocessedInputBatch()
if num_splits == 1:
# Special case. No split is needed.
# this is the place the make 1 gpu different from 4 gpu
return [batch]
assert not py_utils.use_tpu()
print("batch " + str(batch))
print("batch.Flatten " + str(batch.Flatten))
print("num_splits " + str(num_splits))
# batch is ok without any ? its this step that get symbol ?
field_split = ig_helper.SplitTensors(batch.Flatten(), num_splits)
print("field_split " + str(field_split))
num_fields = len(field_split)
ret = []
for j in range(num_splits):
print("j " + str(j))
split_flatten = [field_split[i][j] for i in range(num_fields)]
print("split_flatten " + str(split_flatten))
split = batch.Pack(split_flatten)
print("split " + str(split))
ret += [split]
return ret
