def test_validating_dataset_input_tensors_with_dtype_mismatch(self):
with self.cached_session():
strategy = mirrored_strategy.MirroredStrategy(
['/device:GPU:0', '/device:CPU:0'])
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2],
shape=(1, 2),
dtype=dtypes.float64)
x = values.DistributedValues({
'/device:CPU:0': a,
'/device:GPU:0': b
})
y = values.DistributedValues({
'/device:CPU:0': a,
'/device:GPU:0': a
})
with strategy.scope():
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegexp(
ValueError, 'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
distributed_training_utils.validate_distributed_dataset_inputs(
strategy, x, y)
def test_validating_dataset_input_tensors_with_dtype_mismatch(self,
distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.float64)
x = values.DistributedValues({'/device:CPU:0': a, '/device:GPU:0': b})
y = values.DistributedValues({'/device:CPU:0': a, '/device:GPU:0': a})
with distribution.scope():
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegexp(ValueError,
'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def test_validating_dataset_input_tensors_with_shape_mismatch(self):
with self.test_session():
strategy = mirrored_strategy.MirroredStrategy(['/device:GPU:0',
'/device:CPU:0'])
a = constant_op.constant([1, 2], shape=(1, 2))
b = constant_op.constant([[1, 2], [1, 2]], shape=(2, 2))
x = values.DistributedValues({'/device:CPU:0': a, '/device:GPU:0': b})
y = values.DistributedValues({'/device:CPU:0': a, '/device:GPU:0': a})
with strategy.scope():
# Removed device and input tensor shape details from the error message
# since the order of the device and the corresponding input tensor shape
# is not deterministic over different runs.
with self.assertRaisesRegexp(ValueError,
'Input tensor shapes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
distributed_training_utils.validate_distributed_dataset_inputs(
strategy, x, y)
def test_validating_dataset_input_tensors_with_dtype_mismatch(self,
distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.int32)
b = constant_op.constant([1, 2], shape=(1, 2), dtype=dtypes.float64)
device_map = values.ReplicaDeviceMap(('/device:CPU:0', '/device:GPU:0'))
x = values.DistributedValues(device_map, (a, b))
y = values.DistributedValues(device_map, (a, a))
# Removed device and input tensor dtype details from the error message
# since the order of the device and the corresponding input tensor dtype
# is not deterministic over different runs.
with self.assertRaisesRegexp(ValueError,
'Input tensor dtypes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def test_validating_dataset_input_tensors_with_shape_mismatch(
self, distribution):
with self.cached_session():
a = constant_op.constant([1, 2], shape=(1, 2))
b = constant_op.constant([[1, 2], [1, 2]], shape=(2, 2))
device_map = values.ReplicaDeviceMap(('/device:CPU:0', '/device:GPU:0'))
x = values.DistributedValues(device_map, (a, b))
y = values.DistributedValues(device_map, (a, a))
# Removed device and input tensor shape details from the error message
# since the order of the device and the corresponding input tensor shape
# is not deterministic over different runs.
with self.assertRaisesRegexp(
ValueError, 'Input tensor shapes do not match for '
'distributed tensor inputs '
'DistributedValues:.+'):
with distribution.scope():
distributed_training_utils.validate_distributed_dataset_inputs(
distribution, x, y)
def _get_input_from_iterator(iterator, model):
"""Get elements from the iterator and verify the input shape and type."""
next_element = iterator.get_next()
if len(nest.flatten(next_element)) == len(model.inputs):
x = next_element
y = None
sample_weights = None
elif len(nest.flatten(next_element)) == (len(model.inputs) +
len(model.outputs)):
x, y = next_element
sample_weights = None
else:
x, y, sample_weights = next_element
# Validate that all the elements in x and y are of the same type and shape.
# We can then pass the first element of x and y to `_standardize_weights`
# below and be confident of the output.
distributed_training_utils.validate_distributed_dataset_inputs(
model._distribution_strategy, x, y, sample_weights)
return x, y, sample_weights
