def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type, name="restore_slice", preferred_shard=-1):
"""Restore a tensor slice from a set of files with a given pattern.
Example usage:
RestoreSlice("/foo/bar-?????-of-?????", "w", "10 10 0,2:-", DT_FLOAT)
Args:
file_pattern: the file pattern used to match a set of checkpoint files.
tensor_name: the name of the tensor to restore.
shape_and_slice: the shape-and-slice spec of the slice.
tensor_type: the type of the tensor to restore.
name: string. Optional name for the op.
preferred_shard: Int. Optional shard to open first in the checkpoint file.
Returns:
A tensor of type "tensor_type".
"""
base_type = dtypes.as_dtype(tensor_type).base_dtype
return gen_io_ops._restore_slice(file_pattern, tensor_name, shape_and_slice, base_type, preferred_shard, name=name)
def _set_checkpoint_initializer(variable, file_pattern, tensor_name, slice_spec,
name="checkpoint_initializer"):
"""Sets variable initializer to assign op form value in checkpoint's tensor.
Args:
variable: `Variable` object.
file_pattern: string, where to load checkpoints from.
tensor_name: Name of the `Tensor` to load from checkpoint reader.
slice_spec: Slice specification for loading partitioned variables.
name: Name of the operation.
"""
base_type = variable.dtype.base_dtype
restore_op = gen_io_ops._restore_slice(
file_pattern,
tensor_name,
slice_spec,
base_type,
preferred_shard=-1,
name=name)
variable._initializer_op = state_ops.assign(variable, restore_op)
def _set_checkpoint_initializer(variable,
file_pattern,
tensor_name,
slice_spec,
name="checkpoint_initializer"):
"""Sets variable initializer to assign op form value in checkpoint's tensor.
Args:
variable: `Variable` object.
file_pattern: string, where to load checkpoints from.
tensor_name: Name of the `Tensor` to load from checkpoint reader.
slice_spec: Slice specification for loading partitioned variables.
name: Name of the operation.
"""
base_type = variable.dtype.base_dtype
restore_op = gen_io_ops._restore_slice(file_pattern,
tensor_name,
slice_spec,
base_type,
preferred_shard=-1,
name=name)
variable._initializer_op = state_ops.assign(variable, restore_op)
def _restore_slice(file_pattern, tensor_name, shape_and_slice, tensor_type,
name="restore_slice", preferred_shard=-1):
"""Restore a tensor slice from a set of files with a given pattern.
Example usage:
RestoreSlice("/foo/bar-?????-of-?????", "w", "10 10 0,2:-", DT_FLOAT)
Args:
file_pattern: the file pattern used to match a set of checkpoint files.
tensor_name: the name of the tensor to restore.
shape_and_slice: the shape-and-slice spec of the slice.
tensor_type: the type of the tensor to restore.
name: string. Optional name for the op.
preferred_shard: Int. Optional shard to open first in the checkpoint file.
Returns:
A tensor of type "tensor_type".
"""
base_type = dtypes.as_dtype(tensor_type).base_dtype
return gen_io_ops._restore_slice(
file_pattern, tensor_name, shape_and_slice, base_type,
preferred_shard, name=name)
def testRestoreSlice(self):
op = gen_io_ops._restore_slice("model", "var", "3 4 0,1:-",
dtypes.float32)
self.assertEqual([1, 4], op.get_shape())
