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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Module for constructing GridRNN cells"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import namedtuple
import functools
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import variable_scope as vs
from tensorflow.contrib import layers
from tensorflow.contrib import rnn
class GridRNNCell(rnn.RNNCell):
"""Grid recurrent cell.
This implementation is based on:
http://arxiv.org/pdf/1507.01526v3.pdf
This is the generic implementation of GridRNN. Users can specify arbitrary
number of dimensions,
set some of them to be priority (section 3.2), non-recurrent (section 3.3)
and input/output dimensions (section 3.4).
Weight sharing can also be specified using the `tied` parameter.
Type of recurrent units can be specified via `cell_fn`.
"""
def __init__(self,
num_units,
num_dims=1,
input_dims=None,
output_dims=None,
priority_dims=None,
non_recurrent_dims=None,
tied=False,
cell_fn=None,
non_recurrent_fn=None):
"""Initialize the parameters of a Grid RNN cell
Args:
num_units: int, The number of units in all dimensions of this GridRNN cell
num_dims: int, Number of dimensions of this grid.
input_dims: int or list, List of dimensions which will receive input data.
output_dims: int or list, List of dimensions from which the output will be
recorded.
priority_dims: int or list, List of dimensions to be considered as
priority dimensions.
If None, no dimension is prioritized.
non_recurrent_dims: int or list, List of dimensions that are not
recurrent.
The transfer function for non-recurrent dimensions is specified
via `non_recurrent_fn`,
which is default to be `tensorflow.nn.relu`.
tied: bool, Whether to share the weights among the dimensions of this
GridRNN cell.
If there are non-recurrent dimensions in the grid, weights are
shared between each
group of recurrent and non-recurrent dimensions.
cell_fn: function, a function which returns the recurrent cell object. Has
to be in the following signature:
def cell_func(num_units, input_size):
# ...
and returns an object of type `RNNCell`. If None, LSTMCell with
default parameters will be used.
non_recurrent_fn: a tensorflow Op that will be the transfer function of
the non-recurrent dimensions
Raises:
TypeError: if cell_fn does not return an RNNCell instance.
"""
if num_dims < 1:
raise ValueError('dims must be >= 1: {}'.format(num_dims))
self._config = _parse_rnn_config(num_dims, input_dims, output_dims,
priority_dims, non_recurrent_dims,
non_recurrent_fn or nn.relu, tied,
num_units)
cell_input_size = (self._config.num_dims - 1) * num_units
if cell_fn is None:
my_cell_fn = functools.partial(
rnn.LSTMCell,
num_units=num_units, input_size=cell_input_size,
state_is_tuple=False)
else:
my_cell_fn = lambda: cell_fn(num_units, cell_input_size)
if tied:
self._cells = [my_cell_fn()] * num_dims
else:
self._cells = [my_cell_fn() for _ in range(num_dims)]
if not isinstance(self._cells[0], rnn.RNNCell):
raise TypeError(
'cell_fn must return an RNNCell instance, saw: %s'
% type(self._cells[0]))
@property
def input_size(self):
# temporarily using num_units as the input_size of each dimension.
# The actual input size only determined when this cell get invoked,
# so this information can be considered unreliable.
return self._config.num_units * len(self._config.inputs)
@property
def output_size(self):
return self._cells[0].output_size * len(self._config.outputs)
@property
def state_size(self):
return self._cells[0].state_size * len(self._config.recurrents)
def __call__(self, inputs, state, scope=None):
"""Run one step of GridRNN.
Args:
inputs: input Tensor, 2D, batch x input_size. Or None
state: state Tensor, 2D, batch x state_size. Note that state_size =
cell_state_size * recurrent_dims
scope: VariableScope for the created subgraph; defaults to "GridRNNCell".
Returns:
A tuple containing:
- A 2D, batch x output_size, Tensor representing the output of the cell
after reading "inputs" when previous state was "state".
- A 2D, batch x state_size, Tensor representing the new state of the cell
after reading "inputs" when previous state was "state".
"""
state_sz = state.get_shape().as_list()[1]
if self.state_size != state_sz:
raise ValueError(
'Actual state size not same as specified: {} vs {}.'.format(
state_sz, self.state_size))
conf = self._config
dtype = inputs.dtype if inputs is not None else state.dtype
# c_prev is `m`, and m_prev is `h` in the paper.
# Keep c and m here for consistency with the codebase
c_prev = [None] * self._config.num_dims
m_prev = [None] * self._config.num_dims
cell_output_size = self._cells[0].state_size - conf.num_units
# for LSTM : state = memory cell + output, hence cell_output_size > 0
# for GRU/RNN: state = output (whose size is equal to _num_units),
# hence cell_output_size = 0
for recurrent_dim, start_idx in zip(self._config.recurrents, range(
0, self.state_size, self._cells[0].state_size)):
if cell_output_size > 0:
c_prev[recurrent_dim] = array_ops.slice(state, [0, start_idx],
[-1, conf.num_units])
m_prev[recurrent_dim] = array_ops.slice(
state, [0, start_idx + conf.num_units], [-1, cell_output_size])
else:
m_prev[recurrent_dim] = array_ops.slice(state, [0, start_idx],
[-1, conf.num_units])
new_output = [None] * conf.num_dims
new_state = [None] * conf.num_dims
with vs.variable_scope(scope or type(self).__name__): # GridRNNCell
# project input
if inputs is not None and sum(inputs.get_shape().as_list()) > 0 and len(
conf.inputs) > 0:
input_splits = array_ops.split(
value=inputs, num_or_size_splits=len(conf.inputs), axis=1)
input_sz = input_splits[0].get_shape().as_list()[1]
for i, j in enumerate(conf.inputs):
input_project_m = vs.get_variable(
'project_m_{}'.format(j), [input_sz, conf.num_units], dtype=dtype)
m_prev[j] = math_ops.matmul(input_splits[i], input_project_m)
if cell_output_size > 0:
input_project_c = vs.get_variable(
'project_c_{}'.format(j), [input_sz, conf.num_units],
dtype=dtype)
c_prev[j] = math_ops.matmul(input_splits[i], input_project_c)
_propagate(conf.non_priority, conf, self._cells, c_prev, m_prev,
new_output, new_state, True)
_propagate(conf.priority, conf, self._cells,
c_prev, m_prev, new_output, new_state, False)
output_tensors = [new_output[i] for i in self._config.outputs]
output = array_ops.zeros(
[0, 0], dtype) if len(output_tensors) == 0 else array_ops.concat(
output_tensors, 1)
state_tensors = [new_state[i] for i in self._config.recurrents]
states = array_ops.zeros(
[0, 0],
dtype) if len(state_tensors) == 0 else array_ops.concat(state_tensors,
1)
return output, states
"""Specialized cells, for convenience
"""
class Grid1BasicRNNCell(GridRNNCell):
"""1D BasicRNN cell"""
def __init__(self, num_units):
super(Grid1BasicRNNCell, self).__init__(
num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0, tied=False,
cell_fn=lambda n, i: rnn.BasicRNNCell(num_units=n, input_size=i))
class Grid2BasicRNNCell(GridRNNCell):
"""2D BasicRNN cell
This creates a 2D cell which receives input and gives output in the first
dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is
specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None):
super(Grid2BasicRNNCell, self).__init__(
num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn.BasicRNNCell(num_units=n, input_size=i),
non_recurrent_fn=non_recurrent_fn)
class Grid1BasicLSTMCell(GridRNNCell):
"""1D BasicLSTM cell"""
def __init__(self, num_units, forget_bias=1):
super(Grid1BasicLSTMCell, self).__init__(
num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0, tied=False,
cell_fn=lambda n, i: rnn.BasicLSTMCell(
num_units=n,
forget_bias=forget_bias, input_size=i,
state_is_tuple=False))
class Grid2BasicLSTMCell(GridRNNCell):
"""2D BasicLSTM cell
This creates a 2D cell which receives input and gives output in the first
dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is
specified.
"""
def __init__(self,
num_units,
tied=False,
non_recurrent_fn=None,
forget_bias=1):
super(Grid2BasicLSTMCell, self).__init__(
num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn.BasicLSTMCell(
num_units=n, forget_bias=forget_bias, input_size=i,
state_is_tuple=False),
non_recurrent_fn=non_recurrent_fn)
class Grid1LSTMCell(GridRNNCell):
"""1D LSTM cell
This is different from Grid1BasicLSTMCell because it gives options to
specify the forget bias and enabling peepholes
"""
def __init__(self, num_units, use_peepholes=False, forget_bias=1.0):
super(Grid1LSTMCell, self).__init__(
num_units=num_units, num_dims=1,
input_dims=0, output_dims=0, priority_dims=0,
cell_fn=lambda n, i: rnn.LSTMCell(
num_units=n, input_size=i, use_peepholes=use_peepholes,
forget_bias=forget_bias, state_is_tuple=False))
class Grid2LSTMCell(GridRNNCell):
"""2D LSTM cell
This creates a 2D cell which receives input and gives output in the first
dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is
specified.
"""
def __init__(self,
num_units,
tied=False,
non_recurrent_fn=None,
use_peepholes=False,
forget_bias=1.0):
super(Grid2LSTMCell, self).__init__(
num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn.LSTMCell(
num_units=n, input_size=i, forget_bias=forget_bias,
use_peepholes=use_peepholes, state_is_tuple=False),
non_recurrent_fn=non_recurrent_fn)
class Grid3LSTMCell(GridRNNCell):
"""3D BasicLSTM cell
This creates a 2D cell which receives input and gives output in the first
dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is
specified.
The second and third dimensions are LSTM.
"""
def __init__(self,
num_units,
tied=False,
non_recurrent_fn=None,
use_peepholes=False,
forget_bias=1.0):
super(Grid3LSTMCell, self).__init__(
num_units=num_units, num_dims=3,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn.LSTMCell(
num_units=n, input_size=i, forget_bias=forget_bias,
use_peepholes=use_peepholes, state_is_tuple=False),
non_recurrent_fn=non_recurrent_fn)
class Grid2GRUCell(GridRNNCell):
"""2D LSTM cell
This creates a 2D cell which receives input and gives output in the first
dimension.
The first dimension can optionally be non-recurrent if `non_recurrent_fn` is
specified.
"""
def __init__(self, num_units, tied=False, non_recurrent_fn=None):
super(Grid2GRUCell, self).__init__(
num_units=num_units, num_dims=2,
input_dims=0, output_dims=0, priority_dims=0, tied=tied,
non_recurrent_dims=None if non_recurrent_fn is None else 0,
cell_fn=lambda n, i: rnn.GRUCell(num_units=n, input_size=i),
non_recurrent_fn=non_recurrent_fn)
"""Helpers
"""
_GridRNNDimension = namedtuple(
'_GridRNNDimension',
['idx', 'is_input', 'is_output', 'is_priority', 'non_recurrent_fn'])
_GridRNNConfig = namedtuple('_GridRNNConfig',
['num_dims', 'dims', 'inputs', 'outputs',
'recurrents', 'priority', 'non_priority', 'tied',
'num_units'])
def _parse_rnn_config(num_dims, ls_input_dims, ls_output_dims, ls_priority_dims,
ls_non_recurrent_dims, non_recurrent_fn, tied, num_units):
def check_dim_list(ls):
if ls is None:
ls = []
if not isinstance(ls, (list, tuple)):
ls = [ls]
ls = sorted(set(ls))
if any(_ < 0 or _ >= num_dims for _ in ls):
raise ValueError('Invalid dims: {}. Must be in [0, {})'.format(ls,
num_dims))
return ls
input_dims = check_dim_list(ls_input_dims)
output_dims = check_dim_list(ls_output_dims)
priority_dims = check_dim_list(ls_priority_dims)
non_recurrent_dims = check_dim_list(ls_non_recurrent_dims)
rnn_dims = []
for i in range(num_dims):
rnn_dims.append(
_GridRNNDimension(
idx=i,
is_input=(i in input_dims),
is_output=(i in output_dims),
is_priority=(i in priority_dims),
non_recurrent_fn=non_recurrent_fn if i in non_recurrent_dims else
None))
return _GridRNNConfig(
num_dims=num_dims,
dims=rnn_dims,
inputs=input_dims,
outputs=output_dims,
recurrents=[x for x in range(num_dims) if x not in non_recurrent_dims],
priority=priority_dims,
non_priority=[x for x in range(num_dims) if x not in priority_dims],
tied=tied,
num_units=num_units)
def _propagate(dim_indices, conf, cells, c_prev, m_prev, new_output, new_state,
first_call):
"""Propagates through all the cells in dim_indices dimensions.
"""
if len(dim_indices) == 0:
return
# Because of the way RNNCells are implemented, we take the last dimension
# (H_{N-1}) out and feed it as the state of the RNN cell
# (in `last_dim_output`).
# The input of the cell (H_0 to H_{N-2}) are concatenated into `cell_inputs`
if conf.num_dims > 1:
ls_cell_inputs = [None] * (conf.num_dims - 1)
for d in conf.dims[:-1]:
ls_cell_inputs[d.idx] = new_output[d.idx] if new_output[
d.idx] is not None else m_prev[d.idx]
cell_inputs = array_ops.concat(ls_cell_inputs, 1)
else:
cell_inputs = array_ops.zeros([m_prev[0].get_shape().as_list()[0], 0],
m_prev[0].dtype)
last_dim_output = new_output[-1] if new_output[-1] is not None else m_prev[-1]
for i in dim_indices:
d = conf.dims[i]
if d.non_recurrent_fn:
linear_args = array_ops.concat(
[cell_inputs, last_dim_output],
1) if conf.num_dims > 1 else last_dim_output
with vs.variable_scope('non_recurrent' if conf.tied else
'non_recurrent/cell_{}'.format(i)):
if conf.tied and not (first_call and i == dim_indices[0]):
vs.get_variable_scope().reuse_variables()
new_output[d.idx] = layers.legacy_fully_connected(
linear_args,
num_output_units=conf.num_units,
activation_fn=d.non_recurrent_fn,
weight_init=vs.get_variable_scope().initializer or
layers.initializers.xavier_initializer)
else:
if c_prev[i] is not None:
cell_state = array_ops.concat([c_prev[i], last_dim_output], 1)
else:
# for GRU/RNN, the state is just the previous output
cell_state = last_dim_output
with vs.variable_scope('recurrent' if conf.tied else
'recurrent/cell_{}'.format(i)):
if conf.tied and not (first_call and i == dim_indices[0]):
vs.get_variable_scope().reuse_variables()
cell = cells[i]
new_output[d.idx], new_state[d.idx] = cell(cell_inputs, cell_state)
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