research/lstm_object_detection/lstm/lstm_cells.py
# Copyright 2018 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.
# ==============================================================================
"""BottleneckConvLSTMCell implementation."""
import functools
import tensorflow.compat.v1 as tf
import tf_slim as slim
from tensorflow.contrib import rnn as contrib_rnn
from tensorflow.contrib.framework.python.ops import variables as contrib_variables
import lstm_object_detection.lstm.utils as lstm_utils
class BottleneckConvLSTMCell(contrib_rnn.RNNCell):
"""Basic LSTM recurrent network cell using separable convolutions.
The implementation is based on:
Mobile Video Object Detection with Temporally-Aware Feature Maps
https://arxiv.org/abs/1711.06368.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
This LSTM first projects inputs to the size of the output before doing gate
computations. This saves params unless the input is less than a third of the
state size channel-wise.
"""
def __init__(self,
filter_size,
output_size,
num_units,
forget_bias=1.0,
activation=tf.tanh,
flatten_state=False,
clip_state=False,
output_bottleneck=False,
pre_bottleneck=False,
visualize_gates=False):
"""Initializes the basic LSTM cell.
Args:
filter_size: collection, conv filter size.
output_size: collection, the width/height dimensions of the cell/output.
num_units: int, The number of channels in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
activation: Activation function of the inner states.
flatten_state: if True, state tensor will be flattened and stored as a 2-d
tensor. Use for exporting the model to tfmini.
clip_state: if True, clip state between [-6, 6].
output_bottleneck: if True, the cell bottleneck will be concatenated to
the cell output.
pre_bottleneck: if True, cell assumes that bottlenecking was performing
before the function was called.
visualize_gates: if True, add histogram summaries of all gates and outputs
to tensorboard.
"""
self._filter_size = list(filter_size)
self._output_size = list(output_size)
self._num_units = num_units
self._forget_bias = forget_bias
self._activation = activation
self._viz_gates = visualize_gates
self._flatten_state = flatten_state
self._clip_state = clip_state
self._output_bottleneck = output_bottleneck
self._pre_bottleneck = pre_bottleneck
self._param_count = self._num_units
for dim in self._output_size:
self._param_count *= dim
@property
def state_size(self):
return contrib_rnn.LSTMStateTuple(self._output_size + [self._num_units],
self._output_size + [self._num_units])
@property
def state_size_flat(self):
return contrib_rnn.LSTMStateTuple([self._param_count], [self._param_count])
@property
def output_size(self):
return self._output_size + [self._num_units]
def __call__(self, inputs, state, scope=None):
"""Long short-term memory cell (LSTM) with bottlenecking.
Args:
inputs: Input tensor at the current timestep.
state: Tuple of tensors, the state and output at the previous timestep.
scope: Optional scope.
Returns:
A tuple where the first element is the LSTM output and the second is
a LSTMStateTuple of the state at the current timestep.
"""
scope = scope or 'conv_lstm_cell'
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
c, h = state
# unflatten state if necessary
if self._flatten_state:
c = tf.reshape(c, [-1] + self.output_size)
h = tf.reshape(h, [-1] + self.output_size)
# summary of input passed into cell
if self._viz_gates:
slim.summaries.add_histogram_summary(inputs, 'cell_input')
if self._pre_bottleneck:
bottleneck = inputs
else:
bottleneck = slim.separable_conv2d(
tf.concat([inputs, h], 3),
self._num_units,
self._filter_size,
depth_multiplier=1,
activation_fn=self._activation,
normalizer_fn=None,
scope='bottleneck')
if self._viz_gates:
slim.summaries.add_histogram_summary(bottleneck, 'bottleneck')
concat = slim.separable_conv2d(
bottleneck,
4 * self._num_units,
self._filter_size,
depth_multiplier=1,
activation_fn=None,
normalizer_fn=None,
scope='gates')
i, j, f, o = tf.split(concat, 4, 3)
new_c = (
c * tf.sigmoid(f + self._forget_bias) +
tf.sigmoid(i) * self._activation(j))
if self._clip_state:
new_c = tf.clip_by_value(new_c, -6, 6)
new_h = self._activation(new_c) * tf.sigmoid(o)
# summary of cell output and new state
if self._viz_gates:
slim.summaries.add_histogram_summary(new_h, 'cell_output')
slim.summaries.add_histogram_summary(new_c, 'cell_state')
output = new_h
if self._output_bottleneck:
output = tf.concat([new_h, bottleneck], axis=3)
# reflatten state to store it
if self._flatten_state:
new_c = tf.reshape(new_c, [-1, self._param_count])
new_h = tf.reshape(new_h, [-1, self._param_count])
return output, contrib_rnn.LSTMStateTuple(new_c, new_h)
def init_state(self, state_name, batch_size, dtype, learned_state=False):
"""Creates an initial state compatible with this cell.
Args:
state_name: name of the state tensor
batch_size: model batch size
dtype: dtype for the tensor values i.e. tf.float32
learned_state: whether the initial state should be learnable. If false,
the initial state is set to all 0's
Returns:
The created initial state.
"""
state_size = (
self.state_size_flat if self._flatten_state else self.state_size)
# list of 2 zero tensors or variables tensors, depending on if
# learned_state is true
# pylint: disable=g-long-ternary,g-complex-comprehension
ret_flat = [(contrib_variables.model_variable(
state_name + str(i),
shape=s,
dtype=dtype,
initializer=tf.truncated_normal_initializer(stddev=0.03))
if learned_state else tf.zeros(
[batch_size] + s, dtype=dtype, name=state_name))
for i, s in enumerate(state_size)]
# duplicates initial state across the batch axis if it's learned
if learned_state:
ret_flat = [
tf.stack([tensor
for i in range(int(batch_size))])
for tensor in ret_flat
]
for s, r in zip(state_size, ret_flat):
r.set_shape([None] + s)
return tf.nest.pack_sequence_as(structure=[1, 1], flat_sequence=ret_flat)
def pre_bottleneck(self, inputs, state, input_index):
"""Apply pre-bottleneck projection to inputs.
Pre-bottleneck operation maps features of different channels into the same
dimension. The purpose of this op is to share the features from both large
and small models in the same LSTM cell.
Args:
inputs: 4D Tensor with shape [batch_size x width x height x input_size].
state: 4D Tensor with shape [batch_size x width x height x state_size].
input_index: integer index indicating which base features the inputs
correspoding to.
Returns:
inputs: pre-bottlenecked inputs.
Raises:
ValueError: If pre_bottleneck is not set or inputs is not rank 4.
"""
# Sometimes state is a tuple, in which case it cannot be modified, e.g.
# during training, tf.contrib.training.SequenceQueueingStateSaver
# returns the state as a tuple. This should not be an issue since we
# only need to modify state[1] during export, when state should be a
# list.
if len(inputs.shape) != 4:
raise ValueError('Expect rank 4 feature tensor.')
if not self._flatten_state and len(state.shape) != 4:
raise ValueError('Expect rank 4 state tensor.')
if self._flatten_state and len(state.shape) != 2:
raise ValueError('Expect rank 2 state tensor when flatten_state is set.')
with tf.name_scope(None):
state = tf.identity(state, name='raw_inputs/init_lstm_h')
if self._flatten_state:
batch_size = inputs.shape[0]
height = inputs.shape[1]
width = inputs.shape[2]
state = tf.reshape(state, [batch_size, height, width, -1])
with tf.variable_scope('conv_lstm_cell', reuse=tf.AUTO_REUSE):
scope_name = 'bottleneck_%d' % input_index
inputs = slim.separable_conv2d(
tf.concat([inputs, state], 3),
self.output_size[-1],
self._filter_size,
depth_multiplier=1,
activation_fn=tf.nn.relu6,
normalizer_fn=None,
scope=scope_name)
# For exporting inference graph, we only mark the first timestep.
with tf.name_scope(None):
inputs = tf.identity(
inputs, name='raw_outputs/base_endpoint_%d' % (input_index + 1))
return inputs
class GroupedConvLSTMCell(contrib_rnn.RNNCell):
"""Basic LSTM recurrent network cell using separable convolutions.
The implementation is based on: https://arxiv.org/abs/1903.10172.
We add forget_bias (default: 1) to the biases of the forget gate in order to
reduce the scale of forgetting in the beginning of the training.
This LSTM first projects inputs to the size of the output before doing gate
computations. This saves params unless the input is less than a third of the
state size channel-wise. Computation of bottlenecks and gates is divided
into independent groups for further savings.
"""
def __init__(self,
filter_size,
output_size,
num_units,
is_training,
forget_bias=1.0,
activation=tf.tanh,
use_batch_norm=False,
flatten_state=False,
groups=4,
clip_state=False,
scale_state=False,
output_bottleneck=False,
pre_bottleneck=False,
is_quantized=False,
visualize_gates=False,
conv_op_overrides=None):
"""Initialize the basic LSTM cell.
Args:
filter_size: collection, conv filter size
output_size: collection, the width/height dimensions of the cell/output
num_units: int, The number of channels in the LSTM cell.
is_training: Whether the LSTM is in training mode.
forget_bias: float, The bias added to forget gates (see above).
activation: Activation function of the inner states.
use_batch_norm: if True, use batch norm after convolution
flatten_state: if True, state tensor will be flattened and stored as a 2-d
tensor. Use for exporting the model to tfmini
groups: Number of groups to split the state into. Must evenly divide
num_units.
clip_state: if True, clips state between [-6, 6].
scale_state: if True, scales state so that all values are under 6 at all
times.
output_bottleneck: if True, the cell bottleneck will be concatenated to
the cell output.
pre_bottleneck: if True, cell assumes that bottlenecking was performing
before the function was called.
is_quantized: if True, the model is in quantize mode, which requires
quantization friendly concat and separable_conv2d ops.
visualize_gates: if True, add histogram summaries of all gates and outputs
to tensorboard
conv_op_overrides: A list of convolutional operations that override the
'bottleneck' and 'convolution' layers before lstm gates. If None, the
original implementation of seperable_conv will be used. The length of
the list should be two.
Raises:
ValueError: when both clip_state and scale_state are enabled.
"""
if clip_state and scale_state:
raise ValueError('clip_state and scale_state cannot both be enabled.')
self._filter_size = list(filter_size)
self._output_size = list(output_size)
self._num_units = num_units
self._is_training = is_training
self._forget_bias = forget_bias
self._activation = activation
self._use_batch_norm = use_batch_norm
self._viz_gates = visualize_gates
self._flatten_state = flatten_state
self._param_count = self._num_units
self._groups = groups
self._scale_state = scale_state
self._clip_state = clip_state
self._output_bottleneck = output_bottleneck
self._pre_bottleneck = pre_bottleneck
self._is_quantized = is_quantized
for dim in self._output_size:
self._param_count *= dim
self._conv_op_overrides = conv_op_overrides
if self._conv_op_overrides and len(self._conv_op_overrides) != 2:
raise ValueError('Bottleneck and Convolutional layer should be overriden'
'together')
@property
def state_size(self):
return contrib_rnn.LSTMStateTuple(self._output_size + [self._num_units],
self._output_size + [self._num_units])
@property
def state_size_flat(self):
return contrib_rnn.LSTMStateTuple([self._param_count], [self._param_count])
@property
def output_size(self):
return self._output_size + [self._num_units]
@property
def filter_size(self):
return self._filter_size
@property
def num_groups(self):
return self._groups
def __call__(self, inputs, state, scope=None):
"""Long short-term memory cell (LSTM) with bottlenecking.
Includes logic for quantization-aware training. Note that all concats and
activations use fixed ranges unless stated otherwise.
Args:
inputs: Input tensor at the current timestep.
state: Tuple of tensors, the state at the previous timestep.
scope: Optional scope.
Returns:
A tuple where the first element is the LSTM output and the second is
a LSTMStateTuple of the state at the current timestep.
"""
scope = scope or 'conv_lstm_cell'
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
c, h = state
# Set nodes to be under raw_inputs/ name scope for tfmini export.
with tf.name_scope(None):
c = tf.identity(c, name='raw_inputs/init_lstm_c')
# When pre_bottleneck is enabled, input h handle is in rnn_decoder.py
if not self._pre_bottleneck:
h = tf.identity(h, name='raw_inputs/init_lstm_h')
# unflatten state if necessary
if self._flatten_state:
c = tf.reshape(c, [-1] + self.output_size)
h = tf.reshape(h, [-1] + self.output_size)
c_list = tf.split(c, self._groups, axis=3)
if self._pre_bottleneck:
inputs_list = tf.split(inputs, self._groups, axis=3)
else:
h_list = tf.split(h, self._groups, axis=3)
out_bottleneck = []
out_c = []
out_h = []
# summary of input passed into cell
if self._viz_gates:
slim.summaries.add_histogram_summary(inputs, 'cell_input')
for k in range(self._groups):
if self._pre_bottleneck:
bottleneck = inputs_list[k]
else:
if self._conv_op_overrides:
bottleneck_fn = self._conv_op_overrides[0]
else:
bottleneck_fn = functools.partial(
lstm_utils.quantizable_separable_conv2d,
kernel_size=self._filter_size,
activation_fn=self._activation)
if self._use_batch_norm:
b_x = bottleneck_fn(
inputs=inputs,
num_outputs=self._num_units // self._groups,
is_quantized=self._is_quantized,
depth_multiplier=1,
normalizer_fn=None,
scope='bottleneck_%d_x' % k)
b_h = bottleneck_fn(
inputs=h_list[k],
num_outputs=self._num_units // self._groups,
is_quantized=self._is_quantized,
depth_multiplier=1,
normalizer_fn=None,
scope='bottleneck_%d_h' % k)
b_x = slim.batch_norm(
b_x,
scale=True,
is_training=self._is_training,
scope='BatchNorm_%d_X' % k)
b_h = slim.batch_norm(
b_h,
scale=True,
is_training=self._is_training,
scope='BatchNorm_%d_H' % k)
bottleneck = b_x + b_h
else:
# All concats use fixed quantization ranges to prevent rescaling
# at inference. Both |inputs| and |h_list| are tensors resulting
# from Relu6 operations so we fix the ranges to [0, 6].
bottleneck_concat = lstm_utils.quantizable_concat(
[inputs, h_list[k]],
axis=3,
is_training=False,
is_quantized=self._is_quantized,
scope='bottleneck_%d/quantized_concat' % k)
bottleneck = bottleneck_fn(
inputs=bottleneck_concat,
num_outputs=self._num_units // self._groups,
is_quantized=self._is_quantized,
depth_multiplier=1,
normalizer_fn=None,
scope='bottleneck_%d' % k)
if self._conv_op_overrides:
conv_fn = self._conv_op_overrides[1]
else:
conv_fn = functools.partial(
lstm_utils.quantizable_separable_conv2d,
kernel_size=self._filter_size,
activation_fn=None)
concat = conv_fn(
inputs=bottleneck,
num_outputs=4 * self._num_units // self._groups,
is_quantized=self._is_quantized,
depth_multiplier=1,
normalizer_fn=None,
scope='concat_conv_%d' % k)
# Since there is no activation in the previous separable conv, we
# quantize here. A starting range of [-6, 6] is used because the
# tensors are input to a Sigmoid function that saturates at these
# ranges.
concat = lstm_utils.quantize_op(
concat,
is_training=self._is_training,
default_min=-6,
default_max=6,
is_quantized=self._is_quantized,
scope='gates_%d/act_quant' % k)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
i, j, f, o = tf.split(concat, 4, 3)
f_add = f + self._forget_bias
f_add = lstm_utils.quantize_op(
f_add,
is_training=self._is_training,
default_min=-6,
default_max=6,
is_quantized=self._is_quantized,
scope='forget_gate_%d/add_quant' % k)
f_act = tf.sigmoid(f_add)
a = c_list[k] * f_act
a = lstm_utils.quantize_op(
a,
is_training=self._is_training,
is_quantized=self._is_quantized,
scope='forget_gate_%d/mul_quant' % k)
i_act = tf.sigmoid(i)
j_act = self._activation(j)
# The quantization range is fixed for the relu6 to ensure that zero
# is exactly representable.
j_act = lstm_utils.fixed_quantize_op(
j_act,
fixed_min=0.0,
fixed_max=6.0,
is_quantized=self._is_quantized,
scope='new_input_%d/act_quant' % k)
b = i_act * j_act
b = lstm_utils.quantize_op(
b,
is_training=self._is_training,
is_quantized=self._is_quantized,
scope='input_gate_%d/mul_quant' % k)
new_c = a + b
# The quantization range is fixed to [0, 6] due to an optimization in
# TFLite. The order of operations is as fllows:
# Add -> FakeQuant -> Relu6 -> FakeQuant -> Concat.
# The fakequant ranges to the concat must be fixed to ensure all inputs
# to the concat have the same range, removing the need for rescaling.
# The quantization ranges input to the relu6 are propagated to its
# output. Any mismatch between these two ranges will cause an error.
new_c = lstm_utils.fixed_quantize_op(
new_c,
fixed_min=0.0,
fixed_max=6.0,
is_quantized=self._is_quantized,
scope='new_c_%d/add_quant' % k)
if not self._is_quantized:
if self._scale_state:
normalizer = tf.maximum(1.0,
tf.reduce_max(new_c, axis=(1, 2, 3)) / 6)
new_c /= tf.reshape(normalizer, [tf.shape(new_c)[0], 1, 1, 1])
elif self._clip_state:
new_c = tf.clip_by_value(new_c, -6, 6)
new_c_act = self._activation(new_c)
# The quantization range is fixed for the relu6 to ensure that zero
# is exactly representable.
new_c_act = lstm_utils.fixed_quantize_op(
new_c_act,
fixed_min=0.0,
fixed_max=6.0,
is_quantized=self._is_quantized,
scope='new_c_%d/act_quant' % k)
o_act = tf.sigmoid(o)
new_h = new_c_act * o_act
# The quantization range is fixed since it is input to a concat.
# A range of [0, 6] is used since |new_h| is a product of ranges [0, 6]
# and [0, 1].
new_h_act = lstm_utils.fixed_quantize_op(
new_h,
fixed_min=0.0,
fixed_max=6.0,
is_quantized=self._is_quantized,
scope='new_h_%d/act_quant' % k)
out_bottleneck.append(bottleneck)
out_c.append(new_c_act)
out_h.append(new_h_act)
# Since all inputs to the below concats are already quantized, we can use
# a regular concat operation.
new_c = tf.concat(out_c, axis=3)
new_h = tf.concat(out_h, axis=3)
# |bottleneck| is input to a concat with |new_h|. We must use
# quantizable_concat() with a fixed range that matches |new_h|.
bottleneck = lstm_utils.quantizable_concat(
out_bottleneck,
axis=3,
is_training=False,
is_quantized=self._is_quantized,
scope='out_bottleneck/quantized_concat')
# summary of cell output and new state
if self._viz_gates:
slim.summaries.add_histogram_summary(new_h, 'cell_output')
slim.summaries.add_histogram_summary(new_c, 'cell_state')
output = new_h
if self._output_bottleneck:
output = lstm_utils.quantizable_concat(
[new_h, bottleneck],
axis=3,
is_training=False,
is_quantized=self._is_quantized,
scope='new_output/quantized_concat')
# reflatten state to store it
if self._flatten_state:
new_c = tf.reshape(new_c, [-1, self._param_count], name='lstm_c')
new_h = tf.reshape(new_h, [-1, self._param_count], name='lstm_h')
# Set nodes to be under raw_outputs/ name scope for tfmini export.
with tf.name_scope(None):
new_c = tf.identity(new_c, name='raw_outputs/lstm_c')
new_h = tf.identity(new_h, name='raw_outputs/lstm_h')
states_and_output = contrib_rnn.LSTMStateTuple(new_c, new_h)
return output, states_and_output
def init_state(self, state_name, batch_size, dtype, learned_state=False):
"""Creates an initial state compatible with this cell.
Args:
state_name: name of the state tensor
batch_size: model batch size
dtype: dtype for the tensor values i.e. tf.float32
learned_state: whether the initial state should be learnable. If false,
the initial state is set to all 0's
Returns:
ret: the created initial state
"""
state_size = (
self.state_size_flat if self._flatten_state else self.state_size)
# list of 2 zero tensors or variables tensors,
# depending on if learned_state is true
# pylint: disable=g-long-ternary,g-complex-comprehension
ret_flat = [(contrib_variables.model_variable(
state_name + str(i),
shape=s,
dtype=dtype,
initializer=tf.truncated_normal_initializer(stddev=0.03))
if learned_state else tf.zeros(
[batch_size] + s, dtype=dtype, name=state_name))
for i, s in enumerate(state_size)]
# duplicates initial state across the batch axis if it's learned
if learned_state:
ret_flat = [tf.stack([tensor for i in range(int(batch_size))])
for tensor in ret_flat]
for s, r in zip(state_size, ret_flat):
r = tf.reshape(r, [-1] + s)
ret = tf.nest.pack_sequence_as(structure=[1, 1], flat_sequence=ret_flat)
return ret
def pre_bottleneck(self, inputs, state, input_index):
"""Apply pre-bottleneck projection to inputs.
Pre-bottleneck operation maps features of different channels into the same
dimension. The purpose of this op is to share the features from both large
and small models in the same LSTM cell.
Args:
inputs: 4D Tensor with shape [batch_size x width x height x input_size].
state: 4D Tensor with shape [batch_size x width x height x state_size].
input_index: integer index indicating which base features the inputs
correspoding to.
Returns:
inputs: pre-bottlenecked inputs.
Raises:
ValueError: If pre_bottleneck is not set or inputs is not rank 4.
"""
# Sometimes state is a tuple, in which case it cannot be modified, e.g.
# during training, tf.contrib.training.SequenceQueueingStateSaver
# returns the state as a tuple. This should not be an issue since we
# only need to modify state[1] during export, when state should be a
# list.
if not self._pre_bottleneck:
raise ValueError('Only applied when pre_bottleneck is set to true.')
if len(inputs.shape) != 4:
raise ValueError('Expect a rank 4 feature tensor.')
if not self._flatten_state and len(state.shape) != 4:
raise ValueError('Expect rank 4 state tensor.')
if self._flatten_state and len(state.shape) != 2:
raise ValueError('Expect rank 2 state tensor when flatten_state is set.')
with tf.name_scope(None):
state = tf.identity(
state, name='raw_inputs/init_lstm_h_%d' % (input_index + 1))
if self._flatten_state:
batch_size = inputs.shape[0]
height = inputs.shape[1]
width = inputs.shape[2]
state = tf.reshape(state, [batch_size, height, width, -1])
with tf.variable_scope('conv_lstm_cell', reuse=tf.AUTO_REUSE):
state_split = tf.split(state, self._groups, axis=3)
with tf.variable_scope('bottleneck_%d' % input_index):
bottleneck_out = []
for k in range(self._groups):
with tf.variable_scope('group_%d' % k):
bottleneck_out.append(
lstm_utils.quantizable_separable_conv2d(
lstm_utils.quantizable_concat(
[inputs, state_split[k]],
axis=3,
is_training=self._is_training,
is_quantized=self._is_quantized,
scope='quantized_concat'),
self.output_size[-1] / self._groups,
self._filter_size,
is_quantized=self._is_quantized,
depth_multiplier=1,
activation_fn=tf.nn.relu6,
normalizer_fn=None,
scope='project'))
inputs = lstm_utils.quantizable_concat(
bottleneck_out,
axis=3,
is_training=self._is_training,
is_quantized=self._is_quantized,
scope='bottleneck_out/quantized_concat')
# For exporting inference graph, we only mark the first timestep.
with tf.name_scope(None):
inputs = tf.identity(
inputs, name='raw_outputs/base_endpoint_%d' % (input_index + 1))
return inputs