def LastValueQuantize(inputs,
per_channel=False,
init_min=-6.0,
init_max=6.0,
vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
name_prefix='LastValueQuant',
reuse=None,
is_training=True,
num_bits=8,
narrow_range=False):
"""Adds a layer that collects quantization ranges as last input ranges.
LastValueQuantize creates variables called 'min' and 'max', representing the
interval used for quantization and clamping.
Args:
inputs: a tensor containing values to be quantized.
per_channel: (Optional) a boolean specifying whether to use different
quantization ranges per output channel.
init_min: a float scalar, the initial value for variable min.
init_max: a float scalar, the initial value for variable max.
vars_collection: (Optional) collection where to store variables for
quantization interval ends.
name_prefix: name_prefix for created nodes.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
is_training: Whether the op is applied to a training or eval graph.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
Returns:
a tensor containing quantized values.
"""
with variable_scope.variable_scope(
None, default_name=name_prefix, values=[inputs], reuse=reuse):
input_shape = inputs.get_shape()
input_dim = len(input_shape)
if per_channel:
# Only support quantizing 1-, 2- and 4-dimensional tensors.
assert input_dim in [1, 2, 4], ('Expected 1D, 2D or 4D input, was: %s in '
' scope: %s' % (input_shape, name_prefix))
min_max_shape = [input_shape[-1]]
else:
min_max_shape = []
min_var = model_variable(
'min',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_min),
collections=[vars_collection],
trainable=False)
max_var = model_variable(
'max',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_max),
collections=[vars_collection],
trainable=False)
if not is_training:
return _FakeQuantWithMinMaxVars(
inputs,
min_var,
max_var,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
if per_channel:
if input_dim == 2:
reduce_dims = [0]
elif input_dim == 4:
reduce_dims = [0, 1, 2]
if per_channel:
if input_dim >= 2:
batch_min = math_ops.reduce_min(
inputs, reduction_indices=reduce_dims, name='BatchMin')
else:
batch_min = inputs
else:
batch_min = math_ops.reduce_min(inputs, name='BatchMin')
# TFLite requires that 0.0 if always in the [min; max] range.
batch_min = math_ops.minimum(batch_min, 0.0)
assign_min = state_ops.assign(min_var, batch_min, name='AssignMinLast')
if per_channel:
if input_dim >= 2:
batch_max = math_ops.reduce_max(
inputs, reduction_indices=reduce_dims, name='BatchMax')
else:
batch_max = inputs
else:
batch_max = math_ops.reduce_max(inputs, name='BatchMax')
# TFLite requires that 0.0 if always in the [min; max] range.
batch_max = math_ops.maximum(batch_max, 0.0)
assign_max = state_ops.assign(max_var, batch_max, name='AssignMaxLast')
return _FakeQuantWithMinMaxVars(
inputs,
assign_min,
assign_max,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
def MovingAvgQuantize(inputs,
per_channel=False,
init_min=-6.0,
init_max=6.0,
ema_decay=0.999,
vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
name_prefix='MovingAvgQuantize',
reuse=None,
is_training=True,
num_bits=8,
narrow_range=False):
"""Adds a layer that collects quantization ranges as EMAs of input ranges.
MovingAvgQuantize creates variables called 'min' and 'max', representing the
interval used for quantization and clamping.
Args:
inputs: a tensor containing values to be quantized.
per_channel: (default False) a boolean specifying whether to use different
quantization ranges per output channel.
init_min: a float scalar, the initial value for variable min.
init_max: a float scalar, the initial value for variable max.
ema_decay: EMA decay parameter.
vars_collection: (Optional) collection where to store variables for
quantization interval ends.
name_prefix: name_prefix for created nodes.
reuse: whether or not the layer and its variables should be reused. To be
able to reuse the layer scope must be given.
is_training: Whether the op is applied to a training or eval graph.
num_bits: Number of bits to use for quantization, must be between 2 and 8.
narrow_range: Whether to use the narrow quantization range
[1; 2^num_bits - 1] or wide range [0; 2^num_bits - 1].
Returns:
a tensor containing quantized values.
"""
with variable_scope.variable_scope(None,
default_name=name_prefix,
values=[inputs],
reuse=reuse) as scope:
scope.set_partitioner(None)
input_shape = inputs.get_shape()
input_dim = len(input_shape)
if per_channel:
# Only support quantizing 1-, 2- and 4-dimensional tensors.
assert input_dim in [1, 2, 4
], ('Expected 1D, 2D or 4D input, was: %s in '
' scope: %s' % (input_shape, name_prefix))
min_max_shape = [input_shape[-1]]
else:
min_max_shape = []
min_var = model_variable(
'min',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_min),
collections=[vars_collection],
trainable=False)
max_var = model_variable(
'max',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_max),
collections=[vars_collection],
trainable=False)
# modified by sf
min_po2_var = model_variable(
'min_po2',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_min),
collections=[vars_collection],
trainable=False)
max_po2_var = model_variable(
'max_po2',
shape=min_max_shape,
initializer=init_ops.constant_initializer(init_max),
collections=[vars_collection],
trainable=False)
if not is_training:
return _FakeQuantWithMinMaxVars(
inputs,
#min_var,
#max_var,
min_po2_var,
max_po2_var,
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)
# end modified by sf
if per_channel:
if input_dim == 2:
reduce_dims = [0]
elif input_dim == 4:
reduce_dims = [0, 1, 2]
if per_channel:
if input_dim >= 2:
batch_min = math_ops.reduce_min(inputs,
reduction_indices=reduce_dims,
name='BatchMin')
else:
batch_min = inputs
else:
batch_min = math_ops.reduce_min(inputs, name='BatchMin')
# B-eng requires that 0.0 if always in the [min; max] range.
# modified by sf
# ensure batch_min is not 0
batch_min = math_ops.minimum(batch_min, -0.0000001)
# end modified by sf
assign_min = moving_averages.assign_moving_average(min_var,
batch_min,
ema_decay,
name='AssignMinEma')
if per_channel:
if input_dim >= 2:
batch_max = math_ops.reduce_max(inputs,
reduction_indices=reduce_dims,
name='BatchMax')
else:
batch_max = inputs
else:
batch_max = math_ops.reduce_max(inputs, name='BatchMax')
# B-eng requires that 0.0 if always in the [min; max] range.
# modified by sf
# ensure batch_max is not 0
batch_max = math_ops.maximum(batch_max, 0.0000001)
# end modified by sf
assign_max = moving_averages.assign_moving_average(max_var,
batch_max,
ema_decay,
name='AssignMaxEma')
# modified by sf
min_log = tf.ceil(tf.log(tf.abs(assign_min)) / tf.log(2.0))
max_log = tf.ceil(tf.log(tf.abs(assign_max)) / tf.log(2.0))
minmax_log = math_ops.maximum(min_log, max_log)
min_po2 = tf.negative(tf.pow(2.0, minmax_log))
max_po2 = tf.pow(2.0, minmax_log) - tf.pow(2.0, minmax_log - 7)
assign_min_po2 = state_ops.assign(min_po2_var,
min_po2,
name='AssignMinPo2MovAvg')
assign_max_po2 = state_ops.assign(max_po2_var,
max_po2,
name='AssignMaxPo2MovAvg')
return _FakeQuantWithMinMaxVars(
inputs,
#assign_min,
#assign_max,
assign_min_po2,
assign_max_po2,
# end modified by sf
per_channel=per_channel,
num_bits=num_bits,
narrow_range=narrow_range)