def build(self, input_shape):
conv2d_quantized = _quantize_wrapped_layer(
tf.keras.layers.Conv2D,
configs.Default8BitConvQuantizeConfig(['kernel'], ['activation'],
False))
conv2d_quantized_output_quantized = _quantize_wrapped_layer(
tf.keras.layers.Conv2D,
configs.Default8BitConvQuantizeConfig(['kernel'], ['activation'],
True))
num_reduced_filters = nn_layers.make_divisible(
max(1, int(self._in_filters * self._se_ratio)),
divisor=self._divisible_by,
round_down_protect=self._round_down_protect)
self._se_reduce = conv2d_quantized(
filters=num_reduced_filters,
kernel_size=1,
strides=1,
padding='same',
use_bias=True,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=NoOpActivation())
self._se_expand = conv2d_quantized_output_quantized(
filters=self._out_filters,
kernel_size=1,
strides=1,
padding='same',
use_bias=True,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=NoOpActivation())
self._multiply = tfmot.quantization.keras.QuantizeWrapperV2(
tf.keras.layers.Multiply(),
configs.Default8BitQuantizeConfig([], [], True))
self._reduce_mean_quantizer = (
tfmot.quantization.keras.quantizers.MovingAverageQuantizer(
num_bits=8,
per_axis=False,
symmetric=False,
narrow_range=False))
self._reduce_mean_quantizer_vars = self._reduce_mean_quantizer.build(
None, 'reduce_mean_quantizer_vars', self)
self._activation_layer = tfmot.quantization.keras.QuantizeWrapperV2(
tf_utils.get_activation(self._activation, use_keras_layer=True),
configs.Default8BitActivationQuantizeConfig())
self._create_gating_activation_layer()
self._build_quantizer_vars()
super().build(input_shape)
def build(self, input_shape):
expand_filters = self._in_filters
if self._expand_ratio > 1:
# First 1x1 conv for channel expansion.
expand_filters = nn_layers.make_divisible(
self._in_filters * self._expand_ratio, self._divisible_by)
expand_kernel = 1 if self._use_depthwise else self._kernel_size
expand_stride = 1 if self._use_depthwise else self._strides
self._conv0 = tf.keras.layers.Conv2D(
filters=expand_filters,
kernel_size=expand_kernel,
strides=expand_stride,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm0 = self._norm(axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
if self._use_depthwise:
# Depthwise conv.
self._conv1 = tf.keras.layers.DepthwiseConv2D(
kernel_size=(self._kernel_size, self._kernel_size),
strides=self._strides,
padding='same',
depth_multiplier=1,
dilation_rate=self._dilation_rate,
use_bias=False,
depthwise_initializer=self._kernel_initializer,
depthwise_regularizer=self._depthsize_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm1 = self._norm(axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
# Squeeze and excitation.
if self._se_ratio and self._se_ratio > 0 and self._se_ratio <= 1:
logging.info('Use Squeeze and excitation.')
in_filters = self._in_filters
if self._expand_se_in_filters:
in_filters = expand_filters
self._squeeze_excitation = nn_layers.SqueezeExcitation(
in_filters=in_filters,
out_filters=expand_filters,
se_ratio=self._se_ratio,
divisible_by=self._divisible_by,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=self._se_inner_activation,
gating_activation=self._se_gating_activation)
else:
self._squeeze_excitation = None
# Last 1x1 conv.
self._conv2 = tf.keras.layers.Conv2D(
filters=self._out_filters,
kernel_size=1,
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer)
self._norm2 = self._norm(axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
if self._stochastic_depth_drop_rate:
self._stochastic_depth = nn_layers.StochasticDepth(
self._stochastic_depth_drop_rate)
else:
self._stochastic_depth = None
super(InvertedBottleneckBlock, self).build(input_shape)
def build(self, input_shape: Optional[Union[Sequence[int], tf.Tensor]]):
"""Build variables and child layers to prepare for calling."""
conv2d_quantized = _quantize_wrapped_layer(
tf.keras.layers.Conv2D,
configs.DefaultNBitConvQuantizeConfig(
['kernel'], ['activation'], False,
num_bits_weight=self._num_bits_weight,
num_bits_activation=self._num_bits_activation))
depthwise_conv2d_quantized = _quantize_wrapped_layer(
tf.keras.layers.DepthwiseConv2D,
configs.DefaultNBitConvQuantizeConfig(
['depthwise_kernel'], ['activation'], False,
num_bits_weight=self._num_bits_weight,
num_bits_activation=self._num_bits_activation))
expand_filters = self._in_filters
if self._expand_ratio > 1:
# First 1x1 conv for channel expansion.
expand_filters = nn_layers.make_divisible(
self._in_filters * self._expand_ratio, self._divisible_by)
expand_kernel = 1 if self._use_depthwise else self._kernel_size
expand_stride = 1 if self._use_depthwise else self._strides
self._conv0 = conv2d_quantized(
filters=expand_filters,
kernel_size=expand_kernel,
strides=expand_stride,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=NoOpActivation())
self._norm0 = self._norm_with_quantize(
axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
self._activation_layer = tfmot.quantization.keras.QuantizeWrapperV2(
tf_utils.get_activation(self._activation, use_keras_layer=True),
configs.DefaultNBitActivationQuantizeConfig(
num_bits_weight=self._num_bits_weight,
num_bits_activation=self._num_bits_activation))
if self._use_depthwise:
# Depthwise conv.
self._conv1 = depthwise_conv2d_quantized(
kernel_size=(self._kernel_size, self._kernel_size),
strides=self._strides,
padding='same',
depth_multiplier=1,
dilation_rate=self._dilation_rate,
use_bias=False,
depthwise_initializer=self._kernel_initializer,
depthwise_regularizer=self._depthsize_regularizer,
bias_regularizer=self._bias_regularizer,
activation=NoOpActivation())
self._norm1 = self._norm_with_quantize(
axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
self._depthwise_activation_layer = (
tfmot.quantization.keras.QuantizeWrapperV2(
tf_utils.get_activation(self._depthwise_activation,
use_keras_layer=True),
configs.DefaultNBitActivationQuantizeConfig(
num_bits_weight=self._num_bits_weight,
num_bits_activation=self._num_bits_activation)))
# Squeeze and excitation.
if self._se_ratio and self._se_ratio > 0 and self._se_ratio <= 1:
logging.info('Use Squeeze and excitation.')
in_filters = self._in_filters
if self._expand_se_in_filters:
in_filters = expand_filters
self._squeeze_excitation = qat_nn_layers.SqueezeExcitationNBitQuantized(
in_filters=in_filters,
out_filters=expand_filters,
se_ratio=self._se_ratio,
divisible_by=self._divisible_by,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=self._se_inner_activation,
gating_activation=self._se_gating_activation,
num_bits_weight=self._num_bits_weight,
num_bits_activation=self._num_bits_activation)
else:
self._squeeze_excitation = None
# Last 1x1 conv.
self._conv2 = conv2d_quantized(
filters=self._out_filters,
kernel_size=1,
strides=1,
padding='same',
use_bias=False,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=NoOpActivation())
self._norm2 = self._norm_with_quantize(
axis=self._bn_axis,
momentum=self._norm_momentum,
epsilon=self._norm_epsilon)
if self._stochastic_depth_drop_rate:
self._stochastic_depth = nn_layers.StochasticDepth(
self._stochastic_depth_drop_rate)
else:
self._stochastic_depth = None
self._add = tf.keras.layers.Add()
super().build(input_shape)