def build(self, input_shape: Optional[Union[Sequence[int], tf.Tensor]]):
"""Build variables and child layers to prepare for calling."""
if self._use_explicit_padding and self._kernel_size > 1:
padding_size = nn_layers.get_padding_for_kernel_size(
self._kernel_size)
self._pad = tf.keras.layers.ZeroPadding2D(padding_size)
conv2d_quantized = (helper.Conv2DQuantized if self._use_normalization
else helper.Conv2DOutputQuantized)
self._conv0 = conv2d_quantized(
filters=self._filters,
kernel_size=self._kernel_size,
strides=self._strides,
padding=self._padding,
use_bias=self._use_bias,
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
bias_regularizer=self._bias_regularizer,
activation=helper.NoOpActivation())
if self._use_normalization:
self._norm0 = helper.norm_by_activation(
self._activation, self._norm_with_quantize,
self._norm)(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.Default8BitActivationQuantizeConfig())
super(Conv2DBNBlockQuantized, self).build(input_shape)
def build(self, input_shape: Union[tf.TensorShape, List[tf.TensorShape]]):
"""Creates the variables of the head."""
if self._config_dict['use_separable_conv']:
conv_op = SeparableConv2DQuantized
else:
conv_op = helper.quantize_wrapped_layer(
tf.keras.layers.Conv2D,
configs.Default8BitConvQuantizeConfig(
['kernel'], ['activation'], False))
conv_kwargs = {
'filters': self._config_dict['num_filters'],
'kernel_size': 3,
'padding': 'same',
'bias_initializer': tf.zeros_initializer(),
'bias_regularizer': self._config_dict['bias_regularizer'],
}
if not self._config_dict['use_separable_conv']:
conv_kwargs.update({
'kernel_initializer': tf.keras.initializers.RandomNormal(
stddev=0.01),
'kernel_regularizer': self._config_dict['kernel_regularizer'],
})
base_bn_op = (tf.keras.layers.experimental.SyncBatchNormalization
if self._config_dict['use_sync_bn']
else tf.keras.layers.BatchNormalization)
bn_op = helper.norm_by_activation(
self._config_dict['activation'],
helper.quantize_wrapped_layer(
base_bn_op, configs.Default8BitOutputQuantizeConfig()),
helper.quantize_wrapped_layer(
base_bn_op, configs.NoOpQuantizeConfig()))
bn_kwargs = {
'axis': self._bn_axis,
'momentum': self._config_dict['norm_momentum'],
'epsilon': self._config_dict['norm_epsilon'],
}
# Class net.
self._cls_convs = []
self._cls_norms = []
for level in range(
self._config_dict['min_level'], self._config_dict['max_level'] + 1):
this_level_cls_norms = []
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
cls_conv_name = 'classnet-conv_{}'.format(i)
self._cls_convs.append(conv_op(name=cls_conv_name, **conv_kwargs))
cls_norm_name = 'classnet-conv-norm_{}_{}'.format(level, i)
this_level_cls_norms.append(bn_op(name=cls_norm_name, **bn_kwargs))
self._cls_norms.append(this_level_cls_norms)
classifier_kwargs = {
'filters': (
self._config_dict['num_classes'] *
self._config_dict['num_anchors_per_location']),
'kernel_size': 3,
'padding': 'same',
'bias_initializer': tf.constant_initializer(-np.log((1 - 0.01) / 0.01)),
'bias_regularizer': self._config_dict['bias_regularizer'],
}
if not self._config_dict['use_separable_conv']:
classifier_kwargs.update({
'kernel_initializer': tf.keras.initializers.RandomNormal(stddev=1e-5),
'kernel_regularizer': self._config_dict['kernel_regularizer'],
})
self._classifier = conv_op(
name='scores', last_quantize=True, **classifier_kwargs)
# Box net.
self._box_convs = []
self._box_norms = []
for level in range(
self._config_dict['min_level'], self._config_dict['max_level'] + 1):
this_level_box_norms = []
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
box_conv_name = 'boxnet-conv_{}'.format(i)
self._box_convs.append(conv_op(name=box_conv_name, **conv_kwargs))
box_norm_name = 'boxnet-conv-norm_{}_{}'.format(level, i)
this_level_box_norms.append(bn_op(name=box_norm_name, **bn_kwargs))
self._box_norms.append(this_level_box_norms)
box_regressor_kwargs = {
'filters': (self._config_dict['num_params_per_anchor'] *
self._config_dict['num_anchors_per_location']),
'kernel_size': 3,
'padding': 'same',
'bias_initializer': tf.zeros_initializer(),
'bias_regularizer': self._config_dict['bias_regularizer'],
}
if not self._config_dict['use_separable_conv']:
box_regressor_kwargs.update({
'kernel_initializer': tf.keras.initializers.RandomNormal(
stddev=1e-5),
'kernel_regularizer': self._config_dict['kernel_regularizer'],
})
self._box_regressor = conv_op(
name='boxes', last_quantize=True, **box_regressor_kwargs)
# Attribute learning nets.
if self._config_dict['attribute_heads']:
self._att_predictors = {}
self._att_convs = {}
self._att_norms = {}
for att_config in self._config_dict['attribute_heads']:
att_name = att_config['name']
att_type = att_config['type']
att_size = att_config['size']
att_convs_i = []
att_norms_i = []
# Build conv and norm layers.
for level in range(self._config_dict['min_level'],
self._config_dict['max_level'] + 1):
this_level_att_norms = []
for i in range(self._config_dict['num_convs']):
if level == self._config_dict['min_level']:
att_conv_name = '{}-conv_{}'.format(att_name, i)
att_convs_i.append(conv_op(name=att_conv_name, **conv_kwargs))
att_norm_name = '{}-conv-norm_{}_{}'.format(att_name, level, i)
this_level_att_norms.append(bn_op(name=att_norm_name, **bn_kwargs))
att_norms_i.append(this_level_att_norms)
self._att_convs[att_name] = att_convs_i
self._att_norms[att_name] = att_norms_i
# Build the final prediction layer.
att_predictor_kwargs = {
'filters':
(att_size * self._config_dict['num_anchors_per_location']),
'kernel_size': 3,
'padding': 'same',
'bias_initializer': tf.zeros_initializer(),
'bias_regularizer': self._config_dict['bias_regularizer'],
}
if att_type == 'regression':
att_predictor_kwargs.update(
{'bias_initializer': tf.zeros_initializer()})
elif att_type == 'classification':
att_predictor_kwargs.update({
'bias_initializer':
tf.constant_initializer(-np.log((1 - 0.01) / 0.01))
})
else:
raise ValueError(
'Attribute head type {} not supported.'.format(att_type))
if not self._config_dict['use_separable_conv']:
att_predictor_kwargs.update({
'kernel_initializer':
tf.keras.initializers.RandomNormal(stddev=1e-5),
'kernel_regularizer':
self._config_dict['kernel_regularizer'],
})
self._att_predictors[att_name] = conv_op(
name='{}_attributes'.format(att_name), **att_predictor_kwargs)
super().build(input_shape)
def build(self, input_shape):
height = input_shape[1]
width = input_shape[2]
channels = input_shape[3]
norm_layer = (tf.keras.layers.experimental.SyncBatchNormalization
if self._use_sync_bn else
tf.keras.layers.BatchNormalization)
norm_with_quantize = helper.BatchNormalizationQuantized(norm_layer)
norm_no_quantize = helper.BatchNormalizationNoQuantized(norm_layer)
norm = helper.norm_by_activation(self._activation, norm_with_quantize,
norm_no_quantize)
self.aspp_layers = []
conv1 = helper.Conv2DQuantized(
filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
use_bias=False,
activation=helper.NoOpActivation())
norm1 = norm(axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
self.aspp_layers.append([conv1, norm1])
for dilation_rate in self._dilation_rates:
leading_layers = []
kernel_size = (3, 3)
if self._use_depthwise_convolution:
leading_layers += [
helper.DepthwiseConv2DOutputQuantized(
depth_multiplier=1,
kernel_size=kernel_size,
padding='same',
depthwise_regularizer=self._kernel_regularizer,
depthwise_initializer=self._kernel_initializer,
dilation_rate=dilation_rate,
use_bias=False,
activation=helper.NoOpActivation())
]
kernel_size = (1, 1)
conv_dilation = leading_layers + [
helper.Conv2DQuantized(
filters=self._output_channels,
kernel_size=kernel_size,
padding='same',
kernel_regularizer=self._kernel_regularizer,
kernel_initializer=self._kernel_initializer,
dilation_rate=dilation_rate,
use_bias=False,
activation=helper.NoOpActivation())
]
norm_dilation = norm(axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
self.aspp_layers.append(conv_dilation + [norm_dilation])
if self._pool_kernel_size is None:
pooling = [
helper.GlobalAveragePooling2DQuantized(),
helper.ReshapeQuantized((1, 1, channels))
]
else:
pooling = [
helper.AveragePooling2DQuantized(self._pool_kernel_size)
]
conv2 = helper.Conv2DQuantized(
filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
use_bias=False,
activation=helper.NoOpActivation())
norm2 = norm(axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
self.aspp_layers.append(pooling + [conv2, norm2])
self._resizing_layer = helper.ResizingQuantized(
height, width, interpolation=self._interpolation)
self._projection = [
helper.Conv2DQuantized(filters=self._output_channels,
kernel_size=(1, 1),
kernel_initializer=self._kernel_initializer,
kernel_regularizer=self._kernel_regularizer,
use_bias=False,
activation=helper.NoOpActivation()),
norm_with_quantize(axis=self._bn_axis,
momentum=self._batchnorm_momentum,
epsilon=self._batchnorm_epsilon)
]
self._dropout_layer = tf.keras.layers.Dropout(rate=self._dropout)
self._concat_layer = helper.ConcatenateQuantized(axis=-1)
def build(self, input_shape: Sequence[tf.TensorShape]):
"""Creates the variables of the segmentation head."""
# When input_shape is a list/tuple, the first corresponds to backbone
# features used for resizing the decoder features (the second) if feature
# fusion type is `deeplabv3plus`.
backbone_shape = input_shape[0]
use_depthwise_convolution = self._config_dict[
'use_depthwise_convolution']
random_initializer = tf.keras.initializers.RandomNormal(stddev=0.01)
conv_kwargs = {
'kernel_size': 3 if not use_depthwise_convolution else 1,
'padding': 'same',
'use_bias': False,
'kernel_initializer': random_initializer,
'kernel_regularizer': self._config_dict['kernel_regularizer'],
}
norm_layer = (tf.keras.layers.experimental.SyncBatchNormalization
if self._config_dict['use_sync_bn'] else
tf.keras.layers.BatchNormalization)
norm_with_quantize = helper.BatchNormalizationQuantized(norm_layer)
norm_no_quantize = helper.BatchNormalizationNoQuantized(norm_layer)
norm = helper.norm_by_activation(self._config_dict['activation'],
norm_with_quantize, norm_no_quantize)
bn_kwargs = {
'axis': self._bn_axis,
'momentum': self._config_dict['norm_momentum'],
'epsilon': self._config_dict['norm_epsilon'],
}
if self._config_dict['feature_fusion'] == 'deeplabv3plus':
# Deeplabv3+ feature fusion layers.
self._dlv3p_conv = helper.Conv2DQuantized(
kernel_size=1,
padding='same',
use_bias=False,
kernel_initializer=tf.keras.initializers.RandomNormal(
stddev=0.01),
kernel_regularizer=self._config_dict['kernel_regularizer'],
name='segmentation_head_deeplabv3p_fusion_conv',
filters=self._config_dict['low_level_num_filters'],
activation=helper.NoOpActivation())
self._dlv3p_norm = norm(
name='segmentation_head_deeplabv3p_fusion_norm', **bn_kwargs)
# Segmentation head layers.
self._convs = []
self._norms = []
for i in range(self._config_dict['num_convs']):
if use_depthwise_convolution:
self._convs.append(
helper.DepthwiseConv2DQuantized(
name='segmentation_head_depthwise_conv_{}'.format(i),
kernel_size=3,
padding='same',
use_bias=False,
depthwise_initializer=random_initializer,
depthwise_regularizer=self.
_config_dict['kernel_regularizer'],
depth_multiplier=1,
activation=helper.NoOpActivation()))
norm_name = 'segmentation_head_depthwise_norm_{}'.format(i)
self._norms.append(norm(name=norm_name, **bn_kwargs))
conv_name = 'segmentation_head_conv_{}'.format(i)
self._convs.append(
helper.Conv2DQuantized(
name=conv_name,
filters=self._config_dict['num_filters'],
activation=helper.NoOpActivation(),
**conv_kwargs))
norm_name = 'segmentation_head_norm_{}'.format(i)
self._norms.append(norm(name=norm_name, **bn_kwargs))
self._classifier = helper.Conv2DOutputQuantized(
name='segmentation_output',
filters=self._config_dict['num_classes'],
kernel_size=self._config_dict['prediction_kernel_size'],
padding='same',
bias_initializer=tf.zeros_initializer(),
kernel_initializer=tf.keras.initializers.RandomNormal(stddev=0.01),
kernel_regularizer=self._config_dict['kernel_regularizer'],
bias_regularizer=self._config_dict['bias_regularizer'],
activation=helper.NoOpActivation())
self._upsampling_layer = helper.UpSampling2DQuantized(
size=(self._config_dict['upsample_factor'],
self._config_dict['upsample_factor']),
interpolation='nearest')
self._resizing_layer = tf.keras.layers.Resizing(
backbone_shape[1], backbone_shape[2], interpolation='bilinear')
self._concat_layer = helper.ConcatenateQuantized(axis=self._bn_axis)
super().build(input_shape)
def build(self, input_shape: Optional[Union[Sequence[int], tf.Tensor]]):
"""Build variables and child layers to prepare for calling."""
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 = helper.Conv2DQuantized(
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=helper.NoOpActivation())
self._norm0 = helper.norm_by_activation(
self._activation, self._norm_with_quantize,
self._norm)(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.Default8BitActivationQuantizeConfig())
if self._use_depthwise:
# Depthwise conv.
self._conv1 = helper.DepthwiseConv2DQuantized(
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=helper.NoOpActivation())
self._norm1 = helper.norm_by_activation(
self._depthwise_activation, self._norm_with_quantize,
self._norm)(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.Default8BitActivationQuantizeConfig()))
# 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.SqueezeExcitationQuantized(
in_filters=in_filters,
out_filters=expand_filters,
se_ratio=self._se_ratio,
divisible_by=self._divisible_by,
round_down_protect=self._se_round_down_protect,
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 = helper.Conv2DQuantized(
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=helper.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 = tfmot.quantization.keras.QuantizeWrapperV2(
tf.keras.layers.Add(),
configs.Default8BitQuantizeConfig([], [], True))
super(InvertedBottleneckBlockQuantized, self).build(input_shape)