def Conv2D(self, filters, kernel_size, **kwargs):
"""Builds a Conv2D layer according to the current Object Detection config.
Overrides the Keras MobileNetV1 application's convolutions with ones that
follow the spec specified by the Object Detection hyperparameters.
Args:
filters: The number of filters to use for the convolution.
kernel_size: The kernel size to specify the height and width of the 2D
convolution window. In this function, the kernel size is expected to
be pair of numbers and the numbers must be equal for this function.
**kwargs: Keyword args specified by the Keras application for
constructing the convolution.
Returns:
A one-arg callable that will either directly apply a Keras Conv2D layer to
the input argument, or that will first pad the input then apply a Conv2D
layer.
Raises:
ValueError: if kernel size is not a pair of equal
integers (representing a square kernel).
"""
if not isinstance(kernel_size, tuple):
raise ValueError('kernel is expected to be a tuple.')
if len(kernel_size) != 2:
raise ValueError('kernel is expected to be length two.')
if kernel_size[0] != kernel_size[1]:
raise ValueError('kernel is expected to be square.')
layer_name = kwargs['name']
if self._conv_defs:
conv_filters = model_utils.get_conv_def(self._conv_defs,
layer_name)
if conv_filters:
filters = conv_filters
# Apply the width multiplier and the minimum depth to the convolution layers
filters = int(filters * self._alpha)
if self._min_depth and filters < self._min_depth:
filters = self._min_depth
if self._conv_hyperparams:
kwargs = self._conv_hyperparams.params(**kwargs)
else:
kwargs['kernel_regularizer'] = self.regularizer
kwargs['kernel_initializer'] = self.initializer
kwargs['padding'] = 'same'
if self._use_explicit_padding and kernel_size[0] > 1:
kwargs['padding'] = 'valid'
def padded_conv(features): # pylint: disable=invalid-name
padded_features = self._FixedPaddingLayer(kernel_size)(
features)
return tf.keras.layers.Conv2D(filters, kernel_size,
**kwargs)(padded_features)
return padded_conv
else:
return tf.keras.layers.Conv2D(filters, kernel_size, **kwargs)
def Conv2D(self, filters, **kwargs):
"""Builds a Conv2D layer according to the current Object Detection config.
Overrides the Keras MobileNetV2 application's convolutions with ones that
follow the spec specified by the Object Detection hyperparameters.
Args:
filters: The number of filters to use for the convolution.
**kwargs: Keyword args specified by the Keras application for
constructing the convolution.
Returns:
A one-arg callable that will either directly apply a Keras Conv2D layer to
the input argument, or that will first pad the input then apply a Conv2D
layer.
"""
# Make sure 'alpha' is always applied to the last convolution block's size
# (This overrides the Keras application's functionality)
layer_name = kwargs.get('name')
if layer_name == 'Conv_1':
if self._conv_defs:
filters = model_utils.get_conv_def(self._conv_defs, 'Conv_1')
else:
filters = 1280
if self._alpha < 1.0:
filters = _make_divisible(filters * self._alpha, 8)
# Apply the minimum depth to the convolution layers
if (self._min_depth and (filters < self._min_depth)
and not kwargs.get('name').endswith('expand')):
filters = self._min_depth
if self._conv_hyperparams:
kwargs = self._conv_hyperparams.params(**kwargs)
else:
kwargs['kernel_regularizer'] = self.regularizer
kwargs['kernel_initializer'] = self.initializer
kwargs['padding'] = 'same'
kernel_size = kwargs.get('kernel_size')
if self._use_explicit_padding and kernel_size > 1:
kwargs['padding'] = 'valid'
def padded_conv(features):
padded_features = self._FixedPaddingLayer(kernel_size)(
features)
return tf.keras.layers.Conv2D(filters,
**kwargs)(padded_features)
return padded_conv
else:
return tf.keras.layers.Conv2D(filters, **kwargs)
def Conv2D(self, filters, kernel_size, **kwargs):
"""Builds a Conv2D layer according to the current Object Detection config.
Overrides the Keras MobileNetV1 application's convolutions with ones that
follow the spec specified by the Object Detection hyperparameters.
Args:
filters: The number of filters to use for the convolution.
kernel_size: The kernel size to specify the height and width of the 2D
convolution window.
**kwargs: Keyword args specified by the Keras application for
constructing the convolution.
Returns:
A one-arg callable that will either directly apply a Keras Conv2D layer to
the input argument, or that will first pad the input then apply a Conv2D
layer.
"""
layer_name = kwargs['name']
if self._conv_defs:
conv_filters = model_utils.get_conv_def(self._conv_defs,
layer_name)
if conv_filters:
filters = conv_filters
# Apply the width multiplier and the minimum depth to the convolution layers
filters = int(filters * self._alpha)
if self._min_depth and filters < self._min_depth:
filters = self._min_depth
if self._conv_hyperparams:
kwargs = self._conv_hyperparams.params(**kwargs)
else:
kwargs['kernel_regularizer'] = self.regularizer
kwargs['kernel_initializer'] = self.initializer
kwargs['padding'] = 'same'
if self._use_explicit_padding and kernel_size > 1:
kwargs['padding'] = 'valid'
def padded_conv(features): # pylint: disable=invalid-name
padded_features = self._FixedPaddingLayer(kernel_size)(
features)
return tf.keras.layers.Conv2D(filters, kernel_size,
**kwargs)(padded_features)
return padded_conv
else:
return tf.keras.layers.Conv2D(filters, kernel_size, **kwargs)