def __init__(self, width, depth, num_classes=20, num_anchors=9, freeze_bn=False, name='class_net', **kwargs):
self.name = name
self.width = width
self.depth = depth
self.num_classes = num_classes
self.num_anchors = num_anchors
options = {
'kernel_size': 3,
'strides': 1,
'padding': 'same',
'depthwise_initializer': initializers.VarianceScaling(),
'pointwise_initializer': initializers.VarianceScaling(),
}
self.convs = [layers.SeparableConv2D(filters=width, bias_initializer='zeros', name=f'{self.name}/class-{i}',
**options)
for i in range(depth)]
self.head = layers.SeparableConv2D(filters=num_classes * num_anchors,
bias_initializer=PriorProbability(probability=0.01),
name=f'{self.name}/class-predict', **options)
self.bns = [
[layers.BatchNormalization(momentum=MOMENTUM, epsilon=EPSILON, name=f'{self.name}/class-{i}-bn-{j}') for j
in range(3, 8)]
for i in range(depth)]
self.relu = layers.Lambda(lambda x: tf.nn.swish(x))
self.reshape = layers.Reshape((-1, num_classes))
self.activation = layers.Activation('sigmoid')
def make_last_layer_cls(num_classes,num_anchors,pyramid_feature_size=256):
inputs = keras.layers.Input(shape=(None, None, pyramid_feature_size))
options = {
'kernel_size' : 3,
'strides' : 1,
'padding' : 'same',
}
classification = []
outputs = inputs
for i in range(4):
outputs = keras.layers.Conv2D(
filters=256,
activation='relu',
name='pyramid_classification_{}'.format(i),
kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),
bias_initializer='zeros',
**options
)(outputs)
outputs = keras.layers.Conv2D(filters=num_classes * num_anchors,
kernel_initializer=keras.initializers.normal(mean=0.0, stddev=0.01, seed=None),
bias_initializer=PriorProbability(probability=0.01),
name='pyramid_classification'.format(),
**options
)(outputs)
outputs = keras.layers.Reshape((-1, num_classes), name='pyramid_classification_reshape')(outputs)
classification = keras.layers.Activation('sigmoid', name='pyramid_classification_sigmoid')(outputs)
classification_model = keras.models.Model(inputs=inputs, outputs=classification, name="classification_submodel")
return classification_model