def init_from_pretrained_ssd(self, weights_path):
self.ssd.load_weights(weights_path, by_name=True)
for layer in self.ssd.layers:
if layer in self.classification_headers or layer in self.regression_headers:
if type(layer) == Sequential:
for sub_layer in layer.layers:
w = sub_layer.get_weights()
if w:
if len(w) == 2: # Layer has bias
new_weights = []
new_weights.append(glorot_uniform()(w[0].shape))
new_weights.append(Zeros()(w[1].shape))
elif len(w) == 4:
new_weights = []
new_weights.append(Ones()(w[0].shape))
new_weights.append(Zeros()(w[1].shape))
new_weights.append(Zeros()(w[2].shape))
new_weights.append(Ones()(w[3].shape))
else:
new_weights = glorot_uniform()(w.shape)
sub_layer.set_weights(new_weights)
else:
w = layer.get_weights()
if len(w) > 1: # Layer has bias
new_weights = []
new_weights.append(glorot_uniform()(w[0].shape))
new_weights.append(Zeros()(w[1].shape))
else:
new_weights = glorot_uniform()(w.shape)
layer.set_weights(new_weights)
def build(self, input_shape):
self.gamma = self.add_weight(name='gamma',
shape=input_shape[-1:],
initializer=Ones(),
trainable=True)
self.beta = self.add_weight(name='beta',
shape=input_shape[-1:],
initializer=Zeros(),
trainable=True)
super(LayerNormalization, self).build(input_shape)