77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import numpy as np
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import tensorflow as tf
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# the custom cuda compiled fused_bias_act is not compatible with tf.GradientTape()
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# I think it's because of 2nd order gradient computation
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class BiasAct(tf.keras.layers.Layer):
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def __init__(self, lrmul, act, **kwargs):
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super(BiasAct, self).__init__(**kwargs)
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assert act in ['linear', 'lrelu', 'tanh']
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self.lrmul = lrmul
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if act == 'linear':
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self.act = tf.keras.layers.Lambda(lambda x: tf.identity(x))
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self.gain = 1.0
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elif act == 'tanh':
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self.act = tf.keras.layers.Activation('tanh')
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self.gain = 1.0
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else:
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self.act = tf.keras.layers.LeakyReLU(alpha=0.2)
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self.gain = np.sqrt(2)
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def build(self, input_shape):
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self.len2 = True if len(input_shape) == 2 else False
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b_init = tf.zeros(shape=(input_shape[1],), dtype=tf.dtypes.float32)
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self.b = tf.Variable(b_init, name='b', trainable=True)
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def call(self, inputs, training=None, mask=None):
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b = self.lrmul * self.b
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if self.len2:
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x = inputs + b
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else:
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x = inputs + tf.reshape(b, shape=[1, -1, 1, 1])
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x = self.act(x)
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x = self.gain * x
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return x
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def get_config(self):
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config = super(BiasAct, self).get_config()
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config.update({
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'lrmul': self.lrmul,
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'gain': self.gain,
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'len2': self.len2,
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})
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return config
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# import tensorflow as tf
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#
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#
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# from stylegan2.layers.cuda.fused_bias_act import fused_bias_act
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#
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#
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# class BiasAct(tf.keras.layers.Layer):
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# def __init__(self, lrmul, act, **kwargs):
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# super(BiasAct, self).__init__(**kwargs)
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# self.lrmul = lrmul
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# self.act = act
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#
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# def build(self, input_shape):
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# b_init = tf.zeros(shape=(input_shape[1],), dtype=tf.dtypes.float32)
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# self.b = tf.Variable(b_init, name='b', trainable=True)
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#
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# def call(self, inputs, training=None, mask=None):
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# b = self.lrmul * self.b
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# x = fused_bias_act(inputs, b=b, act=self.act, alpha=None, gain=None)
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# return x
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#
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# def get_config(self):
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# config = super(BiasAct, self).get_config()
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# config.update({
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# 'lrmul': self.lrmul,
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# 'act': self.act,
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# })
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# return config
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