| from core.leras import nn | |
| tf = nn.tf | |
| class InstanceNorm2D(nn.LayerBase): | |
| def __init__(self, in_ch, dtype=None, **kwargs): | |
| self.in_ch = in_ch | |
| if dtype is None: | |
| dtype = nn.floatx | |
| self.dtype = dtype | |
| super().__init__(**kwargs) | |
| def build_weights(self): | |
| kernel_initializer = tf.initializers.glorot_uniform(dtype=self.dtype) | |
| self.weight = tf.get_variable("weight", (self.in_ch,), dtype=self.dtype, initializer=kernel_initializer ) | |
| self.bias = tf.get_variable("bias", (self.in_ch,), dtype=self.dtype, initializer=tf.initializers.zeros() ) | |
| def get_weights(self): | |
| return [self.weight, self.bias] | |
| def forward(self, x): | |
| if nn.data_format == "NHWC": | |
| shape = (1,1,1,self.in_ch) | |
| else: | |
| shape = (1,self.in_ch,1,1) | |
| weight = tf.reshape ( self.weight , shape ) | |
| bias = tf.reshape ( self.bias , shape ) | |
| x_mean = tf.reduce_mean(x, axis=nn.conv2d_spatial_axes, keepdims=True ) | |
| x_std = tf.math.reduce_std(x, axis=nn.conv2d_spatial_axes, keepdims=True ) + 1e-5 | |
| x = (x - x_mean) / x_std | |
| x *= weight | |
| x += bias | |
| return x | |
| nn.InstanceNorm2D = InstanceNorm2D |