group normalization with moving average
import tensorflow as tf
import numpy as np
group namalization implementation
def norm(x, norm_type, is_train, G=32, esp=1e-5):
with tf.variable_scope('{}_norm'.format(norm_type)):
if norm_type == 'none':
output = x
elif norm_type == 'batch':
output = tf.contrib.layers.batch_norm(
x, center=True, scale=True, decay=0.999,
is_training=is_train, updates_collections=None
)
elif norm_type == 'group':
# normalize
# tranpose: [bs, h, w, c] to [bs, c, h, w] following the paper
x = tf.transpose(x, [0, 3, 1, 2])
N, C, H, W = x.get_shape().as_list()
G = min(G, C)
x = tf.reshape(x, [N, G, C // G, H, W])
mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
x = (x - mean) / tf.sqrt(var + esp)
# per channel gamma and beta
gamma = tf.get_variable('gamma', [C],
initializer=tf.constant_initializer(1.0))
beta = tf.get_variable('beta', [C],
initializer=tf.constant_initializer(0.0))
gamma = tf.reshape(gamma, [1, C, 1, 1])
beta = tf.reshape(beta, [1, C, 1, 1])
output = tf.reshape(x, [N, C, H, W]) * gamma + beta
# tranpose: [bs, c, h, w, c] to [bs, h, w, c] following the paper
output = tf.transpose(output, [0, 2, 3, 1])
else:
raise NotImplementedError
return output
构建数据
input_x = np.arange(180).reshape([2,3,3,10]) # [bs=2, h=3, w=3, c=2]
input_x
array([[[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
[ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[ 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]],
[[ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39],
[ 40, 41, 42, 43, 44, 45, 46, 47, 48, 49],
[ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]],
[[ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69],
[ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79],
[ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]]],
[[[ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99],
[100, 101, 102, 103, 104, 105, 106, 107, 108, 109],
[110, 111, 112, 113, 114, 115, 116, 117, 118, 119]],
[[120, 121, 122, 123, 124, 125, 126, 127, 128, 129],
[130, 131, 132, 133, 134, 135, 136, 137, 138, 139],
[140, 141, 142, 143, 144, 145, 146, 147, 148, 149]],
[[150, 151, 152, 153, 154, 155, 156, 157, 158, 159],
[160, 161, 162, 163, 164, 165, 166, 167, 168, 169],
[170, 171, 172, 173, 174, 175, 176, 177, 178, 179]]]])
input_x = tf.Variable(input_x,dtype=tf.float32)
input_x
<tf.Variable 'Variable:0' shape=(2, 3, 3, 10) dtype=float32_ref>
tranpose: [bs, h, w, c] to [bs, c, h, w] following the paper
x = tf.transpose(input_x, [0, 3, 1, 2])
N, C, H, W = x.get_shape().as_list()
print(N,C,H,W)
2 10 3 3
G = 5
G = min(G, C)
G
5
x = tf.reshape(x, [N, G, C // G, H, W])
x
<tf.Tensor 'Reshape_1:0' shape=(2, 5, 2, 3, 3) dtype=float32>
mean, var = tf.nn.moments(x, [2, 3, 4], keep_dims=True)
print('mean:',mean)
print('var:',var)
mean: Tensor("moments/mean:0", shape=(2, 5, 1, 1, 1), dtype=float32)
var: Tensor("moments/variance:0", shape=(2, 5, 1, 1, 1), dtype=float32)
esp=1e-5
x = (x - mean) / tf.sqrt(var + esp)
x
<tf.Tensor 'truediv:0' shape=(2, 5, 2, 3, 3) dtype=float32>
per channel gamma and beta
gamma = tf.get_variable('gamma', [C],
initializer=tf.constant_initializer(1.0))
beta = tf.get_variable('beta', [C],
initializer=tf.constant_initializer(0.0))
gamma = tf.reshape(gamma, [1, C, 1, 1])
beta = tf.reshape(beta, [1, C, 1, 1])
print('gamma:',gamma)
print('beta:',beta)
gamma: Tensor("Reshape_2:0", shape=(1, 10, 1, 1), dtype=float32)
beta: Tensor("Reshape_3:0", shape=(1, 10, 1, 1), dtype=float32)
output = tf.reshape(x, [N, C, H, W]) * gamma + beta
output
<tf.Tensor 'add_1:0' shape=(2, 10, 3, 3) dtype=float32>
tranpose: [bs, c, h, w, c] to [bs, h, w, c] following the paper
output = tf.transpose(output, [0, 2, 3, 1])
output
<tf.Tensor 'transpose_1:0' shape=(2, 3, 3, 10) dtype=float32>
