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nll_compressor.py
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import tensorflow.compat.v1 as tf
import tensorflow_compression as tfc
import numpy as np
# from utils.tools import _debug_func
class ResBlock(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.conv1 = tfc.SignalConv2D(self.num_filters//2, (1, 1), name="conv1", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv2 = tfc.SignalConv2D(self.num_filters//2, (3, 3), name="conv2", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv3 = tfc.SignalConv2D(self.num_filters, (1, 1), name="conv3", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
super().build(input_shape)
def call(self, input):
output = input
output = self.conv1(output)
output = self.conv2(output)
output = self.conv3(output) + input
return output
class AnalysisBlock(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.conv1 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv1", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv2 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv2", corr=True, strides_down=2,
padding="same_zeros", use_bias=True,
activation=tfc.GDN(name="gdn"))
self.conv_sc = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv_sc", corr=True, strides_down=2,
padding="same_zeros", use_bias=True,
activation=None)
self.conv3 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv3", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv4 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv4", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
super().build(input_shape)
def call(self, input):
output = input
output = self.conv1(output)
output = self.conv2(output)
output2 = self.conv_sc(input) + output
output = self.conv3(output2)
output = self.conv4(output)
output = output + output2
return output
class SynthesisBlock(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.conv4 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv4", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv3 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv3", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=tfc.GDN(name="igdn", inverse=True))
self.conv_sc = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv_sc", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=None)
self.conv2 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv2", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
self.conv1 = tfc.SignalConv2D(self.num_filters, (3, 3), name="conv1", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
super().build(input_shape)
def call(self, input):
output = input
output = self.conv1(output)
output = self.conv2(output)
output2 = input + output
output = self.conv3(output2)
output = self.conv4(output)
output = output + self.conv_sc(output2)
return output
class AttentionBlock(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers1 = [
ResBlock(self.num_filters, name="res1_1"),
ResBlock(self.num_filters, name="res1_2"),
ResBlock(self.num_filters, name="res1_3")
]
self.layers2 = [
ResBlock(self.num_filters, name="res2_1"),
ResBlock(self.num_filters, name="res2_2"),
ResBlock(self.num_filters, name="res2_3")
]
self.conv = tfc.SignalConv2D(self.num_filters, (1, 1), name="conv", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=None)
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers1:
output = layer(output)
output2 = input
for layer in self.layers2:
output2 = layer(output2)
output2 = tf.math.sigmoid(self.conv(output2))
output = output * output2 + input
return output
class Encoder(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers = [
AnalysisBlock(self.num_filters, name="analysis1"),
AnalysisBlock(self.num_filters, name="analysis2"),
AttentionBlock(self.num_filters, name="attention1"),
AnalysisBlock(self.num_filters, name="analysis3"),
tfc.SignalConv2D(self.num_filters, (3, 3), name="conv", corr=True, strides_down=2,
padding="same_zeros", use_bias=True,
activation=None),
AttentionBlock(self.num_filters, name="attention2"),
]
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers:
output = layer(output)
return output
class Decoder(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers = [
AttentionBlock(self.num_filters, name="attention1"),
SynthesisBlock(self.num_filters, name="synthesis1"),
SynthesisBlock(self.num_filters, name="synthesis2"),
AttentionBlock(self.num_filters, name="attention2"),
SynthesisBlock(self.num_filters, name="synthesis3"),
]
self.conv_img = tfc.SignalConv2D(3, (3, 3), name="conv_img", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=None)
self.conv_prior = tfc.SignalConv2D(64, (3, 3), name="conv_prior", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=None)
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers:
output = layer(output)
rec_img = self.conv_img(output)
prior = self.conv_prior(output)
return rec_img, prior
class HyperEncoder(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers = [
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_0", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_1", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_2", corr=True, strides_down=2,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_3", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_4", corr=True, strides_down=2,
padding="same_zeros", use_bias=True,
activation=None),
]
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers:
output = layer(output)
return output
class HyperDecoder(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers = [
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_0", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_1", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_2", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_3", corr=False, strides_up=2,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (3, 3), name="layer_4", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=None),
]
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers:
output = layer(output)
return output
class EntropyParameters(tf.keras.layers.Layer):
def __init__(self, num_filters, *args, **kwargs):
self.num_filters = num_filters
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers = [
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_0", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_1", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_2", corr=True, strides_down=1,
padding="same_zeros", use_bias=True,
activation=None),
]
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers:
output = layer(output)
return output
class TailBlock(tf.keras.layers.Layer):
def __init__(self, num_filters, num_mixtures, *args, **kwargs):
self.num_filters = num_filters
self.num_mixtures = num_mixtures
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.layers1 = [
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_0", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_1", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu)
]
self.layers2 = [
tfc.SignalConv2D(self.num_filters, (1, 1), name="layer_2", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=tf.nn.leaky_relu),
tfc.SignalConv2D(self.num_mixtures * 3, (1, 1), name="layer_3", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=None)
]
self.conv_sc = tfc.SignalConv2D(self.num_filters, (1, 1), name="conv_sc", corr=False, strides_up=1,
padding="same_zeros", use_bias=True,
activation=None)
super().build(input_shape)
def call(self, input):
output = input
for layer in self.layers1:
output = layer(output)
output += self.conv_sc(input)
for layer in self.layers2:
output = layer(output)
return output
class MaskedConv2d(tf.keras.layers.Layer):
def __init__(self,
mask_type,
num_filters,
kernel_size,
strides=(1, 1),
padding="SAME",
use_bias=True,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
*args,
**kwargs):
assert mask_type == 'A' or mask_type == 'B'
self.mask_type = mask_type
self.num_filters = num_filters
self.kernel_size = kernel_size
self.strides = strides
self.padding = padding
self.kernel_initializer = kernel_initializer
self.bias_initializer = bias_initializer
self.use_bias = use_bias
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.weight = self.add_weight(shape=(self.kernel_size[0], self.kernel_size[1], input_shape[-1], self.num_filters),
initializer=self.kernel_initializer,
trainable=True)
if self.use_bias:
self.bias = self.add_weight(shape=(self.num_filters,), initializer=self.bias_initializer, trainable=True)
mask = np.zeros(shape=(self.kernel_size[0], self.kernel_size[1], input_shape[-1], self.num_filters), dtype=np.float32)
kh = self.kernel_size[0]
kw = self.kernel_size[1]
mask[:kh//2, :, :, :] = 1
mask[kh//2, :kw//2, :, :] = 1
if self.mask_type == "B":
mask[kh//2, kw//2, :, :] = 1
self.mask = tf.constant(mask, dtype=tf.float32)
super().build(input_shape)
def call(self, input):
out = tf.nn.conv2d(input, self.weight * self.mask, self.strides, self.padding)
if self.use_bias:
out = tf.nn.bias_add(out, self.bias)
return out
class ResidualCompressor(tf.keras.layers.Layer):
def __init__(self, num_filters, num_mixtures, *args, **kwargs):
self.num_filters = num_filters
self.num_mixtures = num_mixtures
super().__init__(*args, **kwargs)
def build(self, input_shape):
self.tail_mu = TailBlock(self.num_filters, self.num_mixtures, name="tail_mu")
self.tail_log_sigma = TailBlock(self.num_filters, self.num_mixtures, name="tail_log_sigma")
self.tail_pi = TailBlock(self.num_filters, self.num_mixtures, name="tail_pi")
self.tail_lambda = TailBlock(self.num_filters, self.num_mixtures, name="tail_lambda")
super().build(input_shape)
def call(self, input):
mu = self.tail_mu(input)
log_sigma = self.tail_log_sigma(input)
pi = self.tail_pi(input)
lamda = self.tail_lambda(input)
return mu, log_sigma, pi, lamda