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CNN_img.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import tensorflow_compression as tfc
def MV_analysis(tensor, num_filters, M):
"""Builds the analysis transform."""
with tf.variable_scope("MV_analysis"):
with tf.variable_scope("layer_0"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_1"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_2"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_3"):
layer = tfc.SignalConv2D(
M, (3, 3), corr=True, strides_down=2, padding="same_zeros",
use_bias=False, activation=None)
tensor = layer(tensor)
return tensor
def MV_synthesis(tensor, num_filters):
"""Builds the synthesis transform."""
with tf.variable_scope("MV_synthesis"):
with tf.variable_scope("layer_0"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_1"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_2"):
layer = tfc.SignalConv2D(
num_filters, (3, 3), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_3"):
layer = tfc.SignalConv2D(
2, (3, 3), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=None)
tensor = layer(tensor)
return tensor
def Res_analysis(tensor, num_filters, M, reuse=False):
"""Builds the analysis transform."""
with tf.variable_scope("analysis", reuse=reuse):
with tf.variable_scope("layer_0"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_1"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_2"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=True, strides_down=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN())
tensor = layer(tensor)
with tf.variable_scope("layer_3"):
layer = tfc.SignalConv2D(
M, (5, 5), corr=True, strides_down=2, padding="same_zeros",
use_bias=False, activation=None)
tensor = layer(tensor)
return tensor
def Res_synthesis(tensor, num_filters, reuse=False):
"""Builds the synthesis transform."""
with tf.variable_scope("synthesis", reuse=reuse):
with tf.variable_scope("layer_0"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_1"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_2"):
layer = tfc.SignalConv2D(
num_filters, (5, 5), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=tfc.GDN(inverse=True))
tensor = layer(tensor)
with tf.variable_scope("layer_3"):
layer = tfc.SignalConv2D(
3, (5, 5), corr=False, strides_up=2, padding="same_zeros",
use_bias=True, activation=None)
tensor = layer(tensor)
return tensor