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## -*- coding: utf-8 -*-
import tensorflow as tf
from utils import BatchNorm, Conv3D
stp = [[0,0], [1,1], [1,1], [1,1], [0,0]]
sp = [[0,0], [0,0], [1,1], [1,1], [0,0]]
def FR_16L(x, is_train):
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,3,64], [1,1,1,1,1], 'VALID', name='conv1')
F = 64
G = 32
for r in range(3):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, stp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x, t], 4)
F += G
for r in range(3,6):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, sp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x[:,1:-1], t], 4)
F += G
x = BatchNorm(x, is_train, name='fbn1')
x = tf.nn.relu(x)
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,256,256], [1,1,1,1,1], 'VALID', name='conv2')
x = tf.nn.relu(x)
r = Conv3D(x, [1,1,1,256,256], [1,1,1,1,1], 'VALID', name='rconv1')
r = tf.nn.relu(r)
r = Conv3D(r, [1,1,1,256,3*16], [1,1,1,1,1], 'VALID', name='rconv2')
f = Conv3D(x, [1,1,1,256,512], [1,1,1,1,1], 'VALID', name='fconv1')
f = tf.nn.relu(f)
f = Conv3D(f, [1,1,1,512,1*5*5*16], [1,1,1,1,1], 'VALID', name='fconv2')
ds_f = tf.shape(f)
f = tf.reshape(f, [ds_f[0], ds_f[1], ds_f[2], ds_f[3], 25, 16])
f = tf.nn.softmax(f, dim=4)
return f, r
def FR_28L(x, is_train):
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,3,64], [1,1,1,1,1], 'VALID', name='conv1')
F = 64
G = 16
for r in range(9):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, stp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x, t], 4)
F += G
for r in range(9,12):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, sp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x[:,1:-1], t], 4)
F += G
x = BatchNorm(x, is_train, name='fbn1')
x = tf.nn.relu(x)
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,256,256], [1,1,1,1,1], 'VALID', name='conv2')
x = tf.nn.relu(x)
r = Conv3D(x, [1,1,1,256,256], [1,1,1,1,1], 'VALID', name='rconv1')
r = tf.nn.relu(r)
r = Conv3D(r, [1,1,1,256,3*16], [1,1,1,1,1], 'VALID', name='rconv2')
f = Conv3D(x, [1,1,1,256,512], [1,1,1,1,1], 'VALID', name='fconv1')
f = tf.nn.relu(f)
f = Conv3D(f, [1,1,1,512,1*5*5*16], [1,1,1,1,1], 'VALID', name='fconv2')
ds_f = tf.shape(f)
f = tf.reshape(f, [ds_f[0], ds_f[1], ds_f[2], ds_f[3], 25, 16])
f = tf.nn.softmax(f, dim=4)
return f, r
def FR_52L(x, is_train):
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,3,64], [1,1,1,1,1], 'VALID', name='conv1')
F = 64
G = 16
for r in range(0,21):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, stp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x, t], 4)
F += G
for r in range(21,24):
t = BatchNorm(x, is_train, name='Rbn'+str(r+1)+'a')
t = tf.nn.relu(t)
t = Conv3D(t, [1,1,1,F,F], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'a')
t = BatchNorm(t, is_train, name='Rbn'+str(r+1)+'b')
t = tf.nn.relu(t)
t = Conv3D(tf.pad(t, sp, mode='CONSTANT'), [3,3,3,F,G], [1,1,1,1,1], 'VALID', name='Rconv'+str(r+1)+'b')
x = tf.concat([x[:,1:-1], t], 4)
F += G
x = BatchNorm(x, is_train, name='fbn1')
x = tf.nn.relu(x)
x = Conv3D(tf.pad(x, sp, mode='CONSTANT'), [1,3,3,448,256], [1,1,1,1,1], 'VALID', name='conv2')
x = tf.nn.relu(x)
r = Conv3D(x, [1,1,1,256,256], [1,1,1,1,1], 'VALID', name='rconv1')
r = tf.nn.relu(r)
r = Conv3D(r, [1,1,1,256,3*16], [1,1,1,1,1], 'VALID', name='rconv2')
f = Conv3D(x, [1,1,1,256,512], [1,1,1,1,1], 'VALID', name='fconv1')
f = tf.nn.relu(f)
f = Conv3D(f, [1,1,1,512,1*5*5*16], [1,1,1,1,1], 'VALID', name='fconv2')
ds_f = tf.shape(f)
f = tf.reshape(f, [ds_f[0], ds_f[1], ds_f[2], ds_f[3], 25, 16])
f = tf.nn.softmax(f, dim=4)
return f, r
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