just code

1 files changed, 150 insertions, 0 deletions

diff --git a/nets.py b/nets.py
new file mode 100644
index 0000000..70ea3e9
--- /dev/null
+++ b/nets.py
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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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