add API for using calling inference_small with a config

resnet.py

@@ -49,8 +49,8 @@ def inference(x, is_training,
         c['conv_filters_out'] = 64
         c['ksize'] = 7
         c['stride'] = 2
-        x = _conv(x, c)
-        x = _bn(x, c)
+        x = conv(x, c)
+        x = bn(x, c)
         x = activation(x)
 
     with tf.variable_scope('scale2'):
@@ -81,7 +81,7 @@ def inference(x, is_training,
 
     if num_classes != None:
         with tf.variable_scope('fc'):
-            x = _fc(x, c)
+            x = fc(x, c)
 
     return x
 
@@ -94,22 +94,25 @@ def inference_small(x,
                     use_bias=False, # defaults to using batch norm
                     num_classes=10):
     c = Config()
-    c['bottleneck'] = False
     c['is_training'] = tf.convert_to_tensor(is_training,
                                             dtype='bool',
                                             name='is_training')
-    c['ksize'] = 3
-    c['stride'] = 1
     c['use_bias'] = use_bias
     c['fc_units_out'] = num_classes
     c['num_blocks'] = num_blocks
+    c['num_classes'] = num_classes
+    inference_small_config(x, c)
 
+def inference_small_config(x, c):
+    c['bottleneck'] = False
+    c['ksize'] = 3
+    c['stride'] = 1
     with tf.variable_scope('scale1'):
         c['conv_filters_out'] = 16
         c['block_filters_internal'] = 16
         c['stack_stride'] = 1
-        x = _conv(x, c)
-        x = _bn(x, c)
+        x = conv(x, c)
+        x = bn(x, c)
         x = activation(x)
         x = stack(x, c)
 
@@ -126,9 +129,9 @@ def inference_small(x,
     # post-net
     x = tf.reduce_mean(x, reduction_indices=[1, 2], name="avg_pool")
 
-    if num_classes != None:
+    if c['num_classes'] != None:
         with tf.variable_scope('fc'):
-            x = _fc(x, c)
+            x = fc(x, c)
 
     return x
 
@@ -180,48 +183,48 @@ def block(x, c):
         with tf.variable_scope('a'):
             c['ksize'] = 1
             c['stride'] = c['block_stride']
-            x = _conv(x, c)
-            x = _bn(x, c)
+            x = conv(x, c)
+            x = bn(x, c)
             x = activation(x)
 
         with tf.variable_scope('b'):
-            x = _conv(x, c)
-            x = _bn(x, c)
+            x = conv(x, c)
+            x = bn(x, c)
             x = activation(x)
 
         with tf.variable_scope('c'):
             c['conv_filters_out'] = filters_out
             c['ksize'] = 1
             assert c['stride'] == 1
-            x = _conv(x, c)
-            x = _bn(x, c)
+            x = conv(x, c)
+            x = bn(x, c)
     else:
         with tf.variable_scope('A'):
             c['stride'] = c['block_stride']
             assert c['ksize'] == 3
-            x = _conv(x, c)
-            x = _bn(x, c)
+            x = conv(x, c)
+            x = bn(x, c)
             x = activation(x)
 
         with tf.variable_scope('B'):
             c['conv_filters_out'] = filters_out
             assert c['ksize'] == 3
             assert c['stride'] == 1
-            x = _conv(x, c)
-            x = _bn(x, c)
+            x = conv(x, c)
+            x = bn(x, c)
 
     with tf.variable_scope('shortcut'):
         if filters_out != filters_in or c['block_stride'] != 1:
             c['ksize'] = 1
             c['stride'] = c['block_stride']
             c['conv_filters_out'] = filters_out
-            shortcut = _conv(shortcut, c)
-            shortcut = _bn(shortcut, c)
+            shortcut = conv(shortcut, c)
+            shortcut = bn(shortcut, c)
 
     return activation(x + shortcut)
 
 
-def _bn(x, c):
+def bn(x, c):
     x_shape = x.get_shape()
     params_shape = x_shape[-1:]
 
@@ -268,7 +271,7 @@ def _bn(x, c):
     return x
 
 
-def _fc(x, c):
+def fc(x, c):
     num_units_in = x.get_shape()[1]
     num_units_out = c['fc_units_out']
     weights_initializer = tf.truncated_normal_initializer(
@@ -307,7 +310,7 @@ def _get_variable(name,
                            trainable=trainable)
 
 
-def _conv(x, c):
+def conv(x, c):
     ksize = c['ksize']
     stride = c['stride']
     filters_out = c['conv_filters_out']