clean-up import in ResNet

examples/ResNet/imagenet-resnet.py

@@ -2,7 +2,6 @@
 # -*- coding: UTF-8 -*-
 # File: imagenet-resnet.py
 
-import cv2
 import sys
 import argparse
 import numpy as np
@@ -10,9 +9,8 @@ import os
 import multiprocessing
 
 import tensorflow as tf
-from tensorflow.contrib.layers import variance_scaling_initializer
+
 from tensorpack import *
-from tensorpack.utils.stats import RatioCounter
 from tensorpack.tfutils.symbolic_functions import *
 from tensorpack.tfutils.summary import *
 
@@ -24,6 +22,13 @@ TOTAL_BATCH_SIZE = 256
 INPUT_SHAPE = 224
 DEPTH = None
 
+RESNET_CONFIG = {
+    18: ([2, 2, 2, 2], resnet_basicblock),
+    34: ([3, 4, 6, 3], resnet_basicblock),
+    50: ([3, 4, 6, 3], resnet_bottleneck),
+    101: ([3, 4, 23, 3], resnet_bottleneck)
+}
+
 
 class Model(ModelDesc):
     def __init__(self, data_format='NCHW'):
@@ -46,23 +51,16 @@ class Model(ModelDesc):
 
         if self.data_format == 'NCHW':
             image = tf.transpose(image, [0, 3, 1, 2])
-
-        cfg = {
-            18: ([2, 2, 2, 2], resnet_basicblock),
-            34: ([3, 4, 6, 3], resnet_basicblock),
-            50: ([3, 4, 6, 3], resnet_bottleneck),
-            101: ([3, 4, 23, 3], resnet_bottleneck)
-        }
-        defs, block_func = cfg[DEPTH]
+        defs, block_func = RESNET_CONFIG[DEPTH]
 
         with argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm], data_format=self.data_format):
             logits = resnet_backbone(image, defs, block_func)
 
         loss = compute_loss_and_error(logits, label)
 
-        wd_cost = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
-        add_moving_summary(loss, wd_cost)
-        self.cost = tf.add_n([loss, wd_cost], name='cost')
+        wd_loss = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
+        add_moving_summary(loss, wd_loss)
+        self.cost = tf.add_n([loss, wd_loss], name='cost')
 
     def _get_optimizer(self):
         lr = get_scalar_var('learning_rate', 0.1, summary=True)