remove duplicate code in svhn-resnet

examples/DisturbLabel/svhn-disturb.py

@@ -14,7 +14,8 @@ from tensorpack.tfutils.summary import *
 from disturb import DisturbLabel
 import imp
 
-svhn_example = imp.load_source('svhn_example', '../svhn-digit-convnet.py')
+svhn_example = imp.load_source('svhn_example',
+        os.path.join(os.path.dirname(__file__), '..', 'svhn-digit-convnet.py')))
 Model = svhn_example.Model
 get_config = svhn_example.get_config
 

examples/ResNet/svhn-resnet.py

@@ -18,98 +18,12 @@ Reach 1.8% validation error after 70 epochs, with 2 TitanX. 2it/s.
 You might need to adjust the learning rate schedule when running with 1 GPU.
 """
 
-BATCH_SIZE = 128
-
-class Model(ModelDesc):
-    def __init__(self, n):
-        super(Model, self).__init__()
-        self.n = n
-
-    def _get_input_vars(self):
-        return [InputVar(tf.float32, [None, 32, 32, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')
-               ]
-
-    def _build_graph(self, input_vars, is_training):
-        image, label = input_vars
-        image = image / 128.0 - 1
-
-        def conv(name, l, channel, stride):
-            return Conv2D(name, l, channel, 3, stride=stride,
-                          nl=tf.identity, use_bias=False,
-                          W_init=tf.random_normal_initializer(stddev=np.sqrt(2.0/9/channel)))
-
-        def residual(name, l, increase_dim=False, first=False):
-            shape = l.get_shape().as_list()
-            in_channel = shape[3]
-
-            if increase_dim:
-                out_channel = in_channel * 2
-                stride1 = 2
-            else:
-                out_channel = in_channel
-                stride1 = 1
-
-            with tf.variable_scope(name) as scope:
-                if not first:
-                    b1 = BatchNorm('bn1', l, is_training)
-                    b1 = tf.nn.relu(b1)
-                else:
-                    b1 = l
-                c1 = conv('conv1', b1, out_channel, stride1)
-                b2 = BatchNorm('bn2', c1, is_training)
-                b2 = tf.nn.relu(b2)
-                c2 = conv('conv2', b2, out_channel, 1)
-
-                if increase_dim:
-                    l = AvgPooling('pool', l, 2)
-                    l = tf.pad(l, [[0,0], [0,0], [0,0], [in_channel//2, in_channel//2]])
+import imp
+cifar_example = imp.load_source('cifar_example',
+        os.path.join(os.path.dirname(__file__), 'cifar10-resnet.py'))
+Model = cifar_example.Model
 
-                l = c2 + l
-                return l
-
-        l = conv('conv0', image, 16, 1)
-        l = BatchNorm('bn0', l, is_training)
-        l = tf.nn.relu(l)
-        l = residual('res1.0', l, first=True)
-        for k in range(1, self.n):
-            l = residual('res1.{}'.format(k), l)
-        # 32,c=16
-
-        l = residual('res2.0', l, increase_dim=True)
-        for k in range(1, self.n):
-            l = residual('res2.{}'.format(k), l)
-        # 16,c=32
-
-        l = residual('res3.0', l, increase_dim=True)
-        for k in range(1, self.n):
-            l = residual('res3.' + str(k), l)
-        l = BatchNorm('bnlast', l, is_training)
-        l = tf.nn.relu(l)
-        # 8,c=64
-        l = GlobalAvgPooling('gap', l)
-        logits = FullyConnected('linear', l, out_dim=10, nl=tf.identity)
-        prob = tf.nn.softmax(logits, name='output')
-
-        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
-        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
-
-        wrong = prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
-        # monitor training error
-        tf.add_to_collection(
-            MOVING_SUMMARY_VARS_KEY, tf.reduce_mean(wrong, name='train_error'))
-
-        # weight decay on all W of fc layers
-        #wd_cost = regularize_cost('.*/W', l2_regularizer(0.0002), name='regularize_loss')
-        wd_w = tf.train.exponential_decay(0.0001, get_global_step_var(),
-                                          960000, 0.5, True)
-        wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='wd_cost')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
-
-        add_param_summary([('.*/W', ['histogram'])])   # monitor W
-        self.cost = tf.add_n([cost, wd_cost], name='cost')
+BATCH_SIZE = 128
 
 def get_data(train_or_test):
     isTrain = train_or_test == 'train'

tensorpack/utils/discretize.py

@@ -14,7 +14,7 @@ __all__ = ['UniformDiscretizer1D', 'UniformDiscretizerND']
 def log_once(s):
     logger.warn(s)
 
-# just placeholder
+# just a placeholder
 class Discretizer(object):
     __metaclass__ = ABCMeta
 
@@ -55,6 +55,9 @@ class UniformDiscretizer1D(Discretizer1D):
                 (v - self.minv) / self.spacing,
                 0, self.nr_bin - 1))
 
+    def get_bin_center(self, bin_id):
+        return self.minv + self.spacing * (bin_id + 0.5)
+
     def get_distribution(self, v, smooth_factor=0.05, smooth_radius=2):
         """ return a smoothed one-hot distribution of the sample v.
         """
@@ -96,6 +99,19 @@ class UniformDiscretizerND(Discretizer):
             acc *= self.nr_bins[k]
         return res
 
+    def _get_bin_id_nd(self, bin_id):
+        ret = []
+        for k in reversed(list(range(self.n))):
+            nr = self.nr_bins[k]
+            v = bin_id % nr
+            bin_id = bin_id / nr
+            ret.append(v)
+        return list(reversed(ret))
+
+    def get_bin_center(self, bin_id):
+        bin_id_nd = self._get_bin_id_nd(bin_id)
+        return [self.discretizers[k].get_bin_center(bin_id_nd[k]) for k in range(self.n)]
+
 if __name__ == '__main__':
     #u = UniformDiscretizer1D(-10, 10, 0.12)
     u = UniformDiscretizerND((0, 100, 1), (0, 100, 1), (0, 100, 1))