DoReFa uses ImageNetModel

examples/DeepQNetwork/atari.py

@@ -96,7 +96,7 @@ class AtariPlayer(gym.Env):
 
         self.action_space = spaces.Discrete(len(self.actions))
         self.observation_space = spaces.Box(
-            low=0, high=255, shape=(self.height, self.width))
+            low=0, high=255, shape=(self.height, self.width), dtype=np.uint8)
         self._restart_episode()
 
     def get_action_meanings(self):

examples/DoReFa-Net/alexnet-dorefa.py

@@ -12,13 +12,12 @@ import sys
 
 
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import prediction_incorrect
-from tensorpack.tfutils.summary import add_moving_summary, add_param_summary
+from tensorpack.tfutils.summary import add_param_summary
 from tensorpack.tfutils.varreplace import remap_variables
 from tensorpack.dataflow import dataset
 from tensorpack.utils.gpu import get_nr_gpu
 
-from imagenet_utils import get_imagenet_dataflow, fbresnet_augmentor
+from imagenet_utils import get_imagenet_dataflow, fbresnet_augmentor, ImageNetModel
 from dorefa import get_dorefa, ternarize
 
 """
@@ -59,15 +58,11 @@ TOTAL_BATCH_SIZE = 256
 BATCH_SIZE = None
 
 
-class Model(ModelDesc):
-    def inputs(self):
-        return [tf.placeholder(tf.float32, [None, 224, 224, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
-
-    def build_graph(self, image, label):
-        image = image / 255.0
-        image = tf.transpose(image, [0, 3, 1, 2])
+class Model(ImageNetModel):
+    weight_decay = 5e-6
+    weight_decay_pattern = 'fc.*/W'
 
+    def get_logits(self, image):
         if BITW == 't':
             fw, fa, fg = get_dorefa(32, 32, 32)
             fw = ternarize
@@ -97,7 +92,7 @@ class Model(ModelDesc):
                 argscope(BatchNorm, momentum=0.9, epsilon=1e-4), \
                 argscope(Conv2D, use_bias=False):
             logits = (LinearWrap(image)
-                      .Conv2D('conv0', 96, 12, strides=4, padding='VALID')
+                      .Conv2D('conv0', 96, 12, strides=4, padding='VALID', use_bias=True)
                       .apply(activate)
                       .Conv2D('conv1', 256, 5, padding='SAME', split=2)
                       .apply(fg)
@@ -132,24 +127,8 @@ class Model(ModelDesc):
                       .BatchNorm('bnfc1')
                       .apply(nonlin)
                       .FullyConnected('fct', 1000, use_bias=True)())
-
-        tf.nn.softmax(logits, name='output')
-
-        cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
-        cost = tf.reduce_mean(cost, name='cross_entropy_loss')
-
-        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
-        add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
-        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
-        add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
-
-        # weight decay on all W of fc layers
-        wd_cost = regularize_cost('fc.*/W', l2_regularizer(5e-6), name='regularize_cost')
-
         add_param_summary(('.*/W', ['histogram', 'rms']))
-        total_cost = tf.add_n([cost, wd_cost], name='cost')
-        add_moving_summary(cost, wd_cost, total_cost)
-        return total_cost
+        return logits
 
     def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)

examples/ImageNetModels/README.md

@@ -30,7 +30,7 @@ This Inception-BN script reaches 27% single-crop error after 300k steps with 6 G
 
 This VGG16 script, when trained with 32x8 batch size, reaches the following
 error rate after 100 epochs (30h with 8 P100s). This reproduces the VGG
-experiements in the paper [Group Normalization](https://arxiv.org/abs/1803.08494).
+experiments in the paper [Group Normalization](https://arxiv.org/abs/1803.08494).
 
  | No Normalization                 | Batch Normalization | Group Normalization |
  |:---------------------------------|---------------------|--------------------:|