add epoch_num stat before each epoch

README.md

@@ -8,7 +8,7 @@ You can actually train them and reproduce the performance... not just to see how
 
 + [DoReFa-Net: training binary / low bitwidth CNN](examples/DoReFa-Net)
 + [InceptionV3 on ImageNet](examples/Inception/inceptionv3.py)
-+ [ResNet for Cifar10 classification](examples/ResNet)
++ [ResNet for ImageNet/Cifar10 classification](examples/ResNet)
 + [Fully-convolutional Network for Holistically-Nested Edge Detection](examples/HED)
 + [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)
 + [Double DQN plays Atari games](examples/Atari2600)

examples/ResNet/README.md

@@ -2,9 +2,10 @@
 ## imagenet-resnet.py
 
 ImageNet training code of pre-activation ResNet. It follows the setup in
-[fb.resnet.torch](https://github.com/facebook/fb.resnet.torch) and get similar performance (with much fewer lines of code),
+[fb.resnet.torch](https://github.com/facebook/fb.resnet.torch) and gets similar performance (with much fewer lines of code).
+More results to come.
 
-| Model (WIP)        | Top 5 Error | Top 1 Error |
+| Model              | Top 5 Error | Top 1 Error |
 |:-------------------|-------------|------------:|
 | ResNet 18          |      10.67% |      29.50% |
 | ResNet 50          |      7.13%  |      24.12% |

examples/ResNet/imagenet-resnet.py

@@ -116,7 +116,6 @@ class Model(ModelDesc):
         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_w = 1e-4
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
         add_moving_summary(loss, wd_cost)

tensorpack/callbacks/stat.py

@@ -107,11 +107,14 @@ class StatPrinter(Callback):
         self._stat_holder.set_print_tag(self.print_tag)
         self._stat_holder.add_blacklist_tag(['global_step', 'epoch_num'])
 
+        # just try to add this stat earlier so SendStat can use
+        self._stat_holder.add_stat('epoch_num', self.epoch_num + 1)
+
     def _trigger_epoch(self):
         # by default, add this two stat
         self._stat_holder.add_stat('global_step', get_global_step())
-        self._stat_holder.add_stat('epoch_num', self.epoch_num)
         self._stat_holder.finalize()
+        self._stat_holder.add_stat('epoch_num', self.epoch_num + 1)
 
 class SendStat(Callback):
     """

tensorpack/predict/common.py

@@ -27,7 +27,6 @@ class PredictConfig(object):
         :param session_init: a `utils.sessinit.SessionInit` instance to
             initialize variables of a session.
         :param input_var_names: a list of input variable names.
-        :param input_data_mapping: deprecated. used to select `input_var_names` from the `InputVars` of the model.
         :param model: a `ModelDesc` instance
         :param output_var_names: a list of names of the output tensors to predict, the
             variables can be any computable tensor in the graph.
@@ -47,13 +46,7 @@ class PredictConfig(object):
 
         # inputs & outputs
         self.input_var_names = kwargs.pop('input_var_names', None)
-        input_mapping = kwargs.pop('input_data_mapping', None)
-        if input_mapping:
-            raw_vars = self.model.get_input_vars_desc()
-            self.input_var_names = [raw_vars[k].name for k in input_mapping]
-            logger.warn('The option `input_data_mapping` was deprecated. \
-Use \'input_var_names=[{}]\' instead'.format(', '.join(self.input_var_names)))
-        elif self.input_var_names is None:
+        if self.input_var_names is None:
             # neither options is set, assume all inputs
             raw_vars = self.model.get_input_vars_desc()
             self.input_var_names = [k.name for k in raw_vars]