Let Keras Model accept a real "tower func" with positional args (#739)

examples/ResNet/imagenet-resnet.py

 #!/usr/bin/env python
-# -*- coding: UTF-8 -*-
+# -*- coding: utf-8 -*-
 # File: imagenet-resnet.py
 
 import argparse

examples/keras/imagenet-resnet-keras.py

@@ -85,8 +85,8 @@ def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2))
     return x
 
 
-def resnet50(inputs):
-    input = tf.layers.Input(tensor=inputs[0])
+def resnet50(image):
+    input = Input(tensor=image)
 
     def image_preprocess(image):
         image = ImageNetModel.image_preprocess(image)

examples/keras/mnist-keras-v2.py

@@ -31,16 +31,16 @@ def get_data():
 
 
 if __name__ == '__main__':
-    logger.auto_set_dir()
+    logger.auto_set_dir('d')
 
-    def model_func(inputs):
+    def model_func(image):
         """
         Keras model has to be created inside this function to be used with tensorpack.
         """
         M = keras.models.Sequential()
         # input_tensor have to be used here for tensorpack trainer to function properly.
         # Just use inputs[1], inputs[2] if you have multiple inputs.
-        M.add(KL.InputLayer(input_tensor=inputs[0]))
+        M.add(KL.InputLayer(input_tensor=image))
         M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))
         M.add(KL.MaxPooling2D())
         M.add(KL.Conv2D(32, 3, activation='relu', padding='same'))

tensorpack/contrib/keras.py

@@ -47,13 +47,15 @@ class KerasModelCaller(object):
 
     def __call__(self, input_tensors):
         """
+        Args:
+            input_tensors ([tf.Tensor])
         Returns:
             output tensors of this tower, evaluated with the input tensors.
         """
         reuse = tf.get_variable_scope().reuse
         if self.cached_model is None:
             assert not reuse
-            self.cached_model = self.get_model(input_tensors)
+            self.cached_model = self.get_model(*input_tensors)
             return self.cached_model.outputs
 
         if reuse:
@@ -63,7 +65,7 @@ class KerasModelCaller(object):
             return self.cached_model.call(input_tensors)
         else:
             # create new Keras model if not reuse
-            M = self.get_model(input_tensors)
+            M = self.get_model(*input_tensors)
             return M.outputs
 
 
@@ -99,7 +101,8 @@ def setup_keras_trainer(
     """
     Args:
         trainer (SingleCostTrainer):
-        get_model ( -> keras.model.Model):
+        get_model (input1, input2, ... -> keras.model.Model):
+            Takes tensors and returns a Keras model. Will be part of the tower function.
         input (InputSource):
         optimizer (tf.tarin.Optimizer):
         loss, metrics: list of strings
@@ -175,7 +178,8 @@ class KerasModel(object):
                  input, trainer=None):
         """
         Args:
-            get_model ( -> keras.model.Model):
+            get_model (input1, input2, ... -> keras.model.Model):
+                Takes tensors and returns a Keras model. Will be part of the tower function.
             inputs_desc ([InputDesc]):
             targets_desc ([InputDesc]):
             input (InputSource | DataFlow):

tensorpack/dataflow/common.py

-# -*- coding: UTF-8 -*-
+# -*- coding: utf-8 -*-
 # File: common.py
 
-
 from __future__ import division
 import six
 import numpy as np

tensorpack/predict/config.py

-# -*- coding: UTF-8 -*-
+# -*- coding: utf-8 -*-
 # File: config.py
 
 
@@ -35,7 +35,7 @@ class PredictConfig(object):
         Args:
             model (ModelDescBase): to be used to obtain inputs_desc and tower_func.
             inputs_desc ([InputDesc]):
-            tower_func: a callable which takes input tensors and construct a tower.
+            tower_func: a callable which takes input tensors (by positional args) and construct a tower.
 
             input_names (list): a list of input tensor names. Defaults to match inputs_desc.
             output_names (list): a list of names of the output tensors to predict, the