input_names instead of input_var_names

examples/Atari2600/DQN.py

@@ -123,7 +123,7 @@ class Model(ModelDesc):
 
         target = reward + (1.0 - tf.cast(isOver, tf.float32)) * GAMMA * tf.stop_gradient(best_v)
 
-        self.cost = symbf.huber_loss(target - pred_action_value, name='cost')
+        self.cost = tf.truediv(symbf.huber_loss(target - pred_action_value), BATCH_SIZE, name='cost')
         summary.add_param_summary([('conv.*/W', ['histogram', 'rms']),
                                    ('fc.*/W', ['histogram', 'rms']) ])   # monitor all W
 
@@ -200,8 +200,8 @@ if __name__ == '__main__':
         cfg = PredictConfig(
                 model=Model(),
                 session_init=SaverRestore(args.load),
-                input_var_names=['state'],
-                output_var_names=['Qvalue'])
+                input_names=['state'],
+                output_names=['Qvalue'])
         if args.task == 'play':
             play_model(cfg)
         elif args.task == 'eval':

examples/DoReFa-Net/alexnet-dorefa.py

@@ -245,8 +245,8 @@ def run_image(model, sess_init, inputs):
         model=model,
         session_init=sess_init,
         session_config=get_default_sess_config(0.9),
-        input_var_names=['input'],
-        output_var_names=['output']
+        input_names=['input'],
+        output_names=['output']
     )
     predict_func = get_predict_func(pred_config)
     meta = dataset.ILSVRCMeta()

examples/HED/hed.py

@@ -184,9 +184,9 @@ def get_config():
 def run(model_path, image_path):
     pred_config = PredictConfig(
             model=Model(),
-            input_data_mapping=[0],
             session_init=get_model_loader(model_path),
-            output_var_names=['output' + str(k) for k in range(1, 7)])
+            input_names=['image'],
+            output_names=['output' + str(k) for k in range(1, 7)])
     predict_func = get_predict_func(pred_config)
     im = cv2.imread(image_path)
     assert im is not None

examples/OpenAIGym/run-atari.py

@@ -95,6 +95,6 @@ if __name__ == '__main__':
     cfg = PredictConfig(
             model=Model(),
             session_init=SaverRestore(args.load),
-            input_var_names=['state'],
-            output_var_names=['logits'])
+            input_names=['state'],
+            output_names=['logits'])
     run_submission(cfg, args.output, args.episode)

examples/OpenAIGym/train-atari.py

@@ -235,8 +235,8 @@ if __name__ == '__main__':
         cfg = PredictConfig(
                 model=Model(),
                 session_init=SaverRestore(args.load),
-                input_var_names=['state'],
-                output_var_names=['logits'])
+                input_names=['state'],
+                output_names=['logits'])
         if args.task == 'play':
             play_model(cfg)
         elif args.task == 'eval':

examples/ResNet/README.md

 
 ## imagenet-resnet.py
 
-ImageNet training code of pre-activation ResNet. It follows the setup in
+Training code of pre-activation ResNet on ImageNet. It follows the setup in
 [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch) and gets similar performance (with much fewer lines of code).
 More results to come.
 

examples/ResNet/imagenet-resnet.py

@@ -213,9 +213,9 @@ def eval_on_ILSVRC12(model_file, data_dir):
     ds = get_data('val')
     pred_config = PredictConfig(
         model=Model(),
-        input_var_names=['input', 'label'],
         session_init=get_model_loader(model_file),
-        output_var_names=['wrong-top1', 'wrong-top5']
+        input_names=['input', 'label'],
+        output_names=['wrong-top1', 'wrong-top5']
     )
     pred = SimpleDatasetPredictor(pred_config, ds)
     acc1, acc5 = RatioCounter(), RatioCounter()

examples/ResNet/load-resnet.py

@@ -111,8 +111,8 @@ def run_test(params, input):
     pred_config = PredictConfig(
         model=Model(),
         session_init=ParamRestore(params),
-        input_var_names=['input'],
-        output_var_names=['prob']
+        input_names=['input'],
+        output_names=['prob']
     )
     predict_func = get_predict_func(pred_config)
 
@@ -134,9 +134,9 @@ def eval_on_ILSVRC12(params, data_dir):
     ds = BatchData(ds, 128, remainder=True)
     pred_config = PredictConfig(
         model=Model(),
-        input_var_names=['input', 'label'],
         session_init=ParamRestore(params),
-        output_var_names=['wrong-top1', 'wrong-top5']
+        input_names=['input', 'label'],
+        output_names=['wrong-top1', 'wrong-top5']
     )
     pred = SimpleDatasetPredictor(pred_config, ds)
     acc1, acc5 = RatioCounter(), RatioCounter()

examples/SpatialTransformer/mnist-addition.py

@@ -109,8 +109,8 @@ def view_warp(modelpath):
     pred = OfflinePredictor(PredictConfig(
        session_init=get_model_loader(modelpath),
        model=Model(),
-       input_var_names=['input'],
-       output_var_names=['viz', 'STN1/affine', 'STN2/affine']))
+       input_names=['input'],
+       output_names=['viz', 'STN1/affine', 'STN2/affine']))
 
     xys = np.array([[0, 0, 1],
         [WARP_TARGET_SIZE, 0, 1],

examples/load-alexnet.py

@@ -53,10 +53,10 @@ def run_test(path, input):
 
     pred_config = PredictConfig(
         model=Model(),
-        input_var_names=['input'],
         session_init=ParamRestore(param_dict),
         session_config=get_default_sess_config(0.9),
-        output_var_names=['output']   # the variable 'output' is the probability distribution
+        input_names=['input'],
+        output_names=['output']   # the variable 'output' is the probability distribution
     )
     predict_func = get_predict_func(pred_config)
 

examples/load-vgg16.py

@@ -72,10 +72,10 @@ def run_test(path, input):
     param_dict = np.load(path).item()
     pred_config = PredictConfig(
         model=Model(),
-        input_var_names=['input'],
+        input_names=['input'],
         session_init=ParamRestore(param_dict),
         session_config=get_default_sess_config(0.9),
-        output_var_names=['output']   # output:0 is the probability distribution
+        output_names=['output']   # output:0 is the probability distribution
     )
     predict_func = get_predict_func(pred_config)
 

tensorpack/dataflow/remote.py

@@ -26,6 +26,7 @@ def serve_data(ds, addr):
     try:
         ds.reset_state()
         logger.info("Serving data at {}".format(addr))
+        # TODO print statistics here
         while True:
             for dp in ds.get_data():
                 socket.send(dumps(dp), copy=False)

tensorpack/predict/base.py

@@ -8,7 +8,7 @@ import tensorflow as tf
 import six
 
 from ..utils import logger
-from ..tfutils import get_vars_by_names, TowerContext
+from ..tfutils import get_tensors_by_names, TowerContext
 
 __all__ = ['OnlinePredictor', 'OfflinePredictor',
         'AsyncPredictorBase',
@@ -41,7 +41,7 @@ class PredictorBase(object):
     @abstractmethod
     def _do_call(self, dp):
         """
-        :param dp: input datapoint.  must have the same length as input_var_names
+        :param dp: input datapoint.  must have the same length as input_names
         :return: output as defined by the config
         """
 
@@ -67,18 +67,18 @@ class AsyncPredictorBase(PredictorBase):
         return fut.result()
 
 class OnlinePredictor(PredictorBase):
-    def __init__(self, sess, input_vars, output_vars, return_input=False):
+    def __init__(self, sess, input_tensors, output_tensors, return_input=False):
         self.session = sess
         self.return_input = return_input
 
-        self.input_vars = input_vars
-        self.output_vars = output_vars
+        self.input_tensors = input_tensors
+        self.output_tensors = output_tensors
 
     def _do_call(self, dp):
-        assert len(dp) == len(self.input_vars), \
-            "{} != {}".format(len(dp), len(self.input_vars))
-        feed = dict(zip(self.input_vars, dp))
-        output = self.session.run(self.output_vars, feed_dict=feed)
+        assert len(dp) == len(self.input_tensors), \
+            "{} != {}".format(len(dp), len(self.input_tensors))
+        feed = dict(zip(self.input_tensors, dp))
+        output = self.session.run(self.output_tensors, feed_dict=feed)
         return output
 
 
@@ -91,8 +91,8 @@ class OfflinePredictor(OnlinePredictor):
             with TowerContext('', False):
                 config.model.build_graph(input_vars)
 
-            input_vars = get_vars_by_names(config.input_var_names)
-            output_vars = get_vars_by_names(config.output_var_names)
+            input_vars = get_tensors_by_names(config.input_names)
+            output_vars = get_tensors_by_names(config.output_names)
 
             sess = tf.Session(config=config.session_config)
             config.session_init.init(sess)
@@ -124,12 +124,12 @@ class MultiTowerOfflinePredictor(OnlinePredictor):
             self.sess = tf.Session(config=config.session_config)
             config.session_init.init(self.sess)
 
-            input_vars = get_vars_by_names(config.input_var_names)
+            input_vars = get_tensors_by_names(config.input_names)
 
             for k in towers:
-                output_vars = get_vars_by_names(
+                output_vars = get_tensors_by_names(
                         ['{}{}/'.format(self.PREFIX, k) + n \
-                                for n in config.output_var_names])
+                                for n in config.output_names])
                 self.predictors.append(OnlinePredictor(
                     self.sess, input_vars, output_vars, config.return_input))
 

tensorpack/predict/common.py

@@ -26,9 +26,9 @@ 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 model: a `ModelDesc` instance
-        :param output_var_names: a list of names of the output tensors to predict, the
+        :param input_names: a list of input variable names.
+        :param output_names: a list of names of the output tensors to predict, the
             variables can be any computable tensor in the graph.
             Predict specific output might not require all input variables.
         :param return_input: whether to return (input, output) pair or just output. default to False.
@@ -45,15 +45,24 @@ class PredictConfig(object):
         assert_type(self.model, ModelDesc)
 
         # inputs & outputs
-        self.input_var_names = kwargs.pop('input_var_names', None)
-        if self.input_var_names is None:
+        # TODO add deprecated warning later
+        self.input_names = kwargs.pop('input_names', None)
+        if self.input_names is None:
+            self.input_names = kwargs.pop('input_var_names', None)
+            if self.input_names is not None:
+                pass
+                #logger.warn("[Deprecated] input_var_names is deprecated in PredictConfig. Use input_names instead!")
+        if self.input_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]
-        self.output_var_names = kwargs.pop('output_var_names')
-        assert len(self.input_var_names), self.input_var_names
-        for v in self.input_var_names: assert_type(v, six.string_types)
-        assert len(self.output_var_names), self.output_var_names
+            self.input_names = [k.name for k in raw_vars]
+        self.output_names = kwargs.pop('output_names', None)
+        if self.output_names is None:
+            self.output_names = kwargs.pop('output_var_names')
+            #logger.warn("[Deprecated] output_var_names is deprecated in PredictConfig. Use output_names instead!")
+        assert len(self.input_names), self.input_names
+        for v in self.input_names: assert_type(v, six.string_types)
+        assert len(self.output_names), self.output_names
 
         self.return_input = kwargs.pop('return_input', False)
         assert len(kwargs) == 0, 'Unknown arguments: {}'.format(str(kwargs.keys()))