standardize the name "predictor" instead of "predict_func"

docs/casestudies/colorize.md

@@ -349,7 +349,7 @@ class OnlineExport(Callback):
         self.example_input = color.rgb2lab(cv2.imread('myimage.jpg')[:, :, ::-1])[:, :, 0] # read rgb image and extract luminance
 
     def _setup_graph(self):
-        self.predictor = self.trainer.get_predict_func(['luminance'], ['prediction/output'])
+        self.predictor = self.trainer.get_predictor(['luminance'], ['prediction/output'])
 
     def _trigger_epoch(self):
         pass
@@ -367,7 +367,7 @@ you can simply `print(prediction)` to find out the name.
 
 These two names allows us to build the inference part of the network in
 ```python
-self.trainer.get_predict_func(['luminance', 'prediction/output'])
+self.trainer.get_predictor(['luminance', 'prediction/output'])
 ```
 
 This is very convenient because in the `_tigger_epoch` we can use:
@@ -385,7 +385,7 @@ class OnlineExport(Callback):
         self.example_input = color.rgb2lab(cv2.imread('myimage.jpg')[:, :, [2, 1, 0]])[:, :, 0]
 
     def _setup_graph(self):
-        self.trainer.get_predict_func(['luminance', 'prediction/output'])
+        self.trainer.get_predictor(['luminance', 'prediction/output'])
 
     def _trigger_epoch(self):
         hopefully_cool_rgb = self.pred([[self.example_input]])[0][0]

examples/A3C-Gym/train-atari.py

@@ -151,7 +151,7 @@ class MySimulatorMaster(SimulatorMaster, Callback):
 
     def _setup_graph(self):
         self.async_predictor = MultiThreadAsyncPredictor(
-            self.trainer.get_predict_funcs(['state'], ['logitsT', 'pred_value'],
+            self.trainer.get_predictors(['state'], ['logitsT', 'pred_value'],
                                         PREDICTOR_THREAD), batch_size=15)
 
     def _before_train(self):

examples/DeepQNetwork/common.py

@@ -38,7 +38,7 @@ def play_model(cfg):
         print("Total:", score)
 
 
-def eval_with_funcs(predict_funcs, nr_eval):
+def eval_with_funcs(predictors, nr_eval):
     class Worker(StoppableThread, ShareSessionThread):
         def __init__(self, func, queue):
             super(Worker, self).__init__()
@@ -62,7 +62,7 @@ def eval_with_funcs(predict_funcs, nr_eval):
                     self.queue_put_stoppable(self.q, score)
 
     q = queue.Queue()
-    threads = [Worker(f, q) for f in predict_funcs]
+    threads = [Worker(f, q) for f in predictors]
 
     for k in threads:
         k.start()
@@ -103,7 +103,7 @@ class Evaluator(Triggerable):
 
     def _setup_graph(self):
         NR_PROC = min(multiprocessing.cpu_count() // 2, 20)
-        self.pred_funcs = [self.trainer.get_predict_func(
+        self.pred_funcs = [self.trainer.get_predictor(
             self.input_names, self.output_names)] * NR_PROC
 
     def _trigger(self):

examples/DeepQNetwork/expreplay.py

@@ -229,7 +229,7 @@ class ExpReplay(DataFlow, Callback):
         return [state, action, reward, isOver]
 
     def _setup_graph(self):
-        self.predictor = self.trainer.get_predict_func(*self.predictor_io_names)
+        self.predictor = self.trainer.get_predictor(*self.predictor_io_names)
 
     def _before_train(self):
         self._init_memory()

examples/DoReFa-Net/alexnet-dorefa.py

@@ -258,7 +258,7 @@ def run_image(model, sess_init, inputs):
         input_names=['input'],
         output_names=['output']
     )
-    predict_func = OfflinePredictor(pred_config)
+    predictor = OfflinePredictor(pred_config)
     meta = dataset.ILSVRCMeta()
     pp_mean = meta.get_per_pixel_mean()
     pp_mean_224 = pp_mean[16:-16, 16:-16, :]
@@ -282,7 +282,7 @@ def run_image(model, sess_init, inputs):
         assert img is not None
 
         img = transformers.augment(img)[np.newaxis, :, :, :]
-        outputs = predict_func([img])[0]
+        outputs = predictor([img])[0]
         prob = outputs[0]
         ret = prob.argsort()[-10:][::-1]
 

examples/HED/hed.py

@@ -192,11 +192,11 @@ def run(model_path, image_path, output):
         session_init=get_model_loader(model_path),
         input_names=['image'],
         output_names=['output' + str(k) for k in range(1, 7)])
-    predict_func = OfflinePredictor(pred_config)
+    predictor = OfflinePredictor(pred_config)
     im = cv2.imread(image_path)
     assert im is not None
     im = cv2.resize(im, (im.shape[1] // 16 * 16, im.shape[0] // 16 * 16))
-    outputs = predict_func([[im.astype('float32')]])
+    outputs = predictor([[im.astype('float32')]])
     if output is None:
         for k in range(6):
             pred = outputs[k][0]

examples/Saliency/saliency-maps.py

@@ -30,7 +30,7 @@ class Model(tp.ModelDesc):
 
 
 def run(model_path, image_path):
-    predict_func = tp.OfflinePredictor(tp.PredictConfig(
+    predictor = tp.OfflinePredictor(tp.PredictConfig(
         model=Model(),
         session_init=tp.get_model_loader(model_path),
         input_names=['image'],
@@ -42,7 +42,7 @@ def run(model_path, image_path):
     im = cv2.resize(im, (IMAGE_SIZE, IMAGE_SIZE))
     im = im.astype(np.float32)[:, :, ::-1]
 
-    saliency_images = predict_func([im])[0]
+    saliency_images = predictor([im])[0]
 
     abs_saliency = np.abs(saliency_images).max(axis=-1)
     pos_saliency = np.maximum(0, saliency_images)

examples/load-alexnet.py

@@ -54,7 +54,7 @@ class Model(ModelDesc):
 
 def run_test(path, input):
     param_dict = np.load(path, encoding='latin1').item()
-    predict_func = OfflinePredictor(PredictConfig(
+    predictor = OfflinePredictor(PredictConfig(
         model=Model(),
         session_init=ParamRestore(param_dict),
         input_names=['input'],
@@ -65,7 +65,7 @@ def run_test(path, input):
     assert im is not None, input
     im = cv2.resize(im, (227, 227))[:, :, ::-1].reshape(
         (1, 227, 227, 3)).astype('float32') - 110
-    outputs = predict_func([im])[0]
+    outputs = predictor([im])[0]
     prob = outputs[0]
     ret = prob.argsort()[-10:][::-1]
     print("Top10 predictions:", ret)

tensorpack/callbacks/inference_runner.py

@@ -92,7 +92,7 @@ class InferenceRunner(Triggerable):
     def _setup_graph(self):
         self._find_input_tensors()  # these are all tensor names
         self._find_output_tensors()  # may be either tensor name or op name
-        self.pred_func = self.trainer.get_predict_func(
+        self.predictor = self.trainer.get_predictor(
             self.input_tensors, self.output_tensors)
 
     def _find_input_tensors(self):
@@ -135,7 +135,7 @@ class InferenceRunner(Triggerable):
         self.ds.reset_state()
         with get_tqdm(total=self.ds.size()) as pbar:
             for dp in self.ds.get_data():
-                outputs = self.pred_func(dp)
+                outputs = self.predictor(dp)
                 for inf, tensormap in zip(self.infs, self.inf_to_tensors):
                     inf_output = [(outputs if k.isOutput else dp)[k.index]
                                   for k in tensormap]

tensorpack/callbacks/param.py

@@ -218,8 +218,8 @@ class ScheduledHyperParamSetter(HyperParamSetter):
             param: same as in :class:`HyperParamSetter`.
             schedule (list): with the format ``[(epoch1, val1), (epoch2, val2), (epoch3, val3)]``.
                 Each ``(ep, val)`` pair means to set the param
-                to "val" after the completion of `ep` th epoch.
-                If ep == 0, the value will be set before training.
+                to "val" __after__ the completion of `ep` th epoch.
+                If ep == 0, the value will be set before the first epoch.
             interp: None: no interpolation. 'linear': linear interpolation
 
         Example:
@@ -263,6 +263,7 @@ class HyperParamSetterWithFunc(HyperParamSetter):
         Args:
             param: same as in :class:`HyperParamSetter`.
             func: ``param`` will be set by ``new_value = func(epoch_num, old_value)``.
+                ``epoch_num`` is the number of epochs that have finished.
 
         Example:
             Decrease by a factor of 0.9 every two epochs:

tensorpack/predict/base.py

@@ -7,7 +7,7 @@ from abc import abstractmethod, ABCMeta
 import tensorflow as tf
 import six
 
-from ..utils import logger
+from ..utils import logger, deprecated
 from ..utils.argtools import memoized
 from ..utils.naming import SUMMARY_BACKUP_KEYS
 from ..tfutils import get_tensors_by_names, TowerContext
@@ -146,6 +146,7 @@ class OfflinePredictor(OnlinePredictor):
                 input_tensors, output_tensors, config.return_input, sess)
 
 
+@deprecated("Use OfflinePredictor instead!", "2017-05-20")
 def get_predict_func(config):
     """
     Equivalent to ``OfflinePredictor(config)``.

tensorpack/train/base.py

@@ -189,7 +189,7 @@ class Trainer(object):
                 self.summary_writer.close()
                 self.monitored_sess.close()
 
-    def get_predict_func(self, input_names, output_names, tower=0):
+    def get_predictor(self, input_names, output_names, tower=0):
         """
         Args:
             input_names (list), output_names(list): list of names
@@ -200,16 +200,25 @@ class Trainer(object):
         """
         if not hasattr(self, '_predictor_factory'):
             self._predictor_factory = PredictorFactory(self)
+        nr_tower = len(self.config.predict_tower)
+        if nr_tower < tower:
+            logger.warn(
+                "Requested the {}th predictor but only have {} predict towers! "
+                "Predictors will be assigned to GPUs in round-robin.".format(tower, nr_tower))
+        tower = tower % nr_tower
         return self._predictor_factory.get_predictor(input_names, output_names, tower)
 
-    def get_predict_funcs(self, input_names, output_names, n):
+    def get_predictors(self, input_names, output_names, n):
         """ Return n predictors. """
-        nr_tower = len(self.config.predict_tower)
-        if nr_tower < n:
-            logger.warn(
-                "Requested {} predictor but only have {} predict towers! "
-                "Predictors will be assigned to GPUs in round-robin.".format(n, nr_tower))
-        return [self.get_predict_func(input_names, output_names, k % nr_tower) for k in range(n)]
+        return [self.get_predictor(input_names, output_names, k) for k in range(n)]
+
+    @deprecated("Use get_predictor instead!", "2017-05-20")
+    def get_predict_func(self, input_names, output_names, tower=0):
+        return self.get_predictor(input_names, output_names, tower)
+
+    @deprecated("Use get_predictors instead!", "2017-05-20")
+    def get_predict_funcs(self, input_names, output_names, n):
+        return self.get_predictors(input_names, output_names, n)
 
     @deprecated("Don't need to call it any more!", "2017-03-20")
     def _setup_predictor_factory(self):

tensorpack/train/predict.py

@@ -26,7 +26,6 @@ class PredictorFactory(object):
         self._tower_builder = PredictorTowerBuilder(fn)
         assert isinstance(self.towers, list)
 
-    # TODO sess option
     def get_predictor(self, input_names, output_names, tower):
         """
         Args: