[breaking] rename ParamRestore to DictRestore

docs/tutorial/faq.md

@@ -12,6 +12,7 @@ for more details.
 It you think:
 1. The framework has limitation so your XYZ cannot be supported, OR
 2. Your XYZ is very common, or very well-defined, so it would be nice to include it.
+
 Then it's a good time to open an issue.
 
 ## How to dump/inspect a model
@@ -25,7 +26,7 @@ expects a path without the extension.
 
 You can dump a cleaner version of the model (with only model/trainable variables), with
 `scripts/dump-model-params.py`, as a simple `var-name: value` dict saved in npy format.
-It expects a metagraph file which is also saved by `ModelSaver`.
+The script expects a metagraph file which is also saved by `ModelSaver`.
 
 
 ## How to load a model / do transfer learning

examples/ConvolutionalPoseMachines/load-cpm.py

@@ -108,7 +108,7 @@ def run_test(model_path, img_file):
     param_dict = np.load(model_path, encoding='latin1').item()
     predict_func = OfflinePredictor(PredictConfig(
         model=Model(),
-        session_init=ParamRestore(param_dict),
+        session_init=DictRestore(param_dict),
         input_names=['input'],
         output_names=['resized_map']
     ))

examples/DoReFa-Net/alexnet-dorefa.py

@@ -308,7 +308,7 @@ if __name__ == '__main__':
 
     if args.run:
         assert args.load.endswith('.npy')
-        run_image(Model(), ParamRestore(np.load(args.load, encoding='latin1').item()), args.run)
+        run_image(Model(), DictRestore(np.load(args.load, encoding='latin1').item()), args.run)
         sys.exit()
 
     assert args.gpu is not None, "Need to specify a list of gpu for training!"

examples/DoReFa-Net/resnet-dorefa.py

@@ -190,5 +190,5 @@ if __name__ == '__main__':
         eval_on_ILSVRC12(args.load, args.data)
     elif args.run:
         assert args.load.endswith('.npy')
-        run_image(Model(), ParamRestore(
+        run_image(Model(), DictRestore(
             np.load(args.load, encoding='latin1').item()), args.run)

examples/ResNet/load-resnet.py

@@ -114,7 +114,7 @@ def get_inference_augmentor():
 def run_test(params, input):
     pred_config = PredictConfig(
         model=Model(),
-        session_init=ParamRestore(params),
+        session_init=DictRestore(params),
         input_names=['input'],
         output_names=['prob']
     )
@@ -139,7 +139,7 @@ def eval_on_ILSVRC12(params, data_dir):
     ds = BatchData(ds, 128, remainder=True)
     pred_config = PredictConfig(
         model=Model(),
-        session_init=ParamRestore(params),
+        session_init=DictRestore(params),
         input_names=['input', 'label'],
         output_names=['wrong-top1', 'wrong-top5']
     )

examples/load-alexnet.py

@@ -56,7 +56,7 @@ def run_test(path, input):
     param_dict = np.load(path, encoding='latin1').item()
     predictor = OfflinePredictor(PredictConfig(
         model=Model(),
-        session_init=ParamRestore(param_dict),
+        session_init=DictRestore(param_dict),
         input_names=['input'],
         output_names=['prob']
     ))

examples/load-vgg16.py

@@ -66,7 +66,7 @@ def run_test(path, input):
     param_dict = np.load(path, encoding='latin1').item()
     predict_func = OfflinePredictor(PredictConfig(
         model=Model(),
-        session_init=ParamRestore(param_dict),
+        session_init=DictRestore(param_dict),
         input_names=['input'],
         output_names=['prob']   # prob:0 is the probability distribution
     ))

scripts/dump-model-params.py

@@ -32,7 +32,7 @@ with tf.Graph().as_default() as G:
 
     # loading...
     if args.model.endswith('.npy'):
-        init = sessinit.ParamRestore(np.load(args.model).item())
+        init = sessinit.DictRestore(np.load(args.model).item())
     else:
         init = sessinit.SaverRestore(args.model)
     sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))

tensorpack/dataflow/common.py

@@ -123,7 +123,10 @@ class BatchData(ProxyDataFlow):
                 elif type(dt) == float:
                     tp = 'float32'
                 else:
+                    try:
                         tp = dt.dtype
+                    except:
+                        raise TypeError("Unsupported type to batch: {}".format(type(dt)))
                 try:
                     result.append(
                         np.asarray([x[k] for x in data_holder], dtype=tp))

tensorpack/dataflow/dftools.py

@@ -4,8 +4,8 @@
 
 import sys
 import os
-import cv2
 import multiprocessing as mp
+import cv2
 from six.moves import range
 
 from .base import DataFlow

tensorpack/tfutils/sessinit.py

@@ -8,12 +8,13 @@ import tensorflow as tf
 import six
 
 from ..utils import logger
+from ..utils.develop import deprecated
 from .common import get_op_tensor_name
 from .varmanip import (SessionUpdate, get_savename_from_varname,
                        is_training_name, get_checkpoint_path)
 
 __all__ = ['SessionInit', 'SaverRestore', 'SaverRestoreRelaxed',
-           'ParamRestore', 'ChainInit',
+           'ParamRestore', 'DictRestore', 'ChainInit',
            'JustCurrentSession', 'get_model_loader']
 
 
@@ -156,7 +157,7 @@ class SaverRestoreRelaxed(SaverRestore):
             self._match_vars(f)
 
 
-class ParamRestore(SessionInit):
+class DictRestore(SessionInit):
     """
     Restore variables from a dictionary.
     """
@@ -190,6 +191,11 @@ class ParamRestore(SessionInit):
         upd.update({name: value for name, value in six.iteritems(self.prms) if name in intersect})
 
 
+@deprecated("Use `DictRestore` instead!", "2017-06-01")
+def ParamRestore(d):
+    return DictRestore(d)
+
+
 class ChainInit(SessionInit):
     """ Initialize a session by a list of :class:`SessionInit` instance, executed one by one.
     This can be useful for, e.g., loading several models from different files
@@ -221,11 +227,11 @@ def get_model_loader(filename):
     Get a corresponding model loader by looking at the file name.
 
     Returns:
-        SessInit: either a :class:`ParamRestore` (if name ends with 'npy') or
+        SessInit: either a :class:`DictRestore` (if name ends with 'npy') or
         :class:`SaverRestore` (otherwise).
     """
     if filename.endswith('.npy'):
         assert os.path.isfile(filename), filename
-        return ParamRestore(np.load(filename, encoding='latin1').item())
+        return DictRestore(np.load(filename, encoding='latin1').item())
     else:
         return SaverRestore(filename)

tensorpack/tfutils/varmanip.py

@@ -119,7 +119,7 @@ class SessionUpdate(object):
 def dump_session_params(path):
     """
     Dump value of all TRAINABLE + MODEL variables to a dict, and save as
-    npy format (loadable by :class:`ParamRestore`).
+    npy format (loadable by :class:`DictRestore`).
 
     Args:
         path(str): the path to save the parameters.

tensorpack/utils/argtools.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # File: argtools.py
-# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 
 import inspect