Support npz format

docs/conf.py

@@ -360,6 +360,7 @@ def autodoc_skip_member(app, what, name, obj, skip, options):
         'remap_get_variable',
         'freeze_get_variable',
         'Triggerable',
+        'dump_chkpt_vars',
         'ParamRestore']:
         return True
     if name in ['get_data', 'size', 'reset_state']:

docs/tutorial/faq.md

@@ -26,7 +26,7 @@ To inspect a checkpoint, the easiest tool is `tf.train.NewCheckpointReader`. Ple
 expects a model path without the extension.
 
 You can dump a cleaner version of the model (without unnecessary variables), using
-`scripts/dump-model-params.py`, as a simple `var-name: value` dict saved in npy format.
+`scripts/dump-model-params.py`, as a simple `var-name: value` dict saved in npy/npz format.
 The script expects a metagraph file which is also saved by `ModelSaver`.
 
 

scripts/checkpoint-manipulate.py

@@ -19,6 +19,8 @@ if __name__ == '__main__':
 
     if args.model.endswith('.npy'):
         params = np.load(args.model).item()
+    elif args.model.endswith('.npz'):
+        params = dict(np.load(args.model))
     else:
         params = dump_chkpt_vars(args.model)
     logger.info("Variables in the model:")

scripts/dump-model-params.py

@@ -9,7 +9,7 @@ import tensorflow as tf
 import imp
 
 from tensorpack import TowerContext, logger
-from tensorpack.tfutils import sessinit, varmanip
+from tensorpack.tfutils import sessinit, varmanip, get_model_loader
 from tensorpack.graph_builder.input_source import PlaceholderInput
 
 parser = argparse.ArgumentParser()
@@ -34,17 +34,14 @@ with tf.Graph().as_default() as G:
         tf.train.import_meta_graph(args.meta)
 
     # loading...
-    if args.model.endswith('.npy'):
-        init = sessinit.DictRestore(np.load(args.model).item())
-    else:
-        init = sessinit.SaverRestore(args.model)
+    init = get_model_loader(args.model)
     sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
     sess.run(tf.global_variables_initializer())
     init.init(sess)
 
     # dump ...
     with sess.as_default():
-        if args.output.endswith('npy'):
+        if args.output.endswith('npy') or args.output.endswith('npz'):
             varmanip.dump_session_params(args.output)
         else:
             var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)

tensorpack/tfutils/sessinit.py

@@ -256,12 +256,16 @@ def get_model_loader(filename):
     Get a corresponding model loader by looking at the file name.
 
     Returns:
-        SessInit: either a :class:`DictRestore` (if name ends with 'npy') or
+        SessInit: either a :class:`DictRestore` (if name ends with 'npy/npz') or
         :class:`SaverRestore` (otherwise).
     """
     if filename.endswith('.npy'):
         assert os.path.isfile(filename), filename
         return DictRestore(np.load(filename, encoding='latin1').item())
+    elif filename.endswith('.npz'):
+        assert os.path.isfile(filename), filename
+        obj = np.load(filename)
+        return DictRestore(dict(obj))
     else:
         return SaverRestore(filename)
 

tensorpack/tfutils/varmanip.py

@@ -12,6 +12,7 @@ from ..utils import logger
 from .common import get_op_tensor_name
 
 __all__ = ['SessionUpdate', 'dump_session_params', 'dump_chkpt_vars',
+           'load_chkpt_vars',
            # 'get_savename_from_varname', 'is_training_name',
            'get_checkpoint_path']
 
@@ -112,10 +113,10 @@ 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:`DictRestore`).
+    npy/npz format (loadable by :class:`DictRestore`).
 
     Args:
-        path(str): the path to save the parameters.
+        path(str): the file name to save the parameters. Must ends with npy or npz.
     """
     var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
     var.extend(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES))
@@ -127,7 +128,12 @@ def dump_session_params(path):
     logger.info("Variables to save to {}:".format(path))
     keys = sorted(list(result.keys()))
     logger.info(pprint.pformat(keys))
+    if path.endswith('.npy'):
         np.save(path, result)
+    elif path.endswith('.npz'):
+        np.savez_compressed(path, **result)
+    else:
+        raise ValueError("Don't know which format to use for {}".format(path))
 
 
 def get_checkpoint_path(model_path):
@@ -160,7 +166,7 @@ def get_checkpoint_path(model_path):
     return model_path
 
 
-def dump_chkpt_vars(model_path):
+def load_chkpt_vars(model_path):
     """ Dump all variables from a checkpoint to a dict.
 
     Args:
@@ -177,6 +183,9 @@ def dump_chkpt_vars(model_path):
         result[n] = reader.get_tensor(n)
     return result
 
+def dump_chkpt_vars(model_path):
+    logger.warn("dump_chkpt_vars was renamed to load_chkpt_vars!")
+    return load_chkpt_vars(model_path)
 
 def is_training_name(name):
     """