dorefa & resnet models

examples/Atari2600/common.py

@@ -21,7 +21,7 @@ def play_one_episode(player, func, verbose=False):
     def f(s):
         spc = player.get_action_space()
         act = func([[s]])[0][0].argmax()
-        if random.random() < 0.01:
+        if random.random() < 0.001:
             act = spc.sample()
         if verbose:
             print(act)

examples/DoReFa-Net/README.md

@@ -5,8 +5,8 @@ Code and model for the paper:
 We hosted a demo at CVPR16 on behalf of Megvii, Inc, running real-time half-VGG size DoReFa-Net on both ARM and FPGA.
 But we're not planning to release those runtime bit-op libraries for now. In these examples, bit operations are run in float32.
 
-Pretrained model for 1-2-6-AlexNet is available at
-[google drive](https://drive.google.com/a/ megvii.com/folderview?id=0B308TeQzmFDLa0xOeVQwcXg1ZjQ).
+Pretrained model for 1-2-6-AlexNet is available
+[here](https://github.com/ppwwyyxx/tensorpack/releases/tag/alexnet-dorefa).
 It's provided in the format of numpy dictionary, so it should be very easy to port into other applications.
 
 ## Preparation:

examples/ResNet/README.md

@@ -9,7 +9,5 @@ The validation error here is computed on test set.
 
 ![cifar10](cifar10-resnet.png)
 
-<!--
-   -Download model:
-   -[Cifar10 n=18](https://drive.google.com/open?id=0B308TeQzmFDLeHpSaHAxWGV1WDg)
-	 -->
+Download model:
+[Cifar10 ResNet-110 (n=18)](https://github.com/ppwwyyxx/tensorpack/releases/tag/cifar10-resnet-110)

tensorpack/callbacks/common.py

@@ -36,7 +36,7 @@ class ModelSaver(Callback):
         vars = tf.all_variables()
         var_dict = {}
         for v in vars:
-            name = v.op.name
+            name = v.name
             if re.match('tower[p1-9]', name):
                 #logger.info("Skip {} when saving model.".format(name))
                 continue

tensorpack/tfutils/sessinit.py

@@ -11,7 +11,7 @@ import six
 
 from ..utils import logger, EXTRA_SAVE_VARS_KEY
 from .common import get_op_var_name
-from .varmanip import SessionUpdate
+from .varmanip import SessionUpdate, get_savename_from_varname
 
 __all__ = ['SessionInit', 'NewSession', 'SaverRestore',
            'ParamRestore', 'ChainInit',
@@ -112,19 +112,17 @@ class SaverRestore(SessionInit):
         var_dict = defaultdict(list)
         chkpt_vars_used = set()
         for v in vars_to_restore:
-            name = v.op.name
-            if 'towerp' in name:
-                logger.error("No variable should be under 'towerp' name scope".format(v.name))
-                # don't overwrite anything in the current prediction graph
-                continue
-            if 'tower' in name:
-                name = re.sub('tower[p0-9]+/', '', name)
-            if self.prefix and name.startswith(self.prefix):
-                name = name[len(self.prefix)+1:]
+            name = get_savename_from_varname(v.name, varname_prefix=self.prefix)
+            # try to load both 'varname' and 'opname' from checkpoint
+            # because some old checkpoint might not have ':0'
+            if name in vars_available:
+                var_dict[name].append(v)
+                chkpt_vars_used.add(name)
+            elif name.endswith(':0'):
+                name = name[:-2]
                 if name in vars_available:
                     var_dict[name].append(v)
                     chkpt_vars_used.add(name)
-                #vars_available.remove(name)
             else:
                 logger.warn("Variable {} in the graph not found in checkpoint!".format(v.op.name))
         if len(chkpt_vars_used) < len(vars_available):
@@ -141,12 +139,13 @@ class ParamRestore(SessionInit):
         """
         :param param_dict: a dict of {name: value}
         """
+        # use varname (with :0) for consistency
         self.prms = {get_op_var_name(n)[1]: v for n, v in six.iteritems(param_dict)}
 
     def _init(self, sess):
         variables = tf.get_collection(tf.GraphKeys.VARIABLES)
 
-        variable_names = set([k.name for k in variables])
+        variable_names = set([get_savename_from_varname(k.name) for k in variables])
         param_names = set(six.iterkeys(self.prms))
 
         intersect = variable_names & param_names
@@ -159,7 +158,9 @@ class ParamRestore(SessionInit):
             logger.warn("Variable {} in the dict not found in the graph!".format(k))
 
 
-        upd = SessionUpdate(sess, [v for v in variables if v.name in intersect])
+        upd = SessionUpdate(sess,
+                [v for v in variables if \
+                    get_savename_from_varname(v.name) in intersect])
         logger.info("Restoring from dict ...")
         upd.update({name: value for name, value in six.iteritems(self.prms) if name in intersect})
 

tensorpack/tfutils/varmanip.py

@@ -5,10 +5,37 @@
 
 import six
 import tensorflow as tf
+from collections import defaultdict
+import re
 import numpy as np
 from ..utils import logger
 
-__all__ = ['SessionUpdate', 'dump_session_params', 'dump_chkpt_vars']
+__all__ = ['SessionUpdate', 'dump_session_params', 'dump_chkpt_vars',
+        'get_savename_from_varname']
+
+def get_savename_from_varname(
+        varname, varname_prefix=None,
+        savename_prefix=None):
+    """
+    :param varname: a variable name in the graph
+    :param varname_prefix: an optional prefix that may need to be removed in varname
+    :param savename_prefix: an optional prefix to append to all savename
+    :returns: the name used to save the variable
+    """
+    name = varname
+    if 'towerp' in name:
+        logger.error("No variable should be under 'towerp' name scope".format(v.name))
+        # don't overwrite anything in the current prediction graph
+        return None
+    if 'tower' in name:
+        name = re.sub('tower[p0-9]+/', '', name)
+    if varname_prefix is not None \
+            and name.startswith(varname_prefix):
+        name = name[len(varname_prefix)+1:]
+    if savename_prefix is not None:
+        name = savename_prefix + '/' + name
+    return name
+
 
 class SessionUpdate(object):
     """ Update the variables in a session """
@@ -17,10 +44,14 @@ class SessionUpdate(object):
         :param vars_to_update: a collection of variables to update
         """
         self.sess = sess
-        self.assign_ops = {}
+        self.assign_ops = defaultdict(list)
         for v in vars_to_update:
-            p = tf.placeholder(v.dtype, shape=v.get_shape())
-            self.assign_ops[v.name] = (p, v.assign(p))
+            #p = tf.placeholder(v.dtype, shape=v.get_shape())
+            with tf.device('/cpu:0'):
+                p = tf.placeholder(v.dtype)
+                savename = get_savename_from_varname(v.name)
+                # multiple vars might share one savename
+                self.assign_ops[savename].append((p, v, v.assign(p)))
 
     def update(self, prms):
         """
@@ -28,15 +59,25 @@ class SessionUpdate(object):
         Any name in prms must be in the graph and in vars_to_update.
         """
         for name, value in six.iteritems(prms):
-            p, op = self.assign_ops[name]
-            varshape = tuple(p.get_shape().as_list())
+            assert name in self.assign_ops
+            for p, v, op in self.assign_ops[name]:
+                if 'fc0/W' in name:
+                    import IPython as IP;
+                    IP.embed(config=IP.terminal.ipapp.load_default_config())
+                varshape = tuple(v.get_shape().as_list())
                 if varshape != value.shape:
                     # TODO only allow reshape when shape different by empty axis
                     assert np.prod(varshape) == np.prod(value.shape), \
                             "{}: {}!={}".format(name, varshape, value.shape)
                     logger.warn("Param {} is reshaped during assigning".format(name))
                     value = value.reshape(varshape)
+                if 'fc0/W' in name:
+                    import IPython as IP;
+                    IP.embed(config=IP.terminal.ipapp.load_default_config())
                 self.sess.run(op, feed_dict={p: value})
+                if 'fc0/W' in name:
+                    import IPython as IP;
+                    IP.embed(config=IP.terminal.ipapp.load_default_config())
 
 def dump_session_params(path):
     """ Dump value of all trainable + to_save variables to a dict and save to `path` as