use pickle to dump inputvars

examples/GAN/Image2Image.py

@@ -160,7 +160,7 @@ def get_config():
             ScheduledHyperParamSetter('learning_rate', [(200, 1e-4)])
         ]),
         model=Model(),
-        step_per_epoch=300,
+        step_per_epoch=dataset.size(),
         max_epoch=300,
     )
 

examples/HED/hed.py

@@ -66,7 +66,6 @@ class Model(ModelDesc):
                 tf.concat(3, [b1, b2, b3, b4, b5]), 1, 1,
                 W_init=tf.constant_initializer(0.2),
                 use_bias=False, nl=tf.identity)
-        final_map = tf.squeeze(final_map, [3], name='predmap')
         costs = []
         for idx, b in enumerate([b1, b2, b3, b4, b5, final_map]):
             output = tf.nn.sigmoid(b, name='output{}'.format(idx+1))

tensorpack/callbacks/inference.py

@@ -93,7 +93,7 @@ class InferenceRunner(Callback):
 
     def _find_input_tensors(self):
         if self.input_tensors is None:
-            input_vars = self.trainer.model.reuse_input_vars()
+            input_vars = self.trainer.model.get_input_vars()
             self.input_tensors = [x.name for x in input_vars]
 
     def _find_output_tensors(self):

tensorpack/dataflow/image.py

@@ -19,7 +19,7 @@ class ImageFromFile(RNGDataFlow):
         :param channel: 1 or 3 channel
         :param resize: a (h, w) tuple. If given, will force a resize
         """
-        assert len(files)
+        assert len(files), "No Image Files!"
         self.files = files
         self.channel = int(channel)
         self.imread_mode = cv2.IMREAD_GRAYSCALE if self.channel == 1 else cv2.IMREAD_COLOR

tensorpack/dataflow/imgaug/crop.py

@@ -154,8 +154,9 @@ class RandomCropRandomShape(ImageAugmentor):
         h = self.rng.randint(self.hmin, hmax+1)
         w = self.rng.randint(self.wmin, wmax+1)
         diffh = img.shape[0] - h
-        y0 = 0 if diffh == 0 else self.rng.randint(diffh)
         diffw = img.shape[1] - w
+        assert diffh >= 0 and diffw >= 0
+        y0 = 0 if diffh == 0 else self.rng.randint(diffh)
         x0 = 0 if diffw == 0 else self.rng.randint(diffw)
         return (y0,x0,h,w)
 

tensorpack/models/model_desc.py

@@ -8,6 +8,7 @@ import re
 import tensorflow as tf
 from collections import namedtuple
 import inspect
+import pickle
 
 from ..utils import logger, INPUT_VARS_KEY
 from ..tfutils.common import get_tensors_by_names
@@ -16,7 +17,13 @@ from ..tfutils.tower import get_current_tower_context
 
 __all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph' ]
 
-InputVar = namedtuple('InputVar', ['type', 'shape', 'name'])
+_InputVar = namedtuple('InputVar', ['type', 'shape', 'name'])
+class InputVar(_InputVar):
+    def dumps(self):
+        return pickle.dumps(self)
+    @staticmethod
+    def loads(buf):
+        return pickle.loads(buf)
 
 class ModelDesc(object):
     """ Base class for a model description """
@@ -29,17 +36,17 @@ class ModelDesc(object):
         :returns: the list of raw input vars in the graph
         """
         try:
-            return self.reuse_input_vars()
+            return self._reuse_input_vars()
         except KeyError:
             pass
-        ret = self.get_placeholders()
-        for v in ret:
-            tf.add_to_collection(INPUT_VARS_KEY, v)
-        return ret
+        return self.get_placeholders()
 
     def get_placeholders(self, prefix=''):
-        """ build placeholders with optional prefix, for each InputVar"""
+        """ build placeholders with optional prefix, for each InputVar
+        """
         input_vars = self._get_input_vars()
+        for v in input_vars:
+            tf.add_to_collection(INPUT_VARS_KEY, v.dumps())
         ret = []
         for v in input_vars:
             ret.append(tf.placeholder(
@@ -47,7 +54,7 @@ class ModelDesc(object):
                 name=prefix + v.name))
         return ret
 
-    def reuse_input_vars(self):
+    def _reuse_input_vars(self):
         """ Find and return already-defined input_vars in default graph"""
         input_var_names = [k.name for k in self._get_input_vars()]
         return get_tensors_by_names(input_var_names)
@@ -104,11 +111,10 @@ class ModelFromMetaGraph(ModelDesc):
             assert k in all_coll, \
                     "Collection {} not found in metagraph!".format(k)
 
-    def get_input_vars(self):
-        return tf.get_collection(INPUT_VARS_KEY)
-
     def _get_input_vars(self):
-        raise NotImplementedError("Shouldn't call here")
+        col = tf.get_collection(INPUT_VARS_KEY)
+        col = [InputVar.loads(v) for v in col]
+        return col
 
     def _build_graph(self, _, __):
         """ Do nothing. Graph was imported already """

tensorpack/tfutils/summary.py

@@ -105,7 +105,7 @@ def add_moving_summary(v, *args):
 @memoized
 def summary_moving_average(tensors=None):
     """
-    Create a MovingAverage op and summary for tensors
+    Create a MovingAverage op and add summary for tensors
     :param tensors: list of tf.Tensor to summary. default to the collection MOVING_SUMMARY_VARS_KEY
     :returns: a op to maintain these average.
     """

tensorpack/train/__init__.py

@@ -8,7 +8,7 @@ import os.path
 
 def global_import(name):
     p = __import__(name, globals(), locals(), level=1)
-    lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    lst = p.__all__ if '__all__' in dir(p) else []
     for k in lst:
         globals()[k] = p.__dict__[k]
     del globals()[name]

tensorpack/train/base.py

@@ -24,20 +24,25 @@ class StopTraining(BaseException):
     pass
 
 class Trainer(object):
-    """
-    Base class for a trainer.
-
-    Available Attritbutes:
-        stat_holder: a `StatHolder` instance
-        summary_writer: a `tf.SummaryWriter`
-        summary_op: a `tf.Operation` which returns summary string
-        config: a `TrainConfig`
-        model: a `ModelDesc`
-        sess: a `tf.Session`
-        coord: a `tf.train.Coordinator`
-    """
+    """ Base class for a trainer."""
     __metaclass__ = ABCMeta
 
+    """a `StatHolder` instance"""
+    stat_holder = None
+
+    """`tf.SummaryWriter`"""
+    summary_writer = None
+    """a tf.Tensor which returns summary string"""
+    summary_op = None
+    """ TrainConfig """
+    config = None
+    """ a ModelDesc"""
+    model = None
+    """ the current session"""
+    sess = None
+    """ the `tf.train.Coordinator` """
+    coord = None
+
     def __init__(self, config):
         """
         :param config: a `TrainConfig` instance

tensorpack/train/multigpu.py

@@ -147,6 +147,7 @@ class AsyncMultiGPUTrainer(QueueInputTrainerBase,
         for th in self.training_threads:
             th.pause()
         try:
+            if self.config.tower > 1:
                 async_step_total_cnt = int(re.findall(
                     '[0-9]+', self.async_step_counter.__str__())[0])
                 self.write_scalar_summary(

tensorpack/train/queue.py

@@ -63,9 +63,11 @@ class QueueInputTrainerBase(FeedlessTrainer):
     def _build_enque_thread(self, input_queue=None):
         """ create a thread that keeps filling the queue """
         self.input_vars = self.model.get_input_vars()
+        assert len(self.input_vars) > 0, "QueueInput can only be used with input placeholders!"
         if input_queue is None:
             self.input_queue = tf.FIFOQueue(
-                    50, [x.dtype for x in self.input_vars], name='input_queue')
+                    50, [x.dtype for x in self.input_vars],
+                    name='input_queue')
         else:
             self.input_queue = input_queue
         input_th = EnqueueThread(self)

tensorpack/train/trainer.py

@@ -125,6 +125,7 @@ class FeedlessTrainer(Trainer):
         """ return a list of actual input tensors.
             Always return new tensors (for multi tower) if called mutliple times.
         """
+        pass
 
 class SingleCostFeedlessTrainer(FeedlessTrainer):
     def _get_cost_and_grad(self):