clean-ups

LICENSE

@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright {yyyy} {name of copyright owner}
+   Copyright Yuxin Wu
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

README.md

@@ -3,7 +3,8 @@ Neural Network Toolbox based on TensorFlow
 
 
 ## Features:
-+ Scoped Abstraction of common models.
++ Scoped abstraction of common models.
 + Provide callbacks to control training behavior (as in [Keras](http://keras.io)).
-+ Use `Dataflow` to fine-grained control data preprocessing.
-+ Write a config file, tensorpack will do the rest.
++ Use `Dataflow` to own fine-grained control on data preprocessing.
++ Automatically use the Queue operator in tensorflow to speed up input.
++ Training and testing graph are modeled together, automatically.

dataflow/base.py

@@ -8,7 +8,6 @@
 __all__ = ['DataFlow']
 
 class DataFlow(object):
-    # TODO private impl
     @abstractmethod
     def get_data(self):
         """

dataflow/batch.py → dataflow/common.py

@@ -11,9 +11,10 @@
 class BatchData(DataFlow):
     def __init__(self, ds, batch_size, remainder=False):
         """
-        Args:
-            ds: a dataflow
-            remainder: whether to return the remaining data smaller than a batch_size
+        Group data in ds into batches
+        ds: a DataFlow instance
+        remainder: whether to return the remaining data smaller than a batch_size.
+            if set, might return a data point of a different shape
         """
         self.ds = ds
         self.batch_size = batch_size

example_mnist.py

@@ -90,8 +90,8 @@ def get_config():
 
     dataset_train = BatchData(Mnist('train'), BATCH_SIZE)
     dataset_test = BatchData(Mnist('test'), 256, remainder=True)
-    dataset_train = FixedSizeData(dataset_train, 20)
-    dataset_test = FixedSizeData(dataset_test, 20)
+    #dataset_train = FixedSizeData(dataset_train, 20)
+    #dataset_test = FixedSizeData(dataset_test, 20)
 
     sess_config = tf.ConfigProto()
     sess_config.device_count['GPU'] = 1

models/_common.py

@@ -12,7 +12,12 @@
 
 def layer_register(summary_activation=False):
     """
-    summary_activation: default behavior of whether to summary the output of this layer
+    Register a layer.
+    Args:
+        summary_activation:
+            Define the default behavior of whether to
+            summary the output(activation) of this layer.
+            Can be overriden when creating the layer.
     """
     def wrapper(func):
         def inner(*args, **kwargs):
@@ -26,24 +31,17 @@ def inner(*args, **kwargs):
                 outputs = func(*args, **kwargs)
                 if name not in _layer_logged:
                     # log shape info and add activation
-                    if isinstance(inputs, list):
-                        shape_str = ",".join(
-                            map(str(x.get_shape().as_list()), inputs))
-                    else:
-                        shape_str = str(inputs.get_shape().as_list())
-                    logger.info("{} input: {}".format(name, shape_str))
+                    logger.info("{} input: {}".format(
+                        name, get_shape_str(inputs)))
+                    logger.info("{} output: {}".format(
+                        name, get_shape_str(outputs)))
 
-                    if isinstance(outputs, list):
-                        shape_str = ",".join(
-                            map(str(x.get_shape().as_list()), outputs))
-                        if do_summary:
+                    if do_summary:
+                        if isinstance(outputs, list):
                             for x in outputs:
                                 add_activation_summary(x, scope.name)
-                    else:
-                        shape_str = str(outputs.get_shape().as_list())
-                        if do_summary:
+                        else:
                             add_activation_summary(outputs, scope.name)
-                    logger.info("{} output: {}".format(name, shape_str))
                     _layer_logged.add(name)
                 return outputs
         return inner
@@ -63,4 +61,3 @@ def shape2d(a):
 def shape4d(a):
     # for use with tensorflow
     return [1] + shape2d(a) + [1]
-

train.py

@@ -5,9 +5,8 @@
 
 import tensorflow as tf
 from utils import *
-from utils.concurrency import *
-from utils.callback import *
-from utils.summary import *
+from utils.concurrency import EnqueueThread,coordinator_guard
+from utils.summary import summary_moving_average, describe_model
 from dataflow import DataFlow
 from itertools import count
 import argparse
@@ -97,7 +96,6 @@ def start_train(config):
 
                 # note that summary_op will take a data from the queue.
                 callbacks.trigger_epoch()
-    sess.close()
 
 def main(get_config_func):
     parser = argparse.ArgumentParser()

utils/__init__.py

@@ -27,20 +27,6 @@ def timed_operation(msg, log_start=False):
     logger.info('finished {}, time={:.2f}sec.'.format(
         msg, time.time() - start))
 
-def describe_model():
-    train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
-    msg = [""]
-    total = 0
-    for v in train_vars:
-        shape = v.get_shape()
-        ele = shape.num_elements()
-        total += ele
-        msg.append("{}: shape={}, dim={}".format(
-            v.name, shape.as_list(), ele))
-    msg.append("Total dim={}".format(total))
-    logger.info("Model Params: {}".format('\n'.join(msg)))
-
-# TODO disable shape output in get_model
 @contextmanager
 def create_test_graph():
     G = tf.get_default_graph()

utils/naming.py

@@ -3,9 +3,6 @@
 # File: naming.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
-IS_TRAINING_OP_NAME = 'is_training'
-IS_TRAINING_VAR_NAME = 'is_training:0'
-
 GLOBAL_STEP_OP_NAME = 'global_step'
 GLOBAL_STEP_VAR_NAME = 'global_step:0'
 

utils/summary.py

@@ -4,6 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
+import logger
 from .naming import *
 
 def create_summary(name, v):
@@ -44,6 +45,10 @@ def add_histogram_summary(regex):
             tf.histogram_summary(name, p)
 
 def summary_moving_average(cost_var):
+    """ Create a MovingAverage op and summary for all variables in
+        COST_VARS_KEY, SUMMARY_VARS_KEY, as well as the argument
+        Return a op to maintain these average
+    """
     global_step_var = tf.get_default_graph().get_tensor_by_name(GLOBAL_STEP_VAR_NAME)
     averager = tf.train.ExponentialMovingAverage(
         0.9, num_updates=global_step_var, name='avg')
@@ -54,3 +59,28 @@ def summary_moving_average(cost_var):
     for c in vars_to_summary:
         tf.scalar_summary(c.op.name, averager.average(c))
     return avg_maintain_op
+
+def describe_model():
+    """ describe the current model parameters"""
+    train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
+    msg = [""]
+    total = 0
+    for v in train_vars:
+        shape = v.get_shape()
+        ele = shape.num_elements()
+        total += ele
+        msg.append("{}: shape={}, dim={}".format(
+            v.name, shape.as_list(), ele))
+    msg.append("Total dim={}".format(total))
+    logger.info("Model Params: {}".format('\n'.join(msg)))
+
+
+def get_shape_str(tensors):
+    """ return the shape string for a tensor or a list of tensors"""
+    if isinstance(tensors, list):
+        shape_str = ",".join(
+            map(str(x.get_shape().as_list()), tensors))
+    else:
+        shape_str = str(tensors.get_shape().as_list())
+    return shape_str
+