util updates

example_cifar10.py

@@ -73,9 +73,7 @@ class Model(ModelDesc):
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ValidationError to use at test time
-        wrong = tf.not_equal(
-            tf.cast(tf.argmax(prob, 1), tf.int32), label)
-        wrong = tf.cast(wrong, tf.float32)
+        wrong = prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         tf.add_to_collection(

example_mnist.py

@@ -64,9 +64,7 @@ class Model(ModelDesc):
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ValidationError to use at test time
-        wrong = tf.not_equal(
-            tf.cast(tf.argmax(prob, 1), tf.int32), label)
-        wrong = tf.cast(wrong, tf.float32)
+        wrong = prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         tf.add_to_collection(
@@ -90,7 +88,6 @@ def get_config():
     dataset_train = BatchData(dataset.Mnist('train'), 128)
     dataset_test = BatchData(dataset.Mnist('test'), 256, remainder=True)
     step_per_epoch = dataset_train.size()
-    step_per_epoch = 30
 
     # prepare session
     sess_config = get_default_sess_config()

tensorpack/dataflow/common.py

@@ -3,13 +3,14 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import numpy as np
+import random
 import copy
 from six.moves import range
 from .base import DataFlow, ProxyDataFlow
 from ..utils import *
 
 __all__ = ['BatchData', 'FixedSizeData', 'FakeData', 'MapData',
-           'MapDataComponent', 'RandomChooseData' ]
+           'MapDataComponent', 'RandomChooseData', 'RandomMixData']
 
 class BatchData(ProxyDataFlow):
     def __init__(self, ds, batch_size, remainder=False):
@@ -182,3 +183,35 @@ class RandomChooseData(DataFlow):
                 yield next(itr)
         except StopIteration:
             return
+
+class RandomMixData(DataFlow):
+    """
+    Randomly choose from several dataflow, will eventually exhaust all dataflow.
+    So it's a perfect mix.
+    """
+    def __init__(self, df_lists):
+        """
+        df_lists: list of dataflow
+        all DataFlow in df_lists must have size() implemented
+        """
+        self.df_lists = df_lists
+        self.sizes = [k.size() for k in self.df_lists]
+
+    def reset_state(self):
+        for d in self.df_lists:
+            d.reset_state()
+
+    def size(self):
+        return sum(self.sizes)
+
+    def get_data(self):
+        sums = np.cumsum(self.sizes)
+        idxs = np.arange(self.size())
+        np.random.shuffle(idxs)
+        idxs = np.array(map(
+            lambda x: np.searchsorted(sums, x, 'right'), idxs))
+        itrs = [k.get_data() for k in self.df_lists]
+        assert idxs.max() == len(itrs) - 1, "{}!={}".format(idxs.max(), len(itrs)-1)
+        for k in idxs:
+            yield next(itrs[k])
+

tensorpack/models/conv2d.py

@@ -3,6 +3,7 @@
 # File: conv2d.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
+import numpy as np
 import tensorflow as tf
 import math
 from ._common import *
@@ -21,6 +22,7 @@ def Conv2D(x, out_channel, kernel_shape,
     split: split channels. used in Alexnet
     """
     in_shape = x.get_shape().as_list()
+    num_in = np.prod(in_shape[1:])
     in_channel = in_shape[-1]
     assert in_channel % split == 0
     assert out_channel % split == 0
@@ -31,7 +33,8 @@ def Conv2D(x, out_channel, kernel_shape,
     stride = shape4d(stride)
 
     if W_init is None:
-        W_init = tf.truncated_normal_initializer(stddev=1e-2)
+        #W_init = tf.truncated_normal_initializer(stddev=3e-2)
+        W_init = tf.contrib.layers.xavier_initializer_conv2d()
     if b_init is None:
         b_init = tf.constant_initializer()
 

tensorpack/utils/concurrency.py

@@ -3,6 +3,7 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import threading
+import multiprocessing
 from contextlib import contextmanager
 import tensorflow as tf
 import atexit

tensorpack/utils/symbolic_functions.py

@@ -16,6 +16,19 @@ def one_hot(y, num_labels):
         onehot_labels.set_shape([None, num_labels])
         return tf.cast(onehot_labels, tf.float32)
 
+def prediction_incorrect(logits, label):
+    """
+    logits: batchxN
+    label: batch
+    return a binary vector with 1 means incorrect prediction
+    """
+    with tf.op_scope([logits, label], 'incorrect'):
+        wrong = tf.not_equal(
+            tf.argmax(logits, 1),
+            tf.cast(label, tf.int64))
+        wrong = tf.cast(wrong, tf.float32)
+        return wrong
+
 def flatten(x):
     return tf.reshape(x, [-1])