update docs. use INTEPR_LINEAR by default.

examples/PennTreebank/PTB-LSTM.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
-# File: ptb-lstm.py
+# File: PTB-LSTM.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
@@ -53,11 +53,10 @@ class Model(ModelDesc):
         input, nextinput = input_vars
         initializer = tf.random_uniform_initializer(-0.05, 0.05)
 
-        with tf.variable_scope('LSTM', initializer=initializer):
-            cell = rnn.BasicLSTMCell(num_units=HIDDEN_SIZE, forget_bias=0.0)
-            if is_training:
-                cell = rnn.DropoutWrapper(cell, output_keep_prob=DROPOUT)
-            cell = rnn.MultiRNNCell([cell] * NUM_LAYER)
+        cell = rnn.BasicLSTMCell(num_units=HIDDEN_SIZE, forget_bias=0.0)
+        if is_training:
+            cell = rnn.DropoutWrapper(cell, output_keep_prob=DROPOUT)
+        cell = rnn.MultiRNNCell([cell] * NUM_LAYER)
 
         def get_v(n):
             return tf.get_variable(n, [BATCH, HIDDEN_SIZE],
@@ -71,13 +70,13 @@ class Model(ModelDesc):
         input_feature = tf.nn.embedding_lookup(embeddingW, input)  # B x seqlen x hiddensize
         input_feature = Dropout(input_feature, DROPOUT)
 
-        input_list = tf.unstack(input_feature, num=SEQ_LEN, axis=1)  # seqlen x (Bxhidden)
-        outputs, last_state = rnn.static_rnn(cell, input_list, state_var, scope='rnn')
+        with tf.variable_scope('LSTM', initializer=initializer):
+            input_list = tf.unstack(input_feature, num=SEQ_LEN, axis=1)  # seqlen x (Bxhidden)
+            outputs, last_state = rnn.static_rnn(cell, input_list, state_var, scope='rnn')
 
         # seqlen x (Bxrnnsize)
         output = tf.reshape(tf.concat_v2(outputs, 1), [-1, HIDDEN_SIZE])  # (Bxseqlen) x hidden
-        logits = FullyConnected('fc', output, VOCAB_SIZE, nl=tf.identity,
-                                W_init=initializer)
+        logits = FullyConnected('fc', output, VOCAB_SIZE, nl=tf.identity, W_init=initializer, b_init=initializer)
         xent_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
             logits=logits, labels=symbolic_functions.flatten(nextinput))
 

examples/README.md

@@ -33,4 +33,4 @@ Note to contributors:
 Example needs to satisfy one of the following:
 + Reproduce performance of a published or well-known paper.
 + Get state-of-the-art performance on some task.
-+ Illustrate a new way of using the library that are currently not covered.
++ Illustrate a new way of using the library that is currently not covered.

tensorpack/dataflow/common.py

@@ -499,12 +499,12 @@ class PrintData(ProxyDataFlow):
         .. code-block:: none
 
             [0110 09:22:21 @common.py:589] DataFlow Info:
-            datapoint 0<2 with 4 elements consists of
+            datapoint 0<2 with 4 components consists of
                dp 0: is float of shape () with range [0.0816501893251]
                dp 1: is ndarray of shape (64, 64) with range [0.1300, 0.6895]
                dp 2: is ndarray of shape (64, 64) with range [-1.2248, 1.2177]
                dp 3: is ndarray of shape (9, 9) with range [-0.6045, 0.6045]
-            datapoint 1<2 with 4 elements consists of
+            datapoint 1<2 with 4 components consists of
                dp 0: is float of shape () with range [5.88252075399]
                dp 1: is ndarray of shape (64, 64) with range [0.0072, 0.9371]
                dp 2: is ndarray of shape (64, 64) with range [-0.9011, 0.8491]
@@ -539,7 +539,7 @@ class PrintData(ProxyDataFlow):
             string: debug message
         """
         if isinstance(el, list):
-            return "%s is list of %i elements " % (" " * (depth * 2), len(el))
+            return "%s is list of %i elements" % (" " * (depth * 2), len(el))
         else:
             el_type = el.__class__.__name__
 
@@ -593,7 +593,7 @@ class PrintData(ProxyDataFlow):
         msg = [""]
         for i, dummy in enumerate(cutoff(ds.get_data(), self.num)):
             if isinstance(dummy, list):
-                msg.append("datapoint %i<%i with %i elements consists of" % (i, self.num, len(dummy)))
+                msg.append("datapoint %i<%i with %i components consists of" % (i, self.num, len(dummy)))
                 for k, entry in enumerate(dummy):
                     msg.append(self._analyze_input_data(entry, k))
         label = "" if self.label is "" else " (" + self.label + ")"

tensorpack/dataflow/image.py

@@ -17,7 +17,7 @@ class ImageFromFile(RNGDataFlow):
         """
         Args:
             files (list): list of file paths.
-            channel (int): 1 or 3. Produce RGB images if channel==3.
+            channel (int): 1 or 3. Will convert grayscale to RGB images if channel==3.
             resize (tuple): (h, w). If given, resize the image.
         """
         assert len(files), "No image files given to ImageFromFile!"

tensorpack/dataflow/imgaug/geometry.py

@@ -14,7 +14,7 @@ class Rotation(ImageAugmentor):
     """ Random rotate the image w.r.t a random center"""
 
     def __init__(self, max_deg, center_range=(0, 1),
-                 interp=cv2.INTER_CUBIC,
+                 interp=cv2.INTER_LINEAR,
                  border=cv2.BORDER_REPLICATE):
         """
         Args:
@@ -43,7 +43,7 @@ class RotationAndCropValid(ImageAugmentor):
         Note that this will produce images of different shapes.
     """
 
-    def __init__(self, max_deg, interp=cv2.INTER_CUBIC):
+    def __init__(self, max_deg, interp=cv2.INTER_LINEAR):
         """
         Args:
             max_deg (float): max abs value of the rotation degree (in angle).

tensorpack/dataflow/imgaug/noname.py

@@ -49,7 +49,7 @@ class Flip(ImageAugmentor):
 class Resize(ImageAugmentor):
     """ Resize image to a target size"""
 
-    def __init__(self, shape, interp=cv2.INTER_CUBIC):
+    def __init__(self, shape, interp=cv2.INTER_LINEAR):
         """
         Args:
             shape: (h, w) tuple or a int
@@ -85,7 +85,7 @@ class ResizeShortestEdge(ImageAugmentor):
         h, w = img.shape[:2]
         scale = self.size / min(h, w)
         desSize = map(int, [scale * w, scale * h])
-        ret = cv2.resize(img, tuple(desSize), interpolation=cv2.INTER_CUBIC)
+        ret = cv2.resize(img, tuple(desSize), interpolation=cv2.INTER_LINEAR)
         if img.ndim == 3 and ret.ndim == 2:
             ret = ret[:, :, np.newaxis]
         return ret
@@ -95,7 +95,7 @@ class RandomResize(ImageAugmentor):
     """ Randomly rescale w and h of the image"""
 
     def __init__(self, xrange, yrange, minimum=(0, 0), aspect_ratio_thres=0.15,
-                 interp=cv2.INTER_CUBIC):
+                 interp=cv2.INTER_LINEAR):
         """
         Args:
             xrange (tuple): (min, max) range of scaling ratio for w

tensorpack/tfutils/varreplace.py

@@ -20,6 +20,11 @@ def replace_get_variable(fn):
     Returns:
         a context where ``tf.get_variable`` and
         ``variable_scope.get_variable`` are replaced with ``fn``.
+
+    Note that originally ``tf.get_variable ==
+    tensorflow.python.ops.variable_scope.get_variable``. But some code such as
+    some in `rnn_cell/`, uses the latter one to get variable, therefore both
+    need to be replaced.
     """
     old_getv = tf.get_variable
     old_vars_getv = variable_scope.get_variable