fix image_sampling, and email

examples/Atari2600/DQN.py

 #!/usr/bin/env python2
 # -*- coding: utf-8 -*-
 # File: DQN.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import numpy as np
 import tensorflow as tf

examples/DisturbLabel/mnist-disturb.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # File: mnist-disturb.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import numpy as np
 import tensorflow as tf

examples/DisturbLabel/svhn-disturb.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: svhn-disturb.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import argparse

examples/DoReFa-Net/svhn-digit-dorefa.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: svhn-digit-dorefa.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import argparse

examples/HED/hed.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: hed.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import cv2
 import tensorflow as tf

examples/Inception/inception-bn.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: inception-bn.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import cv2
 import argparse

examples/Inception/inceptionv3.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: inceptionv3.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import cv2
 import argparse

examples/ResNet/cifar10-resnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: cifar10-resnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import numpy as np
 import tensorflow as tf

examples/ResNet/svhn-resnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: svhn-resnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import argparse

examples/char-rnn/char-rnn.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # File: char-rnn.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import numpy as np

examples/cifar-convnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: cifar-convnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 import tensorflow as tf
 import argparse
 import numpy as np

examples/load-alexnet.py

 #!/usr/bin/env python2
 # -*- coding: UTF-8 -*-
 # File: load-alexnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import numpy as np

examples/load-vgg16.py

 #!/usr/bin/env python2
 # -*- coding: UTF-8 -*-
 # File: load-vgg16.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import cv2
 import tensorflow as tf
@@ -72,7 +72,6 @@ class Model(ModelDesc):
 
 def run_test(path, input):
     param_dict = np.load(path).item()
-
     pred_config = PredictConfig(
         model=Model(),
         input_var_names=['input'],

examples/mnist-convnet.py

 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 # File: mnist-convnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import numpy as np
 import tensorflow as tf

examples/svhn-digit-convnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 # File: svhn-digit-convnet.py
-# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
 import argparse

tensorpack/dataflow/image.py

@@ -12,11 +12,12 @@ from .imgaug import AugmentorList
 __all__ = ['ImageFromFile', 'AugmentImageComponent', 'AugmentImageComponents']
 
 class ImageFromFile(DataFlow):
-    """ Generate rgb images from list of files """
     def __init__(self, files, channel=3, resize=None):
-        """ :param files: list of file paths
-            :param channel: 1 or 3 channel
-            :param resize: a (h, w) tuple. If given, will force a resize
+        """
+        Generate rgb images from list of files
+        :param files: list of file paths
+        :param channel: 1 or 3 channel
+        :param resize: a (h, w) tuple. If given, will force a resize
         """
         assert len(files)
         self.files = files
@@ -38,11 +39,9 @@ class ImageFromFile(DataFlow):
 
 
 class AugmentImageComponent(MapDataComponent):
-    """
-    Augment the image component of datapoints
-    """
     def __init__(self, ds, augmentors, index=0):
         """
+        Augment the image component of datapoints
         :param ds: a `DataFlow` instance.
         :param augmentors: a list of `ImageAugmentor` instance to be applied in order.
         :param index: the index (or list of indices) of the image component in the produced datapoints by `ds`. default to be 0
@@ -57,9 +56,8 @@ class AugmentImageComponent(MapDataComponent):
 
 
 class AugmentImageComponents(MapData):
-    """ Augment a list of images of the same shape, with the same parameters"""
     def __init__(self, ds, augmentors, index=(0,1)):
-        """
+        """ Augment a list of images of the same shape, with the same parameters
         :param ds: a `DataFlow` instance.
         :param augmentors: a list of `ImageAugmentor` instance to be applied in order.
         :param index: tuple of indices of the image components

tensorpack/models/image_sample.py

@@ -12,7 +12,7 @@ __all__ = ['ImageSample']
 # XXX TODO ugly.
 # really need to fix this after tensorflow supports advanced indexing
 # See github:tensorflow#418,#206
-def sample(img, coords, borderMode):
+def sample(img, coords):
     """
     :param img: bxhxwxc
     :param coords: bxh2xw2x2 (y, x) floating point (but is actually holding integer)
@@ -39,14 +39,6 @@ def sample(img, coords, borderMode):
 
     img = tf.reshape(img, [-1, shape[2]])   #bhw x c
     sampled = tf.gather(img, flat_coords)
-
-    if borderMode == 'constant':
-        mask = tf.less_equal(orig_coords, max_coor)
-        mask2 = tf.greater_equal(orig_coords, 0)
-        mask = tf.logical_and(mask, mask2)   #bxh2xw2x2
-        mask = tf.reduce_all(mask, [3]) # bxh2xw2 boolean
-        mask = tf.expand_dims(mask, 3)
-        sampled = sampled * tf.cast(mask, tf.float32)
     return sampled
 
 @layer_register()
@@ -64,8 +56,12 @@ def ImageSample(inputs, borderMode='repeat'):
     """
     template, mapping = inputs
     assert template.get_shape().ndims == 4 and mapping.get_shape().ndims == 4
+    input_shape = template.get_shape().as_list()[1:]
+    assert None not in input_shape, \
+            "Images in ImageSample layer must have fully-defined shape"
     assert borderMode in ['repeat', 'constant']
 
+    orig_mapping = mapping
     mapping = tf.maximum(mapping, 0.0)
     lcoor = tf.floor(mapping)
     ucoor = lcoor + 1
@@ -86,10 +82,19 @@ def ImageSample(inputs, borderMode='repeat'):
     #diff = tf.Print(diff, [tf.is_finite(tf.reduce_sum(diff)), tf.shape(prod),
                           #tf.reduce_max(diff), diff], summarize=50)
 
-    return tf.add_n([sample(template, lcoor, borderMode) * neg_diffx * neg_diffy,
-           sample(template, ucoor, borderMode) * diffx * diffy,
-           sample(template, lyux, borderMode) * neg_diffy * diffx,
-           sample(template, uylx, borderMode) * diffy * neg_diffx], name='sampled')
+    ret = tf.add_n([sample(template, lcoor) * neg_diffx * neg_diffy,
+           sample(template, ucoor) * diffx * diffy,
+           sample(template, lyux) * neg_diffy * diffx,
+           sample(template, uylx) * diffy * neg_diffx], name='sampled')
+    if borderMode == 'constant':
+        max_coor = tf.constant([input_shape[0] - 1, input_shape[1] - 1], dtype=tf.float32)
+        mask = tf.greater_equal(orig_mapping, 0.0)
+        mask2 = tf.less_equal(orig_mapping, max_coor)
+        mask = tf.logical_and(mask, mask2)   #bxh2xw2x2
+        mask = tf.reduce_all(mask, [3]) # bxh2xw2 boolean
+        mask = tf.expand_dims(mask, 3)
+        ret = ret * tf.cast(mask, tf.float32)
+    return ret
 
 from ._test import TestModel
 class TestSample(TestModel):
@@ -137,21 +142,21 @@ if __name__ == '__main__':
 
     h, w = 300, 400
     mapping = np.zeros((1, h, w, 2), dtype='float32')
-    diff = 2000
+    diff = 200
     for x in range(w):
         for y in range(h):
             mapping[0,y,x,:] = np.array([y-diff+0.4, x-diff+0.5])
 
     mapv = tf.Variable(mapping)
-    output = ImageSample('sample', [imv, mapv])
+    output = ImageSample('sample', [imv, mapv], borderMode='constant')
     sess = tf.Session()
     sess.run(tf.initialize_all_variables())
 
-    out = sess.run(tf.gradients(tf.reduce_sum(output), mapv))
+    #out = sess.run(tf.gradients(tf.reduce_sum(output), mapv))
     #out = sess.run(output)
-    print(out[0].min())
-    print(out[0].max())
-    print(out[0].sum())
+    #print(out[0].min())
+    #print(out[0].max())
+    #print(out[0].sum())
 
     out = sess.run([output])[0]
     im = out[0]