update readme

README.md

@@ -4,8 +4,8 @@ Neural Network Toolbox on TensorFlow
 See some [examples](examples) to learn about the framework:
 
 ### Vision:
-+ [DoReFa-Net: training binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
-+ [ResNet for ImageNet/Cifar10/SVHN](examples/ResNet)
++ [DoReFa-Net: train binary / low-bitwidth CNN on ImageNet](examples/DoReFa-Net)
++ [Train ResNet on ImageNet/Cifar10/SVHN](examples/ResNet)
 + [InceptionV3 on ImageNet](examples/Inception/inceptionv3.py)
 + [Fully-convolutional Network for Holistically-Nested Edge Detection(HED)](examples/HED)
 + [Spatial Transformer Networks on MNIST addition](examples/SpatialTransformer)

examples/GAN/README.md

@@ -29,10 +29,12 @@ Play with the [pretrained model](https://drive.google.com/drive/folders/0B9IPQTv
 Image-to-Image following the setup in [pix2pix](https://github.com/phillipi/pix2pix).
 It requires the datasets released by the original authors.
 
-With the cityscapes dataset, it learns to generate semantic segmentation map of urban scene:
+For example, with the cityscapes dataset, it learns to generate semantic segmentation map of urban scene:
 
 ![im2im](demo/im2im-cityscapes.jpg)
 
+This is a visualization from tensorboard. Left to right: original, ground truth, model output.
+
 ## InfoGAN-mnist.py
 
 Reproduce one mnist experiement in InfoGAN.

examples/ResNet/README.md

 
 ## imagenet-resnet.py
 
-Training code of pre-activation ResNet on ImageNet. It follows the setup in
+__Training__ code of pre-activation ResNet on ImageNet. It follows the setup in
 [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch) and gets similar performance (with much fewer lines of code).
 Models can be [downloaded here](https://goo.gl/6XjK9V).
 

tensorpack/models/pool.py

@@ -124,6 +124,19 @@ def BilinearUpSample(x, shape):
     :param x: input NHWC tensor
     :param shape: an integer, the upsample factor
     """
+    #inp_shape = tf.shape(x)
+    #return tf.image.resize_bilinear(x,
+            #tf.pack([inp_shape[1]*shape,inp_shape[2]*shape]),
+            #align_corners=True)
+
+    inp_shape = x.get_shape().as_list()
+    ch = inp_shape[3]
+    assert ch is not None
+
+    shape = int(shape)
+    filter_shape = 2 * shape
+
+
     def bilinear_conv_filler(s):
         """
         s: width, height of the conv filter
@@ -136,13 +149,6 @@ def BilinearUpSample(x, shape):
             for y in range(s):
                 ret[x,y] = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
         return ret
-
-    inp_shape = x.get_shape().as_list()
-    ch = inp_shape[3]
-    assert ch is not None
-
-    shape = int(shape)
-    filter_shape = 2 * shape
     w = bilinear_conv_filler(filter_shape)
     w = np.repeat(w, ch * ch).reshape((filter_shape, filter_shape, ch, ch))
     weight_var = tf.constant(w, tf.float32,

tensorpack/tfutils/sessinit.py

@@ -168,7 +168,6 @@ class ParamRestore(SessionInit):
         for k in param_names - variable_names:
             logger.warn("Variable {} in the dict not found in the graph!".format(k))
 
-
         upd = SessionUpdate(sess,
                 [v for v in variables if \
                     get_savename_from_varname(v.name) in intersect])