add SlimNetsLayer and Inception V3 example / Merge TF-Slim into Tenso…

…rLayer

docs/modules/layers.rst

@@ -270,6 +270,7 @@ In addition, if you want to update the parameters of previous 2 layers at the sa
    FlattenLayer
    ConcatLayer
    ReshapeLayer
+   SlimNetsLayer
    MultiplexerLayer
    EmbeddingAttentionSeq2seqWrapper
    flatten_reshape
@@ -357,10 +358,20 @@ so to implement 1D CNN, you can use Reshape layer as follow.
 
 .. autoclass:: Conv2dLayer
 
+2D Deconvolutional layer
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: DeConv2dLayer
+
+
 3D Convolutional layer
 ^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autoclass:: Conv3dLayer
+
+3D Deconvolutional layer
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. autoclass:: DeConv3dLayer
 
 Pooling layer
@@ -397,6 +408,13 @@ Reshape layer
 
 .. autoclass:: ReshapeLayer
 
+Merge TF-Slim
+^^^^^^^^^^^^^^^
+
+Yes ! TF-Slim models can be merged into TensorLayer, all Google's Pre-trained model can be used easily ,
+see `Slim-model <https://github.com/tensorflow/models/tree/master/slim#Install>`_ .
+
+.. autoclass:: SlimNetsLayer
 
 Flow control layer
 ----------------------

docs/user/example.rst

@@ -19,6 +19,7 @@ Computer Vision
  - Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see ``tutorial_cifar10.py`` and ``tutorial_cifar10_tfrecord.py``on `GitHub`_.
  - VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see ``tutorial_vgg16.py`` on `GitHub`_.
  - VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see ``tutorial_vgg19.py`` on `GitHub`_.
+ - InceptionV3 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see ``tutorial_inceptionV3_tfslim.py`` on `GitHub`_.
 
 
 Natural Language Processing
@@ -36,6 +37,13 @@ Reinforcement Learning
  - Deep Reinforcement Learning - Pong Game. Teach a machine to play Pong games, see ``tutorial_atari_pong.py`` on `GitHub`_.
 
 
+Special Examples
+=================
+
+ - Merge TF-Slim into TensorLayer. ``tutorial_inceptionV3_tfslim.py`` on `GitHub`_.
+ - MultiplexerLayer. ``tutorial_mnist_multiplexer.py`` on `GitHub`_.
+
+
 ..
   Applications
   =============

tensorlayer/layers.py

@@ -1629,6 +1629,58 @@ def __init__(
         self.all_drop = dict(layer.all_drop)
         self.all_layers.extend( [self.outputs] )
 
+## TF-Slim layer
+class SlimNetsLayer(Layer):
+    """
+    The :class:`SlimNetsLayer` class can be used to merge all TF-Slim nets into
+    TensorLayer. Model can be found in `slim-model <https://github.com/tensorflow/models/tree/master/slim#Install>`_ , more about slim
+    see `slim-git <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim>`_ .
+
+    Parameters
+    ----------
+    layer : a list of :class:`Layer` instances
+        The `Layer` class feeding into this layer.
+    slim_layer : a slim network function
+        The network you want to stack onto, end with ``return net, end_points``.
+    name : a string or None
+        An optional name to attach to this layer.
+
+    Note
+    -----
+    The due to TF-Slim stores the layers as dictionary, the ``all_layers`` in this
+    network is not in order ! Fortunately, the ``all_params`` are in order.
+
+    """
+    def __init__(
+        self,
+        layer = None,
+        slim_layer = None,
+        slim_args = {},
+        name ='slim_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  tensorlayer:Instantiate SlimNetsLayer %s: %s" % (self.name, slim_layer.__name__))
+
+        with tf.variable_scope(name) as vs:
+            net, end_points = slim_layer(self.inputs, **slim_args)
+            slim_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope=vs.name)
+
+        self.outputs = net
+
+        slim_layers = []
+        for v in end_points.values():
+            tf.contrib.layers.summaries.summarize_activation(v)
+            slim_layers.append(v)
+
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+
+        self.all_layers.extend( slim_layers )
+        self.all_params.extend( slim_variables )
+
+
 ## Flow control layer
 class MultiplexerLayer(Layer):
     """

tensorlayer/ops.py

@@ -156,9 +156,14 @@ def get_site_packages_directory():
     """Print and return the site-packages directory?
     """
     import site
-    loc = site.getsitepackages()
-    print(loc)
-    return loc
+    try:
+        loc = site.getsitepackages()
+        print("  tl.ops : site-packages in ", loc)
+        return loc
+    except:
+        p = '  tl.ops : You are using virtual environment'
+        print(p)
+        return p
 
 
 

tutorial_inceptionV3_tfslim.py

@@ -0,0 +1,131 @@
+#! /usr/bin/python
+# -*- coding: utf8 -*-
+
+
+import tensorflow as tf
+import tensorlayer as tl
+slim = tf.contrib.slim
+from tensorflow.contrib.slim.python.slim.nets.alexnet import alexnet_v2
+from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base, inception_v3, inception_v3_arg_scope
+# from tensorflow.contrib.slim.python.slim.nets.resnet_v2 import resnet_v2_152
+# from tensorflow.contrib.slim.python.slim.nets.vgg import vgg_16
+import skimage
+import skimage.io
+import skimage.transform
+import time
+from data.imagenet_classes import *
+import numpy as np
+"""
+You will learn:
+1. What is TF-Slim ?
+1. How to combine TensorLayer and TF-Slim ?
+
+Introduction of Slim    : https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim
+Slim Pre-trained Models : https://github.com/tensorflow/models/tree/master/slim
+
+With the help of SlimNetsLayer, all Slim Model can be combined into TensorLayer.
+All models in the following link, end with `return net, end_points`` are available.
+https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim/python/slim/nets
+
+
+Bugs
+-----
+tf.variable_scope :
+        https://groups.google.com/a/tensorflow.org/forum/#!topic/discuss/RoxrU3UnbFA
+load inception_v3 for prediction:
+        http://stackoverflow.com/questions/39357454/restore-checkpoint-in-tensorflow-tensor-name-not-found
+"""
+def load_image(path):
+    # load image
+    img = skimage.io.imread(path)
+    img = img / 255.0
+    assert (0 <= img).all() and (img <= 1.0).all()
+    # print "Original Image Shape: ", img.shape
+    # we crop image from center
+    short_edge = min(img.shape[:2])
+    yy = int((img.shape[0] - short_edge) / 2)
+    xx = int((img.shape[1] - short_edge) / 2)
+    crop_img = img[yy: yy + short_edge, xx: xx + short_edge]
+    # resize to 224, 224
+    resized_img = skimage.transform.resize(crop_img, (299, 299))
+    return resized_img
+
+
+def print_prob(prob):
+    synset = class_names
+    # print prob
+    pred = np.argsort(prob)[::-1]
+    # Get top1 label
+    top1 = synset[pred[0]]
+    print("Top1: ", top1, prob[pred[0]])
+    # Get top5 label
+    top5 = [(synset[pred[i]], prob[pred[i]]) for i in range(5)]
+    print("Top5: ", top5)
+    return top1
+
+
+## Alexnet_v2 / All Slim nets can be merged into TensorLayer
+# x = tf.placeholder(tf.float32, shape=[None, 299, 299, 3])
+# net_in = tl.layers.InputLayer(x, name='input_layer')
+# network = tl.layers.SlimNetsLayer(layer=net_in, slim_layer=alexnet_v2,
+#                                 slim_args= {
+#                                        'num_classes' : 1000,
+#                                        'is_training' : True,
+#                                        'dropout_keep_prob' : 0.5,
+#                                        'spatial_squeeze' : True,
+#                                        'scope' : 'alexnet_v2'
+#                                         }
+#                                     )
+# sess = tf.InteractiveSession()
+# sess.run(tf.initialize_all_variables())
+# network.print_params()
+# exit()
+
+# InceptionV3
+x = tf.placeholder(tf.float32, shape=[None, 299, 299, 3])
+net_in = tl.layers.InputLayer(x, name='input_layer')        # DH
+with slim.arg_scope(inception_v3_arg_scope()):
+    # logits, end_points = inception_v3(X, num_classes=1001,
+    #                                   is_training=False)
+    network = tl.layers.SlimNetsLayer(layer=net_in, slim_layer=inception_v3,
+                                    slim_args= {
+                                             'num_classes' : 1001,
+                                             'is_training' : False,
+                                            #  'dropout_keep_prob' : 0.8,   # for training
+                                            #  'min_depth' : 16,
+                                            #  'depth_multiplier' : 1.0,
+                                            #  'prediction_fn' : slim.softmax,
+                                            #  'spatial_squeeze' : True,
+                                            #  'reuse' : None,
+                                            #  'scope' : 'InceptionV3'
+                                            },
+                                        name=''
+                                        )
+saver = tf.train.Saver()
+
+sess = tf.InteractiveSession()
+sess.run(tf.initialize_all_variables())
+
+# with tf.Session() as sess:
+saver.restore(sess, "inception_v3.ckpt")    # download from https://github.com/tensorflow/models/tree/master/slim#Install
+print("Model Restored")
+network.print_params(False)
+
+
+from scipy.misc import imread, imresize
+y = network.outputs
+probs = tf.nn.softmax(y)
+img1 = load_image("data/puzzle.jpeg")
+img1 = img1.reshape((1, 299, 299, 3))
+
+start_time = time.time()
+prob = sess.run(probs, feed_dict= {x : img1})
+print("End time : %.5ss" % (time.time() - start_time))
+print_prob(prob[0][1:]) # Note : as it have 1001 outputs, the 1st output is nothing
+
+
+
+
+
+
+#