Fix the documentation

bob/learn/tensorflow/dataset/bio.py

@@ -30,13 +30,13 @@ class BioGenerator(object):
         ``data = load_data(database, biofile)``.
         :any:`bob.bio.base.read_original_data` is wrapped to be used by
         default.
-    multiple_samples : bool, optional
+    multiple_samples : :obj:`bool`, optional
         If true, it assumes that the bio database's samples actually contain
         multiple samples. This is useful for when you want to for example treat
         video databases as image databases.
     output_types : (object, object, object)
         The types of the returned samples.
-    output_shapes : (tf.TensorShape, tf.TensorShape, tf.TensorShape)
+    output_shapes : ``(tf.TensorShape, tf.TensorShape, tf.TensorShape)``
         The shapes of the returned samples.
     """
 

bob/learn/tensorflow/network/SimpleCNN.py

 """
-The network using keras (same as new_architecture function below):
-```
-from tensorflow.python.keras import *
-from tensorflow.python.keras.layers import *
-simplecnn = Sequential([
-    Conv2D(32,(3,3),padding='same',use_bias=False, input_shape=(28,28,3)),
-    BatchNormalization(scale=False),
-    Activation('relu'),
-    MaxPool2D(padding='same'),
-    Conv2D(64,(3,3),padding='same',use_bias=False),
-    BatchNormalization(scale=False),
-    Activation('relu'),
-    MaxPool2D(padding='same'),
-    Flatten(),
-    Dense(1024, use_bias=False),
-    BatchNormalization(scale=False),
-    Activation('relu'),
-    Dropout(rate=0.4),
-    Dense(2),
-])
-simplecnn.summary()
-```
-_________________________________________________________________
-Layer (type)                 Output Shape              Param #
-=================================================================
-conv2d_1 (Conv2D)            (None, 28, 28, 32)        864
-_________________________________________________________________
-batch_normalization_1 (Batch (None, 28, 28, 32)        96
-_________________________________________________________________
-activation_1 (Activation)    (None, 28, 28, 32)        0
-_________________________________________________________________
-max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0
-_________________________________________________________________
-conv2d_2 (Conv2D)            (None, 14, 14, 64)        18432
-_________________________________________________________________
-batch_normalization_2 (Batch (None, 14, 14, 64)        192
-_________________________________________________________________
-activation_2 (Activation)    (None, 14, 14, 64)        0
-_________________________________________________________________
-max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0
-_________________________________________________________________
-flatten_1 (Flatten)          (None, 3136)              0
-_________________________________________________________________
-dense_1 (Dense)              (None, 1024)              3211264
-_________________________________________________________________
-batch_normalization_3 (Batch (None, 1024)              3072
-_________________________________________________________________
-activation_3 (Activation)    (None, 1024)              0
-_________________________________________________________________
-dropout_1 (Dropout)          (None, 1024)              0
-_________________________________________________________________
-dense_2 (Dense)              (None, 2)                 2050
-=================================================================
-Total params: 3,235,970
-Trainable params: 3,233,730
-Non-trainable params: 2,240
-_________________________________________________________________
+The network using keras (same as new_architecture function below)::
+
+    from tensorflow.python.keras import *
+    from tensorflow.python.keras.layers import *
+    simplecnn = Sequential([
+        Conv2D(32,(3,3),padding='same',use_bias=False, input_shape=(28,28,3)),
+        BatchNormalization(scale=False),
+        Activation('relu'),
+        MaxPool2D(padding='same'),
+        Conv2D(64,(3,3),padding='same',use_bias=False),
+        BatchNormalization(scale=False),
+        Activation('relu'),
+        MaxPool2D(padding='same'),
+        Flatten(),
+        Dense(1024, use_bias=False),
+        BatchNormalization(scale=False),
+        Activation('relu'),
+        Dropout(rate=0.4),
+        Dense(2),
+    ])
+    simplecnn.summary()
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    conv2d_1 (Conv2D)            (None, 28, 28, 32)        864
+    _________________________________________________________________
+    batch_normalization_1 (Batch (None, 28, 28, 32)        96
+    _________________________________________________________________
+    activation_1 (Activation)    (None, 28, 28, 32)        0
+    _________________________________________________________________
+    max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0
+    _________________________________________________________________
+    conv2d_2 (Conv2D)            (None, 14, 14, 64)        18432
+    _________________________________________________________________
+    batch_normalization_2 (Batch (None, 14, 14, 64)        192
+    _________________________________________________________________
+    activation_2 (Activation)    (None, 14, 14, 64)        0
+    _________________________________________________________________
+    max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0
+    _________________________________________________________________
+    flatten_1 (Flatten)          (None, 3136)              0
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 1024)              3211264
+    _________________________________________________________________
+    batch_normalization_3 (Batch (None, 1024)              3072
+    _________________________________________________________________
+    activation_3 (Activation)    (None, 1024)              0
+    _________________________________________________________________
+    dropout_1 (Dropout)          (None, 1024)              0
+    _________________________________________________________________
+    dense_2 (Dense)              (None, 2)                 2050
+    =================================================================
+    Total params: 3,235,970
+    Trainable params: 3,233,730
+    Non-trainable params: 2,240
+    _________________________________________________________________
 """
 
 

bob/learn/tensorflow/style_transfer/neural_transfer.py

@@ -21,7 +21,7 @@ def compute_features(input_image, architecture, checkpoint_dir, target_end_point
     Parameters
     ----------
 
-    input_image: numpy.array
+    input_image: :any:`numpy.array`
         Input image in the format WxHxC
 
     architecture:
@@ -77,7 +77,7 @@ def compute_gram(features):
     Parameters
     ----------
 
-    features: numpy.array
+    features: :any:`numpy.array`
       Convolved features in the format NxWxHxC
 
     """
@@ -90,7 +90,7 @@ def compute_gram(features):
     return grams
 
 
-def do_style_transfer(content_image, style_images, 
+def do_style_transfer(content_image, style_images,
                       architecture, checkpoint_dir, scopes,
                       content_end_points, style_end_points,
                       preprocess_fn=None, un_preprocess_fn=None, pure_noise=False,
@@ -105,7 +105,7 @@ def do_style_transfer(content_image, style_images,
     Parameters
     ----------
 
-    content_image: numpy.array
+    content_image: :any:`numpy.array`
        Content image in the Bob format (C x W x H)
 
     style_images: :any:`list`
@@ -128,7 +128,7 @@ def do_style_transfer(content_image, style_images,
 
     preprocess_fn:
        Preprocess function. Pointer to a function that preprocess the INPUT signal
-    
+
     unpreprocess_fn:
        Un preprocess function. Pointer to a function that preprocess the OUTPUT signal
 
@@ -138,7 +138,7 @@ def do_style_transfer(content_image, style_images,
 
     iterations:
        Number of iterations to generate the image
-    
+
     learning_rate:
        Adam learning rate
 
@@ -147,7 +147,7 @@ def do_style_transfer(content_image, style_images,
 
     style_weight:
        Weight of the style loss
-    
+
     denoise_weight:
        Weight denoising loss
     """
@@ -178,10 +178,10 @@ def do_style_transfer(content_image, style_images,
                                         content_end_points, preprocess_fn)
 
     # Base style features
-    logger.info("Computing style features")  
+    logger.info("Computing style features")
     style_grams = []
     for image in style_images:
-        style_features = compute_features(image, architecture, checkpoint_dir, 
+        style_features = compute_features(image, architecture, checkpoint_dir,
                                           style_end_points, preprocess_fn)
         style_grams.append(compute_gram(style_features))
 
@@ -206,7 +206,7 @@ def do_style_transfer(content_image, style_images,
         # Computing style_loss
         style_gram_noises = []
         s_loss = 0
-   
+
         for grams_per_image in style_grams:
 
             for c in style_end_points:
@@ -227,7 +227,7 @@ def do_style_transfer(content_image, style_images,
 
         tf.contrib.framework.init_from_checkpoint(tf.train.latest_checkpoint(checkpoint_dir) if os.path.isdir(checkpoint_dir) else checkpoint_dir, scopes)
         # Training
-        with tf.Session() as sess: 
+        with tf.Session() as sess:
             sess.run(tf.global_variables_initializer())
 
             for i in range(iterations):
@@ -258,7 +258,7 @@ def do_style_transfer(content_image, style_images,
                 else:
                     normalized_style_image_yuv = bob.ip.color.rgb_to_yuv(bob.ip.color.gray_to_rgb(bob.ip.color.rgb_to_gray(normalized_style_image)))
                     content_image_yuv = bob.ip.color.rgb_to_yuv(bob.io.base.load(content_image_path))
-                
+
                 output_image = numpy.zeros(shape=content_image_yuv.shape, dtype="uint8")
                 output_image[0,:,:] = normalized_style_image_yuv[0,:,:]
                 output_image[1,:,:] = content_image_yuv[1,:,:]

conda/meta.yaml

@@ -40,6 +40,7 @@ requirements:
   run:
     - python
     - setuptools
+    - numpy
     - scipy
     - six
     - tensorflow >=1.4

doc/py_api.rst

@@ -31,7 +31,7 @@ Architectures
     bob.learn.tensorflow.network.inception_resnet_v1
     bob.learn.tensorflow.network.inception_resnet_v2_batch_norm
     bob.learn.tensorflow.network.inception_resnet_v1_batch_norm
-    bob.learn.tensorflow.network.SimpleCNN.base_architecture
+    bob.learn.tensorflow.network.SimpleCNN.slim_architecture
     bob.learn.tensorflow.network.vgg_19
     bob.learn.tensorflow.network.vgg_16
 

requirements.txt

@@ -17,4 +17,5 @@ bob.db.mnist
 bob.db.atnt
 gridtk
 bob.bio.base
+numpy
 scipy