From 8be1b9ccf9d3e3437d9a89215abda245f4828c64 Mon Sep 17 00:00:00 2001
From: PatWie <mail@patwie.com>
Date: Tue, 2 Oct 2018 13:44:32 +0200
Subject: [PATCH] add pooling, transpose convolution to layers.py and update
 example.py

---
 .github/inference_time.png    | Bin 65047 -> 65212 bytes
 README.md                     |  40 +++----
 example.py                    |  43 ++++++--
 layers.py                     | 194 ++++++++++++++++++++++++++++++++--
 user_ops/CMakeLists.txt       |   2 +-
 user_ops/FindTensorFlow.cmake |   4 +-
 user_ops/__init__.py          |   3 +-
 7 files changed, 241 insertions(+), 45 deletions(-)

diff --git a/.github/inference_time.png b/.github/inference_time.png
index bd56ca2d1ce2c379189cf4e8760a7303c8ae10af..5d2b04b279cbf34c404b6d01c8532f5aa075053c 100644
GIT binary patch
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BQnUa7

diff --git a/README.md b/README.md
index 09fe613..d2fe833 100644
--- a/README.md
+++ b/README.md
@@ -14,7 +14,7 @@ The following figure shows the *raw* network semantic segmentation prediction on
 <p align="center"> <img src=".github/flexconv.jpg" width="100%"> </p>
 
 
-This repository contains contains the source code of our FlexConv Layer from our 2018 ACCV paper "Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)".
+This repository contains the source code of our FlexConv Layer from our 2018 ACCV paper "Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds)".
 
 <p align="center"> <a href="https://www.youtube.com/watch?v=5ftWmuQXU_s"><img src="./.github/youtube.jpg" width="50%"></a> </p>
 
@@ -22,7 +22,7 @@ This repository contains contains the source code of our FlexConv Layer from our
 Example - Usage
 -------------------
 
-We provide GPU-tailored cuda implementation of our novel FlexConv in TensorFlow.
+We provide GPU-tailored CUDA implementations of our novel FlexConv, FlexPool, FlexDeconv operations in TensorFlow.
 
 ```console
 user@host $ cd user_ops
@@ -35,13 +35,13 @@ user@host $ python example.py
 Experiments
 -------------------
 
-Deep learning on point-clouds is a complex matter and our codebase reflect that complexity.
+Deep learning on point-clouds is a complex matter and our codebase reflects that complexity.
 We are currently working on refactoring our research implementation to ease the usage. Therefore,
 `layers.py` contains a Keras/tf.layers compatible implementation. We will add the models later.
 
 ### Benchmark
 
-We benchmarked the inference time of entire network on the 2D-3D-S dataset and with recent tests on V100, we were able to process ~18 Million Points.
+We benchmarked the inference time of *entire* network on the 2D-3D-S dataset and with a recent test on a NVIDIA V100 GPU, we were able to process ~18 Million Points.
 
 <p align="center"> <img src=".github/inference_time.png" width="50%"> </p>
 
@@ -50,14 +50,14 @@ We benchmarked the inference time of entire network on the 2D-3D-S dataset and w
 
 ShapeNet part segmentation results per category and mIoU (%) for different methods and inference speed (on a NVIDIA GeForce GTX 1080 Ti).
 
-|| Airplane        | Bag           | Cap           | Car           | Chair         | Earphones        | Guitar        | Knife         | Lamp          | Laptop        | Motorbike       | Mug           | Pistol        | Rocket        | Skateboard       | Table         | mIoU        | shapes/sec  |
-| ----        | ----           | ----           | ----           | ----         | ----     | ----        | ----         | ----          | ----        | ----       | ----           | ----        | ----        | ----       | ---         | ----        | ----  | --- |
-|Kd-Network  [4]  | 80.1          | 74.6          | 74.3          | 70.3          | 88.6          | 73.5          | 90.2          | **87.2** | 81.0          | 94.9          | 57.4          | 86.7          | 78.1          | 51.8          | 69.9          | 80.3          | 77.4          | n.a.|
-|PointNet  [1]     | 83.4          | 78.7          | 82.5          | 74.9          | 89.6          | 73.0          | 91.5          | 85.9          | 80.8          | 95.3          | 65.2          | 93.0          | 81.2          | 57.9          | 72.8          | 80.6          | 80.4          | n.a. |
-|PointNet++ [2]   | 82.4          | 79.0          | 87.7          | 77.3          | **90.8** | 71.8          | 91.0          | 85.9          | 83.7          | 95.3          | 71.6          | 94.1          | 81.3          | 58.7          | 76.4          | 82.6          | 81.9          | 2.7 |
-|SPLATNet3D [3] | 81.9          | 83.9          | 88.6          | **79.5** | 90.1          | 73.5          | 91.3          | 84.7          | **84.5** | 96.3          | 69.7          | 95.0          | 81.7          | 59.2          | 70.4          | 81.3          | 82.0          | 9.4   |
-|SGPN  [5]  | 80.4          | 78.6          | 78.8          | 71.5          | 88.6          | **78.0** | 90.9          | 83.0          | 78.8          | 95.8          | **77.8** | 93.8          | **87.4** | 60.1          | **92.3** | **89.4** | 82.8          | n.a. |
-Ours                           | **83.6** | **91.2** | **96.7** | **79.5** | 84.7          | 71.7          | **92.0** | 86.5          | 83.2          | **96.6** | 71.7          | **95.7** | 86.1          | **74.8** | 81.4          | 84.5          | **85.0** | **489.3** |
+|                 | mIoU     | shapes/sec | Airplane | Bag      | Cap      | Car      | Chair    | Earphones | Guitar   | Knife    | Lamp     | Laptop   | Motorbike | Mug      | Pistol   | Rocket   | Skateboard | Table    |
+| ----            | ----     | ---        | ----     | ----     | ----     | ----     | ----     | ----      | ----     | ----     | ----     | ----     | ----      | ----     | ----     | ----     | ---        | ----     |
+| Kd-Network  [4] | 77.4     | n.a.       | 80.1     | 74.6     | 74.3     | 70.3     | 88.6     | 73.5      | 90.2     | **87.2** | 81.0     | 94.9     | 57.4      | 86.7     | 78.1     | 51.8     | 69.9       | 80.3     |
+| PointNet  [1]   | 80.4     | n.a.       | 83.4     | 78.7     | 82.5     | 74.9     | 89.6     | 73.0      | 91.5     | 85.9     | 80.8     | 95.3     | 65.2      | 93.0     | 81.2     | 57.9     | 72.8       | 80.6     |
+| PointNet++ [2]  | 81.9     | 2.7        | 82.4     | 79.0     | 87.7     | 77.3     | **90.8** | 71.8      | 91.0     | 85.9     | 83.7     | 95.3     | 71.6      | 94.1     | 81.3     | 58.7     | 76.4       | 82.6     |
+| SPLATNet3D [3]  | 82.0     | 9.4        | 81.9     | 83.9     | 88.6     | **79.5** | 90.1     | 73.5      | 91.3     | 84.7     | **84.5** | 96.3     | 69.7      | 95.0     | 81.7     | 59.2     | 70.4       | 81.3     |
+| SGPN  [5]       | 82.8     | n.a.       | 80.4     | 78.6     | 78.8     | 71.5     | 88.6     | **78.0**  | 90.9     | 83.0     | 78.8     | 95.8     | **77.8**  | 93.8     | **87.4** | 60.1     | **92.3**   | **89.4** |
+| Ours            | **85.0** | **489.3**  | **83.6** | **91.2** | **96.7** | **79.5** | 84.7     | 71.7      | **92.0** | 86.5     | 83.2     | **96.6** | 71.7      | **95.7** | 86.1     | **74.8** | 81.4       | 84.5     |
 
 ![example segmentation](./.github/shapenet.png)
 
@@ -65,14 +65,14 @@ Ours                           | **83.6** | **91.2** | **96.7** | **79.5** | 84.
 
 Class specific average precision (AP) on the 2D-3D-S dataset.
 
-| | Table          | Chair          | Sofa           | Bookcase         | Board          | Ceiling        | Floor          | Wall           | Beam           | Col.           | Wind.          | Door           | mAP  |
-|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
-Armeni [6]     | 46.02          | 16.15          | 6.78           | 54.71        | 3.91           | 71.61          | 88.70          | 72.86          | 66.67          | **91.77** | 25.92          | 54.11          | 49.93|
-Armeni [6]  | 39.87          | 11.43          | 4.91           | **57.76** | 3.73           | 50.74          | 80.48          | 65.59          | 68.53        | 85.08          | 21.17          | 45.39          | 44.19|
-PointNet [1]           | 46.67          | 33.80          | 4.76           | n.a.           | 11.72          | n.a.           | n.a.           | n.a.           | n.a.           | n.a.           | n.a.           | n.a.           | n.a.    |
-SGPN     [5]    | 46.90          | 40.77          | 6.38           | 47.61          | 11.05          | 79.44          | 66.29          | **88.77** | **77.98** | 60.71          | **66.62** | **56.75** | 54.35 |
-Ours                                | 66.03        | 51.75        | 15.59        | 39.03          | 43.50        | 87.20        | 96.00        | 65.53          | 54.76          | 52.74          | 55.34        | 35.81          | 55.27|
-Ours**                              | **67.02** | **52.75** | **16.61** | 39.26        | **47.68** | **87.33** | **96.10** | 65.52        | 56.83        | 55.10        | 57.66        | 36.76        | **56.55**|
+|              | mAP       | Table     | Chair     | Sofa      | Bookcase  | Board     | Ceiling   | Floor     | Wall      | Beam      | Col.      | Wind.     | Door      |
+| ---          | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       | ---       |
+| Armeni [6]   | 49.93     | 46.02     | 16.15     | 6.78      | 54.71     | 3.91      | 71.61     | 88.70     | 72.86     | 66.67     | **91.77** | 25.92     | 54.11     |
+| Armeni [6]   | 44.19     | 39.87     | 11.43     | 4.91      | **57.76** | 3.73      | 50.74     | 80.48     | 65.59     | 68.53     | 85.08     | 21.17     | 45.39     |
+| PointNet [1] | n.a.      | 46.67     | 33.80     | 4.76      | n.a.      | 11.72     | n.a.      | n.a.      | n.a.      | n.a.      | n.a.      | n.a.      | n.a.      |
+| SGPN     [5] | 54.35     | 46.90     | 40.77     | 6.38      | 47.61     | 11.05     | 79.44     | 66.29     | **88.77** | **77.98** | 60.71     | **66.62** | **56.75** |
+| Ours         | 55.27     | 66.03     | 51.75     | 15.59     | 39.03     | 43.50     | 87.20     | 96.00     | 65.53     | 54.76     | 52.74     | 55.34     | 35.81     |
+| Ours**       | **56.55** | **67.02** | **52.75** | **16.61** | 39.26     | **47.68** | **87.33** | **96.10** | 65.52     | 56.83     | 55.10     | 57.66     | 36.76     |
 
 
 ![example segmentation](./.github/2d3ds-results-large-1.jpg)
diff --git a/example.py b/example.py
index 1d07355..fd411a5 100644
--- a/example.py
+++ b/example.py
@@ -19,29 +19,50 @@
 
 
 """
-Demonstration of using FlexConvolution Layer.
+Demonstration of using FlexConvolution, FlexPooling Layer.
 """
 
 import numpy as np
 import tensorflow as tf
-from layers import flex_convolution
+from tabulate import tabulate
+from layers import flex_convolution, flex_convolution_transpose, flex_pooling
 
-B, Din, Dout, Dp, N, K = 1, 2, 4, 3, 10, 5
+B, Din, Dout, Dout2, Dp, N, N2, K, K2 = 1, 2, 4, 8, 3, 10, 5, 5, 3
 
 features = np.random.randn(B, Din, N).astype(np.float32)
 positions = np.random.randn(B, Dp, N).astype(np.float32)
 neighbors = np.random.randint(0, N, [B, K, N]).astype(np.int32)
+neighbors2 = np.random.randint(0, N, [B, K2, N2]).astype(np.int32)
+
+features = tf.convert_to_tensor(features, name='features')
+positions = tf.convert_to_tensor(positions, name='positions')
+neighbors = tf.convert_to_tensor(neighbors, name='neighbors')
+neighbors2 = tf.convert_to_tensor(neighbors2, name='neighbors2')
+
+net = [features]
+# use our FlexConv similar to a traditional convolution layer
+net.append(flex_convolution(net[-1], positions, neighbors, Dout))
+# pool and sub-sampling are different operations
+net.append(flex_pooling(net[-1], neighbors))
+
+# when ordering the points beforehand sub-sampling is simply
+features = net[-1][:, :, :N2]
+positions = positions[:, :, :N2]
+
+net.append(features)
+# we didn't notice any improvements using the transposed version vs. pooling
+net.append(flex_convolution_transpose(net[-1], positions, neighbors2, Dout2))
+# of course any commonly used arguments work here as well
+net.append(flex_convolution(net[-1], positions,
+                            neighbors2, Dout2, trainable=False))
 
-features = tf.convert_to_tensor(features)
-positions = tf.convert_to_tensor(positions)
-neighbors = tf.convert_to_tensor(neighbors)
 
-features2 = flex_convolution(features, positions, neighbors, Dout)
-features3 = flex_convolution(features2, positions, neighbors, Dout, trainable=False)
 
 with tf.Session() as sess:
   sess.run(tf.global_variables_initializer())
-  sess.run(features2)
-  sess.run(features3)
+  sess.run(net[-1])
 
-  print(tf.trainable_variables())
+  print(tabulate([[v.name, v.shape] for v in tf.trainable_variables()],
+                 headers=["Name", "Shape"]))
+
+  print(tabulate([[n.name, n.shape] for n in net], headers=["Name", "Shape"]))
diff --git a/layers.py b/layers.py
index ebbb13d..da84707 100644
--- a/layers.py
+++ b/layers.py
@@ -29,18 +29,88 @@ from tensorflow.python.util.tf_export import tf_export
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import ops
 
-flex_pooling = _flex_pooling
-flex_convolution_transpose = _flex_convolution_transpose
+
+all = ['FlexPooling', 'FlexConvolution', 'FlexConvolutionTranspose',
+       'flex_pooling', 'flex_convolution', 'flex_convolution_transpose']
 
 
-def _remove_dim(x, axis):
-  shape = x.shape.as_list()
-  assert shape[axis] == 1
-  del shape[axis]
-  return tf.reshape(x, shape)
+def _remove_dim(x, axis=2):
+  return tf.squeeze(x, axis=axis)
 
 
-@tf_export('keras.layers.Dense')
+@tf_export('keras.layers.FlexPooling')
+class FlexPooling(Layer):
+  """flex pooling layer.
+
+  This layer performs a max-pooling operation over elements in arbitrary
+  neighborhoods. When `data_format` is 'simple', the input shape should
+  have rank 3, otherwise rank 4 and dimension 2 should be 1.
+
+  Remarks:
+      In contrast to traditional pooling, this operation has no option for
+      sub-sampling.
+
+  Arguments:
+      features: A `Tensor` of the format [B, Din, (1), N].
+      neighborhoods: A `Tensor` of the format [B, K, (1), N] (tf.int32).
+      name: A string, the name of the layer.
+
+  """
+
+  def __init__(self,
+               features,
+               neighborhoods,
+               data_format='simple',
+               name=None):
+
+    super(FlexPooling, self).__init__(name=name)
+    self.features = features
+    self.neighborhoods = neighborhoods
+    self.data_format = data_format
+
+  def compute_output_shape(self, input_shape):
+    return tensor_shape.TensorShape(input_shape)
+
+  def build(self, input_shape):
+    self.built = True
+
+  def call(self, inputs):
+    if not isinstance(inputs, list):
+      raise ValueError('A flexconv layer should be called '
+                       'on a list of inputs.')
+
+    features = ops.convert_to_tensor(inputs[0], dtype=self.dtype)
+    neighborhoods = ops.convert_to_tensor(inputs[1], dtype=tf.int32)
+
+    if self.data_format == 'expanded':
+      features = _remove_dim(inputs[0], 2)
+      neighborhoods = _remove_dim(inputs[1], 2)
+    else:
+      features = inputs[0]
+      neighborhoods = inputs[1]
+
+    y, _ = _flex_pooling(features, neighborhoods)
+
+    if self.data_format == 'expanded':
+      y = tf.expand_dims(y, axis=2)
+
+    return y
+
+
+def flex_pooling(features,
+                 neighborhoods,
+                 data_format='simple',
+                 name=None):
+
+  layer = FlexPooling(features,
+                      neighborhoods,
+                      data_format=data_format,
+                      name=name)
+
+  return layer.apply([features, neighborhoods])
+
+
+@tf_export('keras.layers.FlexConvolution')
 class FlexConvolution(Layer):
   """flex convolution layer.
 
@@ -49,6 +119,8 @@ class FlexConvolution(Layer):
   If `use_feature_bias` is True (and a `features_bias_initializer` is provided),
   a bias vector is created and added to the outputs after te convolution.
   Finally, if `activation` is not `None`, it is applied to the outputs as well.
+  When `data_format` is 'simple', the input shape should have rank 3,
+  otherwise rank 4 and dimension 2 should be 1.
 
   Remarks:
       In contrast to traditional convolutions, this operation has two
@@ -120,7 +192,6 @@ class FlexConvolution(Layer):
     else:
       features = self.features
       positions = self.positions
-
     [B, Din, N] = features.shape
     Din = int(Din)
     N = int(N)
@@ -213,3 +284,108 @@ def flex_convolution(features,
                           name=name)
 
   return layer.apply([features, positions, neighborhoods])
+
+
+@tf_export('keras.layers.FlexConvolutionTranspose')
+class FlexConvolutionTranspose(FlexConvolution):
+  """flex convolution-transpose layer.
+
+  This layer applies a transpose convolution to elements in arbitrary
+  neighborhoods.
+  If `use_feature_bias` is True (and a `features_bias_initializer` is provided),
+  a bias vector is created and added to the outputs after te convolution.
+  Finally, if `activation` is not `None`, it is applied to the outputs as well.
+  When `data_format` is 'simple', the input shape should have rank 3,
+  otherwise rank 4 and dimension 2 should be 1.
+
+  Remarks:
+      In contrast to traditional transposed convolutions, this operation has two
+      bias terms:
+        - bias term when dynamically computing the weight [Din, Dout]
+        - bias term which is added tot the features [Dout]
+
+  Arguments:
+      features: A `Tensor` of the format [B, Din, (1), N].
+      positions: A `Tensor` of the format [B, Dp, (1), N].
+      neighborhoods: A `Tensor` of the format [B, K, (1), N] (tf.int32).
+      filters: Integer, the dimensionality of the output space (i.e. the number
+        of filters in the convolution).
+      activation: Activation function. Set it to None to maintain a
+        linear activation.
+      kernel_initializer: An initializer for the convolution kernel.
+      position_bias_initializer: An initializer for the bias vector within
+        the convolution. If None, the default initializer will be used.
+      features_bias_initializer: An initializer for the bias vector after
+        the convolution. If None, the default initializer will be used.
+      use_feature_bias: Boolean, whether the layer uses a bias.
+      data_format: A string, one of `simple` (default) or `expaned`.
+        If `simple` the shapes are [B, Din, N], when `expanded` the shapes
+        are assumed to be [B, Din, 1, N] to match `channels_first` in trad
+        convolutions.
+      trainable: Boolean, if `True` also add variables to the graph collection
+        `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).
+      name: A string, the name of the layer.
+
+  """
+
+  def call(self, inputs):
+
+    if not isinstance(inputs, list):
+      raise ValueError('A flexconv layer should be called '
+                       'on a list of inputs.')
+
+    features = ops.convert_to_tensor(inputs[0], dtype=self.dtype)
+    positions = ops.convert_to_tensor(inputs[1], dtype=self.dtype)
+    neighborhoods = ops.convert_to_tensor(inputs[2], dtype=tf.int32)
+
+    if self.data_format == 'expanded':
+      features = _remove_dim(inputs[0], 2)
+      positions = _remove_dim(inputs[1], 2)
+      neighborhoods = _remove_dim(inputs[2], 2)
+    else:
+      features = inputs[0]
+      positions = inputs[1]
+      neighborhoods = inputs[2]
+
+    y = _flex_convolution_transpose(features, positions, neighborhoods,
+                                    self.position_theta, self.position_bias)
+
+    if self.use_feature_bias:
+      y = tf.add(y, self.feature_bias)
+
+    if self.activation is not None:
+      y = self.activation(y)
+
+    if self.data_format == 'expanded':
+      y = tf.expand_dims(y, axis=2)
+
+    return y
+
+
+def flex_convolution_transpose(features,
+                               positions,
+                               neighborhoods,
+                               filters,
+                               activation=None,
+                               kernel_initializer=None,
+                               position_bias_initializer=tf.zeros_initializer(),
+                               features_bias_initializer=tf.zeros_initializer(),
+                               use_feature_bias=True,
+                               data_format='simple',
+                               trainable=True,
+                               name=None):
+
+  layer = FlexConvolutionTranspose(features,
+                                   positions,
+                                   neighborhoods,
+                                   filters,
+                                   activation=activation,
+                                   kernel_initializer=kernel_initializer,
+                                   position_bias_initializer=position_bias_initializer,
+                                   features_bias_initializer=features_bias_initializer,
+                                   use_feature_bias=use_feature_bias,
+                                   data_format=data_format,
+                                   trainable=trainable,
+                                   name=name)
+
+  return layer.apply([features, positions, neighborhoods])
diff --git a/user_ops/CMakeLists.txt b/user_ops/CMakeLists.txt
index 7b40ae9..33e0de0 100644
--- a/user_ops/CMakeLists.txt
+++ b/user_ops/CMakeLists.txt
@@ -10,7 +10,7 @@ project( FlexConv )
 
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR})
 
-find_package(CUDA 9 REQUIRED)
+find_package(CUDA REQUIRED)
 find_package(TensorFlow REQUIRED)
 
 
diff --git a/user_ops/FindTensorFlow.cmake b/user_ops/FindTensorFlow.cmake
index d70a4b3..41c9b04 100644
--- a/user_ops/FindTensorFlow.cmake
+++ b/user_ops/FindTensorFlow.cmake
@@ -122,7 +122,7 @@ else()
     # However, only TensorFlow versions 1.9, 1.10 support all header files
     # for custom ops.
     set(_TensorFlow_KNOWN_VERSIONS ${TensorFlow_ADDITIONAL_VERSIONS}
-        "1.9" "1.9.0" "1.10" "1.10.0")
+        "1.9" "1.9.0" "1.10" "1.10.0", "1.11", "1.11.0")
     set(_TensorFlow_TEST_VERSIONS)
 
     if(TF_FIND_VERSION)
@@ -270,4 +270,4 @@ SET(TensorFlow_INCLUDE_DIR ${TensorFlow_INCLUDE_DIR} CACHE PATH "path to tensorf
 SET(TensorFlow_VERSION ${TensorFlow_VERSION} CACHE INTERNAL "The Python executable Version")
 SET(TensorFlow_ABI ${TensorFlow_ABI} CACHE STRING "The Python executable Version")
 SET(TensorFlow_LIBRARY ${TensorFlow_LIBRARY} CACHE PATH "The Python executable Version")
-SET(TensorFlow_FOUND ${TensorFlow_FOUND} CACHE BOOL "The Python executable Version")
\ No newline at end of file
+SET(TensorFlow_FOUND ${TensorFlow_FOUND} CACHE BOOL "The Python executable Version")
diff --git a/user_ops/__init__.py b/user_ops/__init__.py
index 5e1862b..78449c8 100644
--- a/user_ops/__init__.py
+++ b/user_ops/__init__.py
@@ -41,9 +41,8 @@ flex_pool_grad = _flex_pooling_op_so.flex_pool_grad
 flex_deconv = _flex_deconvolution_op_so.flex_deconv
 flex_deconv_grad = _flex_deconvolution_op_so.flex_deconv_grad
 
+
 # pylint: disable=redefined-builtin
-
-
 def flex_convolution(features,
                      position,
                      neighborhood,