Open3D (C++ API)  0.15.1
ContinuousConvTransposeOpKernel.h
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26 
27 #pragma once
28 
30 #include "tensorflow/core/framework/op.h"
31 #include "tensorflow/core/framework/op_kernel.h"
32 #include "tensorflow/core/lib/core/errors.h"
33 
34 template <class TIndex>
35 class ContinuousConvTransposeOpKernel : public tensorflow::OpKernel {
36 public:
38  tensorflow::OpKernelConstruction* construction)
39  : OpKernel(construction) {
40  using namespace tensorflow;
41  using namespace open3d::ml::impl;
42  OP_REQUIRES_OK(construction,
43  construction->GetAttr("align_corners", &align_corners));
44  OP_REQUIRES_OK(construction,
45  construction->GetAttr("normalize", &normalize));
46 
47  std::string interpolation_str;
48  OP_REQUIRES_OK(construction, construction->GetAttr("interpolation",
49  &interpolation_str));
50 
51  if (interpolation_str == "linear")
52  interpolation = InterpolationMode::LINEAR;
53  else if (interpolation_str == "linear_border")
54  interpolation = InterpolationMode::LINEAR_BORDER;
55  else
57 
58  std::string mapping_str;
59  OP_REQUIRES_OK(construction, construction->GetAttr("coordinate_mapping",
60  &mapping_str));
61 
62  if (mapping_str == "ball_to_cube_radial")
63  coordinate_mapping = CoordinateMapping::BALL_TO_CUBE_RADIAL;
64  else if (mapping_str == "ball_to_cube_volume_preserving")
66  CoordinateMapping::BALL_TO_CUBE_VOLUME_PRESERVING;
67  else
68  coordinate_mapping = CoordinateMapping::IDENTITY;
69 
70  OP_REQUIRES_OK(construction, construction->GetAttr("max_temp_mem_MB",
71  &max_temp_mem_MB));
72  }
73 
74  void Compute(tensorflow::OpKernelContext* context) override {
75  using namespace tensorflow;
76  static_assert(sizeof(int64) == sizeof(int64_t),
77  "int64 type is not compatible");
78  const Tensor& filter = context->input(0);
79 
80  const Tensor& out_positions = context->input(1);
81  OP_REQUIRES(context,
82  out_positions.shape().dim_size(0) <=
83  std::numeric_limits<TIndex>::max(),
84  errors::InvalidArgument("Too many output points"));
85 
86  const Tensor& out_importance = context->input(2);
87  OP_REQUIRES(context,
88  out_importance.shape().dim_size(0) == 0 ||
89  out_importance.shape().dim_size(0) ==
90  out_positions.shape().dim_size(0),
91  errors::InvalidArgument("length of out_importance must "
92  "match the number of output points "
93  "or must be 0"));
94 
95  const Tensor& extents = context->input(3);
96 
97  const Tensor& offset = context->input(4);
98  OP_REQUIRES(context, offset.shape().dims() == 1,
99  errors::InvalidArgument("offset must be a rank 1 tensor"));
100  OP_REQUIRES(context, offset.shape().dim_size(0) == 3,
101  errors::InvalidArgument("offset length must be 3"));
102 
103  const Tensor& inp_positions = context->input(5);
104  OP_REQUIRES(context,
105  inp_positions.shape().dim_size(0) <=
106  std::numeric_limits<TIndex>::max(),
107  errors::InvalidArgument("Too many input points"));
108 
109  const Tensor& inp_features = context->input(6);
110 
111  // not used in forward pass
112  // const Tensor& inp_neighbors_index = context->input(7);
113 
114  const Tensor& inp_neighbors_importance_sum = context->input(8);
115 
116  const Tensor& inp_neighbors_row_splits = context->input(9);
117 
118  const Tensor& neighbors_index = context->input(10);
119 
120  const Tensor& neighbors_importance = context->input(11);
121 
122  const Tensor& neighbors_row_splits = context->input(12);
123 
124  OP_REQUIRES(context, extents.shape().dims() == 2,
125  errors::InvalidArgument("extents must be a rank 2 tensor"));
126  OP_REQUIRES(context,
127  extents.shape().dim_size(0) ==
128  inp_positions.shape().dim_size(0) ||
129  extents.shape().dim_size(0) == 1,
130  errors::InvalidArgument("number of extents must match the "
131  "number of inp_positions or must "
132  "be 1"));
133  OP_REQUIRES(context,
134  extents.shape().dim_size(1) == 3 ||
135  extents.shape().dim_size(1) == 1,
136  errors::InvalidArgument(
137  "number of components for extents must be 3 or 1"));
138 
139  OP_REQUIRES(
140  context,
141  inp_positions.shape().dim_size(0) ==
142  inp_features.shape().dim_size(0),
143  errors::InvalidArgument("first dim of inp_positions does not "
144  "match the first dim of inp_features"));
145 
146  OP_REQUIRES(
147  context,
148  inp_neighbors_importance_sum.shape().dim_size(0) ==
149  inp_positions.shape().dim_size(0) ||
150  inp_neighbors_importance_sum.shape().dim_size(0) == 0,
151  errors::InvalidArgument(
152  "first dim of inp_neighbors_importance_sum does not "
153  "match the first dim of inp_positions",
154  inp_neighbors_importance_sum.shape().dim_size(0), " ",
155  inp_positions.shape().dim_size(0)));
156 
157  OP_REQUIRES(context,
158  out_positions.shape().dim_size(0) ==
159  out_importance.shape().dim_size(0) ||
160  out_importance.shape().dim_size(0) == 0,
161  errors::InvalidArgument("first dim of out_positions does "
162  "not match the first dim of "
163  "out_importance"));
164 
165  OP_REQUIRES(context,
166  neighbors_importance.shape().dim_size(0) ==
167  neighbors_index.shape().dim_size(0) ||
168  neighbors_importance.shape().dim_size(0) == 0,
169  errors::InvalidArgument("first dim of neighbors_importance "
170  "does not match the first dim of "
171  "neighbors_index"));
172 
173  OP_REQUIRES(
174  context,
175  filter.shape().dim_size(3) == inp_features.shape().dim_size(1),
176  errors::InvalidArgument("number of input channels in filter "
177  "and inp_features does not match"));
178 
179  TensorShape out_features_shape({out_positions.shape().dim_size(0),
180  filter.shape().dim_size(4)});
181  Tensor* out_features = nullptr;
182  OP_REQUIRES_OK(context, context->allocate_output(0, out_features_shape,
183  &out_features));
184 
185  std::vector<int> filter_dims({
186  int(filter.shape().dim_size(0)),
187  int(filter.shape().dim_size(1)),
188  int(filter.shape().dim_size(2)),
189  int(filter.shape().dim_size(3)),
190  int(filter.shape().dim_size(4)),
191  });
192 
193  bool individual_extents = extents.shape().dim_size(0) ==
194  out_positions.shape().dim_size(0) &&
195  extents.shape().dim_size(0) > 1;
196 
197  bool isotropic_extents = extents.shape().dim_size(1) == 1;
198 
199  bool point_importances = out_importance.shape().dim_size(0) != 0;
200 
201  bool has_neighbors_importances =
202  neighbors_importance.shape().dim_size(0) != 0;
203 
204  Kernel(context, filter, out_positions, out_importance, extents, offset,
205  inp_positions, inp_features, inp_neighbors_importance_sum,
206  inp_neighbors_row_splits, neighbors_index, neighbors_importance,
207  neighbors_row_splits, filter_dims, individual_extents,
208  isotropic_extents, point_importances, has_neighbors_importances,
209  *out_features);
210  }
211 
212  virtual void Kernel(tensorflow::OpKernelContext* context,
213  const tensorflow::Tensor& filter,
214  const tensorflow::Tensor& out_positions,
215  const tensorflow::Tensor& out_importance,
216  const tensorflow::Tensor& extents,
217  const tensorflow::Tensor& offset,
218  const tensorflow::Tensor& inp_positions,
219  const tensorflow::Tensor& inp_features,
220  const tensorflow::Tensor& inp_neighbors_importance_sum,
221  const tensorflow::Tensor& inp_neighbors_row_splits,
222  const tensorflow::Tensor& neighbors_index,
223  const tensorflow::Tensor& neighbors_importance,
224  const tensorflow::Tensor& neighbors_row_splits,
225  const std::vector<int>& filter_dims,
226  const bool individual_extents,
227  const bool isotropic_extents,
228  const bool point_importances,
229  const bool has_neighbors_importances,
230  tensorflow::Tensor& out_features) = 0;
231 
232 public:
234  bool normalize;
238 };
ImGuiContext * context
Definition: Window.cpp:95
Definition: ContinuousConvTransposeOpKernel.h:35
open3d::ml::impl::CoordinateMapping coordinate_mapping
Definition: ContinuousConvTransposeOpKernel.h:236
int max_temp_mem_MB
Definition: ContinuousConvTransposeOpKernel.h:237
open3d::ml::impl::InterpolationMode interpolation
Definition: ContinuousConvTransposeOpKernel.h:235
bool normalize
Definition: ContinuousConvTransposeOpKernel.h:234
ContinuousConvTransposeOpKernel(tensorflow::OpKernelConstruction *construction)
Definition: ContinuousConvTransposeOpKernel.h:37
void Compute(tensorflow::OpKernelContext *context) override
Definition: ContinuousConvTransposeOpKernel.h:74
bool align_corners
Definition: ContinuousConvTransposeOpKernel.h:233
virtual void Kernel(tensorflow::OpKernelContext *context, const tensorflow::Tensor &filter, const tensorflow::Tensor &out_positions, const tensorflow::Tensor &out_importance, const tensorflow::Tensor &extents, const tensorflow::Tensor &offset, const tensorflow::Tensor &inp_positions, const tensorflow::Tensor &inp_features, const tensorflow::Tensor &inp_neighbors_importance_sum, const tensorflow::Tensor &inp_neighbors_row_splits, const tensorflow::Tensor &neighbors_index, const tensorflow::Tensor &neighbors_importance, const tensorflow::Tensor &neighbors_row_splits, const std::vector< int > &filter_dims, const bool individual_extents, const bool isotropic_extents, const bool point_importances, const bool has_neighbors_importances, tensorflow::Tensor &out_features)=0
int offset
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const char const char value recording_handle imu_sample recording_handle uint8_t size_t data_size k4a_record_configuration_t config target_format k4a_capture_t capture_handle k4a_imu_sample_t imu_sample playback_handle k4a_logging_message_cb_t void min_level device_handle k4a_imu_sample_t timeout_in_ms capture_handle capture_handle capture_handle image_handle temperature_c int
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Definition: ContinuousConv.h:35
InterpolationMode
Definition: ContinuousConvTypes.h:37
@ NEAREST_NEIGHBOR
Definition: VoxelPooling.h:40
CoordinateMapping
Definition: ContinuousConvTypes.h:45