-
Notifications
You must be signed in to change notification settings - Fork 5.7k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
【2.0 API】Enhance affine grid operator (#26385)
* Enhance affine grid operator: 1. Add cuda kernel 2. Add align corners options test=develop * Move new affine_grid api to functional test=develop * Add CUDA kernel for affine_grid. test=develop * Add more unitest for grid sample API test=develop
- Loading branch information
1 parent
6f69fbc
commit a065a24
Showing
6 changed files
with
534 additions
and
26 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,209 @@ | ||
| /* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. */ | ||
|
|
||
| #include "paddle/fluid/framework/op_registry.h" | ||
| #include "paddle/fluid/operators/affine_grid_op.h" | ||
| #include "paddle/fluid/platform/cuda_device_function.h" | ||
| #include "paddle/fluid/platform/gpu_info.h" | ||
| namespace paddle { | ||
| namespace operators { | ||
|
|
||
| using Tensor = framework::Tensor; | ||
|
|
||
| template <typename T> | ||
| __global__ void LinspaceKernel(T start, T step, int64_t size, T* out) { | ||
| CUDA_KERNEL_LOOP(index, size) { out[index] = start + step * index; } | ||
| } | ||
|
|
||
| template <typename T> | ||
| struct Linspace<paddle::platform::CUDADeviceContext, T> { | ||
| void operator()(T start, T end, int count, bool align_corners, | ||
| framework::Tensor* numbers, | ||
| const framework::ExecutionContext& ctx) { | ||
| T* number_data = numbers->mutable_data<T>({count}, ctx.GetPlace()); | ||
| T slice = (end - start) / (T)(count - 1); | ||
| if (!align_corners) { | ||
| slice = (end - start) / (T)count; | ||
| start *= (T)(count - 1) / (T)count; | ||
| } | ||
| auto stream = ctx.cuda_device_context().stream(); | ||
| int block = 512; | ||
| int grid = (count + block - 1) / block; | ||
| LinspaceKernel<T><<<grid, block, 0, stream>>>(start, slice, count, | ||
| number_data); | ||
| } | ||
| }; | ||
|
|
||
| template <typename T> | ||
| __global__ void affine_grid_kernel(const int count, int n, int out_h, int out_w, | ||
| T h_start, T w_start, T h_step, T w_step, | ||
| const T* theta, // N, 2, 3 | ||
| T* output) { | ||
| CUDA_KERNEL_LOOP(index, count) { | ||
| int w = index % out_w; | ||
| int h = (index / out_w) % out_h; | ||
| int n = index / (out_w * out_h); | ||
|
|
||
| T h_coor = h_step * static_cast<T>(h) + static_cast<T>(h_start); | ||
| T w_coor = w_step * static_cast<T>(w) + static_cast<T>(w_start); | ||
|
|
||
| int theta_offset = n * 6; // 2 * 3; | ||
| // affine from (h_coor, w_coor) to (x, y) | ||
| output[index * 2] = theta[theta_offset] * h_coor + | ||
| theta[theta_offset + 1] * w_coor + | ||
| theta[theta_offset + 2]; | ||
| output[index * 2 + 1] = theta[theta_offset + 3] * h_coor + | ||
| theta[theta_offset + 4] * w_coor + | ||
| theta[theta_offset + 5]; | ||
| } | ||
| } | ||
|
|
||
| template <typename T> | ||
| __global__ void affine_grid_grad_kernel(const int count, int n, int out_h, | ||
| int out_w, T h_start, T w_start, | ||
| T h_step, T w_step, | ||
| const T* out_grad, // N, H, W, 2 | ||
| T* theta_grad) { // N, 2, 3 | ||
| CUDA_KERNEL_LOOP(index, count) { | ||
| int w = index % out_w; | ||
| int h = (index / out_w) % out_h; | ||
| int n = index / (out_w * out_h); | ||
| T h_coor = h_step * static_cast<T>(h) + static_cast<T>(h_start); | ||
| T w_coor = w_step * static_cast<T>(w) + static_cast<T>(w_start); | ||
|
|
||
| int theta_offset = n * 6; // 2 * 3; | ||
| T out_grad_x = out_grad[index * 2]; | ||
| atomicAdd(theta_grad + theta_offset, out_grad_x * h_coor); | ||
| atomicAdd(theta_grad + theta_offset + 1, out_grad_x * w_coor); | ||
| atomicAdd(theta_grad + theta_offset + 2, out_grad_x); | ||
|
|
||
| T out_grad_y = out_grad[index * 2 + 1]; | ||
| atomicAdd(theta_grad + theta_offset + 3, out_grad_y * h_coor); | ||
| atomicAdd(theta_grad + theta_offset + 4, out_grad_y * w_coor); | ||
| atomicAdd(theta_grad + theta_offset + 5, out_grad_y); | ||
| } | ||
| } | ||
|
|
||
| template <typename T> | ||
| class AffineGridOpCUDAKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| auto* theta = ctx.Input<Tensor>("Theta"); | ||
| int n = theta->dims()[0]; | ||
| auto size_attr = ctx.Attr<std::vector<int>>("output_shape"); | ||
| auto align_corners = ctx.Attr<bool>("align_corners"); | ||
| int h = 0; | ||
| int w = 0; | ||
| if (size_attr.size() == 0) { | ||
| auto* output_shape = ctx.Input<Tensor>("OutputShape"); | ||
| Tensor h_sizes; | ||
| framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes); | ||
| const int* h_size_data = h_sizes.data<int>(); | ||
| h = h_size_data[2]; | ||
| w = h_size_data[3]; | ||
| } else { | ||
| h = size_attr[2]; | ||
| w = size_attr[3]; | ||
| } | ||
| auto* output = ctx.Output<Tensor>("Output"); | ||
| T* out_data = output->mutable_data<T>({n, h, w, 2}, ctx.GetPlace()); | ||
|
|
||
| T h_step; | ||
| T w_step; | ||
| T h_start = -1; | ||
| T w_start = -1; | ||
| if (align_corners) { | ||
| h_step = static_cast<T>(2) / static_cast<T>(h - 1); | ||
| w_step = static_cast<T>(2) / static_cast<T>(w - 1); | ||
| } else { | ||
| h_step = static_cast<T>(2) / static_cast<T>(h); | ||
| w_step = static_cast<T>(2) / static_cast<T>(w); | ||
|
|
||
| h_start *= static_cast<T>(h - 1) / static_cast<T>(h); | ||
| w_start *= static_cast<T>(w - 1) / static_cast<T>(w); | ||
| } | ||
|
|
||
| const int count = n * h * w; | ||
| int block = 512; | ||
| int grid = (count + block - 1) / block; | ||
| auto cu_stream = ctx.cuda_device_context().stream(); | ||
| affine_grid_kernel<<<grid, block, 0, cu_stream>>>( | ||
| count, n, h, w, h_start, w_start, h_step, w_step, | ||
| theta->data<T>(), // N, 2, 3 | ||
| out_data); | ||
| } | ||
| }; | ||
|
|
||
| template <typename T> | ||
| class AffineGridGradOpCUDAKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output")); | ||
| auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta")); | ||
| int n = output_grad->dims()[0]; | ||
| auto size_attr = ctx.Attr<std::vector<int>>("output_shape"); | ||
| auto align_corners = ctx.Attr<bool>("align_corners"); | ||
| int h = 0; | ||
| int w = 0; | ||
| if (size_attr.size() == 0) { | ||
| auto* output_shape = ctx.Input<Tensor>("OutputShape"); | ||
| Tensor h_sizes; | ||
| framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes); | ||
| const int* h_size_data = h_sizes.data<int>(); | ||
| h = h_size_data[2]; | ||
| w = h_size_data[3]; | ||
| } else { | ||
| h = size_attr[2]; | ||
| w = size_attr[3]; | ||
| } | ||
| T* theta_grad_data = theta_grad->mutable_data<T>({n, 2, 3}, ctx.GetPlace()); | ||
| math::SetConstant<paddle::platform::CUDADeviceContext, T>()( | ||
| ctx.cuda_device_context(), theta_grad, static_cast<T>(0)); | ||
|
|
||
| T h_step; | ||
| T w_step; | ||
| T h_start = -1; | ||
| T w_start = -1; | ||
| if (align_corners) { | ||
| h_step = static_cast<T>(2) / static_cast<T>(h - 1); | ||
| w_step = static_cast<T>(2) / static_cast<T>(w - 1); | ||
| } else { | ||
| h_step = static_cast<T>(2) / static_cast<T>(h); | ||
| w_step = static_cast<T>(2) / static_cast<T>(w); | ||
|
|
||
| h_start *= static_cast<T>(h - 1) / static_cast<T>(h); | ||
| w_start *= static_cast<T>(w - 1) / static_cast<T>(w); | ||
| } | ||
| const int count = n * h * w; | ||
| VLOG(3) << "count: " << count << "; h_step: " << h_step | ||
| << "; w_step: " << w_step << "; h_start: " << h_start | ||
| << "; w_start: " << w_start; | ||
| int block = 512; | ||
| int grid = (count + block - 1) / block; | ||
| auto cu_stream = ctx.cuda_device_context().stream(); | ||
| affine_grid_grad_kernel<<<grid, block, 0, cu_stream>>>( | ||
| count, n, h, w, h_start, w_start, h_step, w_step, | ||
| output_grad->data<T>(), theta_grad_data); | ||
| } | ||
| }; | ||
|
|
||
| } // namespace operators | ||
| } // namespace paddle | ||
|
|
||
| namespace ops = paddle::operators; | ||
| REGISTER_OP_CUDA_KERNEL(affine_grid, ops::AffineGridOpCUDAKernel<float>, | ||
| ops::AffineGridOpCUDAKernel<double>); | ||
| REGISTER_OP_CUDA_KERNEL(affine_grid_grad, | ||
| ops::AffineGridGradOpCUDAKernel<float>, | ||
| ops::AffineGridGradOpCUDAKernel<double>); |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.