| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| */ |
| |
| /*! |
| * Copyright (c) 2016 by Contributors |
| * \file spatial_transformer.cc |
| * \brief |
| * \author Wei Wu |
| */ |
| |
| #include "./spatial_transformer-inl.h" |
| |
| namespace mshadow { |
| template<typename DType> |
| static MSHADOW_CINLINE bool between(const DType value, |
| const DType lowerBound, |
| const DType upperBound) { |
| return value >= lowerBound && value <= upperBound; |
| } |
| |
| template<typename DType> |
| inline void BilinearSamplingForward(const Tensor<cpu, 4, DType> &output, |
| const Tensor<cpu, 4, DType> &input, |
| const Tensor<cpu, 3, DType> grid_src) { |
| DType *out = output.dptr_; |
| const DType *data = input.dptr_; |
| const DType *grid = grid_src.dptr_; |
| const int o_n = output.size(0), o_c = output.size(1), o_h = output.size(2), o_w = output.size(3); |
| const int i_c = input.size(1), i_h = input.size(2), i_w = input.size(3); |
| for (index_t n = 0; n < static_cast<index_t>(o_n); ++n) { |
| for (index_t c = 0; c < static_cast<index_t>(o_c); ++c) { |
| for (index_t h = 0; h < static_cast<index_t>(o_h); ++h) { |
| for (index_t w = 0; w < static_cast<index_t>(o_w); ++w) { |
| const index_t out_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w; |
| const index_t grid_index = n * o_h * o_w * 2 + h * o_w + w; |
| const DType y_real = (*(grid + grid_index + o_h * o_w) + 1) * (i_h - 1) / 2; |
| const DType x_real = (*(grid + grid_index) + 1) * (i_w - 1) / 2; |
| const auto top_left_y = static_cast<int>(std::floor(y_real)); |
| const auto top_left_x = static_cast<int>(std::floor(x_real)); |
| const DType top_left_y_w = 1.0 - (y_real - top_left_y); |
| const DType top_left_x_w = 1.0 - (x_real - top_left_x); |
| const int data_index = n * i_c * i_h * i_w + c * i_h * i_w + |
| top_left_y * i_w + top_left_x; |
| DType top_left_v = 0; |
| DType top_right_v = 0; |
| DType bottom_left_v = 0; |
| DType bottom_right_v = 0; |
| if (between(top_left_x, 0, i_w-1) && between(top_left_y, 0, i_h-1)) |
| top_left_v = *(data + data_index); |
| if (between(top_left_x + 1, 0, i_w-1) && between(top_left_y, 0, i_h-1)) |
| top_right_v = *(data + data_index + 1); |
| if (between(top_left_x, 0, i_w-1) && between(top_left_y + 1, 0, i_h-1)) |
| bottom_left_v = *(data + data_index + i_w); |
| if (between(top_left_x+1, 0, i_w-1) && between(top_left_y + 1, 0, i_h-1)) |
| bottom_right_v = *(data + data_index + i_w + 1); |
| *(out+out_index) = top_left_v * top_left_y_w * top_left_x_w + |
| top_right_v * top_left_y_w * (1.0 - top_left_x_w) + |
| bottom_left_v * (1.0 - top_left_y_w) * top_left_x_w + |
| bottom_right_v * (1.0 - top_left_y_w) * (1.0 - top_left_x_w); |
| } |
| } |
| } |
| } |
| } |
| |
| template<typename DType> |
| inline void BilinearSamplingBackward(const Tensor<cpu, 4, DType> &input_grad, |
| const Tensor<cpu, 3, DType> &grid_src_data, |
| const Tensor<cpu, 4, DType> &output_grad, |
| const Tensor<cpu, 4, DType> &input_data) { |
| DType *g_input = input_grad.dptr_; |
| DType *grid_src = grid_src_data.dptr_; |
| const DType *grad = output_grad.dptr_; |
| const DType *data = input_data.dptr_; |
| const int o_n = output_grad.size(0), o_c = output_grad.size(1), |
| o_h = output_grad.size(2), o_w = output_grad.size(3); |
| const int i_c = input_data.size(1), i_h = input_data.size(2), i_w = input_data.size(3); |
| for (index_t n = 0; n < static_cast<index_t>(o_n); ++n) { |
| for (index_t h = 0; h < static_cast<index_t>(o_h); ++h) { |
| for (index_t w = 0; w < static_cast<index_t>(o_w); ++w) { |
| DType top_left_y_gw = 0.0; |
| DType top_left_x_gw = 0.0; |
| const index_t grid_src_index = n * o_h * o_w * 2 + h * o_w + w; |
| const DType y_real = (*(grid_src + grid_src_index + o_h * o_w) + 1) * (i_h - 1) / 2; |
| const DType x_real = (*(grid_src + grid_src_index) + 1) * (i_w - 1) / 2; |
| const auto top_left_y = static_cast<int>(std::floor(y_real)); |
| const auto top_left_x = static_cast<int>(std::floor(x_real)); |
| const DType top_left_y_w = 1.0 - (y_real - top_left_y); |
| const DType top_left_x_w = 1.0 - (x_real - top_left_x); |
| for (index_t c = 0; c < static_cast<index_t>(o_c); ++c) { |
| index_t grad_index = n * o_c * o_h * o_w + c * o_h * o_w + h * o_w + w; |
| const int data_index = n * i_c * i_h * i_w + c * i_h * i_w + |
| top_left_y * i_w + top_left_x; |
| // calc 4 vertex value in input data |
| DType top_left_v = 0; |
| DType top_right_v = 0; |
| DType bottom_left_v = 0; |
| DType bottom_right_v = 0; |
| if (between(top_left_x, 0, i_w-1) && between(top_left_y, 0, i_h-1)) { |
| *(g_input + data_index) += *(grad + grad_index) * top_left_y_w * top_left_x_w; |
| top_left_v = *(data + data_index); |
| } |
| if (between(top_left_x+1, 0, i_w-1) && between(top_left_y, 0, i_h-1)) { |
| *(g_input + data_index + 1) += *(grad + grad_index) * top_left_y_w |
| * (1.0 - top_left_x_w); |
| top_right_v = *(data + data_index + 1); |
| } |
| if (between(top_left_x, 0, i_w-1) && between(top_left_y+1, 0, i_h-1)) { |
| *(g_input + data_index+ i_w) += *(grad + grad_index) * (1.0 - top_left_y_w) |
| * top_left_x_w; |
| bottom_left_v = *(data + data_index + i_w); |
| } |
| if (between(top_left_x+1, 0, i_w-1) && between(top_left_y+1, 0, i_h-1)) { |
| *(g_input + data_index+ i_w + 1) += *(grad + grad_index) * (1.0 - top_left_y_w) |
| * (1.0 - top_left_x_w); |
| bottom_right_v = *(data + data_index + i_w + 1); |
| } |
| // calc weight grad of top_left_w, then multiple -1 is the grad of grid_src |
| top_left_y_gw -= *(grad + grad_index) * (top_right_v - bottom_right_v + |
| (top_left_v - top_right_v - bottom_left_v + bottom_right_v) |
| * top_left_x_w); |
| top_left_x_gw -= *(grad + grad_index) * (bottom_left_v - bottom_right_v + |
| (top_left_v - top_right_v - bottom_left_v + bottom_right_v) |
| * top_left_y_w); |
| } |
| // calc grid_src grad |
| *(grid_src + grid_src_index + o_h * o_w) = top_left_y_gw * (i_h - 1) / 2; |
| *(grid_src + grid_src_index) = top_left_x_gw * (i_w - 1) / 2; |
| } |
| } |
| } |
| } |
| |
| } // namespace mshadow |
| |
| namespace mxnet { |
| namespace op { |
| template<> |
| Operator* CreateOp<cpu>(SpatialTransformerParam param, int dtype) { |
| Operator *op = nullptr; |
| MSHADOW_REAL_TYPE_SWITCH(dtype, DType, { |
| op = new SpatialTransformerOp<cpu, DType>(param); |
| }) |
| return op; |
| } |
| |
| Operator *SpatialTransformerProp::CreateOperatorEx(Context ctx, mxnet::ShapeVector *in_shape, |
| std::vector<int> *in_type) const { |
| DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0]); |
| } |
| |
| DMLC_REGISTER_PARAMETER(SpatialTransformerParam); |
| |
| MXNET_REGISTER_OP_PROPERTY(SpatialTransformer, SpatialTransformerProp) |
| .add_argument("data", "NDArray-or-Symbol", |
| "Input data to the SpatialTransformerOp.") |
| .add_argument("loc", "NDArray-or-Symbol", |
| "localisation net, the output dim should be 6 when transform_type " |
| "is affine. You shold initialize the weight and bias with identity tranform.") |
| .add_arguments(SpatialTransformerParam::__FIELDS__()) |
| .describe("Applies a spatial transformer to input feature map."); |
| |
| } // namespace op |
| } // namespace mxnet |
