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Fix build error of affine grid op in mac os. (#14237)

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* Fix build error of affine grid op in mac os.
test=develop

* Make function return reference.
test=develop
fix_recordio_link
whs 6 years ago committed by GitHub
parent d55481cfeb
commit d6a6a13039
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2 changed files with 56 additions and 74 deletions
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8
paddle/fluid/operators/affine_grid_op.cc
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@ -26,15 +26,13 @@ using Tensor = framework::Tensor;
template <typename T>
struct Linspace<paddle::platform::CPUDeviceContext, T> {
framework::Tensor operator()(T start, T end, int count,
const framework::ExecutionContext& ctx) {
Tensor numbers;
T* number_data = numbers.mutable_data<T>({count}, platform::CPUPlace());
void operator()(T start, T end, int count, framework::Tensor* numbers,
const framework::ExecutionContext& ctx) {
T* number_data = numbers->mutable_data<T>({count}, platform::CPUPlace());
T slice = (end - start) / (T)(count - 1);
for (int i = 0; i < count; ++i) {
number_data[i] = start + (T)i * slice;
}
return numbers;
}
};

122
paddle/fluid/operators/affine_grid_op.h
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@ -37,18 +37,65 @@ using Array4 = Eigen::DSizes<int64_t, 4>;
*/
template <typename DeviceContext, typename T>
struct Linspace {
framework::Tensor operator()(T start, T end, int count,
const framework::ExecutionContext& ctx);
void operator()(T start, T end, int count, framework::Tensor* numbers,
const framework::ExecutionContext& ctx);
};
template <typename DeviceContext, typename T>
inline void GetIdxMap(int n, int h, int w, Tensor* grid,
const framework::ExecutionContext& ctx) {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
grid->mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
auto grid_t = EigenTensor<T, 4>::From(*grid);
// Get indexes of height with shape [height, width, 1]
Tensor h_idx;
Linspace<DeviceContext, T> linspace;
linspace((T)-1, (T)1, h, &h_idx, ctx);
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
// Get indexes of width with shape [height, width, 1]
Tensor w_idx;
linspace((T)-1, (T)1, w, &w_idx, ctx);
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
// Get constant ones tensor with shape [height, width, 1]
Tensor ones;
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor w_idx_map;
w_idx_map.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto w_idx_map_t = EigenTensor<T, 3>::From(w_idx_map);
Tensor h_idx_map;
h_idx_map.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto h_idx_map_t = EigenTensor<T, 3>::From(h_idx_map);
Tensor w_h_idx_map;
w_h_idx_map.mutable_data<T>({h, w, 2}, ctx.GetPlace());
auto w_h_idx_map_t = EigenTensor<T, 3>::From(w_h_idx_map);
Tensor w_h_one_idx_map;
w_h_one_idx_map.mutable_data<T>({h, w, 3}, ctx.GetPlace());
auto w_h_one_idx_map_t = EigenTensor<T, 3>::From(w_h_one_idx_map);
w_idx_map_t.device(place) = w_idx_t.reshape(Array2(1, w))
.broadcast(Array2(h, 1))
.reshape(Array3(h, w, 1));
h_idx_map_t.device(place) = h_idx_t.reshape(Array2(1, h))
.broadcast(Array2(w, 1))
.shuffle(Array2(1, 0))
.reshape(Array3(h, w, 1));
w_h_idx_map_t.device(place) = w_idx_map_t.concatenate(h_idx_map_t, 2);
w_h_one_idx_map_t.device(place) = w_h_idx_map_t.concatenate(ones_t, 2);
grid_t.device(place) = w_h_one_idx_map_t.reshape(Array4(1, h, w, 3))
.broadcast(Array4(n, 1, 1, 1));
}
template <typename DeviceContext, typename T>
class AffineGridOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto* theta = ctx.Input<Tensor>("Theta");
int n = theta->dims()[0];
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
int h = 0;
int w = 0;
@ -63,44 +110,13 @@ class AffineGridOpKernel : public framework::OpKernel<T> {
h = size_attr[2];
w = size_attr[3];
}
auto* output = ctx.Output<Tensor>("Output");
output->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), output,
static_cast<T>(0));
Linspace<DeviceContext, T> linspace;
// Get indexes of height with shape [height, width, 1]
auto h_idx = linspace((T)-1, (T)1, h, ctx);
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
// Get indexes of width with shape [height, width, 1]
auto w_idx = linspace((T)-1, (T)1, w, ctx);
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
// Get constant ones tensor with shape [height, width, 1]
Tensor ones;
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor grid;
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
auto grid_t = EigenTensor<T, 4>::From(grid);
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
.broadcast(Array2(h, 1))
.reshape(Array3(h, w, 1))
.concatenate(h_idx_t.reshape(Array2(1, h))
.broadcast(Array2(w, 1))
.shuffle(Array2(1, 0))
.reshape(Array3(h, w, 1)),
2)
.eval()
.concatenate(ones_t, 2)
.reshape(Array4(1, h, w, 3))
.broadcast(Array4(n, 1, 1, 1));
GetIdxMap<DeviceContext, T>(n, h, w, &grid, ctx);
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);
@ -118,10 +134,8 @@ template <typename DeviceContext, typename T>
class AffineGridGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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");
int h = 0;
@ -137,42 +151,12 @@ class AffineGridGradOpKernel : public framework::OpKernel<T> {
h = size_attr[2];
w = size_attr[3];
}
theta_grad->mutable_data<T>({n, 2, 3}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), theta_grad,
static_cast<T>(0));
Linspace<DeviceContext, T> linspace;
// Get indexes of height with shape [height, width, 1]
auto h_idx = linspace((T)-1, (T)1, h, ctx);
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
// Get indexes of width with shape [height, width, 1]
auto w_idx = linspace((T)-1, (T)1, w, ctx);
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
// Get constant ones tensor with shape [height, width, 1]
Tensor ones;
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor grid;
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
auto grid_t = EigenTensor<T, 4>::From(grid);
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
.broadcast(Array2(h, 1))
.reshape(Array3(h, w, 1))
.concatenate(h_idx_t.reshape(Array2(1, h))
.broadcast(Array2(w, 1))
.shuffle(Array2(1, 0))
.reshape(Array3(h, w, 1)),
2)
.eval()
.concatenate(ones_t, 2)
.reshape(Array4(1, h, w, 3))
.broadcast(Array4(n, 1, 1, 1));
GetIdxMap<DeviceContext, T>(n, h, w, &grid, ctx);
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);

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