提交 67db9d35 编写于 作者: H hedaoyuan

Refine the GemmConvKernel.

上级 a7c18722
......@@ -29,61 +29,68 @@ class GemmConvKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
Tensor* filter = const_cast<Tensor*>(context.Input<Tensor>("Filter"));
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *context.Input<Tensor>("Filter");
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
auto filter_dims = filter->dims();
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_height = filter->dims()[filter->dims().size() - 2];
int filter_width = filter->dims()[filter->dims().size() - 1];
int filter_height = filter.dims()[filter.dims().size() - 2];
int filter_width = filter.dims()[filter.dims().size() - 1];
int output_channels = output->dims()[1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO, Place, T>
im2col;
// use col_shape in the im2col calculation
framework::DDim col_shape = {input_channels, filter_height, filter_width,
output_height, output_width};
// use col_matrix_shape in the gemm calculation
framework::DDim col_matrix_shape = {
input_channels * filter_height * filter_width,
output_height * output_width};
Tensor col;
col.mutable_data<float>(col_shape, context.GetPlace());
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix = col;
col_matrix.Resize(col_matrix_shape);
framework::DDim input_shape = {input->dims()[1], input->dims()[2],
input->dims()[3]};
framework::DDim filter_matrix_shape = {
filter->dims()[0],
filter->dims()[1] * filter->dims()[2] * filter->dims()[3]};
framework::DDim col_matrix_shape = {
input_channels * filter_height * filter_width,
output_height * output_width};
framework::DDim output_matrix_shape = {
output->dims()[1], output->dims()[2] * output->dims()[3]};
filter->Resize(filter_matrix_shape);
output_channels, framework::product(filter.dims()) / output_channels};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {output_channels,
output_height * output_width};
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
// convolution operator: im2col + gemm
for (int i = 0; i < batch_size; i++) {
// im2col
Tensor in_slice = input->Slice<T>(i, i + 1);
in_slice.Resize(input_shape);
col.Resize(col_shape);
im2col(in_slice, col, strides[0], strides[1], paddings[0], paddings[1],
device_context);
// gemm
Tensor out_slice = output->Slice<T>(i, i + 1);
out_slice.Resize(output_matrix_shape);
col.Resize(col_matrix_shape);
math::matmul<Place, T>(*filter, false, col, false, T(1.0), &out_slice,
T(0.0), device_context);
math::matmul<Place, T>(filter, false, col_matrix, false, T(1.0),
&out_slice, T(0.0), device_context);
}
filter->Resize(filter_dims);
}
};
......
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