mirror of
https://github.com/lordmathis/CUDANet.git
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98 lines
2.3 KiB
C++
98 lines
2.3 KiB
C++
#include "max_pool.hpp"
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#include <stdexcept>
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using namespace CUDANet::Layers;
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MaxPool2d::MaxPool2d(
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CUDANet::Shape input_shape,
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CUDANet::Shape pool_shape,
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CUDANet::Shape stride_shape,
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CUDANet::Shape padding_shape,
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CUDANet::Backend* backend
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)
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: MaxPool2d(input_shape, pool_shape, stride_shape, padding_shape, backend->get_default_dtype(), backend) {}
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MaxPool2d::MaxPool2d(
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CUDANet::Shape input_shape,
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CUDANet::Shape pool_shape,
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CUDANet::Shape stride_shape,
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CUDANet::Shape padding_shape,
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CUDANet::DType dtype,
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CUDANet::Backend* backend
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)
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: in_shape(input_shape),
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pool_shape(pool_shape),
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stride_shape(stride_shape),
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padding_shape(padding_shape),
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backend(backend) {
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if (in_shape.size() != 3) {
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throw InvalidShapeException("input", 3, in_shape.size());
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}
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if (pool_shape.size() != 2) {
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throw InvalidShapeException("pool", 2, pool_shape.size());
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}
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if (stride_shape.size() != 2) {
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throw InvalidShapeException("stride", 2, stride_shape.size());
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}
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if (padding_shape.size() != 2) {
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throw InvalidShapeException("padding", 2, padding_shape.size());
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}
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this->dtype = dtype;
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out_shape = {
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(in_shape[0] + 2 * padding_shape[0] - pool_shape[0]) / stride_shape[0] +
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1,
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(in_shape[1] + 2 * padding_shape[1] - pool_shape[1]) / stride_shape[1] +
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1,
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in_shape[2]
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};
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output = CUDANet::Tensor(
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Shape{out_shape[0] * out_shape[1] * out_shape[2]},
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dtype, backend
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);
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}
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MaxPool2d::~MaxPool2d() {}
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CUDANet::Tensor& MaxPool2d::forward(CUDANet::Tensor& input) {
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output.zero();
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backend->max_pool2d(
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input, output, in_shape, pool_shape, stride_shape, padding_shape,
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out_shape
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);
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return output;
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}
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CUDANet::Shape MaxPool2d::input_shape() {
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return in_shape;
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}
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CUDANet::Shape MaxPool2d::output_shape() {
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return out_shape;
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}
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size_t MaxPool2d::input_size() {
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return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
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}
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size_t MaxPool2d::output_size() {
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return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[2];
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}
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void MaxPool2d::set_weights(void* input) {}
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size_t MaxPool2d::get_weights_size() {
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return 0;
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}
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void MaxPool2d::set_biases(void* input) {}
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size_t MaxPool2d::get_biases_size() {
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return 0;
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} |