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Add avgPool2d implementation

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This commit is contained in:
Matúš Námešný
2025-11-21 19:39:30 +01:00
parent c83e1f0c45
commit 5679dc0a50
7 changed files with 102 additions and 67 deletions
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74
src/layers/avg_pooling.cpp
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@@ -49,23 +49,43 @@ AvgPool2d::AvgPool2d(
AvgPool2d::~AvgPool2d() {}
CUDANet::Tensor& AvgPool2d::forward(CUDANet::Tensor& input);
CUDANet::Tensor& AvgPool2d::forward(CUDANet::Tensor& input) {
output.zero();
backend->avgPool2d(
input,
output,
in_shape,
pool_shape,
stride_shape,
padding_shape,
out_shape
);
return output;
}
CUDANet::Shape AvgPool2d::input_shape();
CUDANet::Shape AvgPool2d::input_shape() {
return in_shape;
}
CUDANet::Shape AvgPool2d::output_shape();
CUDANet::Shape AvgPool2d::output_shape() {
return out_shape;
}
size_t AvgPool2d::input_size();
size_t AvgPool2d::input_size() {
return sizeof(float) * in_shape[0] * in_shape[1] * in_shape[2];
}
size_t AvgPool2d::output_size();
size_t AvgPool2d::output_size() {
return sizeof(float) * out_shape[0] * out_shape[1] * out_shape[3];
}
void AvgPool2d::set_weights(void* input);
void AvgPool2d::set_weights(void* input) {}
CUDANet::Tensor& AvgPool2d::get_weights();
CUDANet::Tensor& AvgPool2d::get_weights() {}
void AvgPool2d::set_biases(void* input);
void AvgPool2d::set_biases(void* input) {}
CUDANet::Tensor& AvgPool2d::get_biases();
CUDANet::Tensor& AvgPool2d::get_biases() {}
AdaptiveAvgPool2d::AdaptiveAvgPool2d(
@@ -73,15 +93,29 @@ AdaptiveAvgPool2d::AdaptiveAvgPool2d(
CUDANet::Shape output_shape,
CUDANet::Backend *backend
)
: AvgPool2d(input_shape, {1, 1}, {1, 1}, {0, 0}, backend) {
stride_shape = {
input_shape[0] / output_shape[0],
input_shape[1] / output_shape[1]
};
pool_shape = {
input_shape[0] - (output_shape[0] - 1) * stride_shape[0],
input_shape[1] - (output_shape[1] - 1) * stride_shape[1]
};
padding_shape = {(pool_shape[0] - 1) / 2, (pool_shape[1] - 1) / 2};
: AvgPool2d(
input_shape,
// pool_shape
{
input_shape[0] - (output_shape[0] - 1) * (input_shape[0] / output_shape[0]),
input_shape[1] - (output_shape[1] - 1) * (input_shape[1] / output_shape[1])
},
// stride_shape
{
input_shape[0] / output_shape[0],
input_shape[1] / output_shape[1]
},
// padding_shape
{
(input_shape[0] - (output_shape[0] - 1) * (input_shape[0] / output_shape[0]) - 1) / 2,
(input_shape[1] - (output_shape[1] - 1) * (input_shape[1] / output_shape[1]) - 1) / 2
},
backend
) {
out_shape = output_shape;
}
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[2]},
CUDANet::DType::FLOAT32, backend
);
}