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Add the pooling operators to the pyo3 layer. (#1086)
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@ -1224,6 +1224,28 @@ fn softmax(tensor: PyTensor, dim: i64) -> PyResult<PyTensor> {
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Ok(PyTensor(sm))
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}
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#[pyfunction]
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#[pyo3(signature = (tensor, ksize, *, stride=1), text_signature = "(tensor:Tensor, ksize:int, stride:int=1)")]
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/// Applies the 2d avg-pool function to a given tensor.#
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/// &RETURNS&: Tensor
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fn avg_pool2d(tensor: PyTensor, ksize: usize, stride: usize) -> PyResult<PyTensor> {
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let tensor = tensor
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.avg_pool2d_with_stride(ksize, stride)
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.map_err(wrap_err)?;
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Ok(PyTensor(tensor))
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}
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#[pyfunction]
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#[pyo3(signature = (tensor, ksize, *, stride=1), text_signature = "(tensor:Tensor, ksize:int, stride:int=1)")]
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/// Applies the 2d max-pool function to a given tensor.#
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/// &RETURNS&: Tensor
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fn max_pool2d(tensor: PyTensor, ksize: usize, stride: usize) -> PyResult<PyTensor> {
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let tensor = tensor
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.max_pool2d_with_stride(ksize, stride)
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.map_err(wrap_err)?;
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Ok(PyTensor(tensor))
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}
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#[pyfunction]
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#[pyo3(text_signature = "(tensor:Tensor)")]
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/// Applies the Sigmoid Linear Unit (SiLU) function to a given tensor.
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@ -1263,6 +1285,8 @@ fn tanh(tensor: PyTensor) -> PyResult<PyTensor> {
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fn candle_functional_m(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
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m.add_function(wrap_pyfunction!(silu, m)?)?;
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m.add_function(wrap_pyfunction!(softmax, m)?)?;
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m.add_function(wrap_pyfunction!(max_pool2d, m)?)?;
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m.add_function(wrap_pyfunction!(avg_pool2d, m)?)?;
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m.add_function(wrap_pyfunction!(gelu, m)?)?;
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m.add_function(wrap_pyfunction!(relu, m)?)?;
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m.add_function(wrap_pyfunction!(tanh, m)?)?;
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