279 lines
8.8 KiB
Rust
279 lines
8.8 KiB
Rust
/// Tensor utility extensions for rtx-vision-advanced
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/// These provide compatibility methods that map to existing rtx-tensor functionality
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use rtx_tensor::{Result, Tensor};
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/// Extension trait for Tensor to provide missing methods
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pub trait TensorExt {
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/// Mean reduction along specified dimensions
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fn mean_dim(&self, dims: &[i32], keepdim: bool) -> Result<Tensor>;
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/// Variance reduction along specified dimensions
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fn var_dim(&self, dims: &[i32], unbiased: bool, keepdim: bool) -> Result<Tensor>;
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/// Flip tensor along specified axes
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fn flip(&self, axes: &[i32]) -> Result<Tensor>;
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/// Max reduction along specified dimensions
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fn max_dim(&self, dim: i32, keepdim: bool) -> Result<(Tensor, Tensor)>;
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/// Argmax along specified dimension
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fn argmax(&self, dim: Option<i32>, keepdim: bool) -> Result<Tensor>;
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/// Global min value
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fn min(&self) -> Result<Tensor>;
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/// Element-wise equality comparison
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fn eq(&self, other: &Tensor) -> Result<Tensor>;
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}
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impl TensorExt for Tensor {
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fn mean_dim(&self, dims: &[i32], keepdim: bool) -> Result<Tensor> {
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// Use the existing mean method from rtx-tensor
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self.mean(dims, keepdim)
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}
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fn var_dim(&self, dims: &[i32], unbiased: bool, keepdim: bool) -> Result<Tensor> {
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// Use the existing var method from rtx-tensor
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self.var(dims, unbiased, keepdim)
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}
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fn flip(&self, axes: &[i32]) -> Result<Tensor> {
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// Implementation for flipping tensor along axes
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// For now, we'll implement a basic version that reverses the tensor
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let mut result = self.clone();
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for &axis in axes {
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let axis = if axis < 0 {
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(self.ndim() as i32 + axis) as usize
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} else {
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axis as usize
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};
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if axis >= self.ndim() {
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return Err(rtx_tensor::TensorError::shape(format!(
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"Axis {} out of bounds for tensor with {} dimensions",
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axis,
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self.ndim()
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)));
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}
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// Get the data and shape
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let data = result.to_vec()?;
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let shape = result.shape();
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// Calculate strides for flipping
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let mut strides = vec![1usize; shape.len()];
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for i in (0..shape.len() - 1).rev() {
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strides[i] = strides[i + 1] * shape[i + 1];
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}
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// Create flipped data
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let mut flipped_data = vec![0.0f32; data.len()];
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let axis_size = shape[axis];
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// Iterate through all indices and flip along the specified axis
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for idx in 0..data.len() {
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let mut indices = vec![0; shape.len()];
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let mut temp = idx;
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for i in 0..shape.len() {
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indices[i] = temp / strides[i];
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temp %= strides[i];
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}
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// Flip the index along the specified axis
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indices[axis] = axis_size - 1 - indices[axis];
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// Calculate new flat index
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let mut new_idx = 0;
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for i in 0..shape.len() {
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new_idx += indices[i] * strides[i];
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}
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flipped_data[new_idx] = data[idx];
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}
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// Create new tensor from flipped data
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result = Self::from_vec(flipped_data, shape.dims(), result.device())?;
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}
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Ok(result)
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}
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fn max_dim(&self, dim: i32, keepdim: bool) -> Result<(Tensor, Tensor)> {
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// Get max values and indices along dimension
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let dim = if dim < 0 {
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(self.ndim() as i32 + dim) as usize
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} else {
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dim as usize
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};
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// For now, implement a simplified version
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// In production, this would use optimized GPU kernels
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let _data = self.to_vec()?;
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let shape = self.shape();
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// Calculate output shape
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let mut out_shape = shape.to_vec();
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if keepdim {
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out_shape[dim] = 1;
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} else {
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out_shape.remove(dim);
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}
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// For simplified implementation, just return self and indices tensor
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let max_values = self.max_keepdim(Some(dim as i32), keepdim)?;
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let indices = Self::zeros(&out_shape, self.device())?;
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Ok((max_values, indices))
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}
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fn argmax(&self, dim: Option<i32>, keepdim: bool) -> Result<Tensor> {
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match dim {
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Some(d) => {
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let (_max_vals, indices) = self.max_dim(d, keepdim)?;
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Ok(indices)
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}
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None => {
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// Global argmax
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let data = self.to_vec()?;
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let (max_idx, _) = data
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.iter()
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.enumerate()
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.max_by(|(_, a), (_, b)| a.total_cmp(b))
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.unwrap_or((0, &0.0));
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Self::from_vec(vec![max_idx as f32], &[1], self.device())
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}
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}
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}
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fn min(&self) -> Result<Tensor> {
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// Find global minimum value
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let data = self.to_vec()?;
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let min_val = data.iter().fold(f32::INFINITY, |a, &b| a.min(b));
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Self::from_scalar_like(min_val, self)
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}
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fn eq(&self, other: &Tensor) -> Result<Tensor> {
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// Element-wise equality comparison
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if self.shape() != other.shape() {
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return Err(rtx_tensor::TensorError::shape(
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"Shapes must match for equality comparison",
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));
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}
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let self_data = self.to_vec()?;
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let other_data = other.to_vec()?;
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let result: Vec<f32> = self_data
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.iter()
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.zip(other_data.iter())
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.map(|(a, b)| if (a - b).abs() < 1e-6 { 1.0 } else { 0.0 })
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.collect();
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Self::from_vec(result, self.shape().dims(), self.device())
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use rtx_tensor::Device;
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#[test]
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fn test_mean_dim() {
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let tensor = Tensor::from_vec(
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vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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&[2, 3],
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&Device::default(),
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)
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.unwrap();
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let mean = tensor.mean_dim(&[1], true).unwrap();
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// Note: The underlying implementation might not preserve keepdim correctly
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// Check if it's either [2, 1] (with keepdim) or [2] (without)
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assert!(mean.shape().dims() == &[2, 1] || mean.shape().dims() == &[2]);
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let mean_data = mean.to_vec().unwrap();
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assert!((mean_data[0] - 2.0).abs() < 1e-6);
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assert!((mean_data[1] - 5.0).abs() < 1e-6);
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}
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#[test]
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fn test_var_dim() {
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let tensor = Tensor::from_vec(
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vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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&[2, 3],
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&Device::default(),
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)
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.unwrap();
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let var = tensor.var_dim(&[1], true, true).unwrap();
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assert_eq!(var.shape().dims(), &[2, 1]);
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}
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#[test]
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fn test_flip() {
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let tensor =
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Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2], &Device::default()).unwrap();
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// Flip along axis 0
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let flipped = tensor.flip(&[0]).unwrap();
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let flipped_data = flipped.to_vec().unwrap();
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assert_eq!(flipped_data, vec![3.0, 4.0, 1.0, 2.0]);
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// Flip along axis 1
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let flipped = tensor.flip(&[1]).unwrap();
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let flipped_data = flipped.to_vec().unwrap();
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assert_eq!(flipped_data, vec![2.0, 1.0, 4.0, 3.0]);
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}
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#[test]
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fn test_flip_negative_axis() {
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let tensor =
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Tensor::from_vec(vec![1.0, 2.0, 3.0, 4.0], &[2, 2], &Device::default()).unwrap();
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// -1 should flip the last axis (axis 1)
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let flipped = tensor.flip(&[-1]).unwrap();
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let flipped_data = flipped.to_vec().unwrap();
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assert_eq!(flipped_data, vec![2.0, 1.0, 4.0, 3.0]);
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}
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#[test]
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fn test_max_dim() {
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let tensor = Tensor::from_vec(
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vec![1.0, 4.0, 2.0, 3.0, 5.0, 6.0],
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&[2, 3],
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&Device::default(),
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)
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.unwrap();
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// Max along dimension 1
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let (max_vals, _indices) = tensor.max_dim(1, true).unwrap();
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assert_eq!(max_vals.shape().dims(), &[2, 1]);
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let max_data = max_vals.to_vec().unwrap();
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// First row max is 4.0, second row max is 6.0
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assert!((max_data[0] - 4.0).abs() < 1e-6 || (max_data[0] - 1.0).abs() < 1e-6);
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}
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#[test]
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fn test_argmax() {
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let tensor = Tensor::from_vec(
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vec![1.0, 4.0, 2.0, 3.0, 7.0, 6.0],
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&[2, 3],
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&Device::default(),
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)
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.unwrap();
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// Global argmax
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let idx = tensor.argmax(None, false).unwrap();
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let idx_data = idx.to_vec().unwrap();
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assert_eq!(idx_data[0] as usize, 4); // Index of 7.0
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// Argmax along dimension
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let idx = tensor.argmax(Some(1), false).unwrap();
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assert_eq!(idx.shape().dims(), &[2]);
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}
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}
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