Ben_Kosytorz
3a67c0979c
- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution - Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory - Added Cargo.lock to gitignore with appropriate comment - Reorganized IDE files section in gitignore for better clarity - Added newline at end of file for proper formatting
103 lines
2.6 KiB
Rust
103 lines
2.6 KiB
Rust
use super::*;
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use burn_tensor::module::avg_pool1d;
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use burn_tensor::{Shape, Tolerance};
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#[test]
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fn test_avg_pool1d_simple() {
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let test = AvgPool1dTestCase {
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batch_size: 1,
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channels: 1,
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kernel_size: 3,
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padding: 0,
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stride: 1,
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length: 6,
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count_include_pad: true,
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};
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test.assert_output(TestTensor::from_floats(
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[[[0.33333, 0.66667, 1.0000, 1.0000, 0.66667, 0.33333]]],
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&Default::default(),
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));
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}
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#[test]
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fn test_avg_pool1d_complex() {
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let test = AvgPool1dTestCase {
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batch_size: 1,
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channels: 2,
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kernel_size: 3,
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padding: 1,
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stride: 2,
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length: 6,
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count_include_pad: true,
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};
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test.assert_output(TestTensor::from_floats(
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[[
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[0.33333, 0.66667, 0.33333, 0.66667, 0.33333, 0.33333],
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[0.33333, 0.66667, 0.33333, 0.66667, 0.33333, 0.33333],
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]],
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&Default::default(),
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));
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}
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#[test]
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fn test_avg_pool1d_complex_dont_count_pad() {
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let test = AvgPool1dTestCase {
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batch_size: 1,
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channels: 2,
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kernel_size: 3,
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padding: 1,
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stride: 2,
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length: 6,
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count_include_pad: false,
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};
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test.assert_output(TestTensor::from_floats(
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[[
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[0.5000, 0.83333, 0.33333, 0.66667, 0.33333, 0.33333],
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[0.5000, 0.83333, 0.33333, 0.66667, 0.33333, 0.33333],
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]],
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&Default::default(),
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));
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}
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struct AvgPool1dTestCase {
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batch_size: usize,
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channels: usize,
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kernel_size: usize,
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padding: usize,
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stride: usize,
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length: usize,
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count_include_pad: bool,
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}
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impl AvgPool1dTestCase {
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fn assert_output(self, x_grad: TestTensor<3>) {
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let shape_x = Shape::new([self.batch_size, self.channels, self.length]);
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let device = Default::default();
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let x = TestAutodiffTensor::from_data(
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TestTensorInt::arange(0..shape_x.num_elements() as i64, &device)
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.reshape::<3, _>(shape_x)
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.into_data(),
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&device,
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)
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.require_grad();
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let output = avg_pool1d(
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x.clone(),
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self.kernel_size,
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self.stride,
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self.padding,
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self.count_include_pad,
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false,
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);
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let grads = output.backward();
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let x_grad_actual = x.grad(&grads).unwrap();
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let tolerance = Tolerance::default().set_half_precision_relative(1e-3);
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x_grad
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.to_data()
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.assert_approx_eq::<FloatElem>(&x_grad_actual.into_data(), tolerance);
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}
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}
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