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feat: update workspace paths and enhance gitignore

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- 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
This commit is contained in:
Ben_Kosytorz
2026-03-05 19:39:14 +01:00
parent 4bb7ca9074
commit 3a67c0979c
1605 changed files with 537032 additions and 2 deletions
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271
crates/stable-diffusion-burn/burn-crates/burn-backend-tests/tests/autodiff/maxpool2d.rs Normal file
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use super::*;
use burn_tensor::Tolerance;
use burn_tensor::module::max_pool2d;
#[test]
fn test_max_pool2d_simple_1() {
let kernel_size_1 = 3;
let kernel_size_2 = 3;
let padding_1 = 0;
let padding_2 = 0;
let stride_1 = 1;
let stride_2 = 1;
let dilation_1 = 1;
let dilation_2 = 1;
let device = Default::default();
let x = TestAutodiffTensor::from_floats(
[[[
[0.2479, 0.6386, 0.3166, 0.5742],
[0.7065, 0.1940, 0.6305, 0.8959],
[0.5416, 0.8602, 0.8129, 0.1662],
[0.3358, 0.3059, 0.8293, 0.0990],
]]],
&device,
)
.require_grad();
let x_grad_expected = TestAutodiffTensor::<4>::from_floats(
[[[
[0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 2.0],
[0.0, 2.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0],
]]],
&device,
);
let output = max_pool2d(
x.clone(),
[kernel_size_1, kernel_size_2],
[stride_1, stride_2],
[padding_1, padding_2],
[dilation_1, dilation_2],
false,
);
let grads = output.backward();
// Asserts
let x_grad_actual = x.grad(&grads).unwrap();
x_grad_expected
.to_data()
.assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
}
#[test]
fn test_max_pool2d_simple_2() {
let kernel_size_1 = 2;
let kernel_size_2 = 2;
let padding_1 = 1;
let padding_2 = 1;
let stride_1 = 1;
let stride_2 = 1;
let dilation_1 = 1;
let dilation_2 = 1;
let device = Default::default();
let x = TestAutodiffTensor::from_floats(
[[[
[0.2479, 0.6386, 0.3166, 0.5742],
[0.7065, 0.1940, 0.6305, 0.8959],
[0.5416, 0.8602, 0.8129, 0.1662],
[0.3358, 0.3059, 0.8293, 0.0990],
]]],
&device,
)
.require_grad();
let x_grad_expected = TestAutodiffTensor::<4>::from_floats(
[[[
[1., 3., 0., 2.],
[3., 0., 0., 4.],
[1., 4., 0., 1.],
[2., 0., 3., 1.],
]]],
&device,
);
let output = max_pool2d(
x.clone(),
[kernel_size_1, kernel_size_2],
[stride_1, stride_2],
[padding_1, padding_2],
[dilation_1, dilation_2],
false,
);
let grads = output.backward();
// Asserts
let x_grad_actual = x.grad(&grads).unwrap();
x_grad_expected
.to_data()
.assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
}
#[test]
fn test_max_pool2d_with_dilation() {
let kernel_size_1 = 2;
let kernel_size_2 = 2;
let padding_1 = 1;
let padding_2 = 1;
let stride_1 = 1;
let stride_2 = 1;
let dilation_1 = 2;
let dilation_2 = 2;
let device = Default::default();
let x = TestAutodiffTensor::from_floats(
[[[
[0.2479, 0.6386, 0.3166, 0.5742],
[0.7065, 0.1940, 0.6305, 0.8959],
[0.5416, 0.8602, 0.8129, 0.1662],
[0.3358, 0.3059, 0.8293, 0.0990],
]]],
&device,
)
.require_grad();
let x_grad_expected = TestAutodiffTensor::<4>::from_floats(
[[[
[0., 0., 0., 0.],
[1., 1., 1., 2.],
[0., 4., 4., 0.],
[0., 1., 2., 0.],
]]],
&device,
);
let output = max_pool2d(
x.clone(),
[kernel_size_1, kernel_size_2],
[stride_1, stride_2],
[padding_1, padding_2],
[dilation_1, dilation_2],
false,
);
let grads = output.backward();
// Asserts
let x_grad_actual = x.grad(&grads).unwrap();
x_grad_expected
.to_data()
.assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
}
#[test]
fn test_max_pool2d_complex() {
let kernel_size_1 = 4;
let kernel_size_2 = 2;
let padding_1 = 2;
let padding_2 = 1;
let stride_1 = 1;
let stride_2 = 2;
let dilation_1 = 1;
let dilation_2 = 1;
let device = Default::default();
let x = TestAutodiffTensor::from_floats(
[[[
[0.5388, 0.0676, 0.7122, 0.8316, 0.0653],
[0.9154, 0.1536, 0.9089, 0.8016, 0.7518],
[0.2073, 0.0501, 0.8811, 0.5604, 0.5075],
[0.4384, 0.9963, 0.9698, 0.4988, 0.2609],
[0.3391, 0.2230, 0.4610, 0.5365, 0.6880],
]]],
&device,
)
.require_grad();
let x_grad_expected = TestAutodiffTensor::<4>::from_floats(
[[[
[0., 0., 0., 3., 0.],
[4., 0., 2., 1., 0.],
[0., 0., 0., 0., 0.],
[2., 4., 0., 0., 0.],
[0., 0., 0., 0., 2.],
]]],
&device,
);
let output = max_pool2d(
x.clone(),
[kernel_size_1, kernel_size_2],
[stride_1, stride_2],
[padding_1, padding_2],
[dilation_1, dilation_2],
false,
);
let grads = output.backward();
// Asserts
let x_grad_actual = x.grad(&grads).unwrap();
x_grad_expected
.to_data()
.assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
}
#[test]
fn test_max_pool2d_ceil_mode() {
// Test ceil_mode=true with gradient computation
// Using 1x1x6x6 input with kernel 3x3, stride 2x2, padding 0
// Floor mode: output 2x2
// Ceil mode: output 3x3
let kernel_size_1 = 3;
let kernel_size_2 = 3;
let padding_1 = 0;
let padding_2 = 0;
let stride_1 = 2;
let stride_2 = 2;
let dilation_1 = 1;
let dilation_2 = 1;
let device = Default::default();
// Input (values 1-36):
let x = TestAutodiffTensor::from_floats(
[[[
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
[7.0, 8.0, 9.0, 10.0, 11.0, 12.0],
[13.0, 14.0, 15.0, 16.0, 17.0, 18.0],
[19.0, 20.0, 21.0, 22.0, 23.0, 24.0],
[25.0, 26.0, 27.0, 28.0, 29.0, 30.0],
[31.0, 32.0, 33.0, 34.0, 35.0, 36.0],
]]],
&device,
)
.require_grad();
// Expected gradients for ceil_mode output 3x3:
// Output positions and their max value positions:
// (0,0): max at (2,2)=15 -> grad[2,2] += 1
// (0,1): max at (2,4)=17 -> grad[2,4] += 1
// (0,2): max at (2,5)=18 -> grad[2,5] += 1
// (1,0): max at (4,2)=27 -> grad[4,2] += 1
// (1,1): max at (4,4)=29 -> grad[4,4] += 1
// (1,2): max at (4,5)=30 -> grad[4,5] += 1
// (2,0): max at (5,2)=33 -> grad[5,2] += 1
// (2,1): max at (5,4)=35 -> grad[5,4] += 1
// (2,2): max at (5,5)=36 -> grad[5,5] += 1
let x_grad_expected = TestAutodiffTensor::<4>::from_floats(
[[[
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 1., 1.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 1., 0., 1., 1.],
[0., 0., 1., 0., 1., 1.],
]]],
&device,
);
let output = max_pool2d(
x.clone(),
[kernel_size_1, kernel_size_2],
[stride_1, stride_2],
[padding_1, padding_2],
[dilation_1, dilation_2],
true,
);
let grads = output.backward();
// Asserts
let x_grad_actual = x.grad(&grads).unwrap();
x_grad_expected
.to_data()
.assert_approx_eq::<FloatElem>(&x_grad_actual.to_data(), Tolerance::default());
}