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
59 lines
2.0 KiB
Rust
59 lines
2.0 KiB
Rust
use super::*;
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use burn_tensor::{TensorData, Tolerance, cast::ToElement};
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#[test]
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fn should_diff_abs() {
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let data_1 = TensorData::from([[0.0, -1.0], [3.0, 4.0]]);
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let data_2 = TensorData::from([[6.0, 7.0], [9.0, -10.0]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().abs());
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let tensor_4 = tensor_3.matmul(tensor_2.clone());
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let grads = tensor_4.backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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let expected = TensorData::from([[71.0, 107.0], [71.0, 107.0]]);
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grad_1
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let expected = TensorData::from([[84.0, 42.0], [90.0, 54.0]]);
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grad_2
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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}
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#[test]
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fn should_diff_abs_no_nans() {
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let data_1 = TensorData::from([[6.0, 7.0], [9.0, -10.0]]);
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let data_2 = TensorData::from([[0.0, -1.0], [3.0, 4.0]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<2>::from_data(data_1, &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2, &device).require_grad();
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone().abs());
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let grads = tensor_3.backward();
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let grad_1 = tensor_1.grad(&grads).unwrap();
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let grad_2 = tensor_2.grad(&grads).unwrap();
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let expected = TensorData::from([[1.0, 7.0], [1.0, 7.0]]);
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grad_1
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let expected = TensorData::from([[0.0, -15.0], [-3.0, -3.0]]);
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grad_2
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.to_data()
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.assert_approx_eq::<FloatElem>(&expected, Tolerance::default());
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let contains_nan = grad_2.contains_nan();
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assert!(!contains_nan.into_scalar().to_bool());
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}
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