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
75 lines
2.3 KiB
Rust
75 lines
2.3 KiB
Rust
use super::*;
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use burn_tensor::TensorData;
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#[test]
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fn should_diff_add() {
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<1>::from_floats([2.0, 5.0], &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_floats([4.0, 1.0], &device).require_grad();
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let tensor_3 = tensor_1.clone() + tensor_2.clone();
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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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grad_1
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.to_data()
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.assert_eq(&TensorData::from([1.0, 1.0]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([1.0, 1.0]), false);
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tensor_3
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.to_data()
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.assert_eq(&TensorData::from([6.0, 6.0]), false);
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}
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#[test]
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fn should_diff_add_scalar() {
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let data = TensorData::from([2.0, 10.0]);
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let tensor = TestAutodiffTensor::<1>::from_data(data, &Default::default()).require_grad();
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let tensor_out = tensor.clone().add_scalar(5.0);
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let grads = tensor_out.backward();
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let grad = tensor.grad(&grads).unwrap();
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grad.to_data()
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.assert_eq(&TensorData::from([1.0, 1.0]), false);
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tensor_out
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.into_data()
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.assert_eq(&TensorData::from([7.0, 15.0]), false);
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}
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#[test]
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fn test_add_complex_1() {
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let data_1 = TensorData::from([[1.0, 7.0], [13.0, -3.0]]);
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let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
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let data_3 = TensorData::from([[2.0, 2.0], [2.0, 2.0]]);
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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 = TestAutodiffTensor::from_data(data_3, &device).require_grad();
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let tensor_4 = tensor_1.clone().add(tensor_2.clone());
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let tensor_5 = tensor_4
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.add(tensor_3)
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.add_scalar(5.0)
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.add(tensor_1.clone())
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.add(tensor_2.clone());
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let tensor_6 = tensor_1.clone().add(tensor_5);
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let grads = tensor_6.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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grad_1
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.to_data()
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.assert_eq(&TensorData::from([[3.0, 3.0], [3.0, 3.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[2.0, 2.0], [2.0, 2.0]]), false);
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}
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