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
69 lines
2.2 KiB
Rust
69 lines
2.2 KiB
Rust
use super::*;
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use burn_tensor::TensorData;
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#[test]
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fn should_diff_mul() {
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let data_1 = TensorData::from([1.0, 7.0]);
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let data_2 = TensorData::from([4.0, 7.0]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::<1>::from_data(data_1.clone(), &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data(data_2.clone(), &device).require_grad();
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let tensor_3 = tensor_1.clone().mul(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.to_data().assert_eq(&data_2, false);
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tensor_3
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.into_data()
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.assert_eq(&TensorData::from([4.0, 49.0]), false);
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}
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#[test]
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fn should_diff_mul_scalar() {
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let data = TensorData::from([2.0, 5.0]);
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let tensor = TestAutodiffTensor::<1>::from_data(data, &Default::default()).require_grad();
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let tensor_out = tensor.clone().mul_scalar(4.0);
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let grads = tensor_out.backward();
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let grad = tensor.grad(&grads).unwrap();
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tensor_out
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.into_data()
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.assert_eq(&TensorData::from([8.0, 20.0]), false);
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grad.to_data()
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.assert_eq(&TensorData::from([4.0, 4.0]), false);
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}
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#[test]
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fn test_mul_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().mul(tensor_2.clone());
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let tensor_5 = tensor_4.mul(tensor_3);
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let tensor_6 = tensor_1.clone().mul(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([[16.0, 196.0], [104.0, -36.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[2.0, 98.0], [338.0, 18.0]]), false);
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}
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