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
82 lines
2.7 KiB
Rust
82 lines
2.7 KiB
Rust
use super::*;
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use burn_tensor::TensorData;
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#[test]
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fn should_diff_full_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 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());
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let tensor_4 = tensor_3.matmul(tensor_1.clone());
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let tensor_5 = tensor_4.mul(tensor_2.clone());
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let grads = tensor_5.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([[593., 463.0], [487.0, 539.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[734.0, 294.0], [1414.0, 242.0]]), false);
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}
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#[test]
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fn should_diff_full_complex_2() {
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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 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());
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let tensor_4 = tensor_3.matmul(tensor_1.clone());
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let tensor_5 = tensor_4.add_scalar(17.0).add(tensor_2.clone());
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let grads = tensor_5.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([[166.0, 110.0], [212.0, 156.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[113.0, 141.0], [33.0, 41.0]]), false);
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}
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#[test]
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fn should_diff_full_complex_3() {
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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 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());
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let tensor_4 = tensor_3.matmul(tensor_1.clone());
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let tensor_5 = tensor_4.clone().sub(tensor_2.clone());
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let tensor_6 = tensor_5.add(tensor_4);
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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([[332.0, 220.0], [424.0, 312.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[223.0, 279.0], [63.0, 79.0]]), false);
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}
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