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
66 lines
2.4 KiB
Rust
66 lines
2.4 KiB
Rust
use super::*;
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use burn_tensor::Tolerance;
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use burn_tensor::{Bool, Tensor, TensorData};
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#[test]
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fn should_diff_mask_fill() {
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let data_1 = TensorData::from([[1.0, 7.0], [2.0, 3.0]]);
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let data_2 = TensorData::from([[4.0, 7.0], [2.0, 3.0]]);
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let mask = TensorData::from([[true, false], [false, true]]);
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::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 mask = Tensor::<TestAutodiffBackend, 2, Bool>::from_bool(mask, &device);
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let tensor_3 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_4 = tensor_3.mask_fill(mask, 2.0);
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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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grad_1
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.to_data()
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.assert_eq(&TensorData::from([[7.0, 3.0], [4.0, 2.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([[2.0, 1.0], [3.0, 7.0]]), false);
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}
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#[test]
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fn should_diff_mask_where() {
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let device = Default::default();
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let tensor_1 = TestAutodiffTensor::from_data([[1.0, 7.0], [2.0, 3.0]], &device).require_grad();
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let tensor_2 = TestAutodiffTensor::from_data([[4.0, 7.0], [2.0, 3.0]], &device).require_grad();
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let tensor_3 =
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TestAutodiffTensor::from_data([[8.8, 9.8], [10.8, 11.8]], &device).require_grad();
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let mask =
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Tensor::<TestAutodiffBackend, 2, Bool>::from_data([[true, false], [false, true]], &device);
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let tensor_4 = tensor_1.clone().matmul(tensor_2.clone());
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let tensor_5 = tensor_4.clone().matmul(tensor_3.clone());
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let tensor_6 = tensor_5.mask_where(mask, tensor_3.clone());
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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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let grad_3 = tensor_3.grad(&grads).unwrap();
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let tolerance = Tolerance::default().set_half_precision_relative(1e-3);
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let expected = TensorData::from([[121.8, 55.0], [110.8, 50.0]]);
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grad_1
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.into_data()
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.assert_approx_eq::<FloatElem>(&expected, tolerance);
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let expected = TensorData::from([[27.4, 33.4], [95.0, 115.0]]);
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grad_2
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.into_data()
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.assert_approx_eq::<FloatElem>(&expected, tolerance);
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let expected = TensorData::from([[15., 18.], [23., 29.]]);
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grad_3
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.into_data()
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.assert_approx_eq::<FloatElem>(&expected, tolerance);
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}
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