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
42 lines
1.5 KiB
Rust
42 lines
1.5 KiB
Rust
use super::*;
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use burn_tensor::{Bool, Tensor, TensorData};
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#[test]
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fn should_diff_nonzero() {
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let data_1 = TensorData::from([[1.0, 2.0], [3.0, 4.0]]);
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let data_2 = TensorData::from([-1.0, 1.0]);
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let mask = TensorData::from([[false, true], [true, false]]);
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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::<1>::from_data(data_2, &device).require_grad();
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// Multi-dimensional tensor indexing isn't really supported yet so the easiest way to do
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// this is to flatten the mask and tensor to get proper indexing. Anyway the returned tensor would
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// have dimensions different from the input, so this is somewhat equivalent.
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let mask = Tensor::<TestAutodiffBackend, 2, Bool>::from_bool(mask, &device).flatten::<1>(0, 1);
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let indices = mask.nonzero();
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let tensor_3 = tensor_1
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.clone()
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.flatten::<1>(0, 1)
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.select(0, indices[0].clone());
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// Vector dot product not supported (only 2D matmuls) so unsqueeze for test purposes
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let tensor_4 = tensor_2
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.clone()
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.unsqueeze_dim::<2>(0)
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.matmul(tensor_3.unsqueeze_dim(1));
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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([[0.0, -1.0], [1.0, 0.0]]), false);
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grad_2
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.to_data()
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.assert_eq(&TensorData::from([2.0, 3.0]), false);
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}
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