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
43 lines
1.3 KiB
Rust
43 lines
1.3 KiB
Rust
use super::*;
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use burn_tensor::TensorData;
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/// Example using the sign function with PyTorch:
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// >>> import torch
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// >>> # Create a tensor with requires_grad=True
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// >>> x = torch.tensor([-2.0, -1.0, 0.0, 1.0, 2.0], requires_grad=True)
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// >>> # Forward pass: Apply the sign function
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// >>> y = torch.sign(x)
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// >>> print("Forward pass:")
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// Forward pass:
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// >>> print("x:", x)
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// x: tensor([-2., -1., 0., 1., 2.], requires_grad=True)
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// >>> print("y:", y)
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// y: tensor([-1., -1., 0., 1., 1.], grad_fn=<SignBackward0>)
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// >>> # Compute the loss (just an example)
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// >>> loss = y.sum()
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// >>> # Backward pass: Compute the gradients
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// >>> loss.backward()
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// >>> print("\nBackward pass:")
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// Backward pass:
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// >>> print("x.grad:", x.grad)
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// x.grad: tensor([0., 0., 0., 0., 0.])
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#[test]
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fn should_diff_sign() {
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let data = TensorData::from([-2.0, -1.0, 0.0, 1.0, 2.0]);
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let device = Default::default();
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let x = TestAutodiffTensor::<1>::from_data(data, &device).require_grad();
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let y = x.clone().sign();
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let loss = y.clone().sum();
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let grads = loss.backward();
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let grad = x.grad(&grads).unwrap();
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y.to_data()
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.assert_eq(&TensorData::from([-1., -1., 0., 1., 1.]), false);
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grad.to_data()
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.assert_eq(&TensorData::from([0., 0., 0., 0., 0.]), false);
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}
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