diff options
| -rw-r--r-- | candle-core/src/cpu_backend.rs | 1 | ||||
| -rw-r--r-- | candle-core/tests/conv_tests.rs | 31 |
2 files changed, 31 insertions, 1 deletions
diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs index 05c1f4e8..0ec19559 100644 --- a/candle-core/src/cpu_backend.rs +++ b/candle-core/src/cpu_backend.rs @@ -1060,7 +1060,6 @@ impl<'a> Map2 for Conv2D<'a> { let dst_idx = dst_idx + dst_w; let mut d = T::zero(); for offset_h in 0..p.k_h { - // TODO: Handle the case where padding is larger than p.k_h / 2. let src_h = (p.stride * dst_h + offset_h) .saturating_sub(p.padding) .min(p.i_h - 1); diff --git a/candle-core/tests/conv_tests.rs b/candle-core/tests/conv_tests.rs index 4ef47780..7ec83592 100644 --- a/candle-core/tests/conv_tests.rs +++ b/candle-core/tests/conv_tests.rs @@ -39,6 +39,37 @@ fn conv1d() -> Result<()> { test_utils::to_vec1_round(&res.flatten_all()?, 4)?, [2.6357, -1.3336, 4.1393, -1.1784, 3.5675, 0.5069] ); + let res = t.conv1d(&w, /*padding*/ 1, 1)?; + assert_eq!(res.dims(), [1, 2, 5]); + /* Note that the default for padding is different from PyTorch at the moment: instead of + padding with zeros, the edge value from the input tensor is used, i.e. this is similiar to: + t = torch.nn.functional.pad(t, (1, 1), mode='replicate') + res = torch.nn.functional.conv1d(t, w, padding=0) + */ + assert_eq!( + test_utils::to_vec1_round(&res.flatten_all()?, 4)?, + [2.5209, 2.6357, -1.3336, 4.1393, 0.4951, 3.6855, -1.1784, 3.5675, 0.5069, 4.9562] + ); + Ok(()) +} + +#[test] +fn conv1d_small() -> Result<()> { + let dev = &Device::Cpu; + let t = Tensor::new(&[0.4056f32, -0.8689, -0.0773, -1.5630], dev)?.reshape((1, 1, 4))?; + let w = Tensor::new(&[1f32, 0., 0.], dev)?.reshape((1, 1, 3))?; + let res = t.conv1d(&w, 0, 1)?; + assert_eq!(res.dims(), [1, 1, 2]); + assert_eq!( + test_utils::to_vec1_round(&res.flatten_all()?, 4)?, + [0.4056, -0.8689] + ); + let res = t.conv1d(&w, /*padding*/ 1, 1)?; + assert_eq!(res.dims(), [1, 1, 4]); + assert_eq!( + test_utils::to_vec1_round(&res.flatten_all()?, 4)?, + [0.4056, 0.4056, -0.8689, -0.0773], + ); Ok(()) } |