Add test that unets do not translate state

tests/test_unet_translation.py

@@ -0,0 +1,81 @@
+import jax
+import jax.numpy as jnp
+import pytest
+
+import pdequinox as pdeqx
+
+
+@pytest.mark.parametrize(
+    "num_spatial_dims,num_points,num_levels",
+    [
+        (num_spatial_dims, num_points, num_levels)
+        for num_spatial_dims in (1, 2, 3)
+        for num_points in (36,)
+        for num_levels in (0, 1, 2)
+    ],
+)
+def test_unet_translation(num_spatial_dims: int, num_points: int, num_levels: int):
+    grid_1d = jnp.linspace(0, 1.0, num_points, endpoint=False)
+
+    grid = jnp.stack(
+        jnp.meshgrid(*[grid_1d for _ in range(num_spatial_dims)], indexing="ij")
+    )
+
+    u_0 = jnp.sin(2 * jnp.pi * grid[0:1])
+
+    if num_spatial_dims >= 2:
+        u_0 *= jnp.sin(4 * jnp.pi * grid[1:2])
+
+    if num_spatial_dims >= 3:
+        u_0 *= jnp.sin(6 * jnp.pi * grid[2:3])
+
+    unet = pdeqx.arch.ClassicUNet(
+        num_spatial_dims,
+        1,
+        1,
+        hidden_channels=1,
+        num_levels=num_levels,
+        use_norm=False,
+        key=jax.random.PRNGKey(0),
+    )
+
+    # Change all kernels to identity and biases to zero
+    arr, structure = jax.tree.flatten(unet)
+
+    arr_new = []
+
+    for a in arr:
+        if isinstance(a, jnp.ndarray):
+            if a.ndim == num_spatial_dims + 1:
+                # Bias
+                arr_new.append(jnp.zeros_like(a))
+            elif a.ndim == num_spatial_dims + 2:
+                # Convolutional kernel
+                new_kernel = jnp.zeros_like(a)
+                if a.shape[-1] == 3:
+                    if num_spatial_dims == 1:
+                        new_kernel = new_kernel.at[..., 1].set(1.0)
+                    elif num_spatial_dims == 2:
+                        new_kernel = new_kernel.at[..., 1, 1].set(1.0)
+                    elif num_spatial_dims == 3:
+                        new_kernel = new_kernel.at[..., 1, 1, 1].set(1.0)
+                elif a.shape[-1] == 1:
+                    new_kernel = new_kernel.at[..., 0].set(1.0)
+                else:
+                    raise ValueError(f"Invalid kernel shape {a.shape}")
+                arr_new.append(new_kernel)
+        else:
+            arr_new.append(a)
+
+    print(arr_new)
+
+    unet_new = jax.tree_util.tree_unflatten(structure, arr_new)
+
+    u_0_processed = unet_new(u_0)
+
+    if num_spatial_dims == 1:
+        assert u_0_processed[..., 1] == pytest.approx(u_0[..., 1])
+    elif num_spatial_dims == 2:
+        assert u_0_processed[..., 1, 1] == pytest.approx(u_0[..., 1, 1])
+    elif num_spatial_dims == 3:
+        assert u_0_processed[..., 1, 1, 1] == pytest.approx(u_0[..., 1, 1, 1])