rename files to match new convention

sparsecoding/transforms/images_patch_test.py

@@ -0,0 +1,48 @@
+import torch
+
+from sparsecoding.transforms import patchify, quilt, sample_random_patches
+
+
+def test_patchify_quilt_cycle():
+    X, Y, Z = 3, 4, 5
+    C = 3
+    P = 8
+    H = 6 * P
+    W = 8 * P
+
+    images = torch.rand((X, Y, Z, C, H, W), dtype=torch.float32)
+
+    patches = patchify(P, images)
+    assert patches.shape == (X, Y, Z, int(H / P) * int(W / P), C, P, P)
+
+    quilted_images = quilt(H, W, patches)
+    assert torch.allclose(
+        images,
+        quilted_images,
+    ), "Quilted images should be equal to input images."
+
+def test_sample_random_patches():
+    X, Y, Z = 3, 4, 5
+    C = 3
+    P = 8
+    H = 4 * P
+    W = 8 * P
+    N = 10
+
+    images = torch.rand((X, Y, Z, C, H, W), dtype=torch.float32)
+
+    random_patches = sample_random_patches(P, N, images)
+    assert random_patches.shape == (N, C, P, P)
+
+    # Check that patches are actually taken from one of the images.
+    all_patches = torch.nn.functional.unfold(
+        input=images.reshape(-1, C, H, W),
+        kernel_size=P,
+    )  # [prod(*), C*P*P, L]
+    all_patches = torch.permute(all_patches, (0, 2, 1))  # [prod(*), L, C*P*P]
+    all_patches = torch.reshape(all_patches, (-1, C*P*P))
+    for n in range(N):
+        patch = random_patches[n].reshape(1, C*P*P)
+        delta = torch.abs(patch - all_patches)  # [-1, C*P*P]
+        patchwise_delta = torch.sum(delta, dim=1)  # [-1]
+        assert torch.min(patchwise_delta) == 0.

sparsecoding/transforms/whiten_test.py

@@ -0,0 +1,61 @@
+import torch
+
+from sparsecoding.transforms import whiten
+
+
+def test_zca():
+    N = 5000
+    D = 32*32
+
+    X = torch.rand((N, D), dtype=torch.float32)
+
+    X_whitened = whiten(X)
+
+    assert torch.allclose(
+        torch.mean(X_whitened, dim=0),
+        torch.zeros(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have zero mean."
+    assert torch.allclose(
+        torch.cov(X_whitened.T),
+        torch.eye(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have unit (identity) covariance."
+
+def test_pca():
+    N = 5000
+    D = 32*32
+
+    X = torch.rand((N, D), dtype=torch.float32)
+
+    X_whitened = whiten(X, algorithm='pca')
+
+    assert torch.allclose(
+        torch.mean(X_whitened, dim=0),
+        torch.zeros(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have zero mean."
+    assert torch.allclose(
+        torch.cov(X_whitened.T),
+        torch.eye(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have unit (identity) covariance."
+
+def test_cholesky():
+    N = 5000
+    D = 32*32
+
+    X = torch.rand((N, D), dtype=torch.float32)
+
+    X_whitened = whiten(X, algorithm='cholesky')
+
+    assert torch.allclose(
+        torch.mean(X_whitened, dim=0),
+        torch.zeros(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have zero mean."
+    assert torch.allclose(
+        torch.cov(X_whitened.T),
+        torch.eye(D, dtype=torch.float32),
+        atol=1e-3,
+    ), "Whitened data should have unit (identity) covariance."