1 files changed, 89 insertions, 0 deletions
diff --git a/experiments/cifar_image_smoke.py b/experiments/cifar_image_smoke.py
new file mode 100644
index 0000000..c23da45
--- /dev/null
+++ b/experiments/cifar_image_smoke.py
@@ -0,0 +1,89 @@
+#!/usr/bin/env python3
+"""Deterministic checks for the convolutional CIFAR data path."""
+import argparse
+import os
+import sys
+
+import torch
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from sdil.data import _CIFARImageLoader, get_cifar_image_splits
+
+
+EXPECTED_SPLIT_100_SHA256 = (
+    "a88b2ca885bba36dc7407171e3afbce8dca2dd83c7c1d4222e4418b0ca3de6f0")
+
+
+def synthetic_checks():
+    x = (torch.arange(12 * 3 * 32 * 32, dtype=torch.float32)
+         .reshape(12, 3, 32, 32) / 1000.0)
+    y = torch.arange(12)
+    first = _CIFARImageLoader(x, y, 5, True, augment=True, seed=17)
+    second = _CIFARImageLoader(x.clone(), y.clone(), 5, True, augment=True, seed=17)
+    epoch_sums = []
+    for _ in range(2):
+        stream_a, stream_b = list(first), list(second)
+        assert len(stream_a) == len(stream_b) == 3
+        assert all(torch.equal(xa, xb) and torch.equal(ya, yb)
+                   for (xa, ya), (xb, yb) in zip(stream_a, stream_b))
+        assert sum(len(labels) for _, labels in stream_a) == 12
+        assert all(tuple(images.shape[1:]) == (3, 32, 32)
+                   for images, _ in stream_a)
+        epoch_sums.append(sum(float(images.sum()) for images, _ in stream_a))
+    assert epoch_sums[0] != epoch_sums[1], "augmentation RNG did not advance"
+
+    plain_a = _CIFARImageLoader(x, y, 5, False, augment=False, seed=1)
+    plain_b = _CIFARImageLoader(x, y, 5, False, augment=False, seed=999)
+    assert all(torch.equal(xa, xb) and torch.equal(ya, yb)
+               for (xa, ya), (xb, yb) in zip(plain_a, plain_b))
+    try:
+        _CIFARImageLoader(x, y, 5, False, augment=True)
+    except ValueError:
+        pass
+    else:
+        raise AssertionError("evaluation augmentation guard is missing")
+
+
+def real_data_checks(device):
+    args = dict(
+        batch_size=7, device=device, train_limit=13, val_examples=100,
+        split_seed=2027, loader_seed=11)
+    train, validation, test, shape, n_out, metadata = get_cifar_image_splits(**args)
+    assert shape == (3, 32, 32) and n_out == 10
+    assert tuple(train.x.shape) == (13, 3, 32, 32)
+    assert tuple(validation.x.shape) == (100, 3, 32, 32)
+    assert tuple(test.x.shape) == (10000, 3, 32, 32)
+    assert metadata["validation_class_counts"] == {str(i): 10 for i in range(10)}
+    assert metadata["validation_index_sha256"] == EXPECTED_SPLIT_100_SHA256
+
+    validation_before = validation.x.clone()
+    test_before = test.x[:100].clone()
+    list(validation)
+    list(test)
+    assert torch.equal(validation.x, validation_before)
+    assert torch.equal(test.x[:100], test_before)
+
+    paired, _, _, _, _, paired_metadata = get_cifar_image_splits(**args)
+    for (xa, ya), (xb, yb) in zip(train, paired):
+        assert torch.equal(xa, xb) and torch.equal(ya, yb)
+    assert metadata == paired_metadata
+    return metadata
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--device", default="cpu")
+    parser.add_argument("--synthetic-only", action="store_true")
+    args = parser.parse_args()
+    synthetic_checks()
+    metadata = None if args.synthetic_only else real_data_checks(args.device)
+    print({
+        "synthetic": "passed",
+        "real_data": "skipped" if metadata is None else "passed",
+        "validation_index_sha256": (
+            None if metadata is None else metadata["validation_index_sha256"]),
+    })
+
+
+if __name__ == "__main__":
+    main()