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"""Fast checks for the synthetic depth-scaling task plumbing."""
import os
import sys
import torch
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from experiments.run import build, get_args, load_task
def check_task(dataset, expected_in, expected_out, extra):
argv = [
"run.py", "--dataset", dataset, "--device", "cpu",
"--task_train_examples", "64", "--task_test_examples", "32",
"--batch_size", "16", "--depth", "2", "--width", "12",
] + extra
old = sys.argv
try:
sys.argv = argv
args = get_args()
finally:
sys.argv = old
train, test, n_in, n_out = load_task(args, "cpu")
assert (n_in, n_out) == (expected_in, expected_out)
args.n_in, args.n_out = n_in, n_out
x, y = next(iter(train))
assert x.shape == (16, n_in)
assert y.min() >= 0 and y.max() < n_out
for mode in ("bp", "dfa", "sdil"):
args.mode = mode
net, _ = build(args, "cpu")
assert net.logits(x).shape == (16, n_out)
assert sum(batch_y.numel() for _, batch_y in test) == 32
print(f"{dataset}: input={n_in}, classes={n_out}, fixed task seed={args.task_seed}")
if __name__ == "__main__":
torch.manual_seed(0)
check_task("teacher", 20, 4,
["--task_n_in", "20", "--task_classes", "4",
"--teacher_depth", "3", "--teacher_width", "10"])
check_task("hierarchical", 16, 4,
["--task_levels", "4", "--task_classes", "4"])
check_task("tentmap", 5, 2,
["--task_levels", "4", "--task_n_in", "5"])
print("ALL SYNTHETIC TASK CHECKS PASSED")
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