update test for utils functions

.coveragerc

@@ -1,7 +1,7 @@
 # .coveragerc to control coverage.py
 [run]
 omit =
-    # omit all tests
+    # omit all test files
     tests/*
     # omit algorithms, which will be tested separately
     fl_sim/algorithms/*
@@ -12,6 +12,7 @@ omit =
     fl_sim/data_processing/libsvm_datasets.py
     # visualization panel requires notebook environment
     fl_sim/utils/viz.py
+    # fl_sim/utils/torch_compat.py
     # compressors not used currently
     fl_sim/compressors/*
 

test/test_utils.py

@@ -24,6 +24,7 @@
     make_serializable,
     ordered_dict_to_dict,
 )
+from fl_sim.utils.torch_compat import torch_norm
 
 
 def test_get_scheduler():
@@ -226,5 +227,36 @@ def test_experiment_indicator():
 
 
 def test_url_is_reachable():
-    assert url_is_reachable("https://www.google.com/")
+    assert url_is_reachable("https://www.cloudflare.com/")
     assert not url_is_reachable("https://www.google.com/xxx")
+
+
+def test_torch_norm():
+    a = torch.arange(9, dtype=torch.float) - 4
+    b = a.reshape((3, 3))
+    assert torch.allclose(torch_norm(a, dtype=torch.float64), torch.tensor(7.7460, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(torch_norm(b, dtype=torch.float64), torch.tensor(7.7460, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(torch_norm(a, float("inf"), dtype=torch.float64), torch.tensor(4.0, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(torch_norm(b, float("inf"), dtype=torch.float64), torch.tensor(4.0, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(torch_norm(b, p="nuc", dtype=torch.float64), torch.tensor(9.7980, dtype=torch.float64), atol=1e-4)
+
+    c = torch.tensor([[1, 2, 3], [-1, 1, 4]], dtype=torch.float)
+    assert torch.allclose(
+        torch_norm(c, dim=0, dtype=torch.float64), torch.tensor([1.4142, 2.2361, 5.0000], dtype=torch.float64), atol=1e-4
+    )
+    assert torch.allclose(
+        torch_norm(c, dim=1, dtype=torch.float64), torch.tensor([3.7417, 4.2426], dtype=torch.float64), atol=1e-4
+    )
+    assert torch.allclose(
+        torch_norm(c, p=1, dim=1, dtype=torch.float64), torch.tensor([6.0, 6.0], dtype=torch.float64), atol=1e-4
+    )
+
+    d = torch.arange(8, dtype=torch.float).reshape(2, 2, 2)
+    assert torch.allclose(
+        torch_norm(d, dim=(1, 2), dtype=torch.float64), torch.tensor([3.7417, 11.2250], dtype=torch.float64), atol=1e-4
+    )
+    assert torch.allclose(torch_norm(d[0, :, :], dtype=torch.float64), torch.tensor(3.7417, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(torch_norm(d[1, :, :], dtype=torch.float64), torch.tensor(11.2250, dtype=torch.float64), atol=1e-4)
+    assert torch.allclose(
+        torch_norm(d, p="nuc", dim=[1, 2], dtype=torch.float64), torch.tensor([4.2426, 11.4018], dtype=torch.float64), atol=1e-4
+    )