From 66bd6960471521fac43674d0df4f550a0c2ea886 Mon Sep 17 00:00:00 2001
From: Curtis Chong <curtischong5@gmail.com>
Date: Sat, 24 Aug 2024 21:42:01 -0400
Subject: [PATCH] rm additions

---
 tests/core/models/test_equiformer_v2.py | 120 ------------------------
 1 file changed, 120 deletions(-)

diff --git a/tests/core/models/test_equiformer_v2.py b/tests/core/models/test_equiformer_v2.py
index 2decec393..434916cf3 100644
--- a/tests/core/models/test_equiformer_v2.py
+++ b/tests/core/models/test_equiformer_v2.py
@@ -26,126 +26,6 @@
 from fairchem.core.preprocessing import AtomsToGraphs
 
 
-@pytest.fixture(scope="class")
-def load_data(request):
-    atoms = read(
-        os.path.join(os.path.dirname(os.path.abspath(__file__)), "atoms.json"),
-        index=0,
-        format="json",
-    )
-    a2g = AtomsToGraphs(
-        max_neigh=200,
-        radius=6,
-        r_edges=False,
-        r_fixed=True,
-    )
-    data_list = a2g.convert_all([atoms])
-    request.cls.data = data_list[0]
-
-
-@pytest.fixture(scope="class")
-def load_model(request):
-    torch.manual_seed(4)
-    setup_imports()
-
-    # download and load weights.
-    # checkpoint_url = "https://dl.fbaipublicfiles.com/opencatalystproject/models/2023_06/oc20/s2ef/eq2_31M_ec4_allmd.pt"
-
-    # # load buffer into memory as a stream
-    # # and then load it with torch.load
-    # r = requests.get(checkpoint_url, stream=True)
-    # r.raise_for_status()
-    # checkpoint = torch.load(io.BytesIO(r.content), map_location=torch.device("cpu"))
-
-    model = registry.get_model_class("equiformer_v2")(
-        None,
-        -1,
-        1,
-        use_pbc=True,
-        regress_forces=True,
-        otf_graph=True,
-        max_neighbors=20,
-        max_radius=12.0,
-        max_num_elements=90,
-        num_layers=8,
-        sphere_channels=128,
-        attn_hidden_channels=64,
-        num_heads=8,
-        attn_alpha_channels=64,
-        attn_value_channels=16,
-        ffn_hidden_channels=128,
-        norm_type="layer_norm_sh",
-        lmax_list=[4],
-        mmax_list=[2],
-        grid_resolution=18,
-        num_sphere_samples=128,
-        edge_channels=128,
-        use_atom_edge_embedding=True,
-        distance_function="gaussian",
-        num_distance_basis=512,
-        attn_activation="silu",
-        use_s2_act_attn=False,
-        ffn_activation="silu",
-        use_gate_act=False,
-        use_grid_mlp=True,
-        alpha_drop=0.1,
-        drop_path_rate=0.1,
-        proj_drop=0.0,
-        weight_init="uniform",
-    )
-
-    # new_dict = {k[len("module.") * 2 :]: v for k, v in checkpoint["state_dict"].items()}
-    # load_state_dict(model, new_dict)
-
-    # Precision errors between mac vs. linux compound with multiple layers,
-    # so we explicitly set the number of layers to 1 (instead of all 8).
-    # The other alternative is to have different snapshots for mac vs. linux.
-    model.num_layers = 1
-    request.cls.model = model
-
-
-@pytest.mark.usefixtures("load_data")
-@pytest.mark.usefixtures("load_model")
-class TestEquiformerV2:
-    def test_rotation_invariance(self) -> None:
-        random.seed(1)
-        data = self.data
-
-        # Sampling a random rotation within [-180, 180] for all axes.
-        transform = RandomRotate([-180, 180], [0, 1, 2])
-        data_rotated, rot, inv_rot = transform(data.clone())
-        assert not np.array_equal(data.pos, data_rotated.pos)
-
-        # Pass it through the model.
-        batch = data_list_collater([data, data_rotated])
-        out = self.model(batch)
-
-        # Compare predicted energies and forces (after inv-rotation).
-        energies = out["energy"].detach()
-        np.testing.assert_almost_equal(energies[0], energies[1], decimal=3)
-
-        forces = out["forces"].detach()
-        logging.info(forces)
-        np.testing.assert_array_almost_equal(
-            forces[: forces.shape[0] // 2],
-            torch.matmul(forces[forces.shape[0] // 2 :], inv_rot),
-            decimal=3,
-        )
-    # def test_energy_force_shape(self, snapshot):
-    #     # Recreate the Data object to only keep the necessary features.
-    #     data = self.data
-
-    #     # Pass it through the model.
-    #     outputs = self.model(data_list_collater([data]))
-    #     energy, forces = outputs["energy"], outputs["forces"]
-
-    #     assert snapshot == energy.shape
-    #     assert snapshot == pytest.approx(energy.detach())
-
-    #     assert snapshot == forces.shape
-    #     assert snapshot == pytest.approx(forces.detach().mean(0))
-
-
 class TestMPrimaryLPrimary:
     def test_mprimary_lprimary_mappings(self):
         def sign(x):