Removed checkpointer tests as we are using the lightning callback

tests/test_checkpointer.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-import os
-import unittest
-
-from collections import OrderedDict
-from tempfile import TemporaryDirectory
-
-import torch
-
-from ptbench.utils.checkpointer import Checkpointer
-
-
-class TestCheckpointer(unittest.TestCase):
-    def create_model(self):
-        return torch.nn.Sequential(torch.nn.Linear(2, 3), torch.nn.Linear(3, 1))
-
-    def create_complex_model(self):
-        m = torch.nn.Module()
-        m.block1 = torch.nn.Module()
-        m.block1.layer1 = torch.nn.Linear(2, 3)
-        m.layer2 = torch.nn.Linear(3, 2)
-        m.res = torch.nn.Module()
-        m.res.layer2 = torch.nn.Linear(3, 2)
-
-        state_dict = OrderedDict()
-        state_dict["layer1.weight"] = torch.rand(3, 2)
-        state_dict["layer1.bias"] = torch.rand(3)
-        state_dict["layer2.weight"] = torch.rand(2, 3)
-        state_dict["layer2.bias"] = torch.rand(2)
-        state_dict["res.layer2.weight"] = torch.rand(2, 3)
-        state_dict["res.layer2.bias"] = torch.rand(2)
-
-        return m, state_dict
-
-    def test_from_last_checkpoint_model(self):
-        # test that loading works even if they differ by a prefix
-        trained_model = self.create_model()
-        fresh_model = self.create_model()
-        with TemporaryDirectory() as f:
-            checkpointer = Checkpointer(trained_model, path=f)
-            checkpointer.save("checkpoint_file")
-
-            # in the same folder
-            fresh_checkpointer = Checkpointer(fresh_model, path=f)
-            assert fresh_checkpointer.has_checkpoint()
-            assert fresh_checkpointer.last_checkpoint() == os.path.realpath(
-                os.path.join(f, "checkpoint_file.pth")
-            )
-            _ = fresh_checkpointer.load()
-
-        for trained_p, loaded_p in zip(
-            trained_model.parameters(), fresh_model.parameters()
-        ):
-            # different tensor references
-            assert id(trained_p) != id(loaded_p)
-            # same content
-            assert trained_p.equal(loaded_p)
-
-    def test_from_name_file_model(self):
-        # test that loading works even if they differ by a prefix
-        trained_model = self.create_model()
-        fresh_model = self.create_model()
-        with TemporaryDirectory() as f:
-            checkpointer = Checkpointer(trained_model, path=f)
-            checkpointer.save("checkpoint_file")
-
-            # on different folders
-            with TemporaryDirectory() as g:
-                fresh_checkpointer = Checkpointer(fresh_model, path=g)
-                assert not fresh_checkpointer.has_checkpoint()
-                assert fresh_checkpointer.last_checkpoint() is None
-                _ = fresh_checkpointer.load(
-                    os.path.join(f, "checkpoint_file.pth")
-                )
-
-        for trained_p, loaded_p in zip(
-            trained_model.parameters(), fresh_model.parameters()
-        ):
-            # different tensor references
-            assert id(trained_p) != id(loaded_p)
-            # same content
-            assert trained_p.equal(loaded_p)