From 51efda2652dfd6ec42ed47fa9b85c8f4c7d62958 Mon Sep 17 00:00:00 2001
From: dcarron <daniel.carron@idiap.ch>
Date: Wed, 12 Apr 2023 16:49:10 +0200
Subject: [PATCH] Removed checkpointer tests as we are using the lightning
callback
---
tests/test_checkpointer.py | 85 --------------------------------------
1 file changed, 85 deletions(-)
delete mode 100644 tests/test_checkpointer.py
diff --git a/tests/test_checkpointer.py b/tests/test_checkpointer.py
deleted file mode 100644
index aca95248..00000000
--- a/tests/test_checkpointer.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# 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)
--
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