Python maskrcnn_benchmark.utils.checkpoint.Checkpointer() Examples

The following are 28 code examples of maskrcnn_benchmark.utils.checkpoint.Checkpointer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module maskrcnn_benchmark.utils.checkpoint , or try the search function .
Example #1
Source File: checkpoint.py    From FreeAnchor with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #2
Source File: checkpoint.py    From maskscoring_rcnn with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #3
Source File: checkpoint.py    From maskscoring_rcnn with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #4
Source File: checkpoint.py    From DF-Traffic-Sign-Identification with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #5
Source File: checkpoint.py    From DF-Traffic-Sign-Identification with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #6
Source File: checkpoint.py    From RRPN_pytorch with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #7
Source File: checkpoint.py    From RRPN_pytorch with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #8
Source File: checkpoint.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #9
Source File: checkpoint.py    From NAS-FCOS with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #10
Source File: checkpoint.py    From training with Apache License 2.0 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #11
Source File: checkpoint.py    From training with Apache License 2.0 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #12
Source File: checkpoint.py    From retinamask with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #13
Source File: checkpoint.py    From retinamask with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #14
Source File: checkpoint.py    From FreeAnchor with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #15
Source File: checkpoint.py    From Res2Net-maskrcnn with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #16
Source File: checkpoint.py    From maskrcnn-benchmark with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #17
Source File: checkpoint.py    From maskrcnn-benchmark with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #18
Source File: checkpoint.py    From HRNet-MaskRCNN-Benchmark with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #19
Source File: checkpoint.py    From HRNet-MaskRCNN-Benchmark with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #20
Source File: checkpoint.py    From sampling-free with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #21
Source File: checkpoint.py    From sampling-free with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #22
Source File: checkpoint.py    From remote_sensing_object_detection_2019 with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #23
Source File: checkpoint.py    From remote_sensing_object_detection_2019 with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #24
Source File: checkpoint.py    From DetNAS with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #25
Source File: checkpoint.py    From DetNAS with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #26
Source File: checkpoint.py    From R2CNN.pytorch with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #27
Source File: checkpoint.py    From R2CNN.pytorch with MIT License 5 votes vote down vote up 
def test_from_last_checkpoint_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:

            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # in the same folder
                fresh_checkpointer = Checkpointer(fresh_model, save_dir=f)
                self.assertTrue(fresh_checkpointer.has_checkpoint())
                self.assertEqual(
                    fresh_checkpointer.get_checkpoint_file(),
                    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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))
Example #28
Source File: checkpoint.py    From Res2Net-maskrcnn with MIT License 5 votes vote down vote up 
def test_from_name_file_model(self):
        # test that loading works even if they differ by a prefix
        for trained_model, fresh_model in [
            (self.create_model(), self.create_model()),
            (nn.DataParallel(self.create_model()), self.create_model()),
            (self.create_model(), nn.DataParallel(self.create_model())),
            (
                nn.DataParallel(self.create_model()),
                nn.DataParallel(self.create_model()),
            ),
        ]:
            with TemporaryDirectory() as f:
                checkpointer = Checkpointer(
                    trained_model, save_dir=f, save_to_disk=True
                )
                checkpointer.save("checkpoint_file")

                # on different folders
                with TemporaryDirectory() as g:
                    fresh_checkpointer = Checkpointer(fresh_model, save_dir=g)
                    self.assertFalse(fresh_checkpointer.has_checkpoint())
                    self.assertEqual(fresh_checkpointer.get_checkpoint_file(), "")
                    _ = 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
                self.assertFalse(id(trained_p) == id(loaded_p))
                # same content
                self.assertTrue(trained_p.equal(loaded_p))