def test_checkpoint_resume(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
trainer = SimpleTrainer(model, dataloader, opt)
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
trainer.register_hooks([
hooks.LRScheduler(scheduler=scheduler),
# checkpoint after scheduler to properly save the state of scheduler
hooks.PeriodicCheckpointer(checkpointer, 10),
])
trainer.train(0, 12)
self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
self.assertEqual(scheduler.last_epoch, 12)
del trainer
opt = torch.optim.SGD(model.parameters(), 999) # lr will be loaded
trainer = SimpleTrainer(model, dataloader, opt)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
trainer.register_hooks([
hooks.LRScheduler(scheduler=scheduler),
])
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
checkpointer.resume_or_load("non_exist.pth")
self.assertEqual(
trainer.iter,
11) # last finished iter number (0-based in Trainer)
# number of times `scheduler.step()` was called (1-based)
self.assertEqual(scheduler.last_epoch, 12)
self.assertAlmostEqual(opt.param_groups[0]["lr"], 1e-5)
def test_checkpoint_resume(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
trainer = SimpleTrainer(model, dataloader, opt)
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
trainer.register_hooks(
[
hooks.PeriodicCheckpointer(checkpointer, 10),
hooks.LRScheduler(scheduler=scheduler),
]
)
trainer.train(0, 12)
del trainer
trainer = SimpleTrainer(model, dataloader, opt)
scheduler = torch.optim.lr_scheduler.StepLR(opt, 3)
trainer.register_hooks(
[
hooks.LRScheduler(scheduler=scheduler),
]
)
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
checkpointer.resume_or_load("non_exist.pth")
self.assertEqual(trainer.iter, 11) # last finished iter
self.assertEqual(scheduler.last_epoch, 11)
def training_step(self, batch, batch_idx):
data_time = time.perf_counter() - self.data_start
# Need to manually enter/exit since trainer may launch processes
# This ideally belongs in setup, but setup seems to run before processes are spawned
if self.storage is None:
self.storage = EventStorage(0)
self.storage.__enter__()
self.iteration_timer.trainer = weakref.proxy(self)
self.iteration_timer.before_step()
self.writers = (
default_writers(self.cfg.OUTPUT_DIR, self.max_iter)
if comm.is_main_process()
else {}
)
loss_dict = self.model(batch)
SimpleTrainer.write_metrics(loss_dict, data_time)
opt = self.optimizers()
self.storage.put_scalar(
"lr", opt.param_groups[self._best_param_group_id]["lr"], smoothing_hint=False
)
self.iteration_timer.after_step()
self.storage.step()
# A little odd to put before step here, but it's the best way to get a proper timing
self.iteration_timer.before_step()
if self.storage.iter % 20 == 0:
for writer in self.writers:
writer.write()
return sum(loss_dict.values())
def __init__(self, cfg, optimization_level):
logger = logging.getLogger("detectron2")
if not logger.isEnabledFor(logging.INFO):
setup_logger()
model = self.build_model(cfg)
optimizer = self.build_optimizer(cfg, model)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=optimization_level)
data_loader = self.build_train_loader(cfg)
SimpleTrainer.__init__(self, model, data_loader, optimizer)
self.scheduler = self.build_lr_scheduler(cfg, optimizer)
self.checkpointer = DetectionCheckpointer(
model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=self.scheduler,
)
self.start_iter = 0
self.max_iter = cfg.SOLVER.MAX_ITER
self.cfg = cfg
self.register_hooks(self.build_hooks())
def test_simple_trainer(self, device="cpu"):
device = torch.device(device)
model = SimpleModel(nn.Linear(10, 10)).to(device)
def data_loader():
while True:
yield torch.rand(3, 3).to(device)
trainer = SimpleTrainer(model, data_loader(),
torch.optim.SGD(model.parameters(), 0.1))
trainer.train(0, 10)
def __init__(self, cfg, weights: Union[str, Dict[str, Any]]):
self.start_iter = 0
self.max_iter = cfg.SOLVER.MAX_ITER
self.cfg = cfg
# We do not make any super call here and implement `__init__` from
# `DefaultTrainer`: we need to initialize mixed precision model before
# wrapping to DDP, so we need to do it this way.
model = self.build_model(cfg)
optimizer = self.build_optimizer(cfg, model)
data_loader = self.build_train_loader(cfg)
scheduler = self.build_lr_scheduler(cfg, optimizer)
# Load pre-trained weights before wrapping to DDP because `ApexDDP` has
# some weird issue with `DetectionCheckpointer`.
# fmt: off
if isinstance(weights, str):
# weights are ``str`` means ImageNet init or resume training.
self.start_iter = (DetectionCheckpointer(
model, optimizer=optimizer,
scheduler=scheduler).resume_or_load(weights, resume=True).get(
"iteration", -1) + 1)
elif isinstance(weights, dict):
# weights are a state dict means our pretrain init.
DetectionCheckpointer(model)._load_model(weights)
# fmt: on
# Enable distributed training if we have multiple GPUs. Use Apex DDP for
# non-FPN backbones because its `delay_allreduce` functionality helps with
# gradient checkpointing.
if dist.get_world_size() > 1:
if global_cfg.get("GRADIENT_CHECKPOINT", False):
model = ApexDDP(model, delay_allreduce=True)
else:
model = nn.parallel.DistributedDataParallel(
model,
device_ids=[dist.get_rank()],
broadcast_buffers=False)
# Call `__init__` from grandparent class: `SimpleTrainer`.
SimpleTrainer.__init__(self, model, data_loader, optimizer)
self.scheduler = scheduler
self.checkpointer = DetectionCheckpointer(model,
cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=self.scheduler)
self.register_hooks(self.build_hooks())
def benchmark_train(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if comm.get_world_size() > 1:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
)
optimizer = build_optimizer(cfg, model)
checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
checkpointer.load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0
data_loader = build_detection_train_loader(cfg)
dummy_data = list(itertools.islice(data_loader, 100))
def f():
while True:
yield from DatasetFromList(dummy_data, copy=False)
max_iter = 400
trainer = SimpleTrainer(model, f(), optimizer)
trainer.register_hooks(
[hooks.IterationTimer(), hooks.PeriodicWriter([CommonMetricPrinter(max_iter)])]
)
trainer.train(1, max_iter)
def test_writer_hooks(self):
model = _SimpleModel(sleep_sec=0.1)
trainer = SimpleTrainer(model, self._data_loader("cpu"),
torch.optim.SGD(model.parameters(), 0.1))
max_iter = 50
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
json_file = os.path.join(d, "metrics.json")
writers = [CommonMetricPrinter(max_iter), JSONWriter(json_file)]
trainer.register_hooks([
hooks.EvalHook(0, lambda: {"metric": 100}),
hooks.PeriodicWriter(writers)
])
with self.assertLogs(writers[0].logger) as logs:
trainer.train(0, max_iter)
with open(json_file, "r") as f:
data = [json.loads(line.strip()) for line in f]
self.assertEqual([x["iteration"] for x in data],
[19, 39, 49, 50])
# the eval metric is in the last line with iter 50
self.assertIn("metric", data[-1],
"Eval metric must be in last line of JSON!")
# test logged messages from CommonMetricPrinter
self.assertEqual(len(logs.output), 3)
for log, iter in zip(logs.output, [19, 39, 49]):
self.assertIn(f"iter: {iter}", log)
self.assertIn("eta: 0:00:00", logs.output[-1],
"Last ETA must be 0!")
def test_best_checkpointer(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
metric_name = "metric"
total_iter = 40
test_period = 10
test_cases = [
("max", iter([0.3, 0.4, 0.35, 0.5]), 3),
("min", iter([1.0, 0.8, 0.9, 0.9]), 2),
("min", iter([math.nan, 0.8, 0.9, 0.9]), 1),
]
for mode, metrics, call_count in test_cases:
trainer = SimpleTrainer(model, dataloader, opt)
with tempfile.TemporaryDirectory(prefix="detectron2_test") as d:
checkpointer = Checkpointer(model, d, opt=opt, trainer=trainer)
trainer.register_hooks([
hooks.EvalHook(test_period,
lambda: {metric_name: next(metrics)}),
hooks.BestCheckpointer(test_period,
checkpointer,
metric_name,
mode=mode),
])
with mock.patch.object(checkpointer,
"save") as mock_save_method:
trainer.train(0, total_iter)
self.assertEqual(mock_save_method.call_count, call_count)
def test_build_model(self):
cfg = self._get_default_cfg()
cfg.INPUT.MIN_SIZE_TRAIN = (60, )
cfg.MODEL.KMEANS_ANCHORS.KMEANS_ANCHORS_ON = True
cfg.MODEL.KMEANS_ANCHORS.NUM_CLUSTERS = 3
cfg.MODEL.KMEANS_ANCHORS.NUM_TRAINING_IMG = 5
cfg.MODEL.KMEANS_ANCHORS.DATASETS = ("toy_dataset", )
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.ANCHOR_GENERATOR.NAME = "KMeansAnchorGenerator"
with register_toy_coco_dataset(
"toy_dataset",
image_size=(80, 60), # w, h
num_images=cfg.MODEL.KMEANS_ANCHORS.NUM_TRAINING_IMG,
):
model = self.runner.build_model(cfg)
trainer = SimpleTrainer(model, data_loader=[], optimizer=None)
trainer_hooks = [compute_kmeans_anchors_hook(self.runner, cfg)]
trainer.register_hooks(trainer_hooks)
trainer.before_train()
anchor_generator = model.proposal_generator.anchor_generator
cell_anchors = list(anchor_generator.cell_anchors)
gt_anchors = np.array([
[-20, -15, 20, 15] # toy_dataset's bbox is half size of image
for _ in range(cfg.MODEL.KMEANS_ANCHORS.NUM_CLUSTERS)
])
np.testing.assert_allclose(cell_anchors[0], gt_anchors)
def test_build_model(self):
cfg = self._get_default_cfg()
cfg.INPUT.MIN_SIZE_TRAIN = (60,)
cfg.MODEL.KMEANS_ANCHORS.KMEANS_ANCHORS_ON = True
cfg.MODEL.KMEANS_ANCHORS.NUM_CLUSTERS = 3
cfg.MODEL.KMEANS_ANCHORS.NUM_TRAINING_IMG = 5
cfg.MODEL.KMEANS_ANCHORS.DATASETS = ("toy_dataset",)
cfg.MODEL.DEVICE = "cpu"
cfg.MODEL.ANCHOR_GENERATOR.NAME = "KMeansAnchorGenerator"
with make_temp_directory("detectron2go_tmp_dataset") as dataset_dir:
image_dir = os.path.join(dataset_dir, "images")
os.makedirs(image_dir)
image_generator = LocalImageGenerator(image_dir, width=80, height=60)
with register_toy_dataset(
"toy_dataset",
image_generator,
num_images=cfg.MODEL.KMEANS_ANCHORS.NUM_TRAINING_IMG,
):
model = self.runner.build_model(cfg)
trainer = SimpleTrainer(model, data_loader=[], optimizer=None)
trainer_hooks = [compute_kmeans_anchors_hook(self.runner, cfg)]
trainer.register_hooks(trainer_hooks)
trainer.before_train()
anchor_generator = model.proposal_generator.anchor_generator
cell_anchors = [x for x in anchor_generator.cell_anchors]
gt_anchors = np.array(
[
[-20, -15, 20, 15] # toy_dataset's bbox is half size of image
for _ in range(cfg.MODEL.KMEANS_ANCHORS.NUM_CLUSTERS)
]
)
np.testing.assert_allclose(cell_anchors[0], gt_anchors)
def test_eval_hook(self):
model = _SimpleModel()
dataloader = self._data_loader("cpu")
opt = torch.optim.SGD(model.parameters(), 0.1)
for total_iter, period, eval_count in [(30, 15, 2), (31, 15, 3), (20, 0, 1)]:
test_func = mock.Mock(return_value={"metric": 3.0})
trainer = SimpleTrainer(model, dataloader, opt)
trainer.register_hooks([hooks.EvalHook(period, test_func)])
trainer.train(0, total_iter)
self.assertEqual(test_func.call_count, eval_count)
def test_simple_trainer(self, device="cpu"):
model = _SimpleModel().to(device=device)
trainer = SimpleTrainer(model, self._data_loader(device),
torch.optim.SGD(model.parameters(), 0.1))
trainer.train(0, 10)