def test_output_handler_output_transform(dirname):
wrapper = OutputHandler("tag", output_transform=lambda x: x)
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.output = 12345
mock_engine.state.iteration = 123
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.writer.add_scalar.assert_called_once_with("tag/output", 12345, 123)
wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.writer.add_scalar.assert_called_once_with("another_tag/loss", 12345, 123)
def test_grads_hist_handler(dummy_model_factory):
model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
wrapper = GradsHistHandler(model)
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.writer.add_histogram.call_count == 4
mock_logger.writer.add_histogram.assert_has_calls([
call(tag="grads/fc1/weight", values=ANY, global_step=5),
call(tag="grads/fc1/bias", values=ANY, global_step=5),
call(tag="grads/fc2/weight", values=ANY, global_step=5),
call(tag="grads/fc2/bias", values=ANY, global_step=5),
],
any_order=True)
def _test(tag=None):
wrapper = WeightsScalarHandler(model, tag=tag)
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.experiment = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
tag_prefix = "{}/".format(tag) if tag else ""
assert mock_logger.experiment.log_metric.call_count == 4
mock_logger.experiment.log_metric.assert_has_calls([
call(tag_prefix + "weights_norm/fc1/weight", y=0.0, x=5),
call(tag_prefix + "weights_norm/fc1/bias", y=0.0, x=5),
call(tag_prefix + "weights_norm/fc2/weight", y=12.0, x=5),
call(tag_prefix + "weights_norm/fc2/bias", y=math.sqrt(12.0), x=5),
],
any_order=True)
def test_no_grad():
y_pred = torch.zeros(4, requires_grad=True)
y = torch.zeros(4, requires_grad=False)
class DummyMetric(Metric):
def reset(self):
pass
def compute(self):
pass
def update(self, output):
y_pred, y = output
mse = torch.pow(y_pred - y.view_as(y_pred), 2)
assert y_pred.requires_grad
assert not mse.requires_grad
metric = DummyMetric()
state = State(output=(y_pred, y))
engine = MagicMock(state=state)
metric.iteration_completed(engine)
def test_neptune_saver_integration():
model = torch.nn.Module()
to_save_serializable = {"model": model}
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.log_artifact = MagicMock()
mock_logger.delete_artifacts = MagicMock()
saver = NeptuneSaver(mock_logger)
checkpoint = Checkpoint(to_save=to_save_serializable,
save_handler=saver,
n_saved=1)
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=0, iteration=0)
checkpoint(trainer)
trainer.state.iteration = 1
checkpoint(trainer)
assert mock_logger.log_artifact.call_count == 2
assert mock_logger.delete_artifacts.call_count == 1
def test_output_handler_output_transform():
wrapper = OutputHandler("tag", output_transform=lambda x: x)
mock_logger = MagicMock(spec=PolyaxonLogger)
mock_logger.log_metrics = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.output = 12345
mock_engine.state.iteration = 123
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.log_metrics.assert_called_once_with(step=123, **{"tag/output": 12345})
wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=PolyaxonLogger)
mock_logger.log_metrics = MagicMock()
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.log_metrics.assert_called_once_with(step=123, **{"another_tag/loss": 12345})
def _test_checkpoint_with_ddp(device):
torch.manual_seed(0)
model = DummyModel().to(device)
device_ids = (None if "cpu" in device.type else [
device,
])
ddp_model = nn.parallel.DistributedDataParallel(model,
device_ids=device_ids)
to_save = {"model": ddp_model}
save_handler = MagicMock(spec=BaseSaveHandler)
checkpointer = Checkpoint(to_save, save_handler=save_handler)
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=0, iteration=0)
checkpointer(trainer)
assert save_handler.call_count == 1
metadata = {"basename": "model", "score_name": None, "priority": 0}
save_handler.assert_called_with(model.state_dict(), "model_0.pt", metadata)
def test_output_handler_both():
wrapper = OutputHandler("tag",
metric_names=["a", "b"],
output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.experiment = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State(metrics={"a": 12.23, "b": 23.45})
mock_engine.state.epoch = 5
mock_engine.state.output = 12345
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.experiment.log_metric.call_count == 3
mock_logger.experiment.log_metric.assert_has_calls([
call("tag/a", y=12.23, x=5),
call("tag/b", y=23.45, x=5),
call("tag/loss", y=12345, x=5)
],
any_order=True)
def test_weights_scalar_handler(dummy_model_factory):
model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
wrapper = WeightsScalarHandler(model)
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.writer.add_scalar.call_count == 4
mock_logger.writer.add_scalar.assert_has_calls([
call("weights_norm/fc1/weight", 0.0, 5),
call("weights_norm/fc1/bias", 0.0, 5),
call("weights_norm/fc2/weight", 12.0, 5),
call("weights_norm/fc2/bias", math.sqrt(12.0), 5),
], any_order=True)
def _test(tag=None):
wrapper = WeightsScalarHandler(model, tag=tag)
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
tag_prefix = "{}/".format(tag) if tag else ""
assert mock_logger.writer.add_scalar.call_count == 4
mock_logger.writer.add_scalar.assert_has_calls([
call(tag_prefix + "weights_norm/fc1/weight", 0.0, 5),
call(tag_prefix + "weights_norm/fc1/bias", 0.0, 5),
call(tag_prefix + "weights_norm/fc2/weight", 12.0, 5),
call(tag_prefix + "weights_norm/fc2/bias", math.sqrt(12.0), 5),
],
any_order=True)
def test_output_handler_output_transform(dirname):
wrapper = OutputHandler("tag", output_transform=lambda x: x)
mock_logger = MagicMock(spec=VisdomLogger)
mock_logger.vis = MagicMock()
mock_logger.executor = _DummyExecutor()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.output = 12345
mock_engine.state.iteration = 123
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
assert len(wrapper.windows) == 1 and "tag/output" in wrapper.windows
assert wrapper.windows["tag/output"]['win'] is not None
mock_logger.vis.line.assert_called_once_with(
X=[123, ], Y=[12345, ], env=mock_logger.vis.env,
win=None, update=None,
opts=wrapper.windows['tag/output']['opts'],
name="tag/output"
)
wrapper = OutputHandler("another_tag", output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=VisdomLogger)
mock_logger.vis = MagicMock()
mock_logger.executor = _DummyExecutor()
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
assert len(wrapper.windows) == 1 and "another_tag/loss" in wrapper.windows
assert wrapper.windows["another_tag/loss"]['win'] is not None
mock_logger.vis.line.assert_called_once_with(
X=[123, ], Y=[12345, ], env=mock_logger.vis.env,
win=None, update=None,
opts=wrapper.windows['another_tag/loss']['opts'],
name="another_tag/loss"
)
def _test_gpu_info(device="cpu"):
gpu_info = GpuInfo()
# increase code cov
gpu_info.reset()
gpu_info.update(None)
t = torch.rand(4, 10, 100, 100).to(device)
data = gpu_info.compute()
assert len(data) > 0
assert "fb_memory_usage" in data[0]
mem_report = data[0]["fb_memory_usage"]
assert "used" in mem_report and "total" in mem_report
assert mem_report["total"] > 0.0
assert mem_report["used"] > t.shape[0] * t.shape[1] * t.shape[2] * t.shape[
3] / 1024.0 / 1024.0
assert "utilization" in data[0]
util_report = data[0]["utilization"]
assert "gpu_util" in util_report
# with Engine
engine = Engine(lambda engine, batch: 0.0)
engine.state = State(metrics={})
gpu_info.completed(engine, name="gpu")
assert "gpu:0 mem(%)" in engine.state.metrics
assert isinstance(engine.state.metrics["gpu:0 mem(%)"], int)
assert int(mem_report["used"] * 100.0 /
mem_report["total"]) == engine.state.metrics["gpu:0 mem(%)"]
if util_report["gpu_util"] != "N/A":
assert "gpu:0 util(%)" in engine.state.metrics
assert isinstance(engine.state.metrics["gpu:0 util(%)"], int)
assert int(
util_report["gpu_util"]) == engine.state.metrics["gpu:0 util(%)"]
else:
assert "gpu:0 util(%)" not in engine.state.metrics
def test_optimizer_params():
optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
mock_logger = MagicMock(spec=VisdomLogger)
mock_logger.vis = MagicMock()
mock_logger.executor = _DummyExecutor()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.iteration = 123
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
# mock_logger.vis.line.assert_called_once_with("lr/group_0", 0.01, 123)
assert len(wrapper.windows) == 1 and "lr/group_0" in wrapper.windows
assert wrapper.windows["lr/group_0"]['win'] is not None
mock_logger.vis.line.assert_called_once_with(
X=[123, ], Y=[0.01, ], env=mock_logger.vis.env,
win=None, update=None,
opts=wrapper.windows['lr/group_0']['opts'],
name="lr/group_0"
)
wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr", tag="generator")
mock_logger = MagicMock(spec=VisdomLogger)
mock_logger.vis = MagicMock()
mock_logger.executor = _DummyExecutor()
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
assert len(wrapper.windows) == 1 and "generator/lr/group_0" in wrapper.windows
assert wrapper.windows["generator/lr/group_0"]['win'] is not None
mock_logger.vis.line.assert_called_once_with(
X=[123, ], Y=[0.01, ], env=mock_logger.vis.env,
win=None, update=None,
opts=wrapper.windows['generator/lr/group_0']['opts'],
name="generator/lr/group_0"
)
def _test(to_save, obj, name):
save_handler = MagicMock(spec=BaseSaveHandler)
checkpointer = Checkpoint(to_save, save_handler=save_handler)
assert checkpointer.last_checkpoint is None
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=0, iteration=0)
checkpointer(trainer)
assert save_handler.call_count == 1
save_handler.assert_called_with(obj, "{}_0.pt".format(name))
trainer.state.epoch = 12
trainer.state.iteration = 1234
checkpointer(trainer)
assert save_handler.call_count == 2
save_handler.assert_called_with(obj, "{}_1234.pt".format(name))
assert save_handler.remove.call_count == 1
save_handler.remove.assert_called_with("{}_0.pt".format(name))
assert checkpointer.last_checkpoint == "{}_1234.pt".format(name)
def test_completed_on_cuda():
# Checks https://github.com/pytorch/ignite/issues/1635#issuecomment-863026919
class DummyMetric(Metric):
def reset(self):
pass
def compute(self):
return torch.tensor([1.0, 2.0, 3.0], device="cuda")
def update(self, output):
pass
m = DummyMetric()
# tensor
engine = MagicMock(state=State(metrics={}))
m.completed(engine, "metric")
assert "metric" in engine.state.metrics
assert isinstance(engine.state.metrics["metric"], torch.Tensor)
assert engine.state.metrics["metric"].device.type == "cpu"
def test_output_handler_both(dirname):
wrapper = OutputHandler("tag",
metric_names=["a", "b"],
output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State(metrics={"a": 12.23, "b": 23.45})
mock_engine.state.epoch = 5
mock_engine.state.output = 12345
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.writer.add_scalar.call_count == 3
mock_logger.writer.add_scalar.assert_has_calls([
call("tag/a", 12.23, 5),
call("tag/b", 23.45, 5),
call("tag/loss", 12345, 5)
],
any_order=True)
def _test(filename_prefix, to_save, obj, name):
save_handler = MagicMock(spec=BaseSaveHandler)
checkpointer = Checkpoint(
to_save,
save_handler=save_handler,
filename_prefix=filename_prefix,
global_step_transform=lambda e, _: e.state.epoch,
)
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=1, iteration=1)
checkpointer(trainer)
assert save_handler.call_count == 1
if len(filename_prefix) > 0:
filename_prefix += "_"
metadata = {
"basename": "{}{}".format(filename_prefix, name),
"score_name": None,
"priority": 1
}
save_handler.assert_called_with(
obj, "{}{}_1.pt".format(filename_prefix, name), metadata)
trainer.state.epoch = 12
trainer.state.iteration = 1234
checkpointer(trainer)
assert save_handler.call_count == 2
metadata["priority"] = 1234
save_handler.assert_called_with(
obj, "{}{}_12.pt".format(filename_prefix, name), metadata)
assert save_handler.remove.call_count == 1
save_handler.remove.assert_called_with("{}{}_1.pt".format(
filename_prefix, name))
assert checkpointer.last_checkpoint == "{}{}_12.pt".format(
filename_prefix, name)
def test_trains_disk_saver_integration_no_logger():
model = torch.nn.Module()
to_save_serializable = {"model": model}
with pytest.warns(UserWarning, match="TrainsSaver created a temporary checkpoints directory"):
trains.Task.current_task = Mock(return_value=object())
trains.binding.frameworks.WeightsFileHandler.create_output_model = MagicMock()
trains_saver = TrainsSaver()
checkpoint = Checkpoint(to_save=to_save_serializable, save_handler=trains_saver, n_saved=1)
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=0, iteration=0)
checkpoint(trainer)
trainer.state.iteration = 1
checkpoint(trainer)
if trains_saver._atomic:
assert trains.binding.frameworks.WeightsFileHandler.create_output_model.call_count == 2
else:
saved_files = list(os.listdir(trains_saver.dirname))
assert len(saved_files) == 1
assert saved_files[0] == "model_1.pt"
def test_output_handler_both(dirname):
wrapper = OutputHandler("tag", metric_names=["a", "b"], output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=TrainsLogger)
mock_logger.trains_logger = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State(metrics={"a": 12.23, "b": 23.45})
mock_engine.state.epoch = 5
mock_engine.state.output = 12345
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.trains_logger.report_scalar.call_count == 3
mock_logger.trains_logger.report_scalar.assert_has_calls(
[
call(title="tag", series="a", iteration=5, value=12.23),
call(title="tag", series="b", iteration=5, value=23.45),
call(title="tag", series="loss", iteration=5, value=12345),
],
any_order=True,
)
def test_mlflow_bad_metric_name_handling(dirname):
import mlflow
true_values = [123.0, 23.4, 333.4]
with MLflowLogger(os.path.join(dirname, "mlruns")) as mlflow_logger:
active_run = mlflow.active_run()
handler = OutputHandler(tag="training", metric_names="all")
engine = Engine(lambda e, b: None)
engine.state = State(metrics={
"metric:0 in %": 123.0,
"metric 0": 1000.0
})
with pytest.warns(
UserWarning,
match=
r"MLflowLogger output_handler encountered an invalid metric name"
):
engine.state.epoch = 1
handler(engine, mlflow_logger, event_name=Events.EPOCH_COMPLETED)
for _, v in enumerate(true_values):
engine.state.epoch += 1
engine.state.metrics["metric 0"] = v
handler(engine,
mlflow_logger,
event_name=Events.EPOCH_COMPLETED)
from mlflow.tracking import MlflowClient
client = MlflowClient(tracking_uri=os.path.join(dirname, "mlruns"))
stored_values = client.get_metric_history(active_run.info.run_id,
"training metric 0")
for t, s in zip([1000.0] + true_values, stored_values):
assert t == s.value
def test_output_handler_both():
wrapper = OutputHandler("tag",
metric_names=["a", "b"],
output_transform=lambda x: {"loss": x})
mock_logger = MagicMock(spec=PolyaxonLogger)
mock_logger.log_metrics = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State(metrics={"a": 12.23, "b": 23.45})
mock_engine.state.epoch = 5
mock_engine.state.output = 12345
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.log_metrics.call_count == 1
mock_logger.log_metrics.assert_called_once_with(step=5,
**{
"tag/a": 12.23,
"tag/b": 23.45,
"tag/loss": 12345
})
def test_transform():
y_pred = torch.Tensor([[2.0], [-2.0]])
y = torch.zeros(2)
class DummyMetric(Metric):
def reset(self):
pass
def compute(self):
pass
def update(self, output):
assert output == (y_pred, y)
def transform(output):
pred_dict, target_dict = output
return pred_dict['y'], target_dict['y']
metric = DummyMetric(output_transform=transform)
state = State(output=({'y': y_pred}, {'y': y}))
engine = MagicMock(state=state)
metric.iteration_completed(engine)
def test_checkpoint_last_checkpoint_on_score():
save_handler = MagicMock(spec=BaseSaveHandler)
to_save = {"model": DummyModel()}
checkpointer = Checkpoint(
to_save,
save_handler=save_handler,
n_saved=None,
score_name="val_acc",
score_function=lambda e: e.state.metrics["val_acc"],
)
trainer = Engine(lambda e, b: None)
val_acc = 0.0
for i in range(10):
val_acc = i * 0.1
trainer.state = State(epoch=1, iteration=i, metrics={"val_acc": val_acc})
checkpointer(trainer)
assert save_handler.call_count == 10
assert checkpointer.last_checkpoint == "{}_val_acc=0.9000.pt".format("model")
def test_output_handler_state_attrs():
wrapper = OutputHandler("tag", state_attributes=["alpha", "beta", "gamma"])
mock_logger = MagicMock(spec=MLflowLogger)
mock_logger.log_metrics = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.iteration = 5
mock_engine.state.alpha = 3.899
mock_engine.state.beta = torch.tensor(12.21)
mock_engine.state.gamma = torch.tensor([21.0, 6.0])
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.log_metrics.assert_called_once_with(
{
"tag alpha": 3.899,
"tag beta": torch.tensor(12.21).item(),
"tag gamma 0": 21.0,
"tag gamma 1": 6.0
},
step=5)
def test_best_k_with_suffix(dirname):
scores = [0.3456789, 0.1234, 0.4567, 0.134567]
scores_iter = iter(scores)
def score_function(engine):
return next(scores_iter)
h = ModelCheckpoint(dirname, _PREFIX, create_dir=False, n_saved=2,
score_function=score_function, score_name="val_loss")
engine = Engine(lambda e, b: None)
engine.state = State(epoch=0, iteration=0)
model = DummyModel()
to_save = {'model': model}
for _ in range(4):
engine.state.epoch += 1
h(engine, to_save)
expected = ['{}_{}_val_loss={:.4}.pth'.format(_PREFIX, 'model', scores[e - 1]) for e in [1, 3]]
assert sorted(os.listdir(dirname)) == expected
def _test(ext, require_empty, archived):
previous_fname = os.path.join(dirname, '{}_{}_{}{}'.format(_PREFIX, 'obj', 1, ext))
with open(previous_fname, 'w') as f:
f.write("test")
h = ModelCheckpoint(dirname, _PREFIX, create_dir=True, require_empty=require_empty, archived=archived)
engine = Engine(lambda e, b: None)
engine.state = State(epoch=0, iteration=1)
model = DummyModel()
to_save = {'model': model}
h(engine, to_save)
fname = h.last_checkpoint
ext = ".pth.tar" if archived else ".pth"
assert isinstance(fname, str)
assert os.path.join(dirname, '{}_{}_{}{}'.format(_PREFIX, 'model', 1, ext)) == fname
assert os.path.exists(fname)
assert os.path.exists(previous_fname)
loaded_objects = torch.load(fname)
assert loaded_objects == model.state_dict()
os.remove(fname)
def test_optimizer_params():
optimizer = torch.optim.SGD([torch.Tensor(0)], lr=0.01)
wrapper = OptimizerParamsHandler(optimizer=optimizer, param_name="lr")
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.log_metric = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.iteration = 123
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.log_metric.assert_called_once_with("lr/group_0", y=0.01, x=123)
wrapper = OptimizerParamsHandler(optimizer,
param_name="lr",
tag="generator")
mock_logger = MagicMock(spec=NeptuneLogger)
mock_logger.log_metric = MagicMock()
wrapper(mock_engine, mock_logger, Events.ITERATION_STARTED)
mock_logger.log_metric.assert_called_once_with("generator/lr/group_0",
y=0.01,
x=123)
def test_grads_scalar_handler(dummy_model_factory, norm_mock):
model = dummy_model_factory(with_grads=True, with_frozen_layer=False)
wrapper = GradsScalarHandler(model, reduction=norm_mock)
mock_logger = MagicMock(spec=TensorboardLogger)
mock_logger.writer = MagicMock()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
norm_mock.reset_mock()
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
mock_logger.writer.add_scalar.assert_has_calls([
call("grads_norm/fc1/weight", ANY, 5),
call("grads_norm/fc1/bias", ANY, 5),
call("grads_norm/fc2/weight", ANY, 5),
call("grads_norm/fc2/bias", ANY, 5),
], any_order=True)
assert mock_logger.writer.add_scalar.call_count == 4
assert norm_mock.call_count == 4
def _test(to_save, obj, name):
save_handler = MagicMock(spec=BaseSaveHandler)
checkpointer = Checkpoint(to_save, save_handler=save_handler, include_self=True)
assert checkpointer.last_checkpoint is None
trainer = Engine(lambda e, b: None)
trainer.state = State(epoch=0, iteration=0)
checkpointer(trainer)
assert save_handler.call_count == 1
fname = "{}_0.pt".format(name)
obj["checkpointer"] = OrderedDict([("saved", [(0, fname)])])
metadata = {"basename": name, "score_name": None, "priority": 0}
save_handler.assert_called_with(obj, fname, metadata)
# Swap object, state should be maintained
checkpointer2 = Checkpoint(to_save, save_handler=save_handler, include_self=True)
checkpointer2.load_state_dict(checkpointer.state_dict())
assert checkpointer2.last_checkpoint == fname
trainer.state.epoch = 12
trainer.state.iteration = 1234
checkpointer2(trainer)
assert save_handler.call_count == 2
metadata["priority"] = 1234
# This delete only happens if state was restored correctly.
save_handler.remove.assert_called_with("{}_0.pt".format(name))
fname = "{}_1234.pt".format(name)
obj["checkpointer"] = OrderedDict([("saved", [(1234, fname)])])
save_handler.assert_called_with(obj, fname, metadata)
assert save_handler.remove.call_count == 1
assert checkpointer2.last_checkpoint == fname
def test_output_handler_with_global_step_transform():
def global_step_transform(*args, **kwargs):
return 10
wrapper = OutputHandler(
"tag",
output_transform=lambda x: {"loss": x},
global_step_transform=global_step_transform,
)
mock_logger = MagicMock(spec=VisdomLogger)
mock_logger.vis = MagicMock()
mock_logger.executor = _DummyExecutor()
mock_engine = MagicMock()
mock_engine.state = State()
mock_engine.state.epoch = 5
mock_engine.state.output = 12345
wrapper(mock_engine, mock_logger, Events.EPOCH_STARTED)
assert mock_logger.vis.line.call_count == 1
assert len(wrapper.windows) == 1 and "tag/loss" in wrapper.windows
assert wrapper.windows["tag/loss"]["win"] is not None
mock_logger.vis.line.assert_has_calls([
call(
X=[
10,
],
Y=[
12345,
],
env=mock_logger.vis.env,
win=None,
update=None,
opts=wrapper.windows["tag/loss"]["opts"],
name="tag/loss",
)
])