def test_new_events():
def update(*args, **kwargs):
pass
engine = Engine(update)
cpe = CustomPeriodicEvent(n_iterations=5)
cpe.attach(engine)
assert hasattr(cpe, "Events")
assert hasattr(cpe.Events, "ITERATIONS_5_STARTED")
assert hasattr(cpe.Events, "ITERATIONS_5_COMPLETED")
assert engine._allowed_events[-2] == getattr(cpe.Events,
"ITERATIONS_5_STARTED")
assert engine._allowed_events[-1] == getattr(cpe.Events,
"ITERATIONS_5_COMPLETED")
cpe = CustomPeriodicEvent(n_epochs=5)
cpe.attach(engine)
assert hasattr(cpe, "Events")
assert hasattr(cpe.Events, "EPOCHS_5_STARTED")
assert hasattr(cpe.Events, "EPOCHS_5_COMPLETED")
assert engine._allowed_events[-2] == getattr(cpe.Events,
"EPOCHS_5_STARTED")
assert engine._allowed_events[-1] == getattr(cpe.Events,
"EPOCHS_5_COMPLETED")
def test_integration_epochs():
def update(*args, **kwargs):
pass
engine = Engine(update)
n_epochs = 3
cpe = CustomPeriodicEvent(n_epochs=n_epochs)
cpe.attach(engine)
data = list(range(16))
custom_period = [1]
@engine.on(cpe.Events.EPOCHS_3_STARTED)
def on_my_epoch_started(engine):
assert (engine.state.epoch - 1) % n_epochs == 0
assert engine.state.epochs_3 == custom_period[0]
@engine.on(cpe.Events.EPOCHS_3_COMPLETED)
def on_my_epoch_ended(engine):
assert engine.state.epoch % n_epochs == 0
assert engine.state.epochs_3 == custom_period[0]
custom_period[0] += 1
engine.run(data, max_epochs=10)
assert custom_period[0] == 4
def test_new_events():
def update(*args, **kwargs):
pass
with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"):
engine = Engine(update)
cpe = CustomPeriodicEvent(n_iterations=5)
cpe.attach(engine)
assert hasattr(cpe, "Events")
assert hasattr(cpe.Events, "ITERATIONS_5_STARTED")
assert hasattr(cpe.Events, "ITERATIONS_5_COMPLETED")
assert engine._allowed_events[-2] == getattr(cpe.Events, "ITERATIONS_5_STARTED")
assert engine._allowed_events[-1] == getattr(cpe.Events, "ITERATIONS_5_COMPLETED")
with pytest.warns(DeprecationWarning, match="CustomPeriodicEvent is deprecated"):
cpe = CustomPeriodicEvent(n_epochs=5)
cpe.attach(engine)
assert hasattr(cpe, "Events")
assert hasattr(cpe.Events, "EPOCHS_5_STARTED")
assert hasattr(cpe.Events, "EPOCHS_5_COMPLETED")
assert engine._allowed_events[-2] == getattr(cpe.Events, "EPOCHS_5_STARTED")
assert engine._allowed_events[-1] == getattr(cpe.Events, "EPOCHS_5_COMPLETED")
def _test(n_iterations, max_epochs, n_iters_per_epoch):
def update(*args, **kwargs):
pass
engine = Engine(update)
with pytest.warns(DeprecationWarning,
match="CustomPeriodicEvent is deprecated"):
cpe = CustomPeriodicEvent(n_iterations=n_iterations)
cpe.attach(engine)
data = list(range(n_iters_per_epoch))
custom_period = [0]
n_calls_iter_started = [0]
n_calls_iter_completed = [0]
event_started = getattr(cpe.Events,
"ITERATIONS_{}_STARTED".format(n_iterations))
@engine.on(event_started)
def on_my_event_started(engine):
assert (engine.state.iteration - 1) % n_iterations == 0
custom_period[0] += 1
custom_iter = getattr(engine.state,
"iterations_{}".format(n_iterations))
assert custom_iter == custom_period[0]
n_calls_iter_started[0] += 1
event_completed = getattr(
cpe.Events, "ITERATIONS_{}_COMPLETED".format(n_iterations))
@engine.on(event_completed)
def on_my_event_ended(engine):
assert engine.state.iteration % n_iterations == 0
custom_iter = getattr(engine.state,
"iterations_{}".format(n_iterations))
assert custom_iter == custom_period[0]
n_calls_iter_completed[0] += 1
engine.run(data, max_epochs=max_epochs)
n = len(data) * max_epochs / n_iterations
nf = math.floor(n)
assert custom_period[0] == n_calls_iter_started[0]
assert n_calls_iter_started[0] == nf + 1 if nf < n else nf
assert n_calls_iter_completed[0] == nf
def get_loops(self):
if self.mode == l.TRAIN:
train_loop = get_loop(self, self.mode)
val_loop = get_loop(self, l.VAL)
visualize_loop = get_loop(self, l.VISUALIZE)
train_config = self.metric_config[l.TRAIN]
train_config[l.MODE] = l.TRAIN
val_config = self.metric_config[l.VAL]
val_config[l.MODE] = l.VAL
visualize_config = self.metric_config[l.VISUALIZE]
self.criterion_chain.attach(train_loop.engine, train_config)
self.criterion_chain.attach(val_loop.engine, val_config)
self.model.attach(train_loop.engine, train_config)
self.model.attach(val_loop.engine, val_config)
# TODO. Add to the heart of the container. Calculate only during test with measure_time flag
# AveragePeriodicMetric(TimeTransformer()).attach(engine, n.FORWARD_TIME)
train_loss_event = CustomPeriodicEvent(
n_iterations=train_config[exp.LOSS_LOG_ITER])
train_metric_event = CustomPeriodicEvent(
n_iterations=train_config[exp.METRIC_LOG_ITER])
val_event = CustomPeriodicEvent(
n_iterations=val_config[exp.LOG_ITER])
visualize_event = CustomPeriodicEvent(
n_epochs=visualize_config[exp.LOG_EPOCH])
train_loss_event.attach(train_loop.engine)
train_metric_event.attach(train_loop.engine)
val_event.attach(train_loop.engine)
visualize_event.attach(train_loop.engine)
@train_loop.engine.on(train_loss_event._periodic_event_completed)
def on_train_loss(engine):
plot_losses_tensorboard(self.writer, engine, engine, l.TRAIN)
@train_loop.engine.on(train_metric_event._periodic_event_completed)
def on_train_metric(engine):
plot_metrics_tensorboard(self.writer, engine, engine, l.TRAIN)
@train_loop.engine.on(val_event._periodic_event_completed)
def on_val(engine):
val_loop.run(1)
plot_losses_tensorboard(self.writer, val_loop.engine, engine,
l.VAL)
plot_metrics_tensorboard(self.writer, val_loop.engine, engine,
l.VAL)
@train_loop.engine.on(visualize_event._periodic_event_completed)
def on_visualize(engine):
visualize_loop.run(1)
plot_scores(self.writer, engine, visualize_loop.engine,
(eu.SCORE1, eu.SCORE2))
plot_scores(self.writer, engine, visualize_loop.engine,
(eu.SAL_SCORE1, eu.SAL_SCORE2))
plot_scores(self.writer, engine, visualize_loop.engine,
(eu.CONF_SCORE1, eu.CONF_SCORE2), True)
plot_kp_matches(self.writer, engine, visualize_loop.engine,
visualize_config[exp.PX_THRESH])
plot_desc_matches(self.writer, engine, visualize_loop.engine,
visualize_config[exp.PX_THRESH],
DescriptorDistance.INV_COS_SIM)
return [train_loop, val_loop, visualize_loop]
elif self.mode == l.TEST:
test_loop = get_loop(self, self.mode)
test_config = self.metric_config[l.TEST]
test_config[l.MODE] = l.TEST
test_config[du.DATASET_NAME] = list(
self.dataset_config[self.mode].keys())
self.model.attach(test_loop.engine, test_config)
if du.AACHEN in test_config[du.DATASET_NAME]:
@test_loop.engine.on(Events.ITERATION_COMPLETED)
def on_iteration_completed(engine):
save_aachen_inference(self.dataset_config[self.mode],
engine.state.output)
else:
@test_loop.engine.on(Events.EPOCH_COMPLETED)
def on_epoch(engine):
logs = join_logs(engine)
print_summary(logs, test_config)
test_log_to_csv(self.log_dir, logs, test_config,
self.model_config,
test_config[du.DATASET_NAME])
return [test_loop]
elif self.mode == l.ANALYZE:
analyze_loop = get_loop(self, self.mode)
return [analyze_loop]
else:
return None