def test_loop_steps_only_dp(tmpdir):
os.environ['PL_DEV_DEBUG'] = '1'
batches = 10
epochs = 3
model = EvalModelTemplate()
model.validation_step = None
model.test_step = None
model.training_step = model.training_step_result_obj_dp
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj_dp
model.validation_step_end = None
model.validation_epoch_end = None
model.test_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
distributed_backend='dp',
gpus=[0, 1],
max_epochs=epochs,
early_stop_callback=True,
log_every_n_steps=2,
limit_train_batches=batches,
weights_summary=None,
)
trainer.fit(model)
assert model.training_step_called
assert model.validation_step_called
def test_ckpt_metric_names_results(tmpdir):
model = EvalModelTemplate()
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
gradient_clip_val=1.0,
overfit_batches=0.20,
progress_bar_refresh_rate=0,
limit_train_batches=0.01,
limit_val_batches=0.01,
checkpoint_callback=ModelCheckpoint(monitor='val_loss', filepath=tmpdir + "/{val_loss:.2f}"),
)
trainer.fit(model)
# make sure the checkpoint we saved has the metric in the name
ckpts = os.listdir(tmpdir)
ckpts = [x for x in ckpts if "val_loss" in x]
assert len(ckpts) == 1
val = re.sub("[^0-9.]", "", ckpts[0])
assert len(val) > 3
def test_adding_step_key(tmpdir):
logged_step = 0
def _validation_epoch_end(outputs):
nonlocal logged_step
logged_step += 1
return {"log": {"step": logged_step, "val_acc": logged_step / 10}}
def _training_epoch_end(outputs):
nonlocal logged_step
logged_step += 1
return {"log": {"step": logged_step, "train_acc": logged_step / 10}}
def _log_metrics_decorator(log_metrics_fn):
def decorated(metrics, step):
if "val_acc" in metrics:
assert step == logged_step
return log_metrics_fn(metrics, step)
return decorated
model = EvalModelTemplate()
model.validation_epoch_end = _validation_epoch_end
model.training_epoch_end = _training_epoch_end
trainer = Trainer(
max_epochs=3,
default_root_dir=tmpdir,
train_percent_check=0.001,
val_percent_check=0.01,
num_sanity_val_steps=0,
)
trainer.logger.log_metrics = _log_metrics_decorator(
trainer.logger.log_metrics)
trainer.fit(model)
def test_result_obj_on_tpu(tmpdir):
seed_everything(1234)
batches = 5
epochs = 2
model = EvalModelTemplate()
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
model.test_step = model.test_step_result_obj
model.test_step_end = None
model.test_epoch_end = None
trainer_options = dict(default_root_dir=tmpdir,
max_epochs=epochs,
callbacks=[EarlyStopping()],
log_every_n_steps=2,
limit_train_batches=batches,
weights_summary=None,
tpu_cores=8)
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
def test_result_obj_on_tpu(tmpdir):
seed_everything(1234)
os.environ['PL_DEV_DEBUG'] = '1'
batches = 5
epochs = 2
model = EvalModelTemplate()
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
model.test_step = model.test_step_result_obj
model.test_step_end = None
model.test_epoch_end = None
trainer_options = dict(default_root_dir=tmpdir,
max_epochs=epochs,
early_stop_callback=True,
row_log_interval=2,
limit_train_batches=batches,
weights_summary=None,
tpu_cores=8)
tpipes.run_model_test(trainer_options, model, on_gpu=False, with_hpc=False)
def test_val_step_full_loop_result_dp(tmpdir):
# TODO: finish the full train, val, test loop with dp
os.environ['PL_DEV_DEBUG'] = '1'
batches = 10
epochs = 3
model = EvalModelTemplate()
model.training_step = model.training_step_full_loop_result_obj_dp
model.training_step_end = model.training_step_end_full_loop_result_obj_dp
model.training_epoch_end = model.training_epoch_end_full_loop_result_obj_dp
model.validation_step = model.eval_step_full_loop_result_obj_dp
model.validation_step_end = model.eval_step_end_full_loop_result_obj_dp
model.validation_epoch_end = model.eval_epoch_end_full_loop_result_obj_dp
model.test_step = model.eval_step_full_loop_result_obj_dp
model.test_step_end = model.eval_step_end_full_loop_result_obj_dp
model.test_epoch_end = model.eval_epoch_end_full_loop_result_obj_dp
trainer = Trainer(
default_root_dir=tmpdir,
distributed_backend='dp',
gpus=[0, 1],
max_epochs=epochs,
early_stop_callback=True,
log_every_n_steps=2,
limit_train_batches=batches,
weights_summary=None,
)
trainer.fit(model)
results = trainer.test()
# assert we returned all metrics requested
assert len(results) == 1
results = results[0]
assert 'test_epoch_end_metric' in results
# make sure we saw all the correct keys along all paths
seen_keys = set()
for metric in trainer.dev_debugger.logged_metrics:
seen_keys.update(metric.keys())
assert 'train_step_metric' in seen_keys
assert 'train_step_end_metric' in seen_keys
assert 'train_epoch_end_metric_epoch' in seen_keys
assert 'validation_step_metric_step/epoch_0' in seen_keys
assert 'validation_step_metric_epoch' in seen_keys
assert 'validation_step_end_metric' in seen_keys
assert 'validation_epoch_end_metric' in seen_keys
assert 'test_step_metric_step/epoch_2' in seen_keys
assert 'test_step_metric_epoch' in seen_keys
assert 'test_step_end_metric' in seen_keys
assert 'test_epoch_end_metric' in seen_keys
def test_full_loop_result_cpu(tmpdir):
seed_everything(1234)
os.environ['PL_DEV_DEBUG'] = '1'
batches = 10
epochs = 2
model = EvalModelTemplate()
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
model.test_step = model.test_step_result_obj
model.test_step_end = None
model.test_epoch_end = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=epochs,
early_stop_callback=True,
log_every_n_steps=2,
limit_train_batches=batches,
weights_summary=None,
)
trainer.fit(model)
results = trainer.test()
# assert we returned all metrics requested
assert len(results) == 1
results = results[0]
assert results['test_loss'] < 0.3
assert results['test_acc'] > 0.9
assert len(results) == 2
assert 'early_stop_on' not in results
assert 'checkpoint_on' not in results
results2 = trainer.test()[0]
for k, v in results.items():
assert results2[k] == v
def test_result_monitor_warnings(tmpdir):
"""
Tests that we warn when the monitor key is changed and we use Results obj
"""
model = EvalModelTemplate()
model.test_step = None
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
model.test_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
early_stop_callback=True,
log_every_n_steps=2,
limit_train_batches=2,
weights_summary=None,
checkpoint_callback=ModelCheckpoint(monitor='not_checkpoint_on'))
# warn that the key was changed but metric was not found
with pytest.raises(MisconfigurationException,
match="not found in the returned metrics"):
trainer.fit(model)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
log_every_n_steps=2,
limit_train_batches=2,
weights_summary=None,
early_stop_callback=EarlyStopping(monitor='not_val_loss'))
with pytest.raises(
RuntimeError,
match=
r'.*Early stopping conditioned on metric `not_val_loss` which is not*'
):
trainer.fit(model)
def test_eval_loop_return_none(tmpdir):
"""
Tests that we warn when the monitor key is changed and we use Results obj
"""
model = EvalModelTemplate()
model.test_step = None
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = model.validation_epoch_end_return_none
model.test_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
log_every_n_steps=2,
limit_train_batches=2,
weights_summary=None,
)
trainer.fit(model)
def test_result_monitor_warnings(tmpdir):
"""
Tests that we warn when the monitor key is changed and we use Results obj
"""
model = EvalModelTemplate()
model.test_step = None
model.training_step = model.training_step_result_obj
model.training_step_end = None
model.training_epoch_end = None
model.validation_step = model.validation_step_result_obj
model.validation_step_end = None
model.validation_epoch_end = None
model.test_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
early_stop_callback=True,
row_log_interval=2,
limit_train_batches=2,
weights_summary=None,
checkpoint_callback=ModelCheckpoint(monitor='not_val_loss')
)
with pytest.warns(UserWarning, match='key of ModelCheckpoint has no effect'):
trainer.fit(model)
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
row_log_interval=2,
limit_train_batches=2,
weights_summary=None,
early_stop_callback=EarlyStopping(monitor='not_val_loss')
)
with pytest.warns(UserWarning, match='key of EarlyStopping has no effect'):
trainer.fit(model)
def test_full_train_loop_with_results_obj_dp(tmpdir):
os.environ['PL_DEV_DEBUG'] = '1'
batches = 10
epochs = 3
model = EvalModelTemplate()
model.validation_step = None
model.test_step = None
model.training_step = model.training_step_full_loop_result_obj_dp
model.training_step_end = model.training_step_end_full_loop_result_obj_dp
model.training_epoch_end = model.training_epoch_end_full_loop_result_obj_dp
model.val_dataloader = None
model.test_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
distributed_backend='dp',
gpus=[0, 1],
max_epochs=epochs,
early_stop_callback=True,
log_every_n_steps=2,
limit_train_batches=batches,
weights_summary=None,
)
trainer.fit(model)
# make sure we saw all the correct keys
seen_keys = set()
for metric in trainer.dev_debugger.logged_metrics:
seen_keys.update(metric.keys())
assert 'train_step_metric' in seen_keys
assert 'train_step_end_metric' in seen_keys
assert 'train_epoch_end_metric_epoch' in seen_keys
