def test_full_training_loop_scalar(tmpdir):
"""
Checks train_step + training_step_end + training_epoch_end
(all with scalar return from train_step)
"""
model = DeterministicModel()
model.training_step = model.training_step__scalar_return
model.training_step_end = model.training_step_end__scalar
model.training_epoch_end = model.training_epoch_end__scalar
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert model.training_step_end_called
assert model.training_epoch_end_called
# assert epoch end metrics were added
assert len(trainer.logger_connector.callback_metrics) == 0
assert len(trainer.logger_connector.progress_bar_metrics) == 0
# make sure training outputs what is expected
for batch_idx, batch in enumerate(model.train_dataloader()):
break
out = trainer.train_loop.run_training_batch(batch, batch_idx, 0)
assert out.signal == 0
assert len(out.grad_norm_dic) == 0 and isinstance(out.grad_norm_dic, dict)
train_step_out = out.training_step_output_for_epoch_end
assert len(train_step_out) == 1
train_step_out = train_step_out[0][0]
assert isinstance(train_step_out['minimize'], torch.Tensor)
assert train_step_out['minimize'].item() == 171
# make sure the optimizer closure returns the correct things
opt_closure_result = trainer.train_loop.training_step_and_backward(
batch, batch_idx, 0, trainer.optimizers[0], trainer.hiddens
)
assert opt_closure_result['loss'].item() == 171
def test_train_step_epoch_end(tmpdir):
"""
Checks train_step + training_epoch_end (NO training_step_end)
"""
model = DeterministicModel()
model.training_step = model.training_step__dict_return
model.training_step_end = None
model.training_epoch_end = model.training_epoch_end__dict
model.val_dataloader = None
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
weights_summary=None,
)
trainer.fit(model)
# make sure correct steps were called
assert model.training_step_called
assert not model.training_step_end_called
assert model.training_epoch_end_called
# assert epoch end metrics were added
assert trainer.logger_connector.callback_metrics['epoch_end_log_1'] == 178
assert trainer.logger_connector.progress_bar_metrics[
'epoch_end_pbar_1'] == 234
# make sure training outputs what is expected
batch_idx, batch = 0, next(iter(model.train_dataloader()))
out = trainer.train_loop.run_training_batch(batch, batch_idx, 0)
assert out.signal == 0
assert trainer.logger_connector.logged_metrics['log_acc1'] == 12.0
assert trainer.logger_connector.logged_metrics['log_acc2'] == 7.0
# outputs are for 1 optimizer and no tbptt
train_step_end_out = out.training_step_output_for_epoch_end
assert len(train_step_end_out) == 1
train_step_end_out = train_step_end_out[0][0]
pbar_metrics = train_step_end_out['progress_bar']
assert pbar_metrics['pbar_acc1'] == 17.0
assert pbar_metrics['pbar_acc2'] == 19.0
def test_result_obj_lr_scheduler_step(tmpdir):
"""
test that the LR scheduler was called at the correct time with the correct metrics
"""
model = DeterministicModel()
model.training_step = model.training_step__for_step_end_dict
model.training_step_end = model.training_step_end__dict
model.training_epoch_end = model.training_epoch_end__dict
model.val_dataloader = None
model.configure_optimizers = model.configure_optimizers__lr_on_plateau_step
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=2,
weights_summary=None,
)
trainer.fit(model)
assert len(trainer.dev_debugger.saved_lr_scheduler_updates) == 8
