def test_base_no_logger_warning():
""" Test a warning is displayed when Trainer has no logger. """
monitor = TrainingDataMonitor()
trainer = Trainer(logger=False, callbacks=[monitor])
with pytest.warns(
UserWarning, match="Cannot log histograms because Trainer has no logger"
):
monitor.on_train_start(trainer, pl_module=None)
def test_base_unsupported_logger_warning(tmpdir):
""" Test a warning is displayed when an unsupported logger is used. """
monitor = TrainingDataMonitor()
trainer = Trainer(logger=LoggerCollection([TensorBoardLogger(tmpdir)]),
callbacks=[monitor])
with pytest.warns(UserWarning,
match="does not support logging with LoggerCollection"):
monitor.on_train_start(trainer, pl_module=None)
def test_training_data_monitor(log_histogram, tmpdir, datadir):
""" Test that the TrainingDataMonitor logs histograms of data points going into training_step. """
monitor = TrainingDataMonitor()
model = LitMNIST(data_dir=datadir)
trainer = Trainer(
default_root_dir=tmpdir,
log_every_n_steps=1,
callbacks=[monitor],
)
monitor.on_train_start(trainer, model)
# single tensor
example_data = torch.rand(2, 3, 4)
monitor.on_train_batch_start(trainer,
model,
batch=example_data,
batch_idx=0,
dataloader_idx=0)
assert log_histogram.call_args_list == [
call(example_data, "training_step/[2, 3, 4]"),
]
log_histogram.reset_mock()
# tuple
example_data = (torch.rand(2, 3, 4), torch.rand(5), "non-tensor")
monitor.on_train_batch_start(trainer,
model,
batch=example_data,
batch_idx=0,
dataloader_idx=0)
assert log_histogram.call_args_list == [
call(example_data[0], "training_step/0/[2, 3, 4]"),
call(example_data[1], "training_step/1/[5]"),
]
log_histogram.reset_mock()
# dict
example_data = {
"x0": torch.rand(2, 3, 4),
"x1": torch.rand(5),
"non-tensor": "non-tensor",
}
monitor.on_train_batch_start(trainer,
model,
batch=example_data,
batch_idx=0,
dataloader_idx=0)
assert log_histogram.call_args_list == [
call(example_data["x0"], "training_step/x0/[2, 3, 4]"),
call(example_data["x1"], "training_step/x1/[5]"),
]
def test_base_log_interval_fallback(log_histogram, tmpdir, log_every_n_steps, max_steps, expected_calls, datadir):
""" Test that if log_every_n_steps not set in the callback, fallback to what is defined in the Trainer. """
monitor = TrainingDataMonitor()
model = LitMNIST(data_dir=datadir, num_workers=0)
trainer = Trainer(
default_root_dir=tmpdir,
log_every_n_steps=log_every_n_steps,
max_steps=max_steps,
callbacks=[monitor],
)
trainer.fit(model)
assert log_histogram.call_count == (expected_calls * 2) # 2 tensors per log call
def test_base_log_interval_override(log_histogram, tmpdir, log_every_n_steps, max_steps, expected_calls, datadir):
""" Test logging interval set by log_every_n_steps argument. """
monitor = TrainingDataMonitor(log_every_n_steps=log_every_n_steps)
model = LitMNIST(data_dir=datadir, num_workers=0)
trainer = Trainer(
default_root_dir=tmpdir,
log_every_n_steps=1,
max_steps=max_steps,
callbacks=[monitor],
)
trainer.fit(model)
assert log_histogram.call_count == (expected_calls * 2) # 2 tensors per log call