def test_result_metric_integration():
metric_a = DummyMetric()
metric_b = DummyMetric()
metric_c = DummyMetric()
result = ResultCollection(True, torch.device("cpu"))
for _ in range(3):
cumulative_sum = 0
for i in range(5):
metric_a(i)
metric_b(i)
metric_c(i)
cumulative_sum += i
result.log("h", "a", metric_a, on_step=True, on_epoch=True)
result.log("h", "b", metric_b, on_step=False, on_epoch=True)
result.log("h", "c", metric_c, on_step=True, on_epoch=False)
batch_log = result.metrics(True)["log"]
assert batch_log == {"a_step": i, "c": i}
epoch_log = result.metrics(False)["log"]
result.reset()
# assert metric state reset to default values
assert metric_a.x == metric_a._defaults["x"]
assert metric_b.x == metric_b._defaults["x"]
assert metric_c.x == metric_c._defaults["x"]
assert epoch_log == {"b": cumulative_sum, "a_epoch": cumulative_sum}
result.minimize = torch.tensor(1.0)
result.extra = {}
assert str(result) == ("ResultCollection("
"minimize=1.0, "
"{"
"'h.a': ResultMetric('a', value=DummyMetric()), "
"'h.b': ResultMetric('b', value=DummyMetric()), "
"'h.c': ResultMetric('c', value=DummyMetric())"
"})")
assert repr(result) == ("{"
"True, "
"device(type='cpu'), "
"minimize=tensor(1.), "
"{'h.a': ResultMetric('a', value=DummyMetric()), "
"'h.b': ResultMetric('b', value=DummyMetric()), "
"'h.c': ResultMetric('c', value=DummyMetric()), "
"'_extra': {}}"
"}")
def _ddp_test_fn(rank, worldsize):
_setup_ddp(rank, worldsize)
torch.tensor([1.0])
metric_a = DummyMetric()
metric_b = DummyMetric()
metric_c = DummyMetric()
metric_a = metric_a.to(f"cuda:{rank}")
metric_b = metric_b.to(f"cuda:{rank}")
metric_c = metric_c.to(f"cuda:{rank}")
result = ResultCollection(True, torch.device(f"cuda:{rank}"))
for _ in range(3):
cumulative_sum = 0
for i in range(5):
metric_a(i)
metric_b(i)
metric_c(i)
cumulative_sum += i
result.log("h", "a", metric_a, on_step=True, on_epoch=True)
result.log("h", "b", metric_b, on_step=False, on_epoch=True)
result.log("h", "c", metric_c, on_step=True, on_epoch=False)
batch_log = result.metrics(True)["log"]
assert batch_log == {"a_step": i, "c": i}
epoch_log = result.metrics(False)["log"]
result.reset()
# assert metric state reset to default values
assert metric_a.x == metric_a._defaults["x"], (metric_a.x,
metric_a._defaults["x"])
assert metric_b.x == metric_b._defaults["x"]
assert metric_c.x == metric_c._defaults["x"]
assert epoch_log == {
"b": cumulative_sum * worldsize,
"a_epoch": cumulative_sum * worldsize
}
def test_result_metric_integration():
metric_a = DummyMetric()
metric_b = DummyMetric()
metric_c = DummyMetric()
result = ResultCollection(True, torch.device("cpu"))
for _ in range(3):
cumulative_sum = 0
for i in range(5):
metric_a(i)
metric_b(i)
metric_c(i)
cumulative_sum += i
result.log('h', 'a', metric_a, on_step=True, on_epoch=True)
result.log('h', 'b', metric_b, on_step=False, on_epoch=True)
result.log('h', 'c', metric_c, on_step=True, on_epoch=False)
batch_log = result.metrics(True)[MetricSource.LOG]
assert batch_log == {"a_step": i, "c": i}
epoch_log = result.metrics(False)[MetricSource.LOG]
result.reset()
# assert metric state reset to default values
assert metric_a.x == metric_a._defaults['x']
assert metric_b.x == metric_b._defaults['x']
assert metric_c.x == metric_c._defaults['x']
assert epoch_log == {"b": cumulative_sum, "a_epoch": cumulative_sum}
assert str(result) == (
"ResultCollection(True, cpu, {"
"'h.a': ResultMetric('a', value=DummyMetric()), "
"'h.b': ResultMetric('b', value=DummyMetric()), "
"'h.c': ResultMetric('c', value=DummyMetric())"
"})"
)
def test_result_collection_on_tensor_with_mean_reduction():
result_collection = ResultCollection(True, torch.device("cpu"))
product = [(True, True), (False, True), (True, False), (False, False)]
values = torch.arange(1, 10).float(
) # need to convert to float() due to precision issues using torch 1.4
batches = values * values
for i, v in enumerate(values):
for prog_bar in [False, True]:
for logger in [False, True]:
for on_step, on_epoch in product:
name = "loss"
if on_step:
name += "_on_step"
if on_epoch:
name += "_on_epoch"
if prog_bar:
name += "_prog_bar"
if logger:
name += "_logger"
result_collection.log(
"training_step",
name,
v,
on_step=on_step,
on_epoch=on_epoch,
batch_size=batches[i],
prog_bar=prog_bar,
logger=logger,
)
total_value = sum(values * batches)
total_batches = sum(batches)
assert result_collection[
"training_step.loss_on_step_on_epoch"].value == total_value
assert result_collection[
"training_step.loss_on_step_on_epoch"].cumulated_batch_size == total_batches
batch_metrics = result_collection.metrics(True)
max_ = max(values)
assert batch_metrics[MetricSource.PBAR] == {
"loss_on_step_on_epoch_prog_bar_step": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_prog_bar": max_,
"loss_on_step_prog_bar_logger": max_,
}
assert batch_metrics[MetricSource.LOG] == {
"loss_on_step_on_epoch_logger_step": max_,
"loss_on_step_logger": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_prog_bar_logger": max_,
}
assert batch_metrics[MetricSource.CALLBACK] == {
"loss_on_step": max_,
"loss_on_step_logger": max_,
"loss_on_step_on_epoch": max_,
"loss_on_step_on_epoch_logger": max_,
"loss_on_step_on_epoch_logger_step": max_,
"loss_on_step_on_epoch_prog_bar": max_,
"loss_on_step_on_epoch_prog_bar_logger": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_on_epoch_prog_bar_step": max_,
"loss_on_step_on_epoch_step": max_,
"loss_on_step_prog_bar": max_,
"loss_on_step_prog_bar_logger": max_,
}
epoch_metrics = result_collection.metrics(False)
mean = total_value / total_batches
assert epoch_metrics[MetricSource.PBAR] == {
"loss_on_epoch_prog_bar": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_prog_bar_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
assert epoch_metrics[MetricSource.LOG] == {
"loss_on_epoch_logger": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_logger_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
assert epoch_metrics[MetricSource.CALLBACK] == {
"loss_on_epoch": mean,
"loss_on_epoch_logger": mean,
"loss_on_epoch_prog_bar": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch": mean,
"loss_on_step_on_epoch_epoch": mean,
"loss_on_step_on_epoch_logger": mean,
"loss_on_step_on_epoch_logger_epoch": mean,
"loss_on_step_on_epoch_prog_bar": mean,
"loss_on_step_on_epoch_prog_bar_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
def test_result_collection_on_tensor_with_mean_reduction():
result_collection = ResultCollection(True, torch.device("cpu"))
product = [(True, True), (False, True), (True, False), (False, False)]
values = torch.arange(1, 10).float(
) # need to convert to float() due to precision issues using torch 1.4
batches = values * values
for i, v in enumerate(values):
for prog_bar in [False, True]:
for logger in [False, True]:
for on_step, on_epoch in product:
name = "loss"
if on_step:
name += "_on_step"
if on_epoch:
name += "_on_epoch"
if prog_bar:
name += "_prog_bar"
if logger:
name += "_logger"
log_kwargs = dict(
fx="training_step",
name=name,
value=v,
on_step=on_step,
on_epoch=on_epoch,
batch_size=batches[i],
prog_bar=prog_bar,
logger=logger,
)
if not on_step and not on_epoch:
with pytest.raises(
MisconfigurationException,
match="on_step=False, on_epoch=False"):
result_collection.log(**log_kwargs)
else:
result_collection.log(**log_kwargs)
total_value = sum(values * batches)
total_batches = sum(batches)
assert result_collection[
"training_step.loss_on_step_on_epoch"].value == total_value
assert result_collection[
"training_step.loss_on_step_on_epoch"].cumulated_batch_size == total_batches
batch_metrics = result_collection.metrics(True)
max_ = max(values)
assert batch_metrics["pbar"] == {
"loss_on_step_on_epoch_prog_bar_step": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_prog_bar": max_,
"loss_on_step_prog_bar_logger": max_,
}
assert batch_metrics["log"] == {
"loss_on_step_on_epoch_logger_step": max_,
"loss_on_step_logger": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_prog_bar_logger": max_,
}
assert batch_metrics["callback"] == {
"loss_on_step": max_,
"loss_on_step_logger": max_,
"loss_on_step_on_epoch": max_,
"loss_on_step_on_epoch_logger": max_,
"loss_on_step_on_epoch_logger_step": max_,
"loss_on_step_on_epoch_prog_bar": max_,
"loss_on_step_on_epoch_prog_bar_logger": max_,
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
"loss_on_step_on_epoch_prog_bar_step": max_,
"loss_on_step_on_epoch_step": max_,
"loss_on_step_prog_bar": max_,
"loss_on_step_prog_bar_logger": max_,
}
epoch_metrics = result_collection.metrics(False)
mean = total_value / total_batches
assert epoch_metrics["pbar"] == {
"loss_on_epoch_prog_bar": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_prog_bar_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
assert epoch_metrics["log"] == {
"loss_on_epoch_logger": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_logger_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
assert epoch_metrics["callback"] == {
"loss_on_epoch": mean,
"loss_on_epoch_logger": mean,
"loss_on_epoch_prog_bar": mean,
"loss_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch": mean,
"loss_on_step_on_epoch_epoch": mean,
"loss_on_step_on_epoch_logger": mean,
"loss_on_step_on_epoch_logger_epoch": mean,
"loss_on_step_on_epoch_prog_bar": mean,
"loss_on_step_on_epoch_prog_bar_epoch": mean,
"loss_on_step_on_epoch_prog_bar_logger": mean,
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
}
