88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
from collections import namedtuple
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from functools import partial
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import pytest
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import torch
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from sklearn.metrics import mean_absolute_error as sk_mean_absolute_error
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from sklearn.metrics import mean_squared_error as sk_mean_squared_error
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from sklearn.metrics import mean_squared_log_error as sk_mean_squared_log_error
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from pytorch_lightning.metrics.functional import mean_absolute_error, mean_squared_error, mean_squared_log_error
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from pytorch_lightning.metrics.regression import MeanAbsoluteError, MeanSquaredError, MeanSquaredLogError
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from tests.metrics.utils import BATCH_SIZE, MetricTester, NUM_BATCHES
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torch.manual_seed(42)
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num_targets = 5
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Input = namedtuple('Input', ["preds", "target"])
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_single_target_inputs = Input(
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preds=torch.rand(NUM_BATCHES, BATCH_SIZE),
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target=torch.rand(NUM_BATCHES, BATCH_SIZE),
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)
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_multi_target_inputs = Input(
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preds=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets),
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target=torch.rand(NUM_BATCHES, BATCH_SIZE, num_targets),
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)
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def _single_target_sk_metric(preds, target, sk_fn=mean_squared_error):
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sk_preds = preds.view(-1).numpy()
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sk_target = target.view(-1).numpy()
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return sk_fn(sk_preds, sk_target)
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def _multi_target_sk_metric(preds, target, sk_fn=mean_squared_error):
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sk_preds = preds.view(-1, num_targets).numpy()
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sk_target = target.view(-1, num_targets).numpy()
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return sk_fn(sk_preds, sk_target)
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@pytest.mark.parametrize(
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"preds, target, sk_metric",
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[
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(_single_target_inputs.preds, _single_target_inputs.target, _single_target_sk_metric),
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(_multi_target_inputs.preds, _multi_target_inputs.target, _multi_target_sk_metric),
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],
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)
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@pytest.mark.parametrize(
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"metric_class, metric_functional, sk_fn",
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[
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(MeanSquaredError, mean_squared_error, sk_mean_squared_error),
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(MeanAbsoluteError, mean_absolute_error, sk_mean_absolute_error),
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(MeanSquaredLogError, mean_squared_log_error, sk_mean_squared_log_error),
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],
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)
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class TestMeanError(MetricTester):
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@pytest.mark.parametrize("ddp", [True, False])
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@pytest.mark.parametrize("dist_sync_on_step", [True, False])
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def test_mean_error_class(
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self, preds, target, sk_metric, metric_class, metric_functional, sk_fn, ddp, dist_sync_on_step
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):
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self.run_class_metric_test(
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ddp=ddp,
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preds=preds,
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target=target,
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metric_class=metric_class,
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sk_metric=partial(sk_metric, sk_fn=sk_fn),
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dist_sync_on_step=dist_sync_on_step,
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)
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def test_mean_error_functional(self, preds, target, sk_metric, metric_class, metric_functional, sk_fn):
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self.run_functional_metric_test(
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preds=preds,
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target=target,
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metric_functional=metric_functional,
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sk_metric=partial(sk_metric, sk_fn=sk_fn),
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)
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@pytest.mark.parametrize("metric_class", [MeanSquaredError, MeanAbsoluteError, MeanSquaredLogError])
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def test_error_on_different_shape(metric_class):
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metric = metric_class()
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with pytest.raises(RuntimeError, match='Predictions and targets are expected to have the same shape'):
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metric(torch.randn(100, ), torch.randn(50, ))
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