114 lines
3.7 KiB
Python
114 lines
3.7 KiB
Python
import numpy as np
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import pytest
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import torch
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from sklearn.metrics import accuracy_score
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from pytorch_lightning.metrics.classification.accuracy import Accuracy
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from tests.metrics.classification.inputs import (
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_binary_inputs,
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_binary_prob_inputs,
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_multiclass_inputs,
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_multiclass_prob_inputs,
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_multidim_multiclass_inputs,
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_multidim_multiclass_prob_inputs,
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_multilabel_inputs,
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_multilabel_prob_inputs,
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)
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from tests.metrics.utils import THRESHOLD, MetricTester
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torch.manual_seed(42)
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def _sk_accuracy_binary_prob(preds, target):
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sk_preds = (preds.view(-1).numpy() >= THRESHOLD).astype(np.uint8)
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sk_target = target.view(-1).numpy()
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return accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_binary(preds, target):
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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 accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multilabel_prob(preds, target):
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sk_preds = (preds.view(-1).numpy() >= THRESHOLD).astype(np.uint8)
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sk_target = target.view(-1).numpy()
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return accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multilabel(preds, target):
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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 accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multiclass_prob(preds, target):
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sk_preds = torch.argmax(preds, dim=len(preds.shape) - 1).view(-1).numpy()
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sk_target = target.view(-1).numpy()
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return accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multiclass(preds, target):
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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 accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multidim_multiclass_prob(preds, target):
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sk_preds = torch.argmax(preds, dim=len(preds.shape) - 2).view(-1).numpy()
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sk_target = target.view(-1).numpy()
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return accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def _sk_accuracy_multidim_multiclass(preds, target):
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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 accuracy_score(y_true=sk_target, y_pred=sk_preds)
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def test_accuracy_invalid_shape():
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with pytest.raises(ValueError):
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acc = Accuracy()
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acc.update(preds=torch.rand(1), target=torch.rand(1, 2, 3))
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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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@pytest.mark.parametrize(
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"preds, target, sk_metric",
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[
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(_binary_prob_inputs.preds, _binary_prob_inputs.target, _sk_accuracy_binary_prob),
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(_binary_inputs.preds, _binary_inputs.target, _sk_accuracy_binary),
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(_multilabel_prob_inputs.preds, _multilabel_prob_inputs.target, _sk_accuracy_multilabel_prob),
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(_multilabel_inputs.preds, _multilabel_inputs.target, _sk_accuracy_multilabel),
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(_multiclass_prob_inputs.preds, _multiclass_prob_inputs.target, _sk_accuracy_multiclass_prob),
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(_multiclass_inputs.preds, _multiclass_inputs.target, _sk_accuracy_multiclass),
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(
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_multidim_multiclass_prob_inputs.preds,
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_multidim_multiclass_prob_inputs.target,
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_sk_accuracy_multidim_multiclass_prob,
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),
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(_multidim_multiclass_inputs.preds, _multidim_multiclass_inputs.target, _sk_accuracy_multidim_multiclass),
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],
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)
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class TestAccuracy(MetricTester):
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def test_accuracy(self, ddp, dist_sync_on_step, preds, target, sk_metric):
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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=Accuracy,
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sk_metric=sk_metric,
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dist_sync_on_step=dist_sync_on_step,
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metric_args={"threshold": THRESHOLD},
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)
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