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Fix ROC metric for CUDA tensors #2304

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merged 9 commits into from
Jun 23, 2020
Merged

Fix ROC metric for CUDA tensors #2304

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e71edae
Fix ROC metric for CUDA tensors
tridao Jun 20, 2020
43ae8cc
Update test_classification.py
justusschock Jun 21, 2020
e64305a
Update test_classification.py
justusschock Jun 21, 2020
1325506
chlog
Borda Jun 21, 2020
3f40ba7
Update test_classification.py
justusschock Jun 21, 2020
4ae12f3
Update test_classification.py
justusschock Jun 21, 2020
6661af7
Update tests/metrics/functional/test_classification.py
justusschock Jun 21, 2020
96af46c
Update test_classification.py
justusschock Jun 21, 2020
a266aef
Merge branch 'master' into cuda_metric_fix
Borda Jun 23, 2020
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4 changes: 2 additions & 2 deletions pytorch_lightning/metrics/functional/classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from typing import Optional, Tuple, Callable

import torch
from torch.nn import functional as F

from pytorch_lightning.metrics.functional.reduction import reduce
from pytorch_lightning.utilities import rank_zero_warn
Expand Down Expand Up @@ -500,8 +501,7 @@ def _binary_clf_curve(
# the indices associated with the distinct values. We also
# concatenate a value for the end of the curve.
distinct_value_indices = torch.where(pred[1:] - pred[:-1])[0]
threshold_idxs = torch.cat([distinct_value_indices,
torch.tensor([target.size(0) - 1])])
threshold_idxs = F.pad(distinct_value_indices, (0, 1), value=target.size(0) - 1)

target = (target == pos_label).to(torch.long)
tps = torch.cumsum(target * weight, dim=0)[threshold_idxs]
Expand Down
20 changes: 17 additions & 3 deletions tests/metrics/functional/test_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,23 +47,37 @@ def test_against_sklearn(sklearn_metric, torch_metric):
"""Compare PL metrics to sklearn version."""
pred = torch.randint(10, (500,))
target = torch.randint(10, (500,))

if torch.cuda.is_available():
device = 'cuda'
else:
device = 'cpu'

pred = pred.to(device)
target = target.to(device)

assert torch.allclose(
torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
torch.tensor(sklearn_metric(target, pred), dtype=torch.float, device=device),
torch_metric(pred, target))

pred = torch.randint(10, (200,))
target = torch.randint(5, (200,))

pred = pred.to(device)
target = target.to(device)

assert torch.allclose(
torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
torch.tensor(sklearn_metric(target, pred), dtype=torch.float, device=device),
torch_metric(pred, target))

pred = torch.randint(5, (200,))
target = torch.randint(10, (200,))

pred = pred.to(device)
target = target.to(device)

assert torch.allclose(
torch.tensor(sklearn_metric(target, pred), dtype=torch.float),
torch.tensor(sklearn_metric(target, pred), dtype=torch.float, device=device),
torch_metric(pred, target))


Expand Down