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Merged
vfdev-5 merged 5 commits into from
Aug 19, 2022
Merged

Update test_precision_recall_curve.py #2655

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103302f
Update test_precision_recall_curve.py
puhuk Aug 19, 2022
a25360f
Update test_precision_recall_curve.py
puhuk Aug 19, 2022
3765da3
Update test_precision_recall_curve.py
puhuk Aug 19, 2022
8933d9c
Update test_precision_recall_curve.py
puhuk Aug 19, 2022
3c20004
Merge branch 'master' into test_precision_recall_curve
vfdev-5 Aug 19, 2022
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23 changes: 12 additions & 11 deletions tests/ignite/contrib/metrics/test_precision_recall_curve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,17 +138,13 @@ def test_check_compute_fn():


def _test_distrib_compute(device):

rank = idist.get_rank()
torch.manual_seed(12)

def _test(y_pred, y, batch_size, metric_device):

metric_device = torch.device(metric_device)
prc = PrecisionRecallCurve(device=metric_device)

torch.manual_seed(10 + rank)

prc.reset()
if batch_size > 1:
n_iters = y.shape[0] // batch_size + 1
Expand Down Expand Up @@ -183,7 +179,8 @@ def get_test_cases():
]
return test_cases

for _ in range(3):
for i in range(3):
torch.manual_seed(12 + rank + i)
test_cases = get_test_cases()
for y_pred, y, batch_size in test_cases:
y_pred = y_pred.to(device)
Expand All @@ -196,19 +193,20 @@ def get_test_cases():
def _test_distrib_integration(device):

rank = idist.get_rank()
torch.manual_seed(12)

def _test(n_epochs, metric_device):
metric_device = torch.device(metric_device)
n_iters = 80
size = 151
y_true = torch.randint(0, 2, (size,)).to(device)
y_preds = torch.randint(0, 2, (size,)).to(device)
batch_size = 151
torch.manual_seed(12 + rank)

y_true = torch.randint(0, 2, (n_iters * batch_size,)).to(device)
y_preds = torch.randint(0, 2, (n_iters * batch_size,)).to(device)

def update(engine, i):
return (
y_preds[i * size : (i + 1) * size],
y_true[i * size : (i + 1) * size],
y_preds[i * batch_size : (i + 1) * batch_size],
y_true[i * batch_size : (i + 1) * batch_size],
)

engine = Engine(update)
Expand All @@ -219,6 +217,9 @@ def update(engine, i):
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)

y_true = idist.all_gather(y_true)
y_preds = idist.all_gather(y_preds)

assert "prc" in engine.state.metrics

precision, recall, thresholds = engine.state.metrics["prc"]
Expand Down