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Merged
merged 4 commits into from
Aug 17, 2022
Merged

Update test_average_precision.py #2653

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8c62f90
Update test_average_precision.py
puhuk Aug 17, 2022
42db808
Update test_average_precision.py
puhuk Aug 17, 2022
25df6ff
Update test_average_precision.py
puhuk Aug 17, 2022
59c54d0
Update test_average_precision.py
puhuk Aug 17, 2022
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24 changes: 13 additions & 11 deletions tests/ignite/contrib/metrics/test_average_precision.py
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Original file line number Diff line number Diff line change
Expand Up @@ -215,11 +215,9 @@ def get_test_cases():
def _test_distrib_integration_binary_input(device):

rank = idist.get_rank()
torch.manual_seed(12)
n_iters = 80
s = 16
batch_size = 16
n_classes = 2
offset = n_iters * s

def _test(y_preds, y_true, n_epochs, metric_device, update_fn):
metric_device = torch.device(metric_device)
Expand All @@ -232,6 +230,9 @@ def _test(y_preds, y_true, n_epochs, metric_device, update_fn):
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 "ap" in engine.state.metrics

res = engine.state.metrics["ap"]
Expand All @@ -240,24 +241,25 @@ def _test(y_preds, y_true, n_epochs, metric_device, update_fn):
assert pytest.approx(res) == true_res

def get_tests(is_N):
torch.manual_seed(12 + rank)
if is_N:
y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(),)).to(device)
y_preds = torch.rand(offset * idist.get_world_size()).to(device)
y_true = torch.randint(0, n_classes, size=(n_iters * batch_size,)).to(device)
y_preds = torch.rand(n_iters * batch_size).to(device)

def update_fn(engine, i):
return (
y_preds[i * s + rank * offset : (i + 1) * s + rank * offset],
y_true[i * s + rank * offset : (i + 1) * s + rank * offset],
y_preds[i * batch_size : (i + 1) * batch_size],
y_true[i * batch_size : (i + 1) * batch_size],
)

else:
y_true = torch.randint(0, n_classes, size=(offset * idist.get_world_size(), 10)).to(device)
y_preds = torch.randint(0, n_classes, size=(offset * idist.get_world_size(), 10)).to(device)
y_true = torch.randint(0, n_classes, size=(n_iters * batch_size, 10)).to(device)
y_preds = torch.randint(0, n_classes, size=(n_iters * batch_size, 10)).to(device)

def update_fn(engine, i):
return (
y_preds[i * s + rank * offset : (i + 1) * s + rank * offset, :],
y_true[i * s + rank * offset : (i + 1) * s + rank * offset, :],
y_preds[i * batch_size : (i + 1) * batch_size, :],
y_true[i * batch_size : (i + 1) * batch_size, :],
)

return y_preds, y_true, update_fn
Expand Down