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Update r2_score and wave_hedges_distance in test for generating data with different rank #2666

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merged 2 commits into from
Aug 23, 2022
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Update r2_score and wave_hedges_distance in test for generating data with different rank #2666

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329260a
Update 2 metrics in test for generating data with different rank
puhuk Aug 22, 2022
a8f3b1c
Merge branch 'master' into regression_final
vfdev-5 Aug 23, 2022
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23 changes: 12 additions & 11 deletions tests/ignite/contrib/metrics/regression/test_r2_score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,7 +98,6 @@ def _test_distrib_compute(device, tol=1e-6):
def _test(metric_device):
metric_device = torch.device(metric_device)
m = R2Score(device=metric_device)
torch.manual_seed(10 + rank)

y_pred = torch.randint(0, 10, size=(10,), device=device).float()
y = torch.randint(0, 10, size=(10,), device=device).float()
Expand All @@ -114,7 +113,8 @@ def _test(metric_device):
res = m.compute()
assert r2_score(np_y, np_y_pred) == pytest.approx(res, abs=tol)

for _ in range(3):
for i in range(3):
torch.manual_seed(10 + rank + i)
_test("cpu")
if device.type != "xla":
_test(idist.device())
Expand All @@ -123,22 +123,19 @@ def _test(metric_device):
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
s = 16
n_classes = 2
batch_size = 16

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

def update(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],
)

engine = Engine(update)
Expand All @@ -149,6 +146,9 @@ def update(engine, i):
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)

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

assert "r2" in engine.state.metrics

res = engine.state.metrics["r2"]
Expand All @@ -163,7 +163,8 @@ def update(engine, i):
if device.type != "xla":
metric_devices.append(idist.device())
for metric_device in metric_devices:
for _ in range(2):
for i in range(2):
torch.manual_seed(12 + rank + i)
_test(n_epochs=1, metric_device=metric_device)
_test(n_epochs=2, metric_device=metric_device)

Expand Down
23 changes: 12 additions & 11 deletions tests/ignite/contrib/metrics/regression/test_wave_hedges_distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,7 +88,6 @@ def _test_distrib_compute(device):
def _test(metric_device):
metric_device = torch.device(metric_device)
m = WaveHedgesDistance(device=metric_device)
torch.manual_seed(10 + rank)

y_pred = torch.randint(0, 10, size=(10,), device=device).float()
y = torch.randint(0, 10, size=(10,), device=device).float()
Expand All @@ -108,7 +107,8 @@ def _test(metric_device):

assert np_sum == pytest.approx(res)

for _ in range(3):
for i in range(3):
torch.manual_seed(10 + rank + i)
_test("cpu")
if device.type != "xla":
_test(idist.device())
Expand All @@ -117,22 +117,19 @@ def _test(metric_device):
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
s = 16
n_classes = 2
batch_size = 16

offset = n_iters * s
y_true = torch.rand(size=(offset * idist.get_world_size(),)).to(device)
y_preds = torch.rand(size=(offset * idist.get_world_size(),)).to(device)
y_true = torch.rand(size=(n_iters * batch_size,)).to(device)
y_preds = torch.rand(size=(n_iters * batch_size,)).to(device)

def update(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],
)

engine = Engine(update)
Expand All @@ -143,6 +140,9 @@ def update(engine, i):
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)

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

assert "whm" in engine.state.metrics

res = engine.state.metrics["whm"]
Expand All @@ -158,7 +158,8 @@ def update(engine, i):
if device.type != "xla":
metric_devices.append(idist.device())
for metric_device in metric_devices:
for _ in range(2):
for i in range(2):
torch.manual_seed(12 + rank + i)
_test(n_epochs=1, metric_device=metric_device)
_test(n_epochs=2, metric_device=metric_device)

Expand Down