def _test(metric_device):
engine = Engine(update)
m = RootMeanSquaredError(device=metric_device)
m.attach(engine, "rmse")
data = list(range(n_iters))
engine.run(data=data, max_epochs=1)
assert "rmse" in engine.state.metrics
res = engine.state.metrics["rmse"]
y_preds_full = []
for i in range(idist.get_world_size()):
y_preds_full.append((i + 1) * torch.ones(offset))
y_preds_full = torch.stack(y_preds_full).to(device).flatten()
true_res = np.sqrt(np.mean(np.square((y_true - y_preds_full).cpu().numpy())))
assert pytest.approx(res, rel=tol) == true_res
def _test_distrib_itegration(device):
import numpy as np
import torch.distributed as dist
from ignite.engine import Engine
rank = dist.get_rank()
n_iters = 100
s = 50
offset = n_iters * s
y_true = torch.arange(0, offset * dist.get_world_size(), dtype=torch.float).to(
device
)
y_preds = (rank + 1) * torch.ones(offset, dtype=torch.float).to(device)
def update(engine, i):
return (
y_preds[i * s : (i + 1) * s],
y_true[i * s + offset * rank : (i + 1) * s + offset * rank],
)
engine = Engine(update)
m = RootMeanSquaredError(device=device)
m.attach(engine, "rmse")
data = list(range(n_iters))
engine.run(data=data, max_epochs=1)
assert "rmse" in engine.state.metrics
res = engine.state.metrics["rmse"]
y_preds_full = []
for i in range(dist.get_world_size()):
y_preds_full.append((i + 1) * torch.ones(offset))
y_preds_full = torch.stack(y_preds_full).to(device).flatten()
true_res = np.sqrt(np.mean(np.square((y_true - y_preds_full).cpu().numpy())))
assert pytest.approx(res) == true_res