def _test(metric_device):
engine = Engine(update)
m = MeanPairwiseDistance(device=metric_device)
m.attach(engine, "mpwd")
data = list(range(n_iters))
engine.run(data=data, max_epochs=1)
assert "mpwd" in engine.state.metrics
res = engine.state.metrics["mpwd"]
true_res = []
for i in range(n_iters * idist.get_world_size()):
true_res.append(
torch.pairwise_distance(
y_true[i * s : (i + 1) * s, ...], y_preds[i * s : (i + 1) * s, ...], p=m._p, eps=m._eps
)
.cpu()
.numpy()
)
true_res = np.array(true_res).ravel()
true_res = true_res.mean()
assert pytest.approx(res) == 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()
torch.manual_seed(12)
n_iters = 100
s = 50
offset = n_iters * s
y_true = torch.rand(offset * dist.get_world_size(), 10).to(device)
y_preds = torch.rand(offset * dist.get_world_size(), 10).to(device)
def update(engine, i):
return (
y_preds[i * s + offset * rank : (i + 1) * s + offset * rank, ...],
y_true[i * s + offset * rank : (i + 1) * s + offset * rank, ...],
)
engine = Engine(update)
m = MeanPairwiseDistance(device=device)
m.attach(engine, "mpwd")
data = list(range(n_iters))
engine.run(data=data, max_epochs=1)
assert "mpwd" in engine.state.metrics
res = engine.state.metrics["mpwd"]
true_res = []
for i in range(n_iters * dist.get_world_size()):
true_res.append(
torch.pairwise_distance(
y_true[i * s : (i + 1) * s, ...],
y_preds[i * s : (i + 1) * s, ...],
p=m._p,
eps=m._eps,
)
.cpu()
.numpy()
)
true_res = np.array(true_res).ravel()
true_res = true_res.mean()
assert pytest.approx(res) == true_res