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utils.py
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utils.py
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import random
import numpy as np
import torch
from torch.nn import functional as F
def evaluate(model, graph, feats, labels, idxs):
model.eval()
with torch.no_grad():
logits = model(graph, feats)
results = ()
for idx in idxs:
loss = F.cross_entropy(logits[idx], labels[idx])
acc = torch.sum(
logits[idx].argmax(dim=1) == labels[idx]
).item() / len(idx)
results += (loss, acc)
return results
def generate_random_seeds(seed, nums):
random.seed(seed)
return [random.randint(1, 999999999) for _ in range(nums)]
def set_random_state(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True