wehub-resource-sync
34 lines
850 B
Python
34 lines
850 B
Python
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
|