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0788e19 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import numpy as np
import torch
from sklearn.metrics import accuracy_score, average_precision_score
def validate(model, val_loader):
with torch.no_grad():
y_true, y_pred = [], []
for img, label in val_loader:
in_tens = img.cuda()
y_pred.extend(model(in_tens).sigmoid().flatten().tolist())
y_true.extend(label.flatten().tolist())
y_true, y_pred = np.array(y_true), np.array(y_pred)
r_acc = accuracy_score(y_true[y_true == 0], y_pred[y_true == 0] > 0.5)
f_acc = accuracy_score(y_true[y_true == 1], y_pred[y_true == 1] > 0.5)
acc = accuracy_score(y_true, y_pred > 0.5)
ap = average_precision_score(y_true, y_pred)
return acc, ap, r_acc, f_acc
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