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