import numpy as np
import torch
from data import create_dataloader
from networks.resnet import resnet50
from options.test_options import TestOptions
from sklearn.metrics import accuracy_score, average_precision_score


def validate(model, opt):
    data_loader = create_dataloader(opt)

    with torch.no_grad():
        y_true, y_pred = [], []
        for img, label in data_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, y_true, y_pred


if __name__ == '__main__':
    opt = TestOptions().parse(print_options=False)

    model = resnet50(num_classes=1)
    state_dict = torch.load(opt.model_path, map_location='cpu')
    model.load_state_dict(state_dict['model'])
    model.cuda()
    model.eval()

    acc, avg_precision, r_acc, f_acc, y_true, y_pred = validate(model, opt)

    print('accuracy:', acc)
    print('average precision:', avg_precision)

    print('accuracy of real images:', r_acc)
    print('accuracy of fake images:', f_acc)