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9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import torch
import numpy as np
from networks.resnet import resnet50
from sklearn.metrics import average_precision_score, precision_recall_curve, accuracy_score
from options.test_options import TestOptions
from data import create_dataloader
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)
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