| 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) |
|
|
