def eval(data_loader, model, epoch_train, batch_train, optimizer, args):
model.eval()
corrects, avg_loss, accumulated_loss, size = 0, 0, 0, 0
predicates_all, target_all = [], []
for i_batch, (data) in enumerate(data_loader):
inputs, target = data
# scale target to be zero indexed
if args.target_sub_scaler != 0:
target.sub_(args.target_sub_scaler)
size += len(target)
if args.cuda:
inputs, target = inputs.cuda(), target.cuda()
inputs = Variable(inputs, volatile=True)
target = Variable(target)
logit = model(inputs)
predicates = torch.max(logit, 1)[1].view(target.size()).data
accumulated_loss += F.nll_loss(logit, target,
size_average=False).item()
corrects += (torch.max(logit, 1)[1].view(
target.size()).data == target.data).sum()
predicates_all += predicates.cpu().numpy().tolist()
target_all += target.data.cpu().numpy().tolist()
if args.cuda:
torch.cuda.synchronize()
avg_loss = accumulated_loss / size
accuracy = 100.0 * corrects / size
model.train()
print('\nEvaluation - loss: {:.6f} lr: {:.5f} acc: {:.3f}% ({}/{}) '.
format(avg_loss,
optimizer.state_dict()['param_groups'][0]['lr'], accuracy,
corrects, size))
print_f_score(predicates_all, target_all)
print('\n')
if args.log_result:
with open(os.path.join(args.save_folder, 'result.csv'), 'a') as r:
r.write('\n{:d},{:d},{:.5f},{:.2f},{:f}'.format(
epoch_train, batch_train, avg_loss, accuracy,
optimizer.state_dict()['param_groups'][0]['lr']))
return avg_loss, accuracy
with open(filename, 'wb') as fo:
pickle.dump(result, fo)
save_result(result)
plt.plot(train_acces)
plt.plot(val_acces)
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='lower right')
plt.savefig('./mult_acc.png', format='png', dpi=80)
plt.show()
plt.plot(train_losses)
plt.plot(val_losses)
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper right')
plt.savefig('./mult_loss.png', format='png', dpi=80)
plt.show()
model_path = './model/COVID19_multbest.path.tar'
predicates_all, target_all = test(cnn, model_path, test_loader)
print_f_score(predicates_all, target_all)
print(accuracy_score(target_all, predicates_all))
print(classification_report(target_all, predicates_all))
print("AUC", roc_auc_score(target_all, predicates_all))
print(confusion_matrix(target_all, predicates_all))
