def eval_regression(task):
model_path = './model/{}.model'.format(task)
D_m_text, D_m_audio, D_m_video, D_m_context = 300, 384, 35, 300
D_g, D_p, D_e, D_h, D_a = 150, 150, 100, 100, 100
cuda = torch.cuda.is_available()
print('Loading model...')
model = RegressionModel(D_m_text,
D_m_audio,
D_m_video,
D_m_context,
D_g,
D_p,
D_e,
D_h,
dropout_rec=0.1,
dropout=0.25)
if cuda:
model.cuda()
model.load_state_dict(torch.load(model_path))
loss_function = MaskedMSELoss()
print('Evaluating model...')
_, _, test_loader = train_regression.get_MOSEI_loaders(
'./data/regression.pkl', valid=0.0, batch_size=128, num_workers=0)
_, mae, _, labels, preds, masks, sample_ids = train_regression.train_or_eval_model(
model, loss_function, test_loader, None, cuda)
# gather labels and predictions
df = pd.DataFrame([(sample_id, label, pred)
for label, pred, mask, sample_id in zip(
labels, preds, masks, sample_ids) if mask == 1],
columns=['sample_id', 'label', 'pred'])
df['diff'] = (df.label - df.pred).abs()
df['label_class'] = df.label.apply(discretize)
df['pred_class'] = df.pred.apply(discretize)
df = df.sort_values(by='diff', ascending=False)
if_correct = df.label_class == df.pred_class
print('mae =', mean_absolute_error(df.label, df.pred))
print('acc =', if_correct.sum() / len(if_correct))
df.to_csv('./analysis/{}.csv'.format(task), index=False)
