def test_rnn_dim_check(): L_SEQ, BAT, L_INP, L_STA = 2**31, 4, 2**10, 2 data = np.random.uniform(-1, 1, (L_SEQ, BAT, L_INP)) state = np.random.normal(0, 1, (1, BAT, L_STA)) params = np.random.normal(0, 1, (2056, )) assertRaises(ValueError, npx.rnn, data=data, parameters=params, \ mode='rnn_relu', state=state, state_size=L_STA, num_layers=1)
def test_training_with_trt(): assertRaises(RuntimeError, run_resnet, is_train=True, use_tensorrt=True)
def test_roi_pooling_large_dim(): A = np.ones((1, 1, INT_OVERFLOW, 5)) roi = np.array([[0, 0, 0, 5, 5]]) assertRaises(MXNetError, npx.roi_pooling, A, roi, pooled_size=(3, 3), \ spatial_scale=1)
def test_pooling_large_dim(): A = np.ones((1, 1, INT_OVERFLOW)) assertRaises(MXNetError, npx.pooling, data=A, kernel=(2), stride=(2), \ pool_type='max')
def test_ctc_loss_size_check(A, label): assertRaises(ValueError, npx.ctc_loss, A, label)