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)
def test_training_with_trt():
assertRaises(RuntimeError, run_resnet, is_train=True, use_tensorrt=True)
