tests/python/unittest/test_smoke.py

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from mxnet import np, npx, use_np, autograd, initializer, gluon
from common import with_environment
import pytest

@use_np
@with_environment('MXNET_ENGINE_TYPE', 'NaiveEngine')
def test_18927():
    """test for no error when dealing with zero-size array in bipartite matching"""
    arr = np.random.rand(0,2)
    arr_grad = np.empty_like(arr)
    autograd.mark_variables([arr], [arr_grad])
    with autograd.record():
        a = npx.bipartite_matching(arr, threshold=0.1)
    a[0].backward()

@use_np
@with_environment('MXNET_ENGINE_TYPE', 'NaiveEngine')
def test_18933_batch_0():
    arr = np.random.rand(0,1,1) # batch = 0
    arr_grad = np.empty_like(arr)
    gamma = np.random.rand(1)
    gamma_grad = np.empty_like(gamma)
    beta = np.random.rand(1)
    beta_grad = np.empty_like(beta)
    autograd.mark_variables([arr, gamma, beta], [arr_grad, gamma_grad, beta_grad])
    with autograd.record():
        a = npx.instance_norm(arr, gamma, beta)
    a.backward()

@use_np
@with_environment('MXNET_ENGINE_TYPE', 'NaiveEngine')
def test_18933_channel_0():
    arr = np.random.rand(1,0,1) # channel = 0
    arr_grad = np.empty_like(arr)
    gamma = np.random.rand(1)
    gamma_grad = np.empty_like(gamma)
    beta = np.random.rand(1)
    beta_grad = np.empty_like(beta)
    autograd.mark_variables([arr, gamma, beta], [arr_grad, gamma_grad, beta_grad])
    with autograd.record():
        a = npx.instance_norm(arr, gamma, beta)
    a.backward()

@use_np
@with_environment('MXNET_ENGINE_TYPE', 'NaiveEngine')
def test_18934_empty_leaky_relu():
    arr = np.random.rand(0,2)
    arr_grad = np.empty_like(arr)

    autograd.mark_variables([arr], [arr_grad])
    with autograd.record():
        res = npx.leaky_relu(arr)
    res.backward()

@use_np
@pytest.mark.parametrize('initializer',[
    'zeros', 'ones', initializer.Constant(3),
    initializer.Uniform(),
    initializer.Normal(),
    initializer.Orthogonal(),
    initializer.Orthogonal(rand_type='normal'),
    initializer.Xavier(),
    initializer.Xavier(rnd_type='gaussian'),
    initializer.MSRAPrelu(),
    initializer.MSRAPrelu(factor_type='in'),
    initializer.MSRAPrelu(factor_type='out'),
    initializer.LSTMBias(),
])
@pytest.mark.parametrize('dtype', [
    'float32', 'float64'
])
def test_19118(initializer, dtype):
    net = gluon.nn.Dense(16, in_units=16)
    net.cast(dtype)
    net.initialize(initializer)
    net.hybridize()
    net(np.zeros((16, 16), dtype=dtype))