| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| from mxnet import np, npx, use_np, autograd |
| from common import setup_module, teardown_module, with_environment |
| |
| @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() |