<filename>test/PR_test/unit_test/backend/test_maximum.py
# Copyright 2020 The FastEstimator Authors. All Rights Reserved.
#
# Licensed 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.
# ==============================================================================
import unittest

import numpy as np
import tensorflow as tf
import torch

import fastestimator as fe
from fastestimator.test.unittest_util import is_equal


class TestMaximum(unittest.TestCase):
    def test_maximum_np_input(self):
        n1 = np.array([[2, 7, 6]])
        n2 = np.array([[2, 7, 5]])
        res = fe.backend.maximum(n1, n2)
        self.assertTrue(is_equal(res, n1))

    def test_maximum_tf_input(self):
        t1 = tf.constant([[2, 7, 6]])
        t2 = tf.constant([[2, 7, 5]])
        res = fe.backend.maximum(t1, t2)
        self.assertTrue(is_equal(res, t1))

    def test_maximum_torch_input(self):
        p1 = torch.tensor([[2, 7, 6]])
        p2 = torch.tensor([[2, 7, 5]])
        res = fe.backend.maximum(p1, p2)
        self.assertTrue(is_equal(res, p1))

<filename>test/PR_test/unit_test/backend/test_maximum.py
# Copyright 2020 The FastEstimator Authors. All Rights Reserved.
#
# Licensed 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.
# ==============================================================================
import unittest

import numpy as np
import tensorflow as tf
import torch

import fastestimator as fe
from fastestimator.test.unittest_util import is_equal


class TestMaximum(unittest.TestCase):
    def test_maximum_np_input(self):
        n1 = np.array([[2, 7, 6]])
        n2 = np.array([[2, 7, 5]])
        res = fe.backend.maximum(n1, n2)
        self.assertTrue(is_equal(res, n1))

    def test_maximum_tf_input(self):
        t1 = tf.constant([[2, 7, 6]])
        t2 = tf.constant([[2, 7, 5]])
        res = fe.backend.maximum(t1, t2)
        self.assertTrue(is_equal(res, t1))

    def test_maximum_torch_input(self):
        p1 = torch.tensor([[2, 7, 6]])
        p2 = torch.tensor([[2, 7, 5]])
        res = fe.backend.maximum(p1, p2)
        self.assertTrue(is_equal(res, p1))

# Copyright 2020 The FastEstimator Authors. All Rights Reserved. # # Licensed 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. # ==============================================================================