<gh_stars>10-100
# 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
import fastestimator.test.unittest_util as fet


class TestOnesLike(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.data_np = np.array([[0, 1], [2, 3]])
        cls.data_tf = tf.constant([[0, 1], [2, 3]])
        cls.data_torch = torch.tensor([[0, 1], [2, 3]])
        cls.op = np.array([[1, 1], [1, 1]])
        cls.op_tf = tf.ones((2, 2), dtype=tf.int32)
        cls.op_torch = torch.tensor([[1, 1], [1, 1]])

    def test_ones_like_np(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_np), self.op))

    def test_ones_like_tf(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_tf), self.op_tf))

    def test_ones_like_torch(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_torch), self.op_torch))

# 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
import fastestimator.test.unittest_util as fet


class TestOnesLike(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.data_np = np.array([[0, 1], [2, 3]])
        cls.data_tf = tf.constant([[0, 1], [2, 3]])
        cls.data_torch = torch.tensor([[0, 1], [2, 3]])
        cls.op = np.array([[1, 1], [1, 1]])
        cls.op_tf = tf.ones((2, 2), dtype=tf.int32)
        cls.op_torch = torch.tensor([[1, 1], [1, 1]])

    def test_ones_like_np(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_np), self.op))

    def test_ones_like_tf(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_tf), self.op_tf))

    def test_ones_like_torch(self):
        self.assertTrue(fet.is_equal(fe.backend.ones_like(self.data_torch), self.op_torch))
# 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. # ==============================================================================