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81 lines
3.3 KiB
81 lines
3.3 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest, skip_check_grad_ci
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import paddle
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import paddle.fluid.core as core
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class ApiMinimumTest(unittest.TestCase):
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def setUp(self):
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if core.is_compiled_with_cuda():
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self.place = core.CUDAPlace(0)
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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(10, 15).astype("float32")
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self.input_y = np.random.rand(10, 15).astype("float32")
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self.input_z = np.random.rand(15).astype("float32")
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
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result_min = paddle.minimum(data_x, data_y)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"x": self.input_x,
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"y": self.input_y},
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fetch_list=[result_min])
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self.assertEqual((res == np.minimum(self.input_x, self.input_y)).all(),
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True)
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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data_x = paddle.static.data("x", shape=[10, 15], dtype="float32")
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data_z = paddle.static.data("z", shape=[15], dtype="float32")
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result_min = paddle.minimum(data_x, data_z, axis=1)
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exe = paddle.static.Executor(self.place)
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res, = exe.run(feed={"x": self.input_x,
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"z": self.input_z},
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fetch_list=[result_min])
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self.assertEqual((res == np.minimum(self.input_x, self.input_z)).all(),
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True)
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def test_dynamic_api(self):
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paddle.disable_static()
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np_x = np.array([10, 10]).astype('float64')
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x = paddle.to_tensor(self.input_x)
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y = paddle.to_tensor(self.input_y)
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z = paddle.minimum(x, y)
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np_z = z.numpy()
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z_expected = np.array(np.minimum(self.input_x, self.input_y))
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self.assertEqual((np_z == z_expected).all(), True)
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def test_broadcast_axis(self):
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paddle.disable_static()
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np_x = np.random.rand(5, 4, 3, 2).astype("float64")
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np_y = np.random.rand(4, 3).astype("float64")
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x = paddle.to_tensor(self.input_x)
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y = paddle.to_tensor(self.input_y)
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result_1 = paddle.minimum(x, y, axis=1)
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result_2 = paddle.minimum(x, y, axis=-2)
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self.assertEqual((result_1.numpy() == result_2.numpy()).all(), True)
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