wehub-resource-sync
128 lines
4.3 KiB
Python
128 lines
4.3 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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class TestMVOp(OpTest):
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def setUp(self):
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self.op_type = "mv"
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self.python_api = paddle.mv
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self.init_config()
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self.inputs = {'X': self.x, 'Vec': self.vec}
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self.outputs = {'Out': np.dot(self.x, self.vec)}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X', 'Vec'], 'Out', check_pir=True)
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def init_config(self):
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self.x = np.random.random((2, 100)).astype("float64")
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self.vec = np.random.random(100).astype("float64")
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class TestMVOp_ZeroSize1(TestMVOp):
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def init_config(self):
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self.x = np.random.random((0, 100)).astype("float64")
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self.vec = np.random.random(100).astype("float64")
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class TestMVOp_ZeroSize2(TestMVOp):
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def init_config(self):
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self.x = np.random.random((100, 0)).astype("float64")
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self.vec = np.random.random(0).astype("float64")
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class TestMVOp_ZeroSize3(TestMVOp):
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def init_config(self):
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self.x = np.random.random((0, 0)).astype("float64")
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self.vec = np.random.random(0).astype("float64")
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class TestMVAPI(unittest.TestCase):
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def test_dygraph_api_out(self):
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paddle.disable_static()
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self.x_data = np.random.random((5, 100)).astype("float64")
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self.x = paddle.to_tensor(self.x_data)
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self.vec_data = np.random.random(100).astype("float64")
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self.vec = paddle.to_tensor(self.vec_data)
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z = paddle.mv(self.x, self.vec)
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np_z = z.numpy()
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z_expected = np.array(np.dot(self.x_data, self.vec_data))
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np.testing.assert_allclose(np_z, z_expected, rtol=1e-05)
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paddle.enable_static()
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def test_static_graph(self):
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for x_stop_gradient in [False, True]:
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for vec_stop_gradient in [False, True]:
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paddle.enable_static()
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self.input_x = np.random.rand(5, 100).astype("float64")
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self.input_vec = np.random.rand(100).astype("float64")
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_x = paddle.static.data(
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"x", shape=[5, 100], dtype="float64"
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)
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data_vec = paddle.static.data(
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"vec", shape=[100], dtype="float64"
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)
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data_x.stop_gradient = x_stop_gradient
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data_vec.stop_gradient = vec_stop_gradient
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result_vec = paddle.mv(data_x, data_vec)
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self.place = paddle.CPUPlace()
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "vec": self.input_vec},
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fetch_list=[result_vec],
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)
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z_expected = np.array(np.dot(self.input_x, self.input_vec))
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np.testing.assert_allclose(res, z_expected, rtol=1e-05)
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class TestMVError(unittest.TestCase):
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def test_input(self):
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def test_shape():
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paddle.enable_static()
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self.input_x = np.random.rand(5, 100).astype("float64")
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self.input_vec = np.random.rand(100).astype("float64")
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data_x = paddle.static.data("x", shape=[5, 100], dtype="float64")
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data_vec = paddle.static.data(
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"vec", shape=[100, 2], dtype="float64"
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)
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result_vec = paddle.mv(data_x, data_vec)
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self.assertRaises(ValueError, test_shape)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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