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211 lines
7.4 KiB
211 lines
7.4 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
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import paddle
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import paddle.fluid as fluid
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def p_norm(x, axis, porder, keepdims=False):
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if axis is None: axis = -1
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xp = np.power(np.abs(x), porder)
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s = np.sum(xp, axis=axis, keepdims=keepdims)
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r = np.power(s, 1.0 / porder)
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return r
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def frobenius_norm(x, axis=None, keepdims=False):
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if isinstance(axis, list): axis = tuple(axis)
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if axis is None: axis = (-2, -1)
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r = np.linalg.norm(x, ord='fro', axis=axis, keepdims=keepdims)
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return r
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class TestFrobeniusNormOp(OpTest):
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def setUp(self):
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self.op_type = "frobenius_norm"
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self.init_test_case()
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x = (np.random.random(self.shape) + 1.0).astype(self.dtype)
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norm = frobenius_norm(x, self.axis, self.keepdim)
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self.reduce_all = (len(self.axis) == len(self.shape))
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self.inputs = {'X': x}
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self.attrs = {
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'dim': list(self.axis),
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'keep_dim': self.keepdim,
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'reduce_all': self.reduce_all
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}
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self.outputs = {'Out': norm}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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def init_test_case(self):
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self.shape = [2, 3, 4, 5]
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self.axis = (1, 2)
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self.keepdim = False
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self.dtype = "float64"
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class TestFrobeniusNormOp2(TestFrobeniusNormOp):
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def init_test_case(self):
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self.shape = [5, 5, 5]
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self.axis = (0, 1)
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self.keepdim = True
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self.dtype = "float32"
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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class TestPnormOp(OpTest):
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def setUp(self):
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self.op_type = "p_norm"
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self.init_test_case()
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x = (np.random.random(self.shape) + 0.5).astype(self.dtype)
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norm = p_norm(x, self.axis, self.porder, self.keepdim)
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self.inputs = {'X': x}
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self.attrs = {
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'epsilon': self.epsilon,
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'axis': self.axis,
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'keepdim': self.keepdim,
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'porder': float(self.porder)
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}
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self.outputs = {'Out': norm}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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def init_test_case(self):
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self.shape = [2, 3, 4, 5]
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self.axis = 1
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self.epsilon = 1e-12
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self.porder = 2.0
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self.keepdim = False
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self.dtype = "float64"
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class TestPnormOp2(TestPnormOp):
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def init_test_case(self):
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self.shape = [3, 20, 3]
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self.axis = 2
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self.epsilon = 1e-12
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self.porder = 2.0
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self.keepdim = True
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self.dtype = "float32"
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def test_check_grad(self):
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self.check_grad(['X'], 'Out')
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def run_out(self, p, axis, shape_x, shape_y, dtype):
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with fluid.program_guard(fluid.Program()):
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data1 = fluid.data(name="X", shape=shape_x, dtype=dtype)
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data2 = fluid.data(name="Y", shape=shape_y, dtype=dtype)
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out = paddle.norm(input=data1, p=p, axis=axis, out=data2)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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result = exe.run(feed={"X": np.random.rand(*shape_x).astype(dtype)},
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fetch_list=[data2, out])
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self.assertEqual((result[0] == result[1]).all(), True)
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def run_fro(self, p, axis, shape_x, dtype):
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with fluid.program_guard(fluid.Program()):
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data = fluid.data(name="X", shape=shape_x, dtype=dtype)
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out = paddle.norm(input=data, p=p, axis=axis)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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np_input = (np.random.rand(*shape_x) + 1.0).astype(dtype)
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expected_result = frobenius_norm(np_input, axis=axis)
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result, = exe.run(feed={"X": np_input}, fetch_list=[out])
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self.assertEqual((np.abs(result - expected_result) < 1e-6).all(), True)
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def run_pnorm(self, p, axis, shape_x, dtype):
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with fluid.program_guard(fluid.Program()):
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data = fluid.data(name="X", shape=shape_x, dtype=dtype)
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out = paddle.norm(input=data, p=p, axis=axis)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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np_input = (np.random.rand(*shape_x) + 1.0).astype(dtype)
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expected_result = p_norm(np_input, porder=p, axis=axis).astype(dtype)
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result, = exe.run(feed={"X": np_input}, fetch_list=[out])
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self.assertEqual((np.abs(result - expected_result) < 1e-6).all(), True)
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class API_NormTest(unittest.TestCase):
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def test_output_result(self):
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run_out(self, p=2, axis=1, shape_x=[3, 4], shape_y=[3], dtype="float32")
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run_out(
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self,
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p='fro',
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axis=None,
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shape_x=[3, 4],
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shape_y=[1],
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dtype="float32")
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def test_basic(self):
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run_fro(self, p='fro', axis=None, shape_x=[3, 3, 4], dtype="float32")
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run_fro(self, p='fro', axis=[0, 1], shape_x=[3, 3, 4], dtype="float64")
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run_pnorm(self, p=2, axis=None, shape_x=[3, 4], dtype="float32")
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run_pnorm(self, p=2, axis=1, shape_x=[3, 4], dtype="float64")
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def test_name(self):
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with fluid.program_guard(fluid.Program()):
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x = fluid.data(name="x", shape=[10, 10], dtype="float32")
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y_1 = paddle.norm(x, p='fro', name='frobenius_name')
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y_2 = paddle.norm(x, p=2, name='pnorm_name')
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self.assertEqual(('frobenius_name' in y_1.name), True)
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self.assertEqual(('pnorm_name' in y_2.name), True)
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def test_errors(self):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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def err_dtype(p, shape_x, xdtype, out=None):
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data = fluid.data(shape=shape_x, dtype=xdtype)
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paddle.norm(data, p=p, out=out)
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self.assertRaises(TypeError, err_dtype, "fro", [2, 2], "int64")
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out = fluid.data(name="out", shape=[1], dtype="int64")
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self.assertRaises(TypeError, err_dtype, "fro", [2, 2], "float64",
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out)
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self.assertRaises(TypeError, err_dtype, 2, [10], "int64")
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self.assertRaises(TypeError, err_dtype, 2, [10], "float64", out)
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data = fluid.data(name="data_2d", shape=[2, 2], dtype="float64")
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self.assertRaises(ValueError, paddle.norm, data, p="unsupport norm")
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self.assertRaises(ValueError, paddle.norm, data, p=[1])
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self.assertRaises(ValueError, paddle.norm, data, p=[1], axis=-1)
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self.assertRaises(
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ValueError, paddle.norm, data, p='unspport', axis=[-2, -1])
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data = fluid.data(name="data_3d", shape=[2, 2, 2], dtype="float64")
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self.assertRaises(
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ValueError, paddle.norm, data, p='unspport', axis=[-2, -1])
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self.assertRaises(
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ValueError, paddle.norm, data, p='unspport', axis=[-3, -2, -1])
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if __name__ == '__main__':
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unittest.main()
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