wehub-resource-sync
200 lines
7.0 KiB
Python
200 lines
7.0 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 get_device_place, is_custom_device
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import paddle
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import paddle.nn.functional as F
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from paddle import base
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def p_normalize(x, axis=1, p=2, epsilon=1e-12, keepdims=True):
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xp = np.power(np.abs(x), p)
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s = np.sum(xp, axis=axis, keepdims=keepdims)
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r = np.maximum(np.power(s, 1.0 / p), epsilon)
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return x / r
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class TestNNFunctionalNormalize(unittest.TestCase):
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def setUp(self):
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self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
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self.input_np2 = np.array([0.0, 0.0]).astype(np.float32)
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self.expected0 = p_normalize(self.input_np)
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self.expected1 = p_normalize(self.input_np, p=1.5)
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self.expected2 = p_normalize(self.input_np, axis=0)
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self.expected3 = p_normalize(self.input_np2, axis=0)
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def run_imperative(self):
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x = paddle.to_tensor(self.input_np)
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y = F.normalize(x)
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np.testing.assert_allclose(y.numpy(), self.expected0, rtol=1e-05)
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y = F.normalize(x, p=1.5)
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np.testing.assert_allclose(y.numpy(), self.expected1, rtol=1e-05)
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y = F.normalize(x, axis=0)
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np.testing.assert_allclose(y.numpy(), self.expected2, rtol=1e-05)
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x = paddle.to_tensor(self.input_np2)
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y = F.normalize(x, axis=0)
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np.testing.assert_allclose(y.numpy(), self.expected3, rtol=1e-05)
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self.assertRaisesRegex(
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ValueError,
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r"Attr\(axis\) value should be in range \[-R, R-1\]",
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F.normalize,
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x,
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)
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def run_static(self, use_gpu=False):
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x = paddle.static.data(name='input', shape=[10, 10], dtype='float32')
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x2 = paddle.static.data(name='input2', shape=[2], dtype='float32')
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result0 = F.normalize(x)
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result1 = F.normalize(x, p=1.5)
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result2 = F.normalize(x, axis=0)
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result3 = F.normalize(x, name='aaa')
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result4 = F.normalize(x2, axis=0)
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place = get_device_place() if use_gpu else base.CPUPlace()
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exe = base.Executor(place)
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exe.run(paddle.static.default_startup_program())
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static_result = exe.run(
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feed={"input": self.input_np, "input2": self.input_np2},
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fetch_list=[result0, result1, result2, result4],
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)
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np.testing.assert_allclose(static_result[0], self.expected0, rtol=1e-05)
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np.testing.assert_allclose(static_result[1], self.expected1, rtol=1e-05)
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np.testing.assert_allclose(static_result[2], self.expected2, rtol=1e-05)
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np.testing.assert_allclose(static_result[3], self.expected3, rtol=1e-05)
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self.assertRaises(ValueError, F.normalize, x2)
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def test_cpu(self):
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paddle.disable_static(place=paddle.base.CPUPlace())
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self.run_imperative()
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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self.run_static()
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def test_gpu(self):
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if not (base.core.is_compiled_with_cuda() or is_custom_device()):
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return
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paddle.disable_static(place=get_device_place())
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self.run_imperative()
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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self.run_static(use_gpu=True)
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class TestNormalizeAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(2025)
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self.places = ['cpu', get_device_place()]
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self.shape = [2, 3, 4]
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self.dtype = "float32"
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self.init_data()
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def init_data(self):
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self.np_x = np.random.rand(*self.shape).astype(self.dtype)
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self.p = 2
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self.axis = 1
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self.epsilon = 1e-12
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def test_dygraph_Compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_x)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.nn.functional.normalize(
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x, self.p, self.axis, self.epsilon
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)
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paddle_dygraph_out.append(out1)
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# Key words args (kwargs) for paddle
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out2 = paddle.nn.functional.normalize(
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x=x, p=self.p, axis=self.axis, epsilon=self.epsilon
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)
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paddle_dygraph_out.append(out2)
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# Key words args for torch compatibility
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out3 = paddle.nn.functional.normalize(
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input=x, p=self.p, dim=self.axis, eps=self.epsilon
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)
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paddle_dygraph_out.append(out3)
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# Key words args for out
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out4 = paddle.zeros_like(x)
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paddle.nn.functional.normalize(
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x, self.p, self.axis, self.epsilon, out=out4
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)
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paddle_dygraph_out.append(out4)
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# Numpy reference output
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ref_out = self.np_x / np.maximum(
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np.linalg.norm(
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self.np_x, ord=self.p, axis=self.axis, keepdims=True
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),
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self.epsilon,
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)
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(
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ref_out, out.numpy(), rtol=1e-05, atol=1e-08
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)
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paddle.enable_static()
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def test_static_Compatibility(self):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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# Position args (args)
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out1 = paddle.nn.functional.normalize(
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x, self.p, self.axis, self.epsilon
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)
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# Key words args (kwargs) for paddle
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out2 = paddle.nn.functional.normalize(
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x=x, p=self.p, axis=self.axis, epsilon=self.epsilon
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)
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# Key words args for torch compatibility
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out3 = paddle.nn.functional.normalize(
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input=x, p=self.p, dim=self.axis, eps=self.epsilon
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)
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# Numpy reference output
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ref_out = self.np_x / np.maximum(
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np.linalg.norm(
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self.np_x, ord=self.p, axis=self.axis, keepdims=True
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),
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self.epsilon,
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)
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fetch_list = [out1, out2, out3]
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for place in self.places:
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exe = paddle.base.Executor(place)
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fetches = exe.run(
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main,
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feed={"x": self.np_x},
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fetch_list=fetch_list,
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)
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for out in fetches:
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np.testing.assert_allclose(
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out, ref_out, rtol=1e-05, atol=1e-08
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)
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if __name__ == "__main__":
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unittest.main()
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