wehub-resource-sync
238 lines
7.7 KiB
Python
238 lines
7.7 KiB
Python
# Copyright (c) 2023 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_devices
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import paddle
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def output_hist(out):
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hist, _ = np.histogram(out, range=(-1, 1))
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hist = hist.astype("float32")
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hist /= float(out.size)
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prob = 0.1 * np.ones(10)
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return hist, prob
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class TestNormalRandomInplaceOpDtype(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_dtype(self):
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def test_fp32():
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tensor_fp32 = paddle.ones(self.shape, dtype=paddle.float32)
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tensor_fp32.normal_()
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self.assertEqual(tensor_fp32.dtype, paddle.float32)
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def test_fp64():
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tensor_fp64 = paddle.ones(self.shape, paddle.float64)
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tensor_fp64.normal_()
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self.assertEqual(tensor_fp64.dtype, paddle.float64)
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for place in get_devices():
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paddle.set_device(place)
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test_fp32()
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test_fp64()
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class TestNormalRandomComplexInplaceOpDtype(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_dtype(self):
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def test_fp32():
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tensor_fp32 = paddle.ones(self.shape).astype(paddle.complex64)
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tensor_fp32.normal_()
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self.assertEqual(tensor_fp32.dtype, paddle.complex64)
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def test_fp64():
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tensor_fp64 = paddle.ones(self.shape).astype(paddle.complex128)
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tensor_fp64.normal_()
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self.assertEqual(tensor_fp64.dtype, paddle.complex128)
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for place in get_devices():
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paddle.set_device(place)
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test_fp32()
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test_fp64()
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class TestNormalRandomInplaceOpIsInplace(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_is_inplace(self):
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tensor_a = paddle.ones(self.shape)
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tensor_b = tensor_a.normal_()
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self.assertTrue(tensor_a is tensor_b)
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class TestNormalRandomInplaceOpSeedIsZero(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_not_equal(self):
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tensor = paddle.ones(self.shape)
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tensor.normal_()
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tensor_data_first = tensor.numpy()
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tensor.normal_()
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tensor_data_second = tensor.numpy()
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self.assertFalse((tensor_data_first == tensor_data_second).all())
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class TestNormalRandomComplexInplaceOpSeedIsZero(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_not_equal(self):
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tensor = paddle.ones(self.shape).astype(paddle.complex64)
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tensor.normal_()
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tensor_data_first = tensor.numpy()
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tensor.normal_()
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tensor_data_second = tensor.numpy()
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self.assertFalse((tensor_data_first == tensor_data_second).all())
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class TestNormalRandomInplaceOpShape(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def test_normal_inplace_op_shape(self):
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tensor = paddle.ones(self.shape)
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tensor.normal_()
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tensor_shape_np = np.array(tensor.shape)
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origin_shape = np.array(self.shape)
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self.assertTrue((tensor_shape_np == origin_shape).all())
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class TestNormalRandomInplaceOpDistribution(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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self.mean = -3
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self.std = 5
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def test_normal_inplace_op_distribution(self):
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tensor = paddle.ones(self.shape)
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tensor.normal_(self.mean, self.std)
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ones = paddle.ones(self.shape)
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zeros = paddle.zeros(self.shape)
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all_num = self.shape[0] * self.shape[1]
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std_probs = [0.68, 0.95, 0.997]
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for index, prob in enumerate(std_probs):
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left = self.mean - (index + 1) * self.std
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right = self.mean + (index + 1) * self.std
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cond = paddle.logical_and(tensor >= left, tensor <= right)
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c_sum = paddle.where(cond, ones, zeros).sum()
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np.testing.assert_allclose((c_sum / all_num), prob, 1e-2)
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class TestNormalRandomComplexInplaceOpDistribution(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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self.mean = -3 - 3j
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self.std = 5
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def test_normal_inplace_op_distribution(self):
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tensor = paddle.ones(self.shape).astype(paddle.complex64)
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tensor.normal_(self.mean, self.std)
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mean = np.mean(tensor.numpy())
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var = np.var(tensor.numpy())
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var_real = np.var(tensor.real().numpy())
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var_imag = np.var(tensor.imag().numpy())
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mean_ref = self.mean
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var_ref = self.std**2
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np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
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np.testing.assert_allclose(var_ref, var, rtol=0.2, atol=0.2)
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np.testing.assert_allclose(var_ref / 2.0, var_real, rtol=0.2, atol=0.2)
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np.testing.assert_allclose(var_ref / 2.0, var_imag, rtol=0.2, atol=0.2)
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class TestNormalRandomInplaceOpEmptyTensor(unittest.TestCase):
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def test_normal_inplace_op_empty_tensor(self):
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test_shapes = [(200, 0), (0, 200)]
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for place in get_devices():
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paddle.set_device(place)
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for test_shape in test_shapes:
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tensor = paddle.empty(shape=test_shape)
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tensor.normal_()
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tensor_shape_np = np.array(tensor.shape)
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origin_shape = np.array(test_shape)
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self.assertTrue((tensor_shape_np == origin_shape).all())
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class TestNormalRandomInplaceGrad(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def run_(self):
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def test_grad():
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tensor_a = paddle.ones(self.shape)
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tensor_a.stop_gradient = False
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tensor_b = tensor_a * 0.5
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tensor_b.retain_grads()
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tensor_b.normal_(mean=-2, std=2)
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loss = tensor_b.sum()
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loss.backward()
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normal_grad = tensor_b.grad.numpy()
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self.assertTrue((normal_grad == 0).all())
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for place in get_devices():
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paddle.set_device(place)
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test_grad()
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def test_normal_inplace_grad(self):
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self.run_()
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class TestNormalRandomComplexInplaceGrad(unittest.TestCase):
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def setUp(self):
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self.shape = (1000, 784)
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def run_(self):
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def test_grad():
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tensor_a = paddle.ones(self.shape).astype(paddle.complex64)
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tensor_a.stop_gradient = False
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tensor_b = tensor_a * 0.5
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tensor_b.retain_grads()
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tensor_b.normal_(mean=-2 - 2j, std=2)
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loss = tensor_b.sum()
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loss.backward()
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normal_grad = tensor_b.grad.numpy()
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self.assertTrue((normal_grad.real == 0).all())
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self.assertTrue((normal_grad.imag == 0).all())
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for place in get_devices():
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paddle.set_device(place)
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test_grad()
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def test_normal_inplace_grad(self):
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self.run_()
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class TestNormalRandomComplexInplaceErrors(unittest.TestCase):
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def test_dtype_error(self):
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mean = 1 + 1j
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self.assertRaises(TypeError, paddle.normal, mean, dtype='float32')
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def test_incorrect_mean(self):
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mean = 2 + 0.5j
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self.assertRaises(ValueError, paddle.normal, mean)
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if __name__ == '__main__':
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unittest.main()
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