wehub-resource-sync
429 lines
13 KiB
Python
429 lines
13 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, get_device_place, is_custom_device
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import paddle
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def ref_std(x, axis=None, unbiased=True, keepdim=False):
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ddof = 1 if unbiased else 0
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if isinstance(axis, int):
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axis = (axis,)
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if axis is not None:
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axis = tuple(axis)
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return np.std(x, axis=axis, ddof=ddof, keepdims=keepdim)
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class TestStdAPI(unittest.TestCase):
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def setUp(self):
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self.dtype = 'float64'
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self.shape = [1, 3, 4, 10]
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self.axis = [1, 3]
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self.keepdim = False
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self.unbiased = True
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self.set_attrs()
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self.x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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self.place = get_device_place()
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def set_attrs(self):
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pass
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def static(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.shape, self.dtype)
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out = paddle.std(x, self.axis, self.unbiased, self.keepdim)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x}, fetch_list=[out])
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return res[0]
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def dygraph(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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out = paddle.std(x, self.axis, self.unbiased, self.keepdim)
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paddle.enable_static()
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return out.numpy()
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def test_api(self):
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out_ref = ref_std(self.x, self.axis, self.unbiased, self.keepdim)
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out_dygraph = self.dygraph()
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out_static = self.static()
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for out in [out_dygraph, out_static]:
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np.testing.assert_allclose(out_ref, out, rtol=1e-05)
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self.assertTrue(np.equal(out_ref.shape, out.shape).all())
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class TestStdAPI2(OpTest):
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def setUp(self):
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self.python_api = paddle.std
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self.op_type = "std"
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self.prim_op_type = "prim"
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self.init_dtype_type()
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self.attrs = {
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'axis': self.axis,
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'unbiased': self.unbiased,
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'keepdim': self.keepdim,
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}
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x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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out = ref_std(
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x, axis=self.axis, unbiased=self.unbiased, keepdim=self.keepdim
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)
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self.inputs = {'x': x}
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self.outputs = {'out': out}
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def std_wrapper(x):
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return paddle.std(
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x, axis=self.axis, unbiased=self.unbiased, keepdim=self.keepdim
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)
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self.python_api = std_wrapper
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self.public_python_api = std_wrapper
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def init_dtype_type(self):
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self.dtype = 'float64'
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self.shape = [1, 3, 4, 10]
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self.axis = [1, 3]
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self.keepdim = False
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self.unbiased = True
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def test_check_output(self):
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self.check_output_with_place(
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paddle.CPUPlace(),
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check_prim=True,
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check_pir=True,
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check_symbol_infer=True,
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check_prim_pir=True,
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)
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if paddle.is_compiled_with_cuda():
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self.check_output_with_place(
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paddle.CUDAPlace(0),
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check_prim=True,
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check_pir=True,
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check_symbol_infer=True,
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check_prim_pir=True,
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)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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paddle.CPUPlace(),
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['x'],
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'out',
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check_prim=False,
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check_pir=True,
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check_prim_pir=False,
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)
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if paddle.core.is_compiled_with_cuda():
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self.check_grad_with_place(
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paddle.CUDAPlace(0),
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['x'],
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'out',
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check_prim=False,
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check_pir=True,
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check_prim_pir=False,
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)
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class TestStdAPI_dtype(TestStdAPI):
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def set_attrs(self):
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self.dtype = 'float32'
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class TestStdAPI_axis_int(TestStdAPI):
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def set_attrs(self):
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self.axis = 2
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class TestStdAPI_axis_list(TestStdAPI):
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def set_attrs(self):
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self.axis = [1, 2]
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class TestStdAPI_axis_tuple(TestStdAPI):
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def set_attrs(self):
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self.axis = (1, 3)
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class TestStdAPI_keepdim(TestStdAPI):
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def set_attrs(self):
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self.keepdim = False
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class TestStdAPI_unbiased(TestStdAPI):
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def set_attrs(self):
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self.unbiased = False
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class TestStdAPI_alias(unittest.TestCase):
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def test_alias(self):
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paddle.disable_static()
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x = paddle.to_tensor(np.array([10, 12], 'float32'))
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out1 = paddle.std(x).numpy()
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out2 = paddle.tensor.std(x).numpy()
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out3 = paddle.tensor.stat.std(x).numpy()
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np.testing.assert_allclose(out1, out2, rtol=1e-05)
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np.testing.assert_allclose(out1, out3, rtol=1e-05)
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paddle.enable_static()
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class TestStdAPI_Compatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(2026)
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self.dtype = 'float32'
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self.shape = [1, 3, 4, 10]
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self.x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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self.place = get_device_place()
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def test_dygraph_compatibility(self):
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paddle.disable_static()
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x = paddle.tensor(self.x)
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# input arg
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out1_1 = paddle.std(x=x)
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out1_2 = paddle.std(input=x)
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np.testing.assert_allclose(out1_1.numpy(), out1_2.numpy(), rtol=1e-05)
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# dim arg
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out2_1 = paddle.std(x, axis=3)
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out2_2 = paddle.std(x, dim=3)
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np.testing.assert_allclose(out2_1.numpy(), out2_2.numpy(), rtol=1e-05)
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# out arg
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out3_1 = paddle.empty([])
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out3_2 = paddle.std(x, out=out3_1)
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np.testing.assert_allclose(out3_1.numpy(), out3_2.numpy(), rtol=1e-05)
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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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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.shape, self.dtype)
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# input arg
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out1_1 = paddle.std(x=x)
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out1_2 = paddle.std(input=x)
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# dim arg
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out2_1 = paddle.std(x, axis=3)
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out2_2 = paddle.std(x, dim=3)
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exe = paddle.static.Executor(self.place)
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res = exe.run(
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feed={'x': self.x}, fetch_list=[out1_1, out1_2, out2_1, out2_2]
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)
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np.testing.assert_allclose(res[0], res[1], rtol=1e-05)
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np.testing.assert_allclose(res[2], res[3], rtol=1e-05)
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class TestStdAPI_Correction(unittest.TestCase):
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def setUp(self):
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np.random.seed(2026)
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self.dtype = 'float32'
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self.shape = [1, 3, 4, 10]
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self.set_attrs()
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self.x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
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if self.axis:
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axis = tuple(self.axis)
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self.ref_out = np.std(self.x, axis, ddof=self.correction)
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else:
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self.ref_out = np.std(self.x, ddof=self.correction)
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self.place = get_device_place()
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def set_attrs(self):
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self.correction = 1
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self.axis = None
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def test_dygraph_correction(self):
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paddle.disable_static()
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x = paddle.tensor(self.x)
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if self.axis:
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out = paddle.std(x, self.axis, correction=self.correction)
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else:
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out = paddle.std(x, correction=self.correction)
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np.testing.assert_allclose(out.numpy(), self.ref_out, rtol=1e-05)
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paddle.enable_static()
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def test_static_correction(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.shape, self.dtype)
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if self.axis:
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out = paddle.std(x, self.axis, correction=self.correction)
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else:
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out = paddle.std(x, correction=self.correction)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'x': self.x}, fetch_list=[out])
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np.testing.assert_allclose(res[0], self.ref_out, rtol=1e-05)
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class TestStdAPI_Correction2(TestStdAPI_Correction):
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def set_attrs(self):
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self.correction = 2
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self.axis = None
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class TestStdAPI_CorrectionFloat(TestStdAPI_Correction):
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def set_attrs(self):
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self.correction = 1.5
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self.axis = None
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class TestStdAPI_CorrectionWithAxis(TestStdAPI_Correction):
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def set_attrs(self):
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self.correction = 0
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self.axis = [1, 2]
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class TestStdError(unittest.TestCase):
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def test_error(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', [2, 3, 4], 'int32')
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self.assertRaises(TypeError, paddle.std, x)
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class Testfp16Std(unittest.TestCase):
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def test_fp16_with_gpu(self):
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paddle.enable_static()
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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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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input = np.random.random([12, 14]).astype("float16")
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x = paddle.static.data(
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name="x", shape=[12, 14], dtype="float16"
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)
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y = paddle.std(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": input,
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},
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fetch_list=[y],
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)
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class TestStdAPI_ZeroSize1(unittest.TestCase):
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def init_data(self):
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self.x_shape = [0]
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self.dtype = 'float64'
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self.expact_out = np.nan
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self.x = np.random.uniform(-1, 1, self.x_shape).astype(self.dtype)
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def test_zerosize(self):
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self.init_data()
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paddle.disable_static()
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x = paddle.to_tensor(np.random.random(self.x_shape))
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out1 = paddle.std(x).numpy()
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np.testing.assert_allclose(out1, self.expact_out, equal_nan=True)
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paddle.enable_static()
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def test_static_zero(self):
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paddle.enable_static()
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self.init_data()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.x_shape, self.dtype)
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out = paddle.std(x)
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exe = paddle.static.Executor(paddle.CPUPlace())
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res = exe.run(feed={'X': self.x}, fetch_list=[out])
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np.testing.assert_allclose(self.expact_out, res[0], rtol=1e-05)
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paddle.disable_static()
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class TestStdAPI_UnBiased1(unittest.TestCase):
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def init_data(self):
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self.x_shape = [1]
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# x = torch.randn([1])
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# res= torch.std(x,correction=0) Here, res is 0.
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self.expect_out = 0.0
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def test_api(self):
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self.init_data()
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paddle.disable_static()
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x = paddle.to_tensor(np.random.random(self.x_shape))
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out1 = paddle.std(x, unbiased=False).numpy()
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np.testing.assert_allclose(out1, self.expect_out, equal_nan=True)
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paddle.enable_static()
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class TestStdAPI_UnBiased2(unittest.TestCase):
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def init_data(self):
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self.x_shape = [1]
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# x = torch.randn([1])
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# res= torch.std(x,correction=1) Here, res is 0.
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self.expect_out = np.nan
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def test_api(self):
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self.init_data()
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paddle.disable_static()
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x = paddle.to_tensor(np.random.random(self.x_shape))
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out1 = paddle.std(x, unbiased=True).numpy()
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np.testing.assert_allclose(out1, self.expect_out, equal_nan=True)
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paddle.enable_static()
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class TestVarAPI_Backward1(unittest.TestCase):
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def test_api(self):
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paddle.disable_static()
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self.shape = []
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self.axis = []
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self.x = np.random.uniform(-1, 1, self.shape).astype('float64')
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paddle.set_device(paddle.CPUPlace())
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out_ref = ref_std(self.x, self.axis, True, False)
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x = paddle.to_tensor(self.x)
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x.stop_gradient = False
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out = paddle.std(x, self.axis, True, False)
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out.sum().backward()
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paddle.enable_static()
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class TestVarAPI_Backward2(unittest.TestCase):
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def test_api(self):
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paddle.disable_static()
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self.shape = [2]
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self.axis = []
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self.x = np.random.uniform(-1, 1, self.shape).astype('float64')
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paddle.set_device(paddle.CPUPlace())
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out_ref = ref_std(self.x, self.axis, True, False)
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x = paddle.to_tensor(self.x)
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x.stop_gradient = False
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out = paddle.std(x, self.axis, True, False)
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out.sum().backward()
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paddle.enable_static()
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class TestStdAPI_Backward_ZeroSize1(unittest.TestCase):
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def test_api(self):
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paddle.disable_static()
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self.shape = [1, 3, 0, 10]
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self.axis = [1, 3]
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self.x = np.random.uniform(-1, 1, self.shape).astype('float64')
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paddle.set_device(paddle.CPUPlace())
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out_ref = ref_std(self.x, self.axis, True, False)
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x = paddle.to_tensor(self.x)
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x.stop_gradient = False
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out = paddle.std(x, self.axis, True, False)
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out.sum().backward()
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paddle.enable_static()
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if __name__ == '__main__':
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unittest.main()
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