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145 lines
4.9 KiB
145 lines
4.9 KiB
# Copyright (c) 2018 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.core as core
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import paddle.fluid as fluid
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from paddle.fluid import Program, program_guard
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class TestMeanOp(OpTest):
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def setUp(self):
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self.op_type = "mean"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
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self.outputs = {'Out': np.mean(self.inputs["X"])}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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self.check_output()
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def test_checkout_grad(self):
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self.check_grad(['X'], 'Out')
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class TestMeanOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of mean_op must be Variable.
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input1 = 12
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self.assertRaises(TypeError, fluid.layers.mean, input1)
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# The input dtype of mean_op must be float16, float32, float64.
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input2 = fluid.layers.data(
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name='input2', shape=[12, 10], dtype="int32")
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self.assertRaises(TypeError, fluid.layers.mean, input2)
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input3 = fluid.layers.data(
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name='input3', shape=[4], dtype="float16")
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fluid.layers.softmax(input3)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16MeanOp(TestMeanOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-3)
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def test_checkout_grad(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ['X'], 'Out', max_relative_error=0.8)
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class TestMeanAPI(unittest.TestCase):
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"""
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test paddle.tensor.stat.mean
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"""
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def setUp(self):
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self.x_shape = [2, 3, 4, 5]
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self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
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self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
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else paddle.CPUPlace()
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def test_api_static(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.data('X', self.x_shape)
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out1 = paddle.mean(x)
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out2 = paddle.tensor.mean(x)
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out3 = paddle.tensor.stat.mean(x)
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axis = np.arange(len(self.x_shape)).tolist()
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out4 = paddle.mean(x, axis)
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out5 = paddle.mean(x, tuple(axis))
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x},
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fetch_list=[out1, out2, out3, out4, out5])
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out_ref = np.mean(self.x)
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for out in res:
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self.assertEqual(np.allclose(out, out_ref, rtol=1e-04), True)
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def test_api_dygraph(self):
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paddle.disable_static(self.place)
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def test_case(x, axis=None, keepdim=False):
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x_tensor = paddle.to_variable(x)
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out = paddle.mean(x_tensor, axis, keepdim)
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if isinstance(axis, list):
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axis = tuple(axis)
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if len(axis) == 0:
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axis = None
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out_ref = np.mean(x, axis, keepdims=keepdim)
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self.assertEqual(
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np.allclose(
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out.numpy(), out_ref, rtol=1e-04), True)
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test_case(self.x)
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test_case(self.x, [])
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test_case(self.x, -1)
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test_case(self.x, keepdim=True)
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test_case(self.x, 2, keepdim=True)
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test_case(self.x, [0, 2])
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test_case(self.x, (0, 2))
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test_case(self.x, [0, 1, 2, 3])
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paddle.enable_static()
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def test_errors(self):
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paddle.disable_static()
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x = np.random.uniform(-1, 1, [10, 12]).astype('float32')
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x = paddle.to_tensor(x)
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self.assertRaises(Exception, paddle.mean, x, -3)
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self.assertRaises(Exception, paddle.mean, x, 2)
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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.data('X', [10, 12], 'int32')
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self.assertRaises(TypeError, paddle.mean, x)
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if __name__ == "__main__":
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unittest.main()
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