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334 lines
10 KiB
334 lines
10 KiB
# Copyright (c) 2019 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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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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import gradient_checker
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from decorator_helper import prog_scope
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paddle.enable_static()
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class TestMulGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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prog = fluid.Program()
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with fluid.program_guard(prog):
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x = layers.create_parameter(dtype="float64", shape=[2, 8], name='x')
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y = layers.create_parameter(dtype="float64", shape=[8, 4], name='y')
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z = layers.mul(x=x, y=y)
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gradient_checker.grad_check([x, y], z, place=place)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestSliceOpDoubleGradCheck(unittest.TestCase):
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def func(self, place):
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self.config()
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out = fluid.layers.slice(
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self.inputs, axes=self.axes, starts=self.starts, ends=self.ends)
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gradient_checker.double_grad_check(
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[self.inputs], out, x_init=self.x_arr, place=place)
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def config(self):
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self.starts = [1, 0, -1]
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self.ends = [3, 3, 6]
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self.axes = [0, 1, 2]
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self.x_arr = np.random.random([3, 4, 5, 2]).astype("float64")
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self.inputs = layers.create_parameter(
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dtype="float64", shape=[3, 4, 5, 2], name='x')
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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self.func(place)
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class TestSliceOpDoubleGradCheckCase3(TestSliceOpDoubleGradCheck):
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def config(self):
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self.starts = [1, -1, 1]
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self.ends = [3, 3, 3]
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self.axes = [0, 1, 2]
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self.x_arr = np.random.random([3, 3, 3]).astype("float64")
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self.inputs = layers.create_parameter(
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dtype="float64", shape=[3, 3, 3], name='x3')
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class TestReduceMeanWithDimDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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shape = [7, 11]
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eps = 0.05
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dtype = np.float64
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x = layers.data('x', shape, False, dtype)
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x.persistable = True
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y = layers.reduce_mean(x, dim=0)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], y, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestReduceSumWithDimDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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shape = [7, 11]
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eps = 0.05
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dtype = np.float64
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x = layers.data('x', shape, False, dtype)
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x.persistable = True
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y = layers.reduce_sum(x, dim=0)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], y, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestMulDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not inlcude -1.
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x_shape = [7, 11]
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y_shape = [11, 9]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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y = layers.data('y', y_shape, False, dtype)
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y.persistable = True
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out = layers.mul(x, y)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestMatmulDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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eps = 0.005
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x_shapes = [[2], [2, 3], [2, 4, 3], [2, 3, 4, 5], [2, 3, 4]]
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y_shapes = [[2], [3, 2], [2, 4, 5], [2, 3, 3, 5], [4, 3]]
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transpose_xs = [False, True, True, False, False]
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transpose_ys = [False, True, False, True, False]
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dtype = np.float64
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typename = "float64"
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for i, (x_shape, y_shape, transpose_x, transpose_y) \
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in enumerate(zip(x_shapes, y_shapes, transpose_xs, transpose_ys)):
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x = layers.create_parameter(
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dtype=typename, shape=x_shape, name='x{}'.format(i))
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y = layers.create_parameter(
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dtype=typename, shape=y_shape, name='y{}'.format(i))
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out = layers.matmul(
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x, y, transpose_x, transpose_y, name='out{}'.format(i))
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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y_arr = np.random.uniform(-1, 1, y_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, y], out, x_init=[x_arr, y_arr], place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestReshapeDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [3, 12]
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expand_times = [4, 9]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = layers.expand(x, expand_times)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestExpandDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [3, 12]
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new_shape = [4, 9]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = layers.reshape(x, new_shape)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestTileDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [3, 12]
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repeat_times = [4, 9]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = paddle.tile(x, repeat_times)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestExpandV2DoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [1, 12]
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new_shape = [4, 12]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = paddle.expand(x, new_shape)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestSqueezeDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [1, 3, 1, 40]
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axes = [0, 2]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = paddle.squeeze(x, axes)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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class TestUnsqueezeDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [3, 40]
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axes = [1, 2]
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eps = 0.005
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dtype = np.float64
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x = layers.data('x', x_shape, False, dtype)
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x.persistable = True
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out = paddle.unsqueeze(x, axes)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x], out, x_init=x_arr, place=place, eps=eps)
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def test_grad(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for p in places:
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self.func(p)
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if __name__ == "__main__":
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unittest.main()
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