wehub-resource-sync
581 lines
16 KiB
Python
581 lines
16 KiB
Python
# 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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import unittest
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import gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op_test import get_device_place, get_places, is_custom_device
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import paddle
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from paddle.base import core
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paddle.enable_static()
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class TestSliceOpDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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self.config()
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out = paddle.slice(
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self.inputs, axes=self.axes, starts=self.starts, ends=self.ends
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)
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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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)
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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 = paddle.static.data(
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dtype="float64", shape=[3, 4, 5, 2], name='x'
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)
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def test_grad(self):
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for place in get_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 = paddle.static.data(
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dtype="float64", shape=[3, 3, 3], name='x3'
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)
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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 = paddle.static.data('x', shape, dtype)
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x.persistable = True
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y = paddle.mean(x, axis=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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)
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def test_grad(self):
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for p in get_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 = paddle.static.data('x', shape, dtype)
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x.persistable = True
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y = paddle.sum(x, axis=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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)
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def test_grad(self):
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for p in get_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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new_shape = [4, 9]
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eps = 0.005
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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out = paddle.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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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestTileDoubleGradCheck(unittest.TestCase):
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def tile_wrapper(self, x):
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return paddle.tile(x[0], [4, 9])
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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 = paddle.static.data('x', x_shape, 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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)
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gradient_checker.double_grad_check_for_dygraph(
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self.tile_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestExpandV2DoubleGradCheck(unittest.TestCase):
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def expand_wrapper(self, x):
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return paddle.expand(x[0], [4, 12])
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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 = paddle.static.data('x', x_shape, 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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)
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gradient_checker.double_grad_check_for_dygraph(
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self.expand_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestSqueezeDoubleGradCheck(unittest.TestCase):
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def squeeze_wrapper(self, x):
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axes = [0, 2]
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return paddle.squeeze(x[0], axes)
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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 = paddle.static.data('x', x_shape, 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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)
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gradient_checker.double_grad_check_for_dygraph(
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self.squeeze_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestUnsqueezeDoubleGradCheck(unittest.TestCase):
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def unsqueeze_wrapper(self, x):
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axes = [1, 2]
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return paddle.unsqueeze(x[0], axes)
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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 = paddle.static.data('x', x_shape, 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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)
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gradient_checker.double_grad_check_for_dygraph(
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self.unsqueeze_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestClipDoubleGradCheck(unittest.TestCase):
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def clip_wrapper(self, x):
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return paddle.clip(x[0], min=-1.0, max=1.0)
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@prog_scope()
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def func(self, place):
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x_shape = [2, 4, 10]
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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out = paddle.clip(x, min=-1.0, max=1.0)
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x_arr = np.random.uniform(-5.0, 5.0, x_shape).astype(dtype)
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gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
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gradient_checker.double_grad_check_for_dygraph(
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self.clip_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestTransposeDoubleGradCheck(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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perm = [1, 0]
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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out = paddle.transpose(x, perm)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestTransposeDoubleGradCheckCase1(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 4, 5]
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perm = [0, 2, 3, 1]
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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out = paddle.transpose(x, perm)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestConstantPadDoubleGradCheck(unittest.TestCase):
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def pad_wrapper(self, x):
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pad = [1, 1, 1, 1]
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return paddle.nn.functional.pad(x[0], pad)
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 4, 5]
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pad = [1, 1, 1, 1]
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eps = 0.005
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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x.stop_gradient = False
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out = paddle.nn.functional.pad(x, pad)
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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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)
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gradient_checker.double_grad_check_for_dygraph(
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self.pad_wrapper, [x], out, x_init=x_arr, place=place
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestConstantPadDoubleGradCheckCase1(TestConstantPadDoubleGradCheck):
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 4, 5]
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pad = [1, 0, 1, 0, 1, 0, 1, 0]
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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out = paddle.nn.functional.pad(x, pad)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place)
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class TestConcatDoubleGradCheck(unittest.TestCase):
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def concat_wrapper(self, x):
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return paddle.concat(x, axis=0)
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 4, 5]
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dtype = np.float64
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x1 = paddle.static.data('x', x_shape, dtype)
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x2 = paddle.static.data('x', x_shape, dtype)
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x1.persistable = True
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x1.stop_gradient = False
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x2.persistable = True
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x2.stop_gradient = False
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out = paddle.concat([x1, x2], axis=0)
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x2_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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x1_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x1, x2], out, x_init=[x1_arr, x2_arr], place=place
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.concat_wrapper,
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[x1, x2],
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out,
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x_init=[x1_arr, x2_arr],
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place=place,
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestStackDoubleGradCheck(unittest.TestCase):
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def stack_wrapper(self, x):
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return paddle.stack(x, axis=1)
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 4, 5]
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dtype = np.float64
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x1 = paddle.static.data('x', x_shape, dtype)
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x2 = paddle.static.data('x', x_shape, dtype)
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x1.persistable = True
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x1.stop_gradient = False
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x2.persistable = True
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x2.stop_gradient = False
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out = paddle.stack([x1, x2], axis=0)
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x2_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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x1_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x1, x2], out, x_init=[x1_arr, x2_arr], place=place
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.stack_wrapper,
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[x1, x2],
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out,
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x_init=[x1_arr, x2_arr],
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place=place,
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestIndexSelectDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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x_shape = [2, 2, 2, 2]
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axis = 2
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index_shape = [3]
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dtype = np.float64
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x = paddle.static.data('x', x_shape, dtype)
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x.persistable = True
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x.stop_gradient = False
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index = paddle.static.data('index', index_shape, 'int64')
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index.persistable = True
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out = paddle.index_select(x, index, axis)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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index_arr = np.random.uniform(
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-x_shape[axis], x_shape[axis], index_shape
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).astype('int64')
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gradient_checker.double_grad_check(
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[x, index], out, x_init=[x_arr, index_arr], place=place
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)
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def index_select_wrapper(args):
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return paddle.index_select(*args, axis=axis)
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gradient_checker.double_grad_check_for_dygraph(
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index_select_wrapper,
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[x, index],
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out,
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x_init=[x_arr, index_arr],
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place=place,
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)
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def test_grad(self):
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places = []
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# free(): invalid next size (fast) may occurs when
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# execute in CPU
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if core.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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for p in places:
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self.func(p)
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class TestAvgPool2DDoubleGradCheckCase1(unittest.TestCase):
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@prog_scope()
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def func(self, place):
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input_NCHW = paddle.static.data(
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name="input_NCHW",
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shape=[2, 3, 5, 5],
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dtype="float32",
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)
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input_NCHW.persistable = True
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y = paddle.nn.functional.avg_pool2d(input_NCHW, kernel_size=2)
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x_arr = np.random.uniform(-1, 1, [2, 3, 5, 5]).astype(np.float32)
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gradient_checker.double_grad_check(
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[input_NCHW], y, x_init=x_arr, place=place, eps=0.05
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)
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def test_grad(self):
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for p in get_places():
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self.func(p)
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class TestAvgPool2DDoubleGradCheckCase2(unittest.TestCase):
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def pool2d_wrapper(self, x):
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return paddle.nn.functional.avg_pool2d(
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x[0], kernel_size=2, data_format="NHWC"
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)
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@prog_scope()
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def func(self, place):
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input_NHWC = paddle.static.data(
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name="input_NHWC",
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shape=[2, 5, 5, 3],
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dtype="float32",
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)
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input_NHWC.persistable = True
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y = paddle.nn.functional.avg_pool2d(
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input_NHWC, kernel_size=2, data_format="NHWC"
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)
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x_arr = np.random.uniform(-1, 1, [2, 5, 5, 3]).astype(np.float32)
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gradient_checker.double_grad_check(
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[input_NHWC], y, x_init=x_arr, place=place, eps=0.05
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.pool2d_wrapper, [input_NHWC], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestAvgPool2DDoubleGradCheckCase3(unittest.TestCase):
|
|
def pool2d_wrapper(self, x):
|
|
return paddle.nn.functional.avg_pool2d(
|
|
x[0], kernel_size=2, padding=[1, 1]
|
|
)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
input_NCHW = paddle.static.data(
|
|
name="input_NCHW",
|
|
shape=[2, 3, 5, 5],
|
|
dtype="float32",
|
|
)
|
|
|
|
input_NCHW.persistable = True
|
|
y = paddle.nn.functional.avg_pool2d(
|
|
input_NCHW, kernel_size=2, padding=[1, 1]
|
|
)
|
|
x_arr = np.random.uniform(-1, 1, [2, 3, 5, 5]).astype(np.float32)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[input_NCHW], y, x_init=x_arr, place=place, eps=0.05
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.pool2d_wrapper, [input_NCHW], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestAvgPool2DDoubleGradCheckCase4(unittest.TestCase):
|
|
def pool2d_wrapper(self, x):
|
|
return paddle.nn.functional.avg_pool2d(x[0], kernel_size=[4, 4])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
input_NCHW = paddle.static.data(
|
|
name="input_NCHW",
|
|
shape=[2, 3, 5, 5],
|
|
dtype="float32",
|
|
)
|
|
|
|
input_NCHW.persistable = True
|
|
y = paddle.nn.functional.avg_pool2d(input_NCHW, kernel_size=[4, 4])
|
|
x_arr = np.random.uniform(-1, 1, [2, 3, 5, 5]).astype(np.float32)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[input_NCHW], y, x_init=x_arr, place=place, eps=0.05
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.pool2d_wrapper, [input_NCHW], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|