wehub-resource-sync
481 lines
16 KiB
Python
481 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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import paddle.nn.functional as F
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from paddle import base
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from paddle.base import core
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class TestConvDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 4, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, groups=1)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConvDoubleGradCheckTest0(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 4, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConvDoubleGradCheckTest1(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 3, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding=1)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 4, 3, 4, 2]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DDoubleGradCheckTest1(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 4, 5, 3, 2]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding=1)
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv2DoubleGradCheck_AsyPadding(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding=[1, 0, 0, 1])
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv2DoubleGradCheck_PaddingSAME(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding="SAME")
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv2DoubleGradCheck_PaddingVALID(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding="VALID")
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv2DoubleGradCheck_ChannelLast(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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x_shape = [2, 2, 3, 3]
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w_shape = [2, 3, 1, 1]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding=[1, 1], groups=1, data_format="NHWC")
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv2DoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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x_shape = [2, 2, 3, 3]
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w_shape = [2, 3, 1, 1]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv2d(x, w, padding=[1, 0, 1, 0], groups=1, data_format="NHWC")
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 2, 2, 2]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding=[1, 0, 0, 1, 1, 2])
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 2, 2, 2]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding="SAME")
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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shape = [2, 2, 3, 3, 2]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', shape, dtype)
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w = paddle.static.data('w', shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding="VALID")
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x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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x_shape = [2, 2, 2, 2, 3]
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w_shape = [2, 3, 1, 1, 1]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding=[1, 1, 1], data_format="NDHWC")
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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x_shape = [2, 2, 2, 2, 3]
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w_shape = [2, 3, 1, 1, 1]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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x.persistable = True
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w.persistable = True
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y = F.conv3d(x, w, padding=[1, 0, 1, 0, 1, 0], data_format="NDHWC")
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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_pir(p)
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class TestDepthWiseConvDoubleGradCheck(unittest.TestCase):
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@prog_scope()
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def func_pir(self, place):
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x_shape = [2, 4, 3, 3]
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w_shape = [4, 1, 1, 1]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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# x.persistable = True
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# w.persistable = True
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y = F.conv2d(x, w, groups=4)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_arr], place=place, eps=eps
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)
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def test_grad(self):
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places = []
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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_pir(p)
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class TestDepthWiseConvDoubleGradCheckCase1(unittest.TestCase):
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def depthwise_conv2d_wrapper(self, x):
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return paddle.nn.functional.conv2d(x[0], x[1], groups=4)
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@prog_scope()
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def func(self, place):
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x_shape = [2, 4, 3, 3]
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w_shape = [4, 1, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
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x = paddle.static.data('x', x_shape, dtype)
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w = paddle.static.data('w', w_shape, dtype)
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# condition of depthwise conv:
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# use_cudnn == False
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# groups == filters
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# num_filters % num_channels == 0
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y = paddle.nn.functional.conv2d(x, w, groups=4)
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x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
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w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
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gradient_checker.double_grad_check(
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[x, w], y, x_init=[x_arr, w_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.depthwise_conv2d_wrapper,
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[x, w],
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y,
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x_init=[x_arr, w_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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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 TestConv3DDoubleGradCheck_NN(unittest.TestCase):
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def conv3d_wrapper(self, x):
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return paddle.nn.functional.conv3d(x[0], x[1])
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@prog_scope()
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def func(self, place):
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x_shape = [2, 3, 8, 8, 8]
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w_shape = [6, 3, 3, 3, 3]
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eps = 0.005
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dtype = np.float32 if base.core.is_compiled_with_rocm() else np.float64
|
|
x = paddle.static.data('x', x_shape, dtype)
|
|
w = paddle.static.data('w', w_shape, dtype)
|
|
x.persistable = True
|
|
w.persistable = True
|
|
y = paddle.nn.functional.conv3d(x, w)
|
|
x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype)
|
|
w_arr = np.random.uniform(-1, 1, w_shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x, w], y, x_init=[x_arr, w_arr], place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.conv3d_wrapper, [x, w], y, x_init=[x_arr, w_arr], place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
places = []
|
|
if core.is_compiled_with_cuda() or is_custom_device():
|
|
places.append(get_device_place())
|
|
for p in places:
|
|
self.func(p)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|