|
- # Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import unittest
-
- import gradient_checker
- import numpy as np
- from decorator_helper import prog_scope
-
- import paddle
- from paddle import base
- from paddle.base import core
- from paddle.pir_utils import test_with_pir_api
-
-
- class TestInstanceNormDoubleGradCheck(unittest.TestCase):
- @prog_scope()
- def func(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.create_parameter(dtype=dtype, shape=shape, name='x')
- z = paddle.static.nn.instance_norm(input=x)
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
-
- @test_with_pir_api
- @prog_scope()
- def func_pir(self, place):
- prog = paddle.static.Program()
- with paddle.static.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=shape, name='x')
- z = paddle.nn.functional.instance_norm(x)
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
-
- def test_grad(self):
- paddle.enable_static()
- places = [base.CPUPlace()]
- if core.is_compiled_with_cuda():
- places.append(base.CUDAPlace(0))
- for p in places:
- self.func(p)
- self.func_pir(p)
-
-
- class TestInstanceNormDoubleGradCheckWithoutParamBias(
- TestInstanceNormDoubleGradCheck
- ):
- @prog_scope()
- def func(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.create_parameter(dtype=dtype, shape=shape, name='x')
- z = paddle.static.nn.instance_norm(
- input=x, param_attr=False, bias_attr=False
- )
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
-
- @test_with_pir_api
- @prog_scope()
- def func_pir(self, place):
- prog = paddle.static.Program()
- with paddle.static.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=shape, name='x')
- z = paddle.nn.functional.instance_norm(x, bias=None)
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
-
-
- class TestInstanceNormDoubleGradEagerCheck(unittest.TestCase):
- def instance_norm_wrapper(self, x):
- return paddle.nn.functional.instance_norm(x[0])
-
- @test_with_pir_api
- @prog_scope()
- def func(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=shape, name='x')
- z = paddle.nn.functional.instance_norm(x)
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- # check for static graph mode
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
- # check for eager mode
- gradient_checker.double_grad_check_for_dygraph(
- self.instance_norm_wrapper,
- [x],
- z,
- x_init=x_arr,
- atol=atol,
- place=place,
- )
-
- def test_grad(self):
- paddle.enable_static()
- places = [base.CPUPlace()]
- if core.is_compiled_with_cuda():
- places.append(base.CUDAPlace(0))
- for p in places:
- self.func(p)
-
-
- class TestInstanceNormDoubleGradEagerCheckWithParams(
- TestInstanceNormDoubleGradEagerCheck
- ):
- def instance_norm_wrapper(self, x):
- instance_norm = paddle.nn.InstanceNorm2D(3)
- return instance_norm(x[0])
-
- @test_with_pir_api
- @prog_scope()
- def func(self, place):
- prog = paddle.static.Program()
- with paddle.static.program_guard(prog):
- np.random.seed()
- shape = [2, 3, 4, 5]
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=shape, name='x')
- z = paddle.nn.InstanceNorm2D(3)(x)
- x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
- # check for static graph mode
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
- # check for eager mode
- gradient_checker.double_grad_check_for_dygraph(
- self.instance_norm_wrapper,
- [x],
- z,
- x_init=x_arr,
- atol=atol,
- place=place,
- )
-
-
- class TestBatchNormDoubleGradCheck(unittest.TestCase):
- def setUp(self):
- self.init_test()
-
- def init_test(self):
- self.data_layout = 'NCHW'
- self.use_global_stats = False
- self.shape = [2, 3, 4, 5]
- self.channel_index = 1
-
- def batch_norm_wrapper(self, x):
- batch_norm = paddle.nn.BatchNorm2D(
- self.shape[self.channel_index],
- data_format=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- return batch_norm(x[0])
-
- @prog_scope()
- def func(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
- z = paddle.static.nn.batch_norm(
- input=x,
- data_layout=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
- gradient_checker.double_grad_check_for_dygraph(
- self.batch_norm_wrapper,
- [x],
- z,
- x_init=x_arr,
- atol=atol,
- place=place,
- )
-
- @test_with_pir_api
- @prog_scope()
- def func_pir(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
- bn = paddle.nn.BatchNorm2D(
- self.shape[self.channel_index],
- data_format=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- z = bn(x)
- x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
- gradient_checker.double_grad_check_for_dygraph(
- self.batch_norm_wrapper,
- [x],
- z,
- x_init=x_arr,
- atol=atol,
- place=place,
- )
-
- def test_grad(self):
- paddle.enable_static()
- places = [base.CPUPlace()]
- if core.is_compiled_with_cuda():
- places.append(base.CUDAPlace(0))
- for p in places:
- self.func(p)
- self.func_pir(p)
-
-
- class TestBatchNormDoubleGradCheckCase1(TestBatchNormDoubleGradCheck):
- def init_test(self):
- self.data_layout = 'NHWC'
- self.use_global_stats = False
- self.shape = [2, 3, 4, 5]
- self.channel_index = 3
-
-
- class TestBatchNormDoubleGradCheckCase2(TestBatchNormDoubleGradCheck):
- def init_test(self):
- self.data_layout = 'NCHW'
- self.use_global_stats = True
- self.shape = [2, 3, 4, 5]
- self.channel_index = 1
-
-
- class TestBatchNormDoubleGradCheckCase3(TestBatchNormDoubleGradCheck):
- def init_test(self):
- self.data_layout = 'NHWC'
- self.use_global_stats = True
- self.shape = [2, 3, 4, 5]
- self.channel_index = 3
-
-
- class TestBatchNormDoubleGradCheckCase4(TestBatchNormDoubleGradCheck):
- def init_test(self):
- self.data_layout = 'NCHW'
- self.use_global_stats = False
- self.shape = [2, 2, 3, 4, 5]
- self.channel_index = 1
-
- def batch_norm_wrapper(self, x):
- batch_norm = paddle.nn.BatchNorm3D(
- self.shape[self.channel_index],
- data_format=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- return batch_norm(x[0])
-
- @test_with_pir_api
- @prog_scope()
- def func_pir(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed()
- dtype = "float32"
- eps = 0.005
- atol = 1e-4
- x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
- bn = paddle.nn.BatchNorm3D(
- self.shape[self.channel_index],
- data_format=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- z = bn(x)
- x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
- gradient_checker.double_grad_check(
- [x], z, x_init=x_arr, atol=atol, place=place, eps=eps
- )
- gradient_checker.double_grad_check_for_dygraph(
- self.batch_norm_wrapper,
- [x],
- z,
- x_init=x_arr,
- atol=atol,
- place=place,
- )
-
-
- class TestBatchNormDoubleGradCheckCase5(TestBatchNormDoubleGradCheck):
- @prog_scope()
- def func(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed(37)
- dtype = "float32"
- eps = 0.005
- atol = 2e-4
- chn = (
- self.shape[1] if self.data_layout == 'NCHW' else self.shape[-1]
- )
- x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
- z = paddle.static.nn.batch_norm(
- input=x,
- data_layout=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
- w, b = prog.global_block().all_parameters()[1:3]
- w_arr = np.ones(chn).astype(dtype)
- b_arr = np.zeros(chn).astype(dtype)
- gradient_checker.double_grad_check(
- [x, w, b],
- z,
- x_init=[x_arr, w_arr, b_arr],
- atol=atol,
- place=place,
- eps=eps,
- )
-
- @test_with_pir_api
- @prog_scope()
- def func_pir(self, place):
- prog = base.Program()
- with base.program_guard(prog):
- np.random.seed(37)
- dtype = "float32"
- eps = 0.005
- atol = 2e-4
- chn = (
- self.shape[1] if self.data_layout == 'NCHW' else self.shape[-1]
- )
- x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
- w = paddle.static.data(dtype=dtype, shape=[chn], name='w')
- b = paddle.static.data(dtype=dtype, shape=[chn], name='b')
- bn = paddle.nn.BatchNorm2D(
- self.shape[self.channel_index],
- data_format=self.data_layout,
- use_global_stats=self.use_global_stats,
- )
- z = bn(x)
- x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
- w_arr = np.ones(chn).astype(dtype)
- b_arr = np.zeros(chn).astype(dtype)
- gradient_checker.double_grad_check(
- [x, w, b],
- z,
- x_init=[x_arr, w_arr, b_arr],
- atol=atol,
- place=place,
- eps=eps,
- )
-
-
- class TestBatchNormDoubleGradCheckCase6(TestBatchNormDoubleGradCheckCase5):
- def init_test(self):
- self.data_layout = 'NCHW'
- self.use_global_stats = True
- self.shape = [2, 3, 4, 5]
- self.channel_index = 1
-
-
- if __name__ == "__main__":
- unittest.main()
|