# 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. from __future__ import print_function import unittest import numpy as np import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core import gradient_checker from decorator_helper import prog_scope class TestConvDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, groups=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] places = [] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConvDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConvDoubleGradCheckTest1(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 3, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d(x, 2, 1, padding=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 3, 4, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d(x, 2, 1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DDoubleGradCheckTest1(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 5, 3, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d(x, 2, 1, padding=1, bias_attr=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv2DoubleGradCheck_AsyPadding(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, num_filters=2, filter_size=1, padding=[1, 0, 0, 1], bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv2DoubleGradCheck_PaddingSAME(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, num_filters=2, filter_size=1, padding="SAME", bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv2DoubleGradCheck_PaddingVALID(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, num_filters=2, filter_size=1, padding="VALID", bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv2DoubleGradCheck_ChannelLast(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, num_filters=2, filter_size=1, padding=[1, 1], bias_attr=False, use_cudnn=True, groups=1, data_format="NHWC") x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv2DoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv2d( input=x, num_filters=2, filter_size=1, padding=[1, 0, 1, 0], bias_attr=False, use_cudnn=True, groups=1, data_format="NHWC") x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 2, 2, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, num_filters=2, filter_size=1, padding=[1, 0, 0, 1, 1, 2], bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 2, 2, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, num_filters=2, filter_size=1, padding="SAME", groups=1, bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 3, 3, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, num_filters=2, filter_size=1, padding="VALID", bias_attr=False, use_cudnn=True) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 2, 2, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, num_filters=2, filter_size=1, padding=[1, 1, 1], bias_attr=False, use_cudnn=True, groups=1, data_format="NDHWC") x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 2, 2, 2, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) y = layers.conv3d( input=x, num_filters=2, filter_size=1, padding=[1, 0, 1, 0, 1, 0], bias_attr=False, use_cudnn=True, groups=1, data_format="NDHWC") x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) class TestDepthWiseConvDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [2, 4, 3, 3] eps = 0.005 dtype = np.float64 x = layers.data('x', shape, False, dtype) # condition of depthwise conv: # use_cudnn == False # groups == filters # num_filters % num_channels == 0 y = layers.conv2d( x, shape[1], 1, groups=shape[1], bias_attr=False, use_cudnn=False) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) w = fluid.default_main_program().global_block().all_parameters() w_arr = [] for p in w: w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype)) gradient_checker.double_grad_check( [x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps) def test_grad(self): places = [] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) if __name__ == "__main__": unittest.main()