# 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 import paddle.fluid as fluid import paddle.fluid.core as core import paddle.fluid.layers as layers paddle.enable_static() class TestSliceOpDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): self.config() out = paddle.slice( self.inputs, axes=self.axes, starts=self.starts, ends=self.ends ) gradient_checker.double_grad_check( [self.inputs], out, x_init=self.x_arr, place=place ) def config(self): self.starts = [1, 0, -1] self.ends = [3, 3, 6] self.axes = [0, 1, 2] self.x_arr = np.random.random([3, 4, 5, 2]).astype("float64") self.inputs = paddle.create_parameter( dtype="float64", shape=[3, 4, 5, 2], name='x' ) def test_grad(self): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self.func(place) class TestSliceOpDoubleGradCheckCase3(TestSliceOpDoubleGradCheck): def config(self): self.starts = [1, -1, 1] self.ends = [3, 3, 3] self.axes = [0, 1, 2] self.x_arr = np.random.random([3, 3, 3]).astype("float64") self.inputs = paddle.create_parameter( dtype="float64", shape=[3, 3, 3], name='x3' ) class TestReduceMeanWithDimDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [7, 11] eps = 0.05 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = paddle.mean(x, axis=0) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_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 TestReduceSumWithDimDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): shape = [7, 11] eps = 0.05 dtype = np.float64 x = layers.data('x', shape, False, dtype) x.persistable = True y = paddle.sum(x, axis=0) x_arr = np.random.uniform(-1, 1, shape).astype(dtype) gradient_checker.double_grad_check( [x], y, x_init=x_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 TestReshapeDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): x_shape = [3, 12] new_shape = [4, 9] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.reshape(x, new_shape) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_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 TestTileDoubleGradCheck(unittest.TestCase): def tile_wrapper(self, x): return paddle.tile(x[0], [4, 9]) @prog_scope() def func(self, place): x_shape = [3, 12] repeat_times = [4, 9] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.tile(x, repeat_times) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_arr, place=place, eps=eps ) gradient_checker.double_grad_check_for_dygraph( self.tile_wrapper, [x], out, x_init=x_arr, place=place ) 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 TestExpandV2DoubleGradCheck(unittest.TestCase): def expand_wrapper(self, x): return paddle.expand(x[0], [4, 12]) @prog_scope() def func(self, place): x_shape = [1, 12] new_shape = [4, 12] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.expand(x, new_shape) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_arr, place=place, eps=eps ) gradient_checker.double_grad_check_for_dygraph( self.expand_wrapper, [x], out, x_init=x_arr, place=place ) 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 TestSqueezeDoubleGradCheck(unittest.TestCase): def squeeze_warpper(self, x): axes = [0, 2] return paddle.squeeze(x[0], axes) @prog_scope() def func(self, place): x_shape = [1, 3, 1, 40] axes = [0, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.squeeze(x, axes) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_arr, place=place, eps=eps ) gradient_checker.double_grad_check_for_dygraph( self.squeeze_warpper, [x], out, x_init=x_arr, place=place ) 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 TestUnsqueezeDoubleGradCheck(unittest.TestCase): def unsqueeze_wrapper(self, x): axes = [1, 2] return paddle.unsqueeze(x[0], axes) @prog_scope() def func(self, place): x_shape = [3, 40] axes = [1, 2] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.unsqueeze(x, axes) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_arr, place=place, eps=eps ) gradient_checker.double_grad_check_for_dygraph( self.unsqueeze_wrapper, [x], out, x_init=x_arr, place=place ) 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 TestClipDoubleGradCheck(unittest.TestCase): def clip_wrapper(self, x): return paddle.clip(x[0], min=-1.0, max=1.0) @prog_scope() def func(self, place): x_shape = [2, 4, 10] dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.clip(x, min=-1.0, max=1.0) x_arr = np.random.uniform(-5.0, 5.0, x_shape).astype(dtype) gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place) gradient_checker.double_grad_check_for_dygraph( self.clip_wrapper, [x], out, x_init=x_arr, place=place ) 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 TestTransposeDoubleGradCheck(unittest.TestCase): @prog_scope() def func(self, place): x_shape = [3, 40] perm = [1, 0] dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.transpose(x, perm) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place) 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 TestTransposeDoubleGradCheckCase1(unittest.TestCase): @prog_scope() def func(self, place): x_shape = [2, 3, 4, 5] perm = [0, 2, 3, 1] dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.transpose(x, perm) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place) 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 TestConstantPadDoubleGradCheck(unittest.TestCase): def pad_wrapper(self, x): pad = [1, 1, 1, 1] return paddle.nn.functional.pad(x[0], pad) @prog_scope() def func(self, place): x_shape = [2, 3, 4, 5] pad = [1, 1, 1, 1] eps = 0.005 dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.nn.functional.pad(x, pad) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x], out, x_init=x_arr, place=place, eps=eps ) gradient_checker.double_grad_check_for_dygraph( self.pad_wrapper, [x], out, x_init=x_arr, place=place ) 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 TestConstantPadDoubleGradCheckCase1(TestConstantPadDoubleGradCheck): @prog_scope() def func(self, place): x_shape = [2, 3, 4, 5] pad = [1, 0, 1, 0, 1, 0, 1, 0] dtype = np.float64 x = layers.data('x', x_shape, False, dtype) x.persistable = True out = paddle.nn.functional.pad(x, pad) x_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check([x], out, x_init=x_arr, place=place) class TestConcatDoubleGradCheck(unittest.TestCase): def concat_wrapper(self, x): return paddle.concat(x, axis=0) @prog_scope() def func(self, place): x_shape = [2, 3, 4, 5] pad = [1, 1, 1, 1] dtype = np.float64 x1 = layers.data('x', x_shape, False, dtype) x2 = layers.data('x', x_shape, False, dtype) x1.persistable = True x2.persistable = True out = paddle.concat([x1, x2], axis=0) x2_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) x1_arr = np.random.uniform(-1, 1, x_shape).astype(dtype) gradient_checker.double_grad_check( [x1, x2], out, x_init=[x1_arr, x2_arr], place=place ) gradient_checker.double_grad_check_for_dygraph( self.concat_wrapper, [x1, x2], out, x_init=[x1_arr, x2_arr], place=place, ) 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 TestAvgPool2DDoubleGradCheckCase1(unittest.TestCase): @prog_scope() def func(self, place): input_NCHW = fluid.layers.data( name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, dtype="float32", ) input_NCHW.persistable = True y = layers.pool2d(input_NCHW, pool_size=2, pool_type="avg") 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 ) 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 TestAvgPool2DDoubleGradCheckCase2(unittest.TestCase): def pool2d_wrapper(self, x): return paddle.nn.functional.avg_pool2d( x[0], kernel_size=2, data_format="NHWC" ) @prog_scope() def func(self, place): input_NHWC = fluid.layers.data( name="input_NHWC", shape=[2, 5, 5, 3], append_batch_size=False, dtype="float32", ) input_NHWC.persistable = True y = paddle.nn.functional.avg_pool2d( input_NHWC, kernel_size=2, data_format="NHWC" ) x_arr = np.random.uniform(-1, 1, [2, 5, 5, 3]).astype(np.float32) gradient_checker.double_grad_check( [input_NHWC], y, x_init=x_arr, place=place, eps=0.05 ) gradient_checker.double_grad_check_for_dygraph( self.pool2d_wrapper, [input_NHWC], y, x_init=x_arr, place=place ) 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 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 = fluid.layers.data( name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, 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): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in 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 = fluid.layers.data( name="input_NCHW", shape=[2, 3, 5, 5], append_batch_size=False, dtype="float32", ) input_NCHW.persistable = True y = layers.pool2d(input_NCHW, pool_size=[4, 4], pool_type="avg") 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): places = [fluid.CPUPlace()] if core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for p in places: self.func(p) if __name__ == "__main__": unittest.main()