# Copyright (c) 2020 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 numpy as np from op_test import OpTest import paddle import paddle.nn as nn import paddle.nn.functional as F import paddle.fluid.core as core from paddle.fluid import Program, program_guard, Executor, default_main_program class TestPad3dOp(OpTest): def setUp(self): paddle.enable_static() self.value = 0.0 self.variable_paddings = False self.initTestCase() self.op_type = "pad3d" self.inputs = {'X': np.random.random(self.shape).astype("float64")} self.attrs = {} if self.variable_paddings: self.attrs['paddings'] = [] self.inputs['Paddings'] = np.array(self.paddings).flatten().astype( "int32") else: self.attrs['paddings'] = np.array(self.paddings).flatten().astype( "int32") self.attrs['value'] = self.value self.attrs['mode'] = self.mode self.attrs['data_format'] = self.data_format if self.data_format == "NCDHW": paddings = [ (0, 0), (0, 0), (self.paddings[4], self.paddings[5]), (self.paddings[2], self.paddings[3]), (self.paddings[0], self.paddings[1]), ] else: paddings = [ (0, 0), (self.paddings[4], self.paddings[5]), (self.paddings[2], self.paddings[3]), (self.paddings[0], self.paddings[1]), (0, 0), ] if self.mode == "constant": out = np.pad(self.inputs['X'], paddings, mode=self.mode, constant_values=self.value) elif self.mode == "reflect": out = np.pad(self.inputs['X'], paddings, mode=self.mode) elif self.mode == "replicate": out = np.pad(self.inputs['X'], paddings, mode="edge") elif self.mode == "circular": out = np.pad(self.inputs['X'], paddings, mode="wrap") self.outputs = {'Out': out} def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X'], 'Out') def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [0, 0, 0, 0, 0, 0] self.mode = "constant" self.data_format = "NCDHW" self.pad_value = 0.0 class TestCase1(TestPad3dOp): def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [0, 1, 2, 3, 4, 5] self.mode = "constant" self.data_format = "NCDHW" self.value = 1.0 class TestCase2(TestPad3dOp): def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [1, 1, 1, 1, 1, 1] self.mode = "constant" self.data_format = "NDHWC" self.value = 1.0 class TestCase3(TestPad3dOp): def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [0, 1, 1, 0, 2, 3] self.mode = "reflect" self.data_format = "NCDHW" class TestCase4(TestPad3dOp): def initTestCase(self): self.shape = (4, 4, 4, 4, 4) self.paddings = [0, 1, 2, 1, 2, 3] self.mode = "reflect" self.data_format = "NDHWC" class TestCase5(TestPad3dOp): def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [0, 1, 2, 3, 2, 1] self.mode = "replicate" self.data_format = "NCDHW" class TestCase6(TestPad3dOp): def initTestCase(self): self.shape = (4, 4, 4, 4, 4) self.paddings = [5, 4, 2, 1, 2, 3] self.mode = "replicate" self.data_format = "NDHWC" class TestCase7(TestPad3dOp): def initTestCase(self): self.shape = (2, 3, 4, 5, 6) self.paddings = [0, 1, 2, 3, 2, 1] self.mode = "circular" self.data_format = "NCDHW" class TestCase8(TestPad3dOp): def initTestCase(self): self.shape = (4, 4, 4, 4, 4) self.paddings = [0, 1, 2, 1, 2, 3] self.mode = "circular" self.data_format = "NDHWC" class TestPadAPI(unittest.TestCase): def setUp(self): self.places = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.places.append(paddle.CUDAPlace(0)) def check_static_result_1(self, place): paddle.enable_static() with program_guard(Program(), Program()): input_shape = (1, 2, 3, 4, 5) pad = [1, 2, 1, 1, 3, 4] mode = "constant" value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) result = F.pad(x=x, pad=pad, value=value, mode=mode, data_format="NCDHW") exe = Executor(place) fetches = exe.run(default_main_program(), feed={"x": input_data}, fetch_list=[result]) np_out = self._get_numpy_out(input_data, pad, mode, value) self.assertTrue(np.allclose(fetches[0], np_out)) def check_static_result_2(self, place): paddle.enable_static() with program_guard(Program(), Program()): input_shape = (2, 3, 4, 5, 6) pad = [1, 2, 1, 1, 1, 2] mode = "reflect" input_data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW") result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC") exe = Executor(place) fetches = exe.run(default_main_program(), feed={"x": input_data}, fetch_list=[result1, result2]) np_out1 = self._get_numpy_out( input_data, pad, mode, data_format="NCDHW") np_out2 = self._get_numpy_out( input_data, pad, mode, data_format="NDHWC") self.assertTrue(np.allclose(fetches[0], np_out1)) self.assertTrue(np.allclose(fetches[1], np_out2)) def check_static_result_3(self, place): paddle.enable_static() with program_guard(Program(), Program()): input_shape = (2, 3, 4, 5, 6) pad = [1, 2, 1, 1, 3, 4] mode = "replicate" input_data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW") result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC") exe = Executor(place) fetches = exe.run(default_main_program(), feed={"x": input_data}, fetch_list=[result1, result2]) np_out1 = self._get_numpy_out( input_data, pad, mode, data_format="NCDHW") np_out2 = self._get_numpy_out( input_data, pad, mode, data_format="NDHWC") self.assertTrue(np.allclose(fetches[0], np_out1)) self.assertTrue(np.allclose(fetches[1], np_out2)) def check_static_result_4(self, place): paddle.enable_static() with program_guard(Program(), Program()): input_shape = (2, 3, 4, 5, 6) pad = [1, 2, 1, 1, 3, 4] mode = "circular" input_data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) result1 = F.pad(x=x, pad=pad, mode=mode, data_format="NCDHW") result2 = F.pad(x=x, pad=pad, mode=mode, data_format="NDHWC") exe = Executor(place) fetches = exe.run(default_main_program(), feed={"x": input_data}, fetch_list=[result1, result2]) np_out1 = self._get_numpy_out( input_data, pad, mode, data_format="NCDHW") np_out2 = self._get_numpy_out( input_data, pad, mode, data_format="NDHWC") self.assertTrue(np.allclose(fetches[0], np_out1)) self.assertTrue(np.allclose(fetches[1], np_out2)) def _get_numpy_out(self, input_data, pad, mode, value=0, data_format="NCDHW"): if data_format == "NCDHW": pad = [ (0, 0), (0, 0), (pad[4], pad[5]), (pad[2], pad[3]), (pad[0], pad[1]), ] elif data_format == "NDHWC": pad = [ (0, 0), (pad[4], pad[5]), (pad[2], pad[3]), (pad[0], pad[1]), (0, 0), ] elif data_format == "NCHW": pad = [ (0, 0), (0, 0), (pad[2], pad[3]), (pad[0], pad[1]), ] elif data_format == "NHWC": pad = [ (0, 0), (pad[2], pad[3]), (pad[0], pad[1]), (0, 0), ] elif data_format == "NCL": pad = [ (0, 0), (0, 0), (pad[0], pad[1]), ] elif data_format == "NLC": pad = [ (0, 0), (pad[0], pad[1]), (0, 0), ] if mode == "constant": out = np.pad(input_data, pad, mode=mode, constant_values=value) elif mode == "reflect": out = np.pad(input_data, pad, mode=mode) elif mode == "replicate": out = np.pad(input_data, pad, mode="edge") elif mode == "circular": out = np.pad(input_data, pad, mode="wrap") return out def test_static(self): for place in self.places: self.check_static_result_1(place=place) self.check_static_result_2(place=place) self.check_static_result_3(place=place) self.check_static_result_4(place=place) def test_dygraph_1(self): paddle.disable_static() input_shape = (1, 2, 3, 4, 5) pad = [1, 2, 1, 1, 3, 4] mode = "constant" value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) np_out1 = self._get_numpy_out( input_data, pad, mode, value, data_format="NCDHW") np_out2 = self._get_numpy_out( input_data, pad, mode, value, data_format="NDHWC") tensor_data = paddle.to_tensor(input_data) y1 = F.pad(tensor_data, pad=pad, mode=mode, value=value, data_format="NCDHW") y2 = F.pad(tensor_data, pad=pad, mode=mode, value=value, data_format="NDHWC") self.assertTrue(np.allclose(y1.numpy(), np_out1)) self.assertTrue(np.allclose(y2.numpy(), np_out2)) def test_dygraph_2(self): paddle.disable_static() input_shape = (2, 3, 4, 5) pad = [1, 1, 3, 4] mode = "constant" value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) np_out1 = self._get_numpy_out( input_data, pad, mode, value, data_format="NCHW") np_out2 = self._get_numpy_out( input_data, pad, mode, value, data_format="NHWC") tensor_data = paddle.to_tensor(input_data) tensor_pad = paddle.to_tensor(pad, dtype="int32") y1 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NCHW") y2 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NHWC") self.assertTrue(np.allclose(y1.numpy(), np_out1)) self.assertTrue(np.allclose(y2.numpy(), np_out2)) def test_dygraph_2(self): paddle.disable_static() input_shape = (2, 3, 4, 5) pad = [1, 1, 3, 4] mode = "constant" value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) np_out1 = self._get_numpy_out( input_data, pad, mode, value, data_format="NCHW") np_out2 = self._get_numpy_out( input_data, pad, mode, value, data_format="NHWC") tensor_data = paddle.to_tensor(input_data) tensor_pad = paddle.to_tensor(pad, dtype="int32") y1 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NCHW") y2 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NHWC") self.assertTrue(np.allclose(y1.numpy(), np_out1)) self.assertTrue(np.allclose(y2.numpy(), np_out2)) def test_dygraph_3(self): paddle.disable_static() input_shape = (3, 4, 5) pad = [3, 4] mode = "constant" value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) np_out1 = self._get_numpy_out( input_data, pad, mode, value, data_format="NCL") np_out2 = self._get_numpy_out( input_data, pad, mode, value, data_format="NLC") tensor_data = paddle.to_tensor(input_data) tensor_pad = paddle.to_tensor(pad, dtype="int32") y1 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NCL") y2 = F.pad(tensor_data, pad=tensor_pad, mode=mode, value=value, data_format="NLC") self.assertTrue(np.allclose(y1.numpy(), np_out1)) self.assertTrue(np.allclose(y2.numpy(), np_out2)) class TestPad1dAPI(unittest.TestCase): def _get_numpy_out(self, input_data, pad, mode, value=0.0, data_format="NCL"): if data_format == "NCL": pad = [ (0, 0), (0, 0), (pad[0], pad[1]), ] else: pad = [ (0, 0), (pad[0], pad[1]), (0, 0), ] if mode == "constant": out = np.pad(input_data, pad, mode=mode, constant_values=value) elif mode == "reflect": out = np.pad(input_data, pad, mode=mode) elif mode == "replicate": out = np.pad(input_data, pad, mode="edge") return out def setUp(self): self.places = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.places.append(paddle.CUDAPlace(0)) def test_class(self): paddle.disable_static() for place in self.places: input_shape = (3, 4, 5) pad = [1, 2] value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) pad_reflection = nn.ReflectionPad1d(padding=pad) pad_replication = nn.ReplicationPad1d(padding=pad) pad_constant = nn.ConstantPad1d(padding=pad, value=value) data = paddle.to_tensor(input_data) output = pad_reflection(data) np_out = self._get_numpy_out( input_data, pad, "reflect", data_format="NCL") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_replication(data) np_out = self._get_numpy_out( input_data, pad, "replicate", data_format="NCL") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_constant(data) np_out = self._get_numpy_out( input_data, pad, "constant", value=value, data_format="NCL") self.assertTrue(np.allclose(output.numpy(), np_out)) class TestPad2dAPI(unittest.TestCase): def _get_numpy_out(self, input_data, pad, mode, value=0.0, data_format="NCHW"): if data_format == "NCHW": pad = [ (0, 0), (0, 0), (pad[2], pad[3]), (pad[0], pad[1]), ] else: pad = [ (0, 0), (pad[2], pad[3]), (pad[0], pad[1]), (0, 0), ] if mode == "constant": out = np.pad(input_data, pad, mode=mode, constant_values=value) elif mode == "reflect": out = np.pad(input_data, pad, mode=mode) elif mode == "replicate": out = np.pad(input_data, pad, mode="edge") return out def setUp(self): self.places = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.places.append(paddle.CUDAPlace(0)) def test_class(self): paddle.disable_static() for place in self.places: input_shape = (3, 4, 5, 6) pad = [1, 2, 2, 1] value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) pad_reflection = nn.ReflectionPad2d(padding=pad) pad_replication = nn.ReplicationPad2d(padding=pad) pad_constant = nn.ConstantPad2d(padding=pad, value=value) pad_zero = nn.ZeroPad2d(padding=pad) data = paddle.to_tensor(input_data) output = pad_reflection(data) np_out = self._get_numpy_out( input_data, pad, "reflect", data_format="NCHW") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_replication(data) np_out = self._get_numpy_out( input_data, pad, "replicate", data_format="NCHW") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_constant(data) np_out = self._get_numpy_out( input_data, pad, "constant", value=value, data_format="NCHW") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_zero(data) np_out = self._get_numpy_out( input_data, pad, "constant", value=0, data_format="NCHW") self.assertTrue(np.allclose(output.numpy(), np_out)) class TestPad3dAPI(unittest.TestCase): def _get_numpy_out(self, input_data, pad, mode, value=0.0, data_format="NCDHW"): if data_format == "NCDHW": pad = [ (0, 0), (0, 0), (pad[4], pad[5]), (pad[2], pad[3]), (pad[0], pad[1]), ] else: pad = [ (0, 0), (pad[4], pad[5]), (pad[2], pad[3]), (pad[0], pad[1]), (0, 0), ] if mode == "constant": out = np.pad(input_data, pad, mode=mode, constant_values=value) elif mode == "reflect": out = np.pad(input_data, pad, mode=mode) elif mode == "replicate": out = np.pad(input_data, pad, mode="edge") return out def setUp(self): self.places = [paddle.CPUPlace()] if core.is_compiled_with_cuda(): self.places.append(paddle.CUDAPlace(0)) def test_class(self): paddle.disable_static() for place in self.places: input_shape = (3, 4, 5, 6, 7) pad = [1, 2, 2, 1, 1, 0] value = 100 input_data = np.random.rand(*input_shape).astype(np.float32) pad_replication = nn.ReplicationPad3d(padding=pad) pad_constant = nn.ConstantPad3d(padding=pad, value=value) data = paddle.to_tensor(input_data) output = pad_replication(data) np_out = self._get_numpy_out( input_data, pad, "replicate", data_format="NCDHW") self.assertTrue(np.allclose(output.numpy(), np_out)) output = pad_constant(data) np_out = self._get_numpy_out( input_data, pad, "constant", value=value, data_format="NCDHW") self.assertTrue(np.allclose(output.numpy(), np_out)) class TestPad3dOpError(unittest.TestCase): def test_errors(self): def test_variable(): input_shape = (1, 2, 3, 4, 5) data = np.random.rand(*input_shape).astype(np.float32) F.pad(x=data, paddings=[1, 1, 1, 1, 1, 1]) def test_reflect_1(): input_shape = (1, 2, 3, 4, 5) data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) y = F.pad(x, pad=[5, 6, 1, 1, 1, 1], value=1, mode='reflect') place = paddle.CPUPlace() exe = Executor(place) outputs = exe.run(feed={'x': data}, fetch_list=[y.name]) def test_reflect_2(): input_shape = (1, 2, 3, 4, 5) data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) y = F.pad(x, pad=[1, 1, 4, 3, 1, 1], value=1, mode='reflect') place = paddle.CPUPlace() exe = Executor(place) outputs = exe.run(feed={'x': data}, fetch_list=[y.name]) def test_reflect_3(): input_shape = (1, 2, 3, 4, 5) data = np.random.rand(*input_shape).astype(np.float32) x = paddle.data(name="x", shape=input_shape) y = F.pad(x, pad=[1, 1, 1, 1, 2, 3], value=1, mode='reflect') place = paddle.CPUPlace() exe = Executor(place) outputs = exe.run(feed={'x': data}, fetch_list=[y.name]) self.assertRaises(TypeError, test_variable) self.assertRaises(Exception, test_reflect_1) self.assertRaises(Exception, test_reflect_2) self.assertRaises(Exception, test_reflect_3) class TestPadDataformatError(unittest.TestCase): def test_errors(self): def test_ncl(): paddle.disable_static(paddle.CPUPlace()) input_shape = (1, 2, 3, 4) pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32')) data = np.arange( np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1 my_pad = nn.ReplicationPad1d(padding=pad, data_format="NCL") data = paddle.to_tensor(data) result = my_pad(data) def test_nchw(): paddle.disable_static(paddle.CPUPlace()) input_shape = (1, 2, 4) pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32')) data = np.arange( np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1 my_pad = nn.ReplicationPad1d(padding=pad, data_format="NCHW") data = paddle.to_tensor(data) result = my_pad(data) def test_ncdhw(): paddle.disable_static(paddle.CPUPlace()) input_shape = (1, 2, 3, 4) pad = paddle.to_tensor(np.array([2, 1, 2, 1]).astype('int32')) data = np.arange( np.prod(input_shape), dtype=np.float64).reshape(input_shape) + 1 my_pad = nn.ReplicationPad1d(padding=pad, data_format="NCDHW") data = paddle.to_tensor(data) result = my_pad(data) self.assertRaises(AssertionError, test_ncl) self.assertRaises(AssertionError, test_nchw) self.assertRaises(AssertionError, test_ncdhw) if __name__ == '__main__': unittest.main()