# 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. from __future__ import print_function import unittest import numpy as np import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.op import Operator from paddle.fluid import compiler, Program, program_guard from op_test import OpTest, skip_check_grad_ci class TestAddcmulLayer(unittest.TestCase): def setUp(self): self._dtype = "float64" self.input = np.random.uniform(0.1, 1, [3, 100]).astype(self._dtype) self.tensor1 = np.random.uniform(0.1, 1, [100]).astype(self._dtype) self.tensor2 = np.random.uniform(0.1, 1, [3, 100]).astype(self._dtype) def static(self, value=1.0): prog = fluid.Program() with fluid.program_guard(prog): input = fluid.data(name="input", dtype=self._dtype, shape=[3, 100]) tensor1 = fluid.data(name="tensor1", dtype=self._dtype, shape=[100]) tensor2 = fluid.data( name="tensor2", dtype=self._dtype, shape=[3, 100]) out = paddle.tensor.math.addcmul(input, tensor1, tensor2, value) exe = fluid.Executor(self._place) return exe.run(feed={ "input": self.input, "tensor1": self.tensor1, "tensor2": self.tensor2 }, program=prog, fetch_list=[out])[0] def dynamic(self, value=1.0): with fluid.dygraph.guard(self._place): input = fluid.dygraph.to_variable(self.input) tensor1 = fluid.dygraph.to_variable(self.tensor1) tensor2 = fluid.dygraph.to_variable(self.tensor2) out = paddle.tensor.math.addcmul(input, tensor1, tensor2, value) return out.numpy() def numpy(self, value=1.0): self.out = np.add(self.input, np.multiply(self.tensor1, self.tensor2) * value) return self.out def test_equal(self): places = [] if fluid.core.is_compiled_with_cuda(): places.append(fluid.CUDAPlace(0)) for place in places: self._place = place self.assertTrue(np.allclose(self.numpy(), self.static())) self.assertTrue( np.allclose( self.numpy(value=0.9), self.dynamic(value=0.9))) self.assertTrue( np.allclose( self.numpy(value=0), self.dynamic(value=0))) class TestAddcmul(unittest.TestCase): def test_addcmul(self): program = Program() with program_guard(program): data_shape = [3, 64, 64] input = fluid.data(name='in', shape=data_shape, dtype='float32') tensor1 = fluid.data(name='t1', shape=data_shape, dtype='float32') tensor2 = fluid.data(name='t2', shape=data_shape, dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertEqual(out.shape, input.shape) def test_addcmul_with_broadcast0(self): program = Program() with program_guard(program): input = fluid.data(name='in', shape=[3, 100], dtype='float32') tensor1 = fluid.data(name='t1', shape=[3, 100], dtype='float32') tensor2 = fluid.data(name='t2', shape=[100], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertEqual(out.shape, input.shape) def test_addcmul_with_broadcast1(self): program = Program() with program_guard(program): input = fluid.data(name='in', shape=[4, 100], dtype='float32') tensor1 = fluid.data(name='t1', shape=[100], dtype='float32') tensor2 = fluid.data(name='t2', shape=[4, 100], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertEqual(out.shape, input.shape) def test_addcmul_with_broadcast2(self): program = Program() with program_guard(program): input = fluid.data(name='in', shape=[4, 100], dtype='float32') tensor1 = fluid.data(name='t1', shape=[100], dtype='float32') tensor2 = fluid.data(name='t2', shape=[100], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertEqual(out.shape, input.shape) class InvalidInputTest(unittest.TestCase): def test_error(self): def test_invalid_input(): program = Program() with program_guard(program): input = [20, 20] tensor1 = fluid.data( name='tensor1', shape=[20, 20], dtype='float32') tensor2 = fluid.data( name='tensor2', shape=[20, 20], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertRaises(TypeError, test_invalid_input) def test_invalid_tensor1(): program = Program() with program_guard(program): input = fluid.data( name='input', shape=[20, 20], dtype='float32') tensor1 = [20, 20] tensor2 = fluid.data( name='tensor2', shape=[20, 20], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertRaises(TypeError, test_invalid_tensor1) def test_invalid_tensor2(): program = Program() with program_guard(program): input = fluid.data( name='input', shape=[20, 20], dtype='float32') tensor1 = fluid.data( name='tensor1', shape=[20, 20], dtype='float32') tensor2 = [20, 20] out = paddle.tensor.math.addcmul(input, tensor1, tensor2) self.assertRaises(TypeError, test_invalid_tensor2) def test_invalid_value_int(): program = Program() with program_guard(program): input = fluid.data( name='input', shape=[20, 20], dtype='float32') tensor1 = fluid.data( name='tensor1', shape=[20, 20], dtype='float32') tensor2 = fluid.data( name='tensor2', shape=[20, 20], dtype='float32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2, value=1) self.assertRaises(TypeError, test_invalid_value_int) def test_invalid_value_float(): program = Program() with program_guard(program): input = fluid.data(name='input', shape=[20, 20], dtype='int32') tensor1 = fluid.data( name='tensor1', shape=[20, 20], dtype='int32') tensor2 = fluid.data( name='tensor2', shape=[20, 20], dtype='int32') out = paddle.tensor.math.addcmul(input, tensor1, tensor2, value=1.0) self.assertRaises(TypeError, test_invalid_value_float) if __name__ == '__main__': unittest.main()