# Copyright (c) 2018 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 from op_test import OpTest import paddle.fluid.core as core from paddle.fluid.op import Operator import paddle.fluid as fluid import paddle from paddle.fluid import compiler, Program, program_guard from paddle.fluid.framework import _test_eager_guard # Test python API class TestFullAPI(unittest.TestCase): def test_api(self): positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2) positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2) shape_tensor_int32 = fluid.data( name="shape_tensor_int32", shape=[2], dtype="int32") shape_tensor_int64 = fluid.data( name="shape_tensor_int64", shape=[2], dtype="int64") out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1) out_2 = paddle.full( shape=[1, positive_2_int32], dtype="float32", fill_value=1.1) out_3 = paddle.full( shape=[1, positive_2_int64], dtype="float32", fill_value=1.1) out_4 = paddle.full( shape=shape_tensor_int32, dtype="float32", fill_value=1.2) out_5 = paddle.full( shape=shape_tensor_int64, dtype="float32", fill_value=1.1) out_6 = paddle.full( shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1) val = fluid.layers.fill_constant(shape=[1], dtype=np.float32, value=1.1) out_7 = paddle.full( shape=shape_tensor_int64, dtype=np.float32, fill_value=val) exe = fluid.Executor(place=fluid.CPUPlace()) res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run( fluid.default_main_program(), feed={ "shape_tensor_int32": np.array([1, 2]).astype("int32"), "shape_tensor_int64": np.array([1, 2]).astype("int64"), }, fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7]) assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_4, np.full([1, 2], 1.2, dtype="float32")) assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32")) assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32")) def test_api_eager(self): with fluid.dygraph.base.guard(): with _test_eager_guard(): positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2) positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2) out_1 = paddle.full( shape=[1, 2], dtype="float32", fill_value=1.1) out_2 = paddle.full( shape=[1, positive_2_int32.item()], dtype="float32", fill_value=1.1) out_3 = paddle.full( shape=[1, positive_2_int64.item()], dtype="float32", fill_value=1.1) out_4 = paddle.full( shape=[1, 2], dtype="float32", fill_value=1.2) out_5 = paddle.full( shape=[1, 2], dtype="float32", fill_value=1.1) out_6 = paddle.full( shape=[1, 2], dtype=np.float32, fill_value=1.1) val = fluid.layers.fill_constant( shape=[1], dtype=np.float32, value=1.1) out_7 = paddle.full( shape=[1, 2], dtype=np.float32, fill_value=val) assert np.array_equal( out_1, np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( out_2, np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( out_3, np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( out_4, np.full( [1, 2], 1.2, dtype="float32")) assert np.array_equal( out_5, np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( out_6, np.full( [1, 2], 1.1, dtype="float32")) assert np.array_equal( out_7, np.full( [1, 2], 1.1, dtype="float32")) class TestFullOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): #for ci coverage self.assertRaises( TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint4') # The argument dtype of full must be one of bool, float16, #float32, float64, uint8, int16, int32 or int64 # The argument shape's type of full_op must be list, tuple or Variable. def test_shape_type(): paddle.full(shape=1, dtype="float32", fill_value=1) self.assertRaises(TypeError, test_shape_type) # The argument shape's size of full_op must not be 0. def test_shape_size(): paddle.full(shape=[], dtype="float32", fill_value=1) self.assertRaises(AssertionError, test_shape_size) # The shape dtype of full op must be int32 or int64. def test_shape_tensor_dtype(): shape = fluid.data( name="shape_tensor", shape=[2], dtype="float32") paddle.full(shape=shape, dtype="float32", fill_value=1) self.assertRaises(TypeError, test_shape_tensor_dtype) def test_shape_tensor_list_dtype(): shape = fluid.data( name="shape_tensor_list", shape=[1], dtype="bool") paddle.full(shape=[shape, 2], dtype="float32", fill_value=1) self.assertRaises(TypeError, test_shape_tensor_list_dtype) if __name__ == "__main__": unittest.main()