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122 lines
4.6 KiB
122 lines
4.6 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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import paddle
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from paddle.fluid import compiler, Program, program_guard
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# Test python API
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class TestFullAPI(unittest.TestCase):
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def test_api(self):
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positive_2_int32 = fluid.layers.fill_constant([1], "int32", 2)
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positive_2_int64 = fluid.layers.fill_constant([1], "int64", 2)
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shape_tensor_int32 = fluid.data(
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name="shape_tensor_int32", shape=[2], dtype="int32")
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shape_tensor_int64 = fluid.data(
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name="shape_tensor_int64", shape=[2], dtype="int64")
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out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)
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out_2 = paddle.full(
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shape=[1, positive_2_int32], dtype="float32", fill_value=1.1)
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out_3 = paddle.full(
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shape=[1, positive_2_int64], dtype="float32", fill_value=1.1)
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out_4 = paddle.full(
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shape=shape_tensor_int32, dtype="float32", fill_value=1.2)
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out_5 = paddle.full(
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shape=shape_tensor_int64, dtype="float32", fill_value=1.1)
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out_6 = paddle.full(
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shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1)
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val = fluid.layers.fill_constant(shape=[1], dtype=np.float32, value=1.1)
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out_7 = paddle.full(
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shape=shape_tensor_int64, dtype=np.float32, fill_value=val)
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exe = fluid.Executor(place=fluid.CPUPlace())
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res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
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fluid.default_main_program(),
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feed={
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"shape_tensor_int32": np.array([1, 2]).astype("int32"),
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"shape_tensor_int64": np.array([1, 2]).astype("int64"),
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},
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fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
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assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_4, np.full([1, 2], 1.2, dtype="float32"))
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assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
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assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32"))
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class TestFullOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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#for ci coverage
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self.assertRaises(
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ValueError, paddle.full, shape=[1], fill_value=5, dtype='uint4')
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# The argument dtype of full must be one of bool, float16,
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#float32, float64, int32 or int64
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self.assertRaises(
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TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint8')
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# The argument shape's type of full_op must be list, tuple or Variable.
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def test_shape_type():
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paddle.full(shape=1, dtype="float32", fill_value=1)
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self.assertRaises(TypeError, test_shape_type)
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# The argument shape's size of full_op must not be 0.
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def test_shape_size():
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paddle.full(shape=[], dtype="float32", fill_value=1)
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self.assertRaises(AssertionError, test_shape_size)
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# The shape dtype of full op must be int32 or int64.
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def test_shape_tensor_dtype():
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shape = fluid.data(
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name="shape_tensor", shape=[2], dtype="float32")
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paddle.full(shape=shape, dtype="float32", fill_value=1)
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self.assertRaises(TypeError, test_shape_tensor_dtype)
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def test_shape_tensor_list_dtype():
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shape = fluid.data(
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name="shape_tensor_list", shape=[1], dtype="bool")
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paddle.full(shape=[shape, 2], dtype="float32", fill_value=1)
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self.assertRaises(TypeError, test_shape_tensor_list_dtype)
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if __name__ == "__main__":
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unittest.main()
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