wehub-resource-sync
478 lines
16 KiB
Python
478 lines
16 KiB
Python
# 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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import unittest
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import numpy as np
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from utils import dygraph_guard
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import paddle
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from paddle import base
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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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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
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positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
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shape_tensor_int32 = paddle.static.data(
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name="shape_tensor_int32", shape=[2], dtype="int32"
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)
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shape_tensor_int64 = paddle.static.data(
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name="shape_tensor_int64", shape=[2], dtype="int64"
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)
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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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)
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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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)
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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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)
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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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)
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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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)
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val = paddle.tensor.fill_constant(
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shape=[1], dtype=np.float32, value=1.1
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)
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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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)
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out_8 = paddle.full(shape=10, dtype=np.float32, fill_value=val)
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out_9 = paddle.full(
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shape=10, dtype="complex64", fill_value=1.1 + 1.1j
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)
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out_10 = paddle.full(
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shape=10, dtype="complex128", fill_value=1.1 + 1.1j
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)
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out_11 = paddle.full(
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shape=10, dtype="complex64", fill_value=1.1 + np.inf * 1j
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)
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out_12 = paddle.full(
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shape=10, dtype="complex128", fill_value=1.1 + np.inf * 1j
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)
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out_13 = paddle.full(
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shape=10, dtype="complex64", fill_value=1.1 - np.inf * 1j
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)
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out_14 = paddle.full(
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shape=10, dtype="complex128", fill_value=1.1 - np.inf * 1j
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)
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out_15 = paddle.full(
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shape=10, dtype="complex64", fill_value=1.1 + np.nan * 1j
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)
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out_16 = paddle.full(
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shape=10, dtype="complex128", fill_value=1.1 + np.nan * 1j
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)
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out_17 = paddle.full(shape=10, fill_value=1.1 + 1.1j)
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out_18 = paddle.full(shape=10, fill_value=True)
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exe = base.Executor(place=base.CPUPlace())
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(
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res_1,
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res_2,
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res_3,
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res_4,
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res_5,
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res_6,
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res_7,
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res_8,
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res_9,
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res_10,
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res_11,
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res_12,
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res_13,
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res_14,
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res_15,
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res_16,
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res_17,
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res_18,
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) = exe.run(
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paddle.static.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=[
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out_1,
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out_2,
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out_3,
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out_4,
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out_5,
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out_6,
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out_7,
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out_8,
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out_9,
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out_10,
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out_11,
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out_12,
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out_13,
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out_14,
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out_15,
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out_16,
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out_17,
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out_18,
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],
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)
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np.testing.assert_array_equal(
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res_1, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_2, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_3, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_4, np.full([1, 2], 1.2, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_5, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_6, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_7, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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res_8, np.full([10], 1.1, dtype="float32")
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)
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np.testing.assert_allclose(
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res_9, np.full([10], 1.1 + 1.1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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res_10, np.full([10], 1.1 + 1.1j, dtype="complex128")
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)
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np.testing.assert_allclose(
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res_9, np.full([10], 1.1 + 1.1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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res_10, np.full([10], 1.1 + 1.1j, dtype="complex128")
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)
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np.testing.assert_allclose(
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res_11, np.full([10], 1.1 + np.inf * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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res_12, np.full([10], 1.1 + np.inf * 1j, dtype="complex128")
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)
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np.testing.assert_allclose(
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res_13, np.full([10], 1.1 - np.inf * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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res_14, np.full([10], 1.1 - np.inf * 1j, dtype="complex128")
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)
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np.testing.assert_allclose(
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res_15, np.full([10], 1.1 + np.nan * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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res_16, np.full([10], 1.1 + np.nan * 1j, dtype="complex128")
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)
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np.testing.assert_allclose(res_17, np.full([10], 1.1 + 1.1j))
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np.testing.assert_array_equal(res_18, np.full([10], True))
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paddle.disable_static()
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def test_api_eager(self):
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with base.dygraph.base.guard():
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positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
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positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
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positive_4_int64 = paddle.tensor.fill_constant(
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[1], "int64", 4, True
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)
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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.item()],
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dtype="float32",
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fill_value=1.1,
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)
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out_3 = paddle.full(
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shape=[1, positive_2_int64.item()],
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dtype="float32",
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fill_value=1.1,
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)
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out_4 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.2)
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out_5 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)
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out_6 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=1.1)
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val = paddle.tensor.fill_constant(
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shape=[1], dtype=np.float32, value=1.1
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)
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out_7 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=val)
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out_8 = paddle.full(
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shape=positive_2_int32, dtype="float32", fill_value=1.1
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)
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out_9 = paddle.full(
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shape=[
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positive_2_int32,
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positive_2_int64,
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positive_4_int64,
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],
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dtype="float32",
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fill_value=1.1,
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)
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# test for numpy.float64 as fill_value
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out_10 = paddle.full_like(
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out_7, dtype=np.float32, fill_value=np.abs(1.1)
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)
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out_11 = paddle.full(shape=10, dtype="float32", fill_value=1.1)
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out_12 = paddle.full(
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shape=[1, 2, 3], dtype="complex64", fill_value=1.1 + 1.1j
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)
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out_13 = paddle.full(
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shape=[1, 2, 3], dtype="complex128", fill_value=1.1 + 1.1j
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)
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out_14 = paddle.full(
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shape=[1, 2, 3], dtype="complex64", fill_value=1.1 + np.inf * 1j
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)
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out_15 = paddle.full(
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shape=[1, 2, 3],
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dtype="complex128",
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fill_value=1.1 + np.inf * 1j,
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)
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out_16 = paddle.full(
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shape=[1, 2, 3], dtype="complex64", fill_value=1.1 - np.inf * 1j
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)
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out_17 = paddle.full(
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shape=[1, 2, 3],
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dtype="complex128",
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fill_value=1.1 - np.inf * 1j,
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)
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out_18 = paddle.full(
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shape=[1, 2, 3], dtype="complex64", fill_value=1.1 + np.nan * 1j
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)
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out_19 = paddle.full(
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shape=[1, 2, 3],
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dtype="complex128",
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fill_value=1.1 + np.nan * 1j,
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)
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# test without dtype input for complex
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out_20 = paddle.full(shape=[1, 2, 3], fill_value=1.1 + 1.1j)
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# test without dtype input for bool
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out_21 = paddle.full(shape=[1, 2, 3], fill_value=True)
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np.testing.assert_array_equal(
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out_1, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_2, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_3, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_4, np.full([1, 2], 1.2, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_5, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_6, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_7, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_8, np.full([2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_9, np.full([2, 2, 4], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_10, np.full([1, 2], 1.1, dtype="float32")
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)
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np.testing.assert_array_equal(
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out_11, np.full([10], 1.1, dtype="float32")
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)
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np.testing.assert_allclose(
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out_12, np.full([1, 2, 3], 1.1 + 1.1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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out_13, np.full([1, 2, 3], 1.1 + 1.1j, dtype="complex128")
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)
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np.testing.assert_allclose(
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out_14, np.full([1, 2, 3], 1.1 + np.inf * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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out_15,
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np.full([1, 2, 3], 1.1 + np.inf * 1j, dtype="complex128"),
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)
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np.testing.assert_allclose(
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out_16, np.full([1, 2, 3], 1.1 - np.inf * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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out_17,
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np.full([1, 2, 3], 1.1 - np.inf * 1j, dtype="complex128"),
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)
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np.testing.assert_allclose(
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out_18, np.full([1, 2, 3], 1.1 + np.nan * 1j, dtype="complex64")
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)
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np.testing.assert_allclose(
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out_19,
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np.full([1, 2, 3], 1.1 + np.nan * 1j, dtype="complex128"),
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)
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np.testing.assert_allclose(out_20, np.full([1, 2, 3], 1.1 + 1.1j))
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np.testing.assert_array_equal(out_21, np.full([1, 2, 3], True))
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def test_full_alias(self):
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"""
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Test the alias of full function.
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``full(shape=[1])`` is equivalent to ``full(size=[1])``
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"""
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paddle.disable_static()
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shape_cases = [
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[2],
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[2, 4],
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[2, 4, 8],
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]
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dtype_cases = [
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"float32",
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"float64",
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"int32",
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"int64",
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"bool",
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]
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fill_value_cases = [
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1,
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0,
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-1,
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True,
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False,
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3.14,
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]
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for shape in shape_cases:
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for param_alias in ["shape", "size"]:
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for dtype in dtype_cases:
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for fill_value in fill_value_cases:
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if dtype == "bool" and not isinstance(fill_value, bool):
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continue # skip invalid bool cases
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out = paddle.full(
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**{param_alias: shape},
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fill_value=fill_value,
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dtype=dtype,
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)
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expected = np.full(shape, fill_value, dtype=dtype)
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if dtype == "bool":
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np.testing.assert_array_equal(out, expected)
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else:
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np.testing.assert_allclose(out, expected)
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class TestFullOpError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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# for ci coverage
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# The argument dtype of full must be one of bool, float16,
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# float32, float64, uint8, int16, int32 or int64
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self.assertRaises(
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TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint4'
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)
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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 = paddle.static.data(
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name="shape_tensor", shape=[2], dtype="float32"
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)
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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 = paddle.static.data(
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name="shape_tensor_list", shape=[1], dtype="bool"
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)
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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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paddle.disable_static()
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def test_fill_value_errors(self):
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with dygraph_guard():
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# The fill_value must be one of [int, float, bool, complex, np.number, Tensor].
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self.assertRaises(
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TypeError,
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paddle.full,
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shape=[1],
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dtype="float32",
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fill_value=np.array([1.0], dtype=np.float32),
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)
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self.assertRaises(
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TypeError,
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paddle.full,
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shape=[1],
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dtype="float32",
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fill_value=[1.0],
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)
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self.assertRaises(
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TypeError,
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paddle.full,
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shape=[1],
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dtype="bool",
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fill_value=np.bool_(True),
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)
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if __name__ == "__main__":
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unittest.main()
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