wehub-resource-sync
1138 lines
39 KiB
Python
1138 lines
39 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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from functools import reduce
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import numpy as np
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from op_test import get_device_place, get_places, is_custom_device
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.framework import (
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Program,
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convert_nptype_to_datatype_or_vartype,
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default_main_program,
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)
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paddle.enable_static()
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class TestVariable(unittest.TestCase):
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def setUp(self):
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np.random.seed(2022)
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def test_np_dtype_convert(self):
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convert = convert_nptype_to_datatype_or_vartype
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self.assertEqual(paddle.float32, convert(np.float32))
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self.assertEqual(paddle.float16, convert("float16"))
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self.assertEqual(paddle.float64, convert("float64"))
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self.assertEqual(paddle.int32, convert("int32"))
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self.assertEqual(paddle.int16, convert("int16"))
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self.assertEqual(paddle.int64, convert("int64"))
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self.assertEqual(paddle.bool, convert("bool"))
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self.assertEqual(paddle.int8, convert("int8"))
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self.assertEqual(paddle.uint8, convert("uint8"))
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self.assertEqual(paddle.float32, convert(paddle.float32))
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self.assertEqual(paddle.float16, convert(paddle.float16))
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self.assertEqual(paddle.float64, convert(paddle.float64))
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self.assertEqual(paddle.int32, convert(paddle.int32))
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self.assertEqual(paddle.int16, convert(paddle.int16))
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self.assertEqual(paddle.int64, convert(paddle.int64))
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self.assertEqual(paddle.bool, convert(paddle.bool))
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self.assertEqual(paddle.int8, convert(paddle.int8))
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self.assertEqual(paddle.uint8, convert(paddle.uint8))
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def test_var(self):
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b = default_main_program().current_block()
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w = b.create_var(dtype="float64", shape=[784, 100], name="fc.w")
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w_dtype = w.dtype
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if paddle.framework.use_pir_api() and isinstance(
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w_dtype, paddle.base.libpaddle.VarDesc.VarType
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):
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w_dtype = paddle.pir.core.vartype_to_datatype[w_dtype]
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self.assertNotEqual(str(w), "")
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self.assertEqual(paddle.float64, w_dtype)
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self.assertEqual((784, 100), w.shape)
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self.assertEqual("fc.w", w.name)
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self.assertEqual("fc.w@GRAD", w.grad_name)
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self.assertEqual(0, w.lod_level)
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w = b.create_var(name='fc.w')
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self.assertEqual(paddle.float64, w_dtype)
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self.assertEqual((784, 100), w.shape)
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self.assertEqual("fc.w", w.name)
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self.assertEqual("fc.w@GRAD", w.grad_name)
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self.assertEqual(0, w.lod_level)
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self.assertRaises(
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ValueError, lambda: b.create_var(name="fc.w", shape=(24, 100))
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)
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w = b.create_var(
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dtype=paddle.base.core.VarDesc.VarType.STRINGS,
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shape=[1],
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name="str_var",
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)
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self.assertEqual(None, w.lod_level)
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def test_element_size(self):
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if not paddle.framework.use_pir_api():
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with base.program_guard(Program(), Program()):
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x = paddle.static.data(name='x1', shape=[2], dtype='bool')
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self.assertEqual(x.element_size(), 1)
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x = paddle.static.data(name='x2', shape=[2], dtype='float16')
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self.assertEqual(x.element_size(), 2)
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x = paddle.static.data(name='x3', shape=[2], dtype='float32')
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self.assertEqual(x.element_size(), 4)
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x = paddle.static.data(name='x4', shape=[2], dtype='float64')
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self.assertEqual(x.element_size(), 8)
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x = paddle.static.data(name='x5', shape=[2], dtype='int8')
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self.assertEqual(x.element_size(), 1)
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x = paddle.static.data(name='x6', shape=[2], dtype='int16')
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self.assertEqual(x.element_size(), 2)
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x = paddle.static.data(name='x7', shape=[2], dtype='int32')
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self.assertEqual(x.element_size(), 4)
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x = paddle.static.data(name='x8', shape=[2], dtype='int64')
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self.assertEqual(x.element_size(), 8)
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x = paddle.static.data(name='x9', shape=[2], dtype='uint8')
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self.assertEqual(x.element_size(), 1)
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def test_step_scopes(self):
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prog = Program()
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b = prog.current_block()
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var = b.create_var(
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name='step_scopes', type=core.VarDesc.VarType.STEP_SCOPES
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)
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self.assertEqual(core.VarDesc.VarType.STEP_SCOPES, var.type)
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def _test_slice_index_tensor(self, place):
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data = np.random.rand(2, 3).astype("float32")
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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idx0 = [1, 0]
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idx1 = [0, 1]
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idx2 = [0, 0]
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idx3 = [1, 1]
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out0 = x[paddle.assign(np.array(idx0))]
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out1 = x[paddle.assign(np.array(idx1))]
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out2 = x[paddle.assign(np.array(idx2))]
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out3 = x[paddle.assign(np.array(idx3))]
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exe = paddle.static.Executor(place)
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result = exe.run(prog, fetch_list=[out0, out1, out2, out3])
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expected = [data[idx0], data[idx1], data[idx2], data[idx3]]
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self.assertTrue((result[0] == expected[0]).all())
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self.assertTrue((result[1] == expected[1]).all())
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self.assertTrue((result[2] == expected[2]).all())
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self.assertTrue((result[3] == expected[3]).all())
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def _test_slice_index_list(self, place):
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data = np.random.rand(2, 3).astype("float32")
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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idx0 = [1, 0]
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idx1 = [0, 1]
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idx2 = [0, 0]
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idx3 = [1, 1]
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out0 = x[idx0]
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out1 = x[idx1]
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out2 = x[idx2]
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out3 = x[idx3]
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exe = paddle.static.Executor(place)
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result = exe.run(prog, fetch_list=[out0, out1, out2, out3])
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expected = [data[idx0], data[idx1], data[idx2], data[idx3]]
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self.assertTrue((result[0] == expected[0]).all())
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self.assertTrue((result[1] == expected[1]).all())
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self.assertTrue((result[2] == expected[2]).all())
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self.assertTrue((result[3] == expected[3]).all())
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def _test_slice_index_ellipsis(self, place):
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data = np.random.rand(2, 3, 4).astype("float32")
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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y = paddle.assign([1, 2, 3, 4])
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out1 = x[0:, ..., 1:]
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out2 = x[0:, ...]
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out3 = x[..., 1:]
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out4 = x[...]
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out5 = x[[1, 0], [0, 0]]
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out6 = x[([1, 0], [0, 0])]
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out7 = y[..., 0]
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exe = paddle.static.Executor(place)
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result = exe.run(
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prog, fetch_list=[out1, out2, out3, out4, out5, out6, out7]
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)
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expected = [
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data[0:, ..., 1:],
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data[0:, ...],
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data[..., 1:],
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data[...],
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data[[1, 0], [0, 0]],
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data[([1, 0], [0, 0])],
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np.array([1]),
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]
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self.assertTrue((result[0] == expected[0]).all())
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self.assertTrue((result[1] == expected[1]).all())
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self.assertTrue((result[2] == expected[2]).all())
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self.assertTrue((result[3] == expected[3]).all())
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self.assertTrue((result[4] == expected[4]).all())
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self.assertTrue((result[5] == expected[5]).all())
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self.assertTrue((result[6] == expected[6]).all())
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def _test_slice_index_list_bool(self, place):
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data = np.random.rand(2, 3, 4).astype("float32")
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np_idx = np.array([[True, False, False], [True, False, True]])
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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idx0 = [True, False]
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idx1 = [False, True]
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idx2 = [True, True]
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idx3 = [False, False, 1]
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idx4 = [True, False, 0]
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idx5 = paddle.assign(np_idx)
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out0 = x[idx0]
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out1 = x[idx1]
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out2 = x[idx2]
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out3 = x[idx3]
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out4 = x[idx4]
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out5 = x[idx5]
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out6 = x[x < 0.36]
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out7 = x[x > 0.6]
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exe = paddle.static.Executor(place)
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result = exe.run(
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prog, fetch_list=[out0, out1, out2, out3, out4, out5, out6, out7]
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)
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expected = [
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data[idx0],
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data[idx1],
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data[idx2],
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data[idx3],
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data[idx4],
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data[np_idx],
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data[data < 0.36],
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data[data > 0.6],
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]
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self.assertTrue((result[0] == expected[0]).all())
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self.assertTrue((result[1] == expected[1]).all())
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self.assertTrue((result[2] == expected[2]).all())
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self.assertTrue((result[3] == expected[3]).all())
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self.assertTrue((result[4] == expected[4]).all())
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self.assertTrue((result[5] == expected[5]).all())
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self.assertTrue((result[6] == expected[6]).all())
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self.assertTrue((result[7] == expected[7]).all())
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with self.assertRaises(IndexError):
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res = x[[True, False, False]]
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def _test_slice_index_scalar_bool(self, place):
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data = np.random.rand(1, 3, 4).astype("float32")
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np_idx = np.array([True])
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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idx = paddle.assign(np_idx)
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out = x[idx]
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exe = paddle.static.Executor(place)
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result = exe.run(prog, fetch_list=[out])
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expected = [data[np_idx]]
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self.assertTrue((result[0] == expected[0]).all())
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def test_slice(self):
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places = get_places()
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for place in places:
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self._test_slice_index_tensor(place)
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self._test_slice_index_list(place)
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self._test_slice_index_ellipsis(place)
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self._test_slice_index_list_bool(place)
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self._test_slice_index_scalar_bool(place)
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def _tostring(self):
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b = default_main_program().current_block()
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w = b.create_var(dtype="float64")
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self.assertTrue(isinstance(str(w), str))
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if core.is_compiled_with_cuda() or is_custom_device():
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wc = b.create_var(dtype="int")
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self.assertTrue(isinstance(str(wc), str))
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def test_tostring(self):
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with base.dygraph.guard():
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self._tostring()
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with base.program_guard(paddle.base.default_main_program()):
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self._tostring()
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def test_fake_interface_only_api(self):
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b = default_main_program().current_block()
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var = b.create_var(dtype="float64")
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with base.dygraph.guard():
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self.assertRaises(AssertionError, var.numpy)
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self.assertRaises(AssertionError, var.backward)
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self.assertRaises(AssertionError, var.gradient)
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self.assertRaises(AssertionError, var.clear_gradient)
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def test_variable_in_dygraph_mode(self):
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b = default_main_program().current_block()
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var = b.create_var(dtype="float64", shape=[1, 1])
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var_dtype = var.dtype
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if paddle.framework.use_pir_api() and isinstance(
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var_dtype, paddle.base.libpaddle.VarDesc.VarType
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):
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var_dtype = paddle.pir.core.vartype_to_datatype[var_dtype]
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with base.dygraph.guard():
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self.assertTrue(var.to_string(True).startswith('name:'))
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self.assertFalse(var.persistable)
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var.persistable = True
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self.assertTrue(var.persistable)
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self.assertFalse(var.stop_gradient)
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var.stop_gradient = True
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self.assertTrue(var.stop_gradient)
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self.assertTrue(var.name.startswith('_generated_var_'))
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self.assertEqual(var.shape, (1, 1))
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self.assertEqual(var_dtype, paddle.float64)
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self.assertEqual(var.type, base.core.VarDesc.VarType.DENSE_TENSOR)
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def test_create_selected_rows(self):
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b = default_main_program().current_block()
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var = b.create_var(
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name="var",
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shape=[1, 1],
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dtype="float32",
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type=base.core.VarDesc.VarType.SELECTED_ROWS,
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persistable=True,
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)
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def _test():
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var.lod_level()
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self.assertRaisesRegex(
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NotImplementedError, "SelectedRows DO NOT support lod", _test
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)
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def test_size(self):
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(np.random.rand(2, 3, 4).astype("float32"))
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exe = paddle.static.Executor(base.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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if paddle.framework.use_pir_api():
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output = exe.run(prog, fetch_list=[x.size])
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else:
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output = exe.run(prog, fetch_list=[x.size()])
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self.assertEqual(output[0], [24])
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def test_detach(self):
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b = default_main_program().current_block()
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x = b.create_var(shape=[2, 3, 5], dtype="float64")
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detach_x = x.detach()
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self.assertEqual(x.persistable, detach_x.persistable)
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self.assertEqual(x.shape, detach_x.shape)
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self.assertEqual(x.dtype, detach_x.dtype)
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self.assertEqual(x.type, detach_x.type)
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self.assertTrue(detach_x.stop_gradient)
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xx = b.create_var(name='xx', type=core.VarDesc.VarType.STEP_SCOPES)
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self.assertRaises(AssertionError, xx.detach)
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with paddle.pir_utils.OldIrGuard():
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startup = paddle.static.Program()
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main = paddle.static.Program()
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scope = base.core.Scope()
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with (
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paddle.static.scope_guard(scope),
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paddle.static.program_guard(main, startup),
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):
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x = paddle.static.data(
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name='x', shape=[3, 2, 1], dtype='float32'
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)
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x.persistable = True
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feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)
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detach_x = x.detach()
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup)
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result = exe.run(
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main, feed={'x': feed_data}, fetch_list=[x, detach_x]
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)
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self.assertTrue((result[1] == feed_data).all())
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self.assertTrue((result[0] == result[1]).all())
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modified_value = np.zeros(shape=[3, 2, 1], dtype=np.float32)
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detach_x.set_value(modified_value, scope)
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result = exe.run(main, fetch_list=[x, detach_x])
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self.assertTrue((result[1] == modified_value).all())
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self.assertTrue((result[0] == result[1]).all())
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modified_value = np.random.uniform(
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-1, 1, size=[3, 2, 1]
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).astype('float32')
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x.set_value(modified_value, scope)
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result = exe.run(main, fetch_list=[x, detach_x])
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self.assertTrue((result[1] == modified_value).all())
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self.assertTrue((result[0] == result[1]).all())
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class TestVariableSlice(unittest.TestCase):
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def setUp(self):
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np.random.seed(2022)
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def _test_item_none(self, place):
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data = np.random.rand(2, 3, 4).astype("float32")
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prog = paddle.static.Program()
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with paddle.static.program_guard(prog):
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x = paddle.assign(data)
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out0 = x[0:, None, 1:]
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out1 = x[0:, None]
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out2 = x[None, 1:]
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out3 = x[None]
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out4 = x[..., None, :, None]
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outs = [out0, out1, out2, out3, out4]
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exe = paddle.static.Executor(place)
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result = exe.run(prog, fetch_list=outs)
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expected = [
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data[0:, None, 1:],
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data[0:, None],
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data[None, 1:],
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data[None],
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data[..., None, :, None],
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]
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for i in range(len(outs)):
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outs_i_shape = outs[i].shape
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expected_i_shape = expected[i].shape
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if paddle.framework.use_pir_api():
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if type(outs_i_shape) == list:
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outs_i_shape = tuple(outs_i_shape)
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if type(expected_i_shape) == list:
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expected_i_shape = tuple(expected_i_shape)
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self.assertEqual(outs_i_shape, expected_i_shape)
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self.assertTrue((result[i] == expected[i]).all())
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def _test_item_none_and_decrease(self, place):
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data = np.random.rand(2, 3, 4).astype("float32")
|
|
prog = paddle.static.Program()
|
|
with paddle.static.program_guard(prog):
|
|
x = paddle.assign(data)
|
|
out0 = x[0, 1:, None]
|
|
out1 = x[0, None]
|
|
out2 = x[None, 1]
|
|
out3 = x[None]
|
|
out4 = x[0, 0, 0, None]
|
|
out5 = x[None, 0, 0, 0, None]
|
|
|
|
outs = [out0, out1, out2, out3, out4, out5]
|
|
exe = paddle.static.Executor(place)
|
|
result = exe.run(prog, fetch_list=outs)
|
|
expected = [
|
|
data[0, 1:, None],
|
|
data[0, None],
|
|
data[None, 1],
|
|
data[None],
|
|
data[0, 0, 0, None],
|
|
data[None, 0, 0, 0, None],
|
|
]
|
|
|
|
for i in range(len(outs)):
|
|
outs_i_shape = outs[i].shape
|
|
expected_i_shape = expected[i].shape
|
|
if paddle.framework.use_pir_api():
|
|
if type(outs_i_shape) == list:
|
|
outs_i_shape = tuple(outs_i_shape)
|
|
if type(expected_i_shape) == list:
|
|
expected_i_shape = tuple(expected_i_shape)
|
|
self.assertEqual(outs_i_shape, expected_i_shape)
|
|
self.assertTrue((result[i] == expected[i]).all())
|
|
|
|
def test_slice(self):
|
|
for place in get_places():
|
|
self._test_item_none(place)
|
|
self._test_item_none_and_decrease(place)
|
|
|
|
|
|
class TestListIndex(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2022)
|
|
|
|
def numel(self, shape):
|
|
return reduce(lambda x, y: x * y, shape, 1)
|
|
|
|
def test_static_graph_list_index(self):
|
|
paddle.enable_static()
|
|
|
|
inps_shape = [3, 4, 5, 2]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [3, 3, 2, 1]
|
|
index = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
|
|
for _ in range(3):
|
|
program = paddle.static.Program()
|
|
|
|
index_mod = (index % (array.shape[0])).tolist()
|
|
|
|
with paddle.static.program_guard(program):
|
|
x = paddle.static.data(
|
|
name='x', shape=array.shape, dtype='float32'
|
|
)
|
|
|
|
y = x[index_mod]
|
|
|
|
place = (
|
|
paddle.base.CPUPlace()
|
|
if not (
|
|
paddle.base.core.is_compiled_with_cuda()
|
|
or is_custom_device()
|
|
)
|
|
else get_device_place()
|
|
)
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [y]
|
|
|
|
getitem_np = array[np.array(index_mod)]
|
|
getitem_pp = exe.run(
|
|
prog, feed={x.name: array}, fetch_list=fetch_list
|
|
)
|
|
np.testing.assert_array_equal(getitem_np, getitem_pp[0])
|
|
|
|
array = array[0]
|
|
index = index[0]
|
|
|
|
def test_dygraph_list_index(self):
|
|
paddle.disable_static()
|
|
|
|
inps_shape = [3, 4, 5, 3]
|
|
array = np.arange(self.numel(inps_shape)).reshape(inps_shape)
|
|
|
|
index_shape = [2, 3, 4, 5, 6]
|
|
index = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
for _ in range(len(inps_shape) - 1):
|
|
pt = paddle.to_tensor(array)
|
|
index_mod = (index % (array.shape[-1])).tolist()
|
|
try:
|
|
getitem_np = array[np.array(index_mod)]
|
|
|
|
except:
|
|
with self.assertRaises(ValueError):
|
|
getitem_pp = pt[index_mod]
|
|
array = array[0]
|
|
index = index[0]
|
|
continue
|
|
getitem_pp = pt[index_mod]
|
|
np.testing.assert_array_equal(getitem_np, getitem_pp.numpy())
|
|
|
|
array = array[0]
|
|
index = index[0]
|
|
|
|
def test_static_graph_list_index_multi_dim(self):
|
|
paddle.enable_static()
|
|
inps_shape = [3, 4, 5]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [2, 2]
|
|
index1 = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2
|
|
|
|
value_shape = [3, 2, 2, 3]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
|
|
index_mod1 = (index1 % (min(array.shape))).tolist()
|
|
index_mod2 = (index2 % (min(array.shape))).tolist()
|
|
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
x = paddle.static.data(name='x', shape=array.shape, dtype='float32')
|
|
index1 = paddle.static.data(
|
|
name='index1', shape=index1.shape, dtype='int32'
|
|
)
|
|
index2 = paddle.static.data(
|
|
name='index2', shape=index2.shape, dtype='int32'
|
|
)
|
|
|
|
y = x[index1, index2]
|
|
|
|
place = (
|
|
paddle.base.CPUPlace()
|
|
if not (
|
|
paddle.base.core.is_compiled_with_cuda()
|
|
or is_custom_device()
|
|
)
|
|
else get_device_place()
|
|
)
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [y]
|
|
array2 = array.copy()
|
|
|
|
y2 = array2[index_mod1, index_mod2]
|
|
|
|
getitem_pp = exe.run(
|
|
prog,
|
|
feed={
|
|
x.name: array,
|
|
index1.name: index_mod1,
|
|
index2.name: index_mod2,
|
|
},
|
|
fetch_list=fetch_list,
|
|
)
|
|
|
|
np.testing.assert_array_equal(
|
|
y2,
|
|
getitem_pp[0],
|
|
err_msg=f'\n numpy:{y2},\n paddle:{getitem_pp[0]}',
|
|
)
|
|
|
|
def test_dygraph_list_index_multi_dim(self):
|
|
paddle.disable_static()
|
|
inps_shape = [3, 4, 5]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [2, 2]
|
|
index1 = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
index2 = np.arange(self.numel(index_shape)).reshape(index_shape) + 2
|
|
|
|
value_shape = [3, 2, 2, 3]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
|
|
index_mod1 = (index1 % (min(array.shape))).tolist()
|
|
index_mod2 = (index2 % (min(array.shape))).tolist()
|
|
|
|
x = paddle.to_tensor(array)
|
|
index_t1 = paddle.to_tensor(index_mod1)
|
|
index_t2 = paddle.to_tensor(index_mod2)
|
|
|
|
y_np = array[index_t1, index_t2]
|
|
y = x[index_t1, index_t2]
|
|
np.testing.assert_array_equal(y.numpy(), y_np)
|
|
|
|
def run_getitem_list_index(self, array, index):
|
|
x = paddle.static.data(name='x', shape=array.shape, dtype='float32')
|
|
|
|
y = x[index]
|
|
place = paddle.base.CPUPlace()
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [y]
|
|
array2 = array.copy()
|
|
|
|
try:
|
|
value_np = array2[index]
|
|
except:
|
|
with self.assertRaises(ValueError):
|
|
getitem_pp = exe.run(
|
|
prog, feed={x.name: array}, fetch_list=fetch_list
|
|
)
|
|
return
|
|
getitem_pp = exe.run(prog, feed={x.name: array}, fetch_list=fetch_list)
|
|
|
|
np.testing.assert_allclose(
|
|
value_np, getitem_pp[0], rtol=1e-5, atol=1e-8
|
|
)
|
|
|
|
def test_static_graph_getitem_bool_index(self):
|
|
paddle.enable_static()
|
|
|
|
# case 1:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([True, False, False, False])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_getitem_list_index(array, index)
|
|
|
|
# case 2:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([False, True, False, False])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_getitem_list_index(array, index)
|
|
|
|
# case 3:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([True, True, True, True])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_getitem_list_index(array, index)
|
|
|
|
def run_setitem_list_index(self, array, index, value_np):
|
|
x = paddle.static.data(name='x', shape=array.shape, dtype='float32')
|
|
|
|
value = paddle.static.data(
|
|
name='value', shape=value_np.shape, dtype='float32'
|
|
)
|
|
|
|
y = paddle.static.setitem(x, index, value)
|
|
place = paddle.base.CPUPlace()
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [y]
|
|
array2 = array.copy()
|
|
try:
|
|
index = (
|
|
np.array(index)
|
|
if isinstance(index, list) and isinstance(index[0], list)
|
|
else index
|
|
)
|
|
array2[index] = value_np
|
|
except:
|
|
with self.assertRaises(ValueError):
|
|
setitem_pp = exe.run(
|
|
prog,
|
|
feed={x.name: array, value.name: value_np},
|
|
fetch_list=fetch_list,
|
|
)
|
|
return
|
|
setitem_pp = exe.run(
|
|
prog,
|
|
feed={x.name: array, value.name: value_np},
|
|
fetch_list=fetch_list,
|
|
)
|
|
|
|
np.testing.assert_allclose(array2, setitem_pp[0], rtol=1e-5, atol=1e-8)
|
|
|
|
def test_static_graph_setitem_list_index(self):
|
|
paddle.enable_static()
|
|
# case 1:
|
|
inps_shape = [4, 5, 2]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [3, 3, 1]
|
|
index = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
|
|
value_shape = inps_shape[3:]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
|
|
for _ in range(3):
|
|
program = paddle.static.Program()
|
|
|
|
index_mod = (index % (min(array.shape))).tolist()
|
|
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index_mod, value_np)
|
|
|
|
array = array[0]
|
|
index = index[0]
|
|
|
|
# case 2:
|
|
inps_shape = [4, 5, 4]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [4, 3, 2]
|
|
index = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
|
|
value_shape = [3]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
|
|
for _ in range(3):
|
|
program = paddle.static.Program()
|
|
index_mod = (index % (min(array.shape))).tolist()
|
|
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index_mod, value_np)
|
|
|
|
array = array[0]
|
|
index = index[0]
|
|
|
|
# case 3:
|
|
inps_shape = [3, 4, 5, 3, 3]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [4, 3, 2, 2]
|
|
index = np.arange(self.numel(index_shape)).reshape(index_shape)
|
|
|
|
value_shape = [3, 2, 2, 3]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
index_mod = (index % (min(array.shape))).tolist()
|
|
self.run_setitem_list_index(array, index_mod, value_np)
|
|
|
|
def test_static_graph_setitem_bool_index(self):
|
|
paddle.enable_static()
|
|
|
|
# case 1:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([True, False, False, False])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index, value_np)
|
|
|
|
# case 2:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([False, True, False, False])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index, value_np)
|
|
|
|
# case 3:
|
|
array = np.ones((4, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([True, True, True, True])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index, value_np)
|
|
|
|
def test_static_graph_setitem_bool_scalar_index(self):
|
|
paddle.enable_static()
|
|
array = np.ones((1, 2, 3), dtype='float32')
|
|
value_np = np.random.random((2, 3)).astype('float32')
|
|
index = np.array([True])
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
self.run_setitem_list_index(array, index, value_np)
|
|
|
|
def test_static_graph_tensor_index_setitem_multi_dim(self):
|
|
paddle.enable_static()
|
|
inps_shape = [3, 4, 5, 4]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [2, 3, 4]
|
|
index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
index2 = (
|
|
np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
+ 2
|
|
)
|
|
|
|
value_shape = [4]
|
|
value_np = (
|
|
np.arange(self.numel(value_shape), dtype='float32').reshape(
|
|
value_shape
|
|
)
|
|
+ 100
|
|
)
|
|
for _ in range(3):
|
|
index_mod1 = index1 % (min(array.shape))
|
|
index_mod2 = index2 % (min(array.shape))
|
|
|
|
array2 = array.copy()
|
|
array2[index_mod1, index_mod2] = value_np
|
|
array3 = array.copy()
|
|
array3[index_mod1] = value_np
|
|
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
x1 = paddle.static.data(
|
|
name='x1', shape=array.shape, dtype='float32'
|
|
)
|
|
x2 = paddle.static.data(
|
|
name='x2', shape=array.shape, dtype='float32'
|
|
)
|
|
|
|
value = paddle.static.data(
|
|
name='value', shape=value_np.shape, dtype='float32'
|
|
)
|
|
index_1 = paddle.static.data(
|
|
name='index_1', shape=index1.shape, dtype='int32'
|
|
)
|
|
index_2 = paddle.static.data(
|
|
name='index_2', shape=index2.shape, dtype='int32'
|
|
)
|
|
|
|
x1_out = paddle.static.setitem(x1, (index_1, index_2), value)
|
|
x2_out = paddle.static.setitem(x2, index_1, value)
|
|
place = (
|
|
paddle.base.CPUPlace()
|
|
if not (
|
|
paddle.base.core.is_compiled_with_cuda()
|
|
or is_custom_device()
|
|
)
|
|
else get_device_place()
|
|
)
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [x1_out, x2_out]
|
|
|
|
setitem_pp = exe.run(
|
|
prog,
|
|
feed={
|
|
x1.name: array,
|
|
x2.name: array,
|
|
value.name: value_np,
|
|
index_1.name: index_mod1,
|
|
index_2.name: index_mod2,
|
|
},
|
|
fetch_list=fetch_list,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
array2,
|
|
setitem_pp[0],
|
|
err_msg=f'\n numpy:{array2},\n paddle:{setitem_pp[0]}',
|
|
)
|
|
np.testing.assert_array_equal(
|
|
array3,
|
|
setitem_pp[1],
|
|
err_msg=f'\n numpy:{array3},\n paddle:{setitem_pp[1]}',
|
|
)
|
|
array = array[0]
|
|
index1 = index1[0]
|
|
index2 = index2[0]
|
|
|
|
def test_static_graph_array_index_multi_dim(self):
|
|
paddle.enable_static()
|
|
inps_shape = [3, 4, 5, 4]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
|
|
index_shape = [2, 3, 4]
|
|
index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
index2 = (
|
|
np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
+ 2
|
|
)
|
|
|
|
for _ in range(3):
|
|
index_mod1 = index1 % (min(array.shape))
|
|
index_mod2 = index2 % (min(array.shape))
|
|
|
|
array2 = array.copy()
|
|
array2[index_mod1, index_mod2] = 1
|
|
y_np1 = array2[index_mod2, index_mod1]
|
|
array3 = array.copy()
|
|
array3[index_mod1] = 2.5
|
|
y_np2 = array3[index_mod2]
|
|
|
|
program = paddle.static.Program()
|
|
with paddle.static.program_guard(program):
|
|
x1 = paddle.static.data(
|
|
name='x1', shape=array.shape, dtype='float32'
|
|
)
|
|
x2 = paddle.static.data(
|
|
name='x2', shape=array.shape, dtype='float32'
|
|
)
|
|
|
|
x1_out = paddle.static.setitem(x1, (index_mod1, index_mod2), 1)
|
|
x2_out = paddle.static.setitem(x2, index_mod1, 2.5)
|
|
y1 = x1_out[index_mod2, index_mod1]
|
|
y2 = x2_out[index_mod2]
|
|
place = (
|
|
paddle.base.CPUPlace()
|
|
if not (
|
|
paddle.base.core.is_compiled_with_cuda()
|
|
or is_custom_device()
|
|
)
|
|
else get_device_place()
|
|
)
|
|
|
|
prog = paddle.static.default_main_program()
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(paddle.static.default_startup_program())
|
|
fetch_list = [x1_out, x2_out, y1, y2]
|
|
|
|
setitem_pp = exe.run(
|
|
prog,
|
|
feed={x1.name: array, x2.name: array},
|
|
fetch_list=fetch_list,
|
|
)
|
|
np.testing.assert_array_equal(
|
|
array2,
|
|
setitem_pp[0],
|
|
err_msg=f'\n numpy:{array2},\n paddle:{setitem_pp[0]}',
|
|
)
|
|
np.testing.assert_array_equal(
|
|
array3,
|
|
setitem_pp[1],
|
|
err_msg=f'\n numpy:{array3},\n paddle:{setitem_pp[1]}',
|
|
)
|
|
|
|
np.testing.assert_array_equal(
|
|
y_np1,
|
|
setitem_pp[2],
|
|
err_msg=f'\n numpy:{y_np1},\n paddle:{setitem_pp[2]}',
|
|
)
|
|
np.testing.assert_array_equal(
|
|
y_np2,
|
|
setitem_pp[3],
|
|
err_msg=f'\n numpy:{y_np2},\n paddle:{setitem_pp[3]}',
|
|
)
|
|
array = array[0]
|
|
index1 = index1[0]
|
|
index2 = index2[0]
|
|
|
|
def test_dygraph_array_index_multi_dim(self):
|
|
paddle.disable_static()
|
|
inps_shape = [3, 4, 5, 4]
|
|
array = np.arange(self.numel(inps_shape), dtype='float32').reshape(
|
|
inps_shape
|
|
)
|
|
index_shape = [2, 3, 4]
|
|
index1 = np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
index2 = (
|
|
np.arange(self.numel(index_shape), dtype='int32').reshape(
|
|
index_shape
|
|
)
|
|
+ 2
|
|
)
|
|
|
|
for _ in range(3):
|
|
index_mod1 = index1 % (min(array.shape))
|
|
index_mod2 = index2 % (min(array.shape))
|
|
index_mod_t1 = paddle.to_tensor(index_mod1)
|
|
index_mod_t2 = paddle.to_tensor(index_mod2)
|
|
# 2 dim getitem
|
|
array1 = array.copy()
|
|
y_np1 = array1[index_mod2, index_mod1]
|
|
tensor1 = paddle.to_tensor(array)
|
|
|
|
y_t1 = tensor1[index_mod_t2, index_mod_t1]
|
|
|
|
np.testing.assert_array_equal(
|
|
y_t1.numpy(),
|
|
y_np1,
|
|
err_msg=f'\n numpy:{y_np1},\n paddle:{y_t1.numpy()}',
|
|
)
|
|
# 1 dim getitem
|
|
array2 = array.copy()
|
|
y_np2 = array2[index_mod2]
|
|
tensor2 = paddle.to_tensor(array)
|
|
|
|
y_t2 = tensor2[index_mod_t2]
|
|
np.testing.assert_array_equal(
|
|
y_t2.numpy(),
|
|
y_np2,
|
|
err_msg=f'\n numpy:{y_np2},\n paddle:{y_t2.numpy()}',
|
|
)
|
|
|
|
# 2 dim setitem
|
|
array1 = array.copy()
|
|
array1[index_mod1, index_mod2] = 1
|
|
tensor1[index_mod_t1, index_mod_t2] = 1
|
|
np.testing.assert_array_equal(
|
|
tensor1.numpy(),
|
|
array1,
|
|
err_msg=f'\n numpy:{array1},\n paddle:{tensor1.numpy()}',
|
|
)
|
|
# 1 dim setitem
|
|
array2 = array.copy()
|
|
|
|
array2[index_mod1] = 2.5
|
|
|
|
tensor2[index_mod_t1] = 2.5
|
|
|
|
np.testing.assert_array_equal(
|
|
tensor2.numpy(),
|
|
array2,
|
|
err_msg=f'\n numpy:{array2},\n paddle:{tensor2.numpy()}',
|
|
)
|
|
|
|
array = array[0]
|
|
index1 = index1[0]
|
|
index2 = index2[0]
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|