wehub-resource-sync
259 lines
10 KiB
Python
259 lines
10 KiB
Python
# Copyright (c) 2022 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 op_test import get_places
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import paddle
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np.random.seed(10)
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class TestBucketizeAPI(unittest.TestCase):
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# test paddle.tensor.math.nanmean
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def setUp(self):
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self.sorted_sequence = np.array([2, 4, 8, 16]).astype("float64")
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self.x = np.array([[0, 8, 4, 16], [-1, 2, 8, 4]]).astype("float64")
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self.place = get_places()
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def test_api_static(self):
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paddle.enable_static()
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def run(place):
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence',
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shape=self.sorted_sequence.shape,
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dtype="float64",
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)
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x = paddle.static.data('x', shape=self.x.shape, dtype="float64")
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out1 = paddle.bucketize(x, sorted_sequence)
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out2 = paddle.bucketize(x, sorted_sequence, right=True)
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exe = paddle.static.Executor(place)
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res = exe.run(
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feed={'SortedSequence': self.sorted_sequence, 'x': self.x},
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fetch_list=[out1, out2],
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)
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out_ref = np.searchsorted(self.sorted_sequence, self.x)
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out_ref1 = np.searchsorted(
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self.sorted_sequence, self.x, side='right'
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)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-05)
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np.testing.assert_allclose(out_ref1, res[1], rtol=1e-05)
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for place in self.place:
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run(place)
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def test_api_dygraph(self):
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def run(place):
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paddle.disable_static(place)
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x)
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out1 = paddle.bucketize(x, sorted_sequence)
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out2 = paddle.bucketize(x, sorted_sequence, right=True)
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out_ref1 = np.searchsorted(self.sorted_sequence, self.x)
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out_ref2 = np.searchsorted(
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self.sorted_sequence, self.x, side='right'
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)
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np.testing.assert_allclose(out_ref1, out1.numpy(), rtol=1e-05)
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np.testing.assert_allclose(out_ref2, out2.numpy(), rtol=1e-05)
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paddle.enable_static()
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for place in self.place:
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run(place)
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def test_out_int32(self):
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paddle.disable_static()
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x)
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out = paddle.bucketize(x, sorted_sequence, out_int32=True)
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self.assertTrue(out.type, 'int32')
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def test_bucketize_dims_error(self):
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with paddle.static.program_guard(paddle.static.Program()):
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sorted_sequence = paddle.static.data(
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'SortedSequence', shape=[2, 2], dtype="float64"
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)
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x = paddle.static.data('x', shape=[2, 5], dtype="float64")
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self.assertRaises(ValueError, paddle.bucketize, x, sorted_sequence)
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def test_input_error(self):
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for place in self.place:
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paddle.disable_static(place)
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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self.assertRaises(
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ValueError, paddle.bucketize, self.x, sorted_sequence
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)
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def test_empty_input_error(self):
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for place in self.place:
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paddle.disable_static(place)
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sorted_sequence = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x)
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self.assertRaises(
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ValueError, paddle.bucketize, None, sorted_sequence
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)
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self.assertRaises(AttributeError, paddle.bucketize, x, None)
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class TestBucketizeAPI_Extended(unittest.TestCase):
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def setUp(self):
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self.sorted_sequence = np.array([2, 4, 8, 16]).astype("float64")
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self.x2d = np.array([[0, 8, 4, 16], [-1, 2, 8, 4]]).astype("float64")
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self.x1d = np.array([0, 8, 4, 16]).astype("float64")
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self.sorted_dup = np.array([1, 2, 2, 2, 3]).astype("float64")
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self.x_dup = np.array([2, 2, 1, 3]).astype("float64")
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self.place = get_places()
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def test_dygraph_out_and_out_int32_and_name(self):
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# Dynamic diagram: Testing the out parameter (inplace write) and out_int32
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x2d)
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res32 = paddle.bucketize(
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x, seq, out_int32=True, name="test_name"
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)
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self.assertEqual(res32.dtype, paddle.int32)
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ref32 = np.searchsorted(self.sorted_sequence, self.x2d)
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np.testing.assert_allclose(
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ref32, res32.numpy().astype("int64"), rtol=1e-05
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)
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# out parameter: supply existing tensor, should be written and returned
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out_tensor = paddle.empty(shape=self.x2d.shape, dtype="int64")
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ret = paddle.bucketize(x, seq, out=out_tensor)
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ref = np.searchsorted(self.sorted_sequence, self.x2d)
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np.testing.assert_allclose(ref, out_tensor.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_static_out_int32_and_right(self):
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# Static image: Testing out_int32 and right=True/False
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paddle.enable_static()
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for place in self.place:
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with paddle.static.program_guard(paddle.static.Program()):
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seq = paddle.static.data(
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name="seq",
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shape=self.sorted_sequence.shape,
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dtype="float64",
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)
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x = paddle.static.data(
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name="x", shape=self.x2d.shape, dtype="float64"
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)
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out_left = paddle.bucketize(
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x, seq, right=False, out_int32=False
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)
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out_right = paddle.bucketize(x, seq, right=True, out_int32=True)
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exe = paddle.static.Executor(place)
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res_left, res_right = exe.run(
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feed={"seq": self.sorted_sequence, "x": self.x2d},
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fetch_list=[out_left, out_right],
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)
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ref_left = np.searchsorted(
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self.sorted_sequence, self.x2d, side="left"
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)
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ref_right = np.searchsorted(
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self.sorted_sequence, self.x2d, side="right"
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)
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np.testing.assert_allclose(ref_left, res_left, rtol=1e-05)
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# out_int32 True -> numpy result must be cast-compatible to int32
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self.assertEqual(res_right.dtype, np.int32)
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np.testing.assert_allclose(
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ref_right, res_right.astype("int64"), rtol=1e-05
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)
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paddle.disable_static()
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def test_dygraph_1d_input(self):
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# Dynamic image: 1D x test
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x1d)
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out = paddle.bucketize(x, seq)
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ref = np.searchsorted(self.sorted_sequence, self.x1d)
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np.testing.assert_allclose(ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_dup_elements_side_behavior(self):
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# Left/right difference when testing duplicate elements
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_dup)
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x = paddle.to_tensor(self.x_dup)
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out_left = paddle.bucketize(x, seq, right=False)
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out_right = paddle.bucketize(x, seq, right=True)
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ref_left = np.searchsorted(
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self.sorted_dup, self.x_dup, side="left"
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)
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ref_right = np.searchsorted(
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self.sorted_dup, self.x_dup, side="right"
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)
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np.testing.assert_allclose(
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ref_left, out_left.numpy(), rtol=1e-05
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)
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np.testing.assert_allclose(
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ref_right, out_right.numpy(), rtol=1e-05
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)
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paddle.enable_static()
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def test_static_and_dygraph_sort_of_api_stability(self):
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# Simple coverage: Both static and dynamic calls can succeed (without checking for duplicate results)
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paddle.enable_static()
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for place in self.place:
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with paddle.static.program_guard(paddle.static.Program()):
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seq = paddle.static.data(
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name="seq",
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shape=self.sorted_sequence.shape,
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dtype="float64",
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)
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x = paddle.static.data(
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name="x", shape=self.x2d.shape, dtype="float64"
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)
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_ = paddle.bucketize(
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x, seq, out_int32=False, right=False, name="static_case"
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)
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exe = paddle.static.Executor(place)
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exe.run(
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feed={"seq": self.sorted_sequence, "x": self.x2d},
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fetch_list=[],
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)
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paddle.disable_static()
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paddle.disable_static()
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for place in self.place:
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with paddle.base.dygraph.guard():
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seq = paddle.to_tensor(self.sorted_sequence)
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x = paddle.to_tensor(self.x2d)
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_ = paddle.bucketize(
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x, seq, out_int32=False, right=False, name="dy_case"
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)
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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