# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import get_places import paddle np.random.seed(10) class TestBucketizeAPI(unittest.TestCase): # test paddle.tensor.math.nanmean def setUp(self): self.sorted_sequence = np.array([2, 4, 8, 16]).astype("float64") self.x = np.array([[0, 8, 4, 16], [-1, 2, 8, 4]]).astype("float64") self.place = get_places() def test_api_static(self): paddle.enable_static() def run(place): with paddle.static.program_guard(paddle.static.Program()): sorted_sequence = paddle.static.data( 'SortedSequence', shape=self.sorted_sequence.shape, dtype="float64", ) x = paddle.static.data('x', shape=self.x.shape, dtype="float64") out1 = paddle.bucketize(x, sorted_sequence) out2 = paddle.bucketize(x, sorted_sequence, right=True) exe = paddle.static.Executor(place) res = exe.run( feed={'SortedSequence': self.sorted_sequence, 'x': self.x}, fetch_list=[out1, out2], ) out_ref = np.searchsorted(self.sorted_sequence, self.x) out_ref1 = np.searchsorted( self.sorted_sequence, self.x, side='right' ) np.testing.assert_allclose(out_ref, res[0], rtol=1e-05) np.testing.assert_allclose(out_ref1, res[1], rtol=1e-05) for place in self.place: run(place) def test_api_dygraph(self): def run(place): paddle.disable_static(place) sorted_sequence = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x) out1 = paddle.bucketize(x, sorted_sequence) out2 = paddle.bucketize(x, sorted_sequence, right=True) out_ref1 = np.searchsorted(self.sorted_sequence, self.x) out_ref2 = np.searchsorted( self.sorted_sequence, self.x, side='right' ) np.testing.assert_allclose(out_ref1, out1.numpy(), rtol=1e-05) np.testing.assert_allclose(out_ref2, out2.numpy(), rtol=1e-05) paddle.enable_static() for place in self.place: run(place) def test_out_int32(self): paddle.disable_static() sorted_sequence = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x) out = paddle.bucketize(x, sorted_sequence, out_int32=True) self.assertTrue(out.type, 'int32') def test_bucketize_dims_error(self): with paddle.static.program_guard(paddle.static.Program()): sorted_sequence = paddle.static.data( 'SortedSequence', shape=[2, 2], dtype="float64" ) x = paddle.static.data('x', shape=[2, 5], dtype="float64") self.assertRaises(ValueError, paddle.bucketize, x, sorted_sequence) def test_input_error(self): for place in self.place: paddle.disable_static(place) sorted_sequence = paddle.to_tensor(self.sorted_sequence) self.assertRaises( ValueError, paddle.bucketize, self.x, sorted_sequence ) def test_empty_input_error(self): for place in self.place: paddle.disable_static(place) sorted_sequence = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x) self.assertRaises( ValueError, paddle.bucketize, None, sorted_sequence ) self.assertRaises(AttributeError, paddle.bucketize, x, None) class TestBucketizeAPI_Extended(unittest.TestCase): def setUp(self): self.sorted_sequence = np.array([2, 4, 8, 16]).astype("float64") self.x2d = np.array([[0, 8, 4, 16], [-1, 2, 8, 4]]).astype("float64") self.x1d = np.array([0, 8, 4, 16]).astype("float64") self.sorted_dup = np.array([1, 2, 2, 2, 3]).astype("float64") self.x_dup = np.array([2, 2, 1, 3]).astype("float64") self.place = get_places() def test_dygraph_out_and_out_int32_and_name(self): # Dynamic diagram: Testing the out parameter (inplace write) and out_int32 paddle.disable_static() for place in self.place: with paddle.base.dygraph.guard(): seq = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x2d) res32 = paddle.bucketize( x, seq, out_int32=True, name="test_name" ) self.assertEqual(res32.dtype, paddle.int32) ref32 = np.searchsorted(self.sorted_sequence, self.x2d) np.testing.assert_allclose( ref32, res32.numpy().astype("int64"), rtol=1e-05 ) # out parameter: supply existing tensor, should be written and returned out_tensor = paddle.empty(shape=self.x2d.shape, dtype="int64") ret = paddle.bucketize(x, seq, out=out_tensor) ref = np.searchsorted(self.sorted_sequence, self.x2d) np.testing.assert_allclose(ref, out_tensor.numpy(), rtol=1e-05) paddle.enable_static() def test_static_out_int32_and_right(self): # Static image: Testing out_int32 and right=True/False paddle.enable_static() for place in self.place: with paddle.static.program_guard(paddle.static.Program()): seq = paddle.static.data( name="seq", shape=self.sorted_sequence.shape, dtype="float64", ) x = paddle.static.data( name="x", shape=self.x2d.shape, dtype="float64" ) out_left = paddle.bucketize( x, seq, right=False, out_int32=False ) out_right = paddle.bucketize(x, seq, right=True, out_int32=True) exe = paddle.static.Executor(place) res_left, res_right = exe.run( feed={"seq": self.sorted_sequence, "x": self.x2d}, fetch_list=[out_left, out_right], ) ref_left = np.searchsorted( self.sorted_sequence, self.x2d, side="left" ) ref_right = np.searchsorted( self.sorted_sequence, self.x2d, side="right" ) np.testing.assert_allclose(ref_left, res_left, rtol=1e-05) # out_int32 True -> numpy result must be cast-compatible to int32 self.assertEqual(res_right.dtype, np.int32) np.testing.assert_allclose( ref_right, res_right.astype("int64"), rtol=1e-05 ) paddle.disable_static() def test_dygraph_1d_input(self): # Dynamic image: 1D x test paddle.disable_static() for place in self.place: with paddle.base.dygraph.guard(): seq = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x1d) out = paddle.bucketize(x, seq) ref = np.searchsorted(self.sorted_sequence, self.x1d) np.testing.assert_allclose(ref, out.numpy(), rtol=1e-05) paddle.enable_static() def test_dup_elements_side_behavior(self): # Left/right difference when testing duplicate elements paddle.disable_static() for place in self.place: with paddle.base.dygraph.guard(): seq = paddle.to_tensor(self.sorted_dup) x = paddle.to_tensor(self.x_dup) out_left = paddle.bucketize(x, seq, right=False) out_right = paddle.bucketize(x, seq, right=True) ref_left = np.searchsorted( self.sorted_dup, self.x_dup, side="left" ) ref_right = np.searchsorted( self.sorted_dup, self.x_dup, side="right" ) np.testing.assert_allclose( ref_left, out_left.numpy(), rtol=1e-05 ) np.testing.assert_allclose( ref_right, out_right.numpy(), rtol=1e-05 ) paddle.enable_static() def test_static_and_dygraph_sort_of_api_stability(self): # Simple coverage: Both static and dynamic calls can succeed (without checking for duplicate results) paddle.enable_static() for place in self.place: with paddle.static.program_guard(paddle.static.Program()): seq = paddle.static.data( name="seq", shape=self.sorted_sequence.shape, dtype="float64", ) x = paddle.static.data( name="x", shape=self.x2d.shape, dtype="float64" ) _ = paddle.bucketize( x, seq, out_int32=False, right=False, name="static_case" ) exe = paddle.static.Executor(place) exe.run( feed={"seq": self.sorted_sequence, "x": self.x2d}, fetch_list=[], ) paddle.disable_static() paddle.disable_static() for place in self.place: with paddle.base.dygraph.guard(): seq = paddle.to_tensor(self.sorted_sequence) x = paddle.to_tensor(self.x2d) _ = paddle.bucketize( x, seq, out_int32=False, right=False, name="dy_case" ) paddle.enable_static() if __name__ == "__main__": unittest.main()