wehub-resource-sync
576 lines
18 KiB
Python
576 lines
18 KiB
Python
# Copyright (c) 2019 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 (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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paddle_static_guard,
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)
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import paddle
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from paddle.base import core
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class TestUniqueOp(OpTest):
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def setUp(self):
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self.op_type = "unique"
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self.init_dtype()
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self.init_config()
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def test_check_output(self):
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self.check_output(
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check_dygraph=False
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) # unique return sorted data in dygraph
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def init_dtype(self):
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self.dtype = np.int64
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def init_config(self):
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self.inputs = {
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'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype),
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}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
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self.outputs = {
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'Out': np.array([2, 3, 1, 5], dtype=self.dtype),
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'Index': np.array([0, 1, 1, 2, 3, 1], dtype='int32'),
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}
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class TestOne(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': np.array([2], dtype=self.dtype),
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}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
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self.outputs = {
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'Out': np.array([2], dtype=self.dtype),
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'Index': np.array([0], dtype='int32'),
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}
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class TestRandom(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.random.randint(0, 100, (150,), dtype=self.dtype)}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
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np_unique, np_index, reverse_index = np.unique(
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self.inputs['X'], True, True
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)
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np_tuple = [(np_unique[i], np_index[i]) for i in range(len(np_unique))]
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np_tuple.sort(key=lambda x: x[1])
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target_out = np.array([i[0] for i in np_tuple], dtype=self.dtype)
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target_index = np.array(
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[list(target_out).index(i) for i in self.inputs['X']], dtype='int64'
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)
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self.outputs = {'Out': target_out, 'Index': target_index}
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class TestUniqueRaiseError(unittest.TestCase):
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def test_errors(self):
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with paddle_static_guard():
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def test_type():
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paddle.unique([10])
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self.assertRaises(TypeError, test_type)
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def test_dtype():
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data = paddle.static.data(
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shape=[10], dtype="int16", name="input"
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)
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paddle.unique(data)
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self.assertRaises(TypeError, test_dtype)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestOneGPU(TestUniqueOp):
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def init_config(self):
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self.inputs = {
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'X': np.array([2], dtype=self.dtype),
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}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
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self.outputs = {
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'Out': np.array([2], dtype=self.dtype),
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'Index': np.array([0], dtype='int32'),
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}
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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self.check_output_with_place(
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place, atol=1e-5, check_dygraph=False
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) # unique return sorted data in dygraph
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestRandomGPU(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.random.randint(0, 100, (150,), dtype=self.dtype)}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
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np_unique, np_index, reverse_index = np.unique(
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self.inputs['X'], True, True
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)
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np_tuple = [(np_unique[i], np_index[i]) for i in range(len(np_unique))]
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np_tuple.sort(key=lambda x: x[1])
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target_out = np.array([i[0] for i in np_tuple], dtype=self.dtype)
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target_index = np.array(
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[list(target_out).index(i) for i in self.inputs['X']], dtype='int64'
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)
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self.outputs = {'Out': target_out, 'Index': target_index}
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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self.check_output_with_place(
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place, atol=1e-5, check_dygraph=False
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) # unique return sorted data in dygraph
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class TestSortedUniqueOp(TestUniqueOp):
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def init_dtype(self):
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self.dtype = np.float64
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def init_config(self):
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if self.dtype == np.uint16:
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self.inputs = {
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'X': convert_float_to_uint16(
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np.array([2, 3, 3, 1, 5, 3], dtype=np.float32)
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)
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}
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else:
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self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype)}
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unique, indices, inverse, count = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=None,
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)
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": None,
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": count,
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}
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class TestSortedUniqueFP16Op(TestSortedUniqueOp):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support the bfloat16",
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)
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class TestSortedUniqueBF16Op(TestSortedUniqueOp):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_dygraph=False
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) # unique return sorted data in dygraph
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class TestUniqueOpAxisNone(TestUniqueOp):
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def init_dtype(self):
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self.dtype = np.float64
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def init_config(self):
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if self.dtype == np.uint16:
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self.inputs = {
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'X': convert_float_to_uint16(
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np.random.randint(0, 100, (4, 7, 10)).astype(np.float32)
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)
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}
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else:
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self.inputs = {
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'X': np.random.randint(0, 100, (4, 7, 10)).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=None,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": None,
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestUniqueOpAxisNoneFP16Op(TestUniqueOpAxisNone):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support the bfloat16",
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)
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class TestUniqueOpAxisNoneBF16Op(TestUniqueOpAxisNone):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_dygraph=False
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) # unique return sorted data in dygraph
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class TestUniqueOpAxisNeg(TestUniqueOp):
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def init_dtype(self):
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self.dtype = np.float64
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def init_config(self):
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self.inputs = {
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'X': np.random.randint(0, 100, (6, 1, 8)).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=-1,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": [-1],
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestUniqueOpAxisNegFP16Op(TestUniqueOpAxisNeg):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support the bfloat16",
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)
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class TestUniqueOpAxisNegBF16Op(TestUniqueOpAxisNeg):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_dygraph=False
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) # unique return sorted data in dygraph
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class TestUniqueOpAxis1(TestUniqueOp):
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def init_dtype(self):
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self.dtype = np.float64
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def init_config(self):
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self.inputs = {
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'X': np.random.randint(0, 100, (3, 8, 8)).astype(self.dtype)
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}
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unique, indices, inverse, counts = np.unique(
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self.inputs['X'],
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=1,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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inverse = inverse.flatten()
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self.attrs = {
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'dtype': int(core.VarDesc.VarType.INT32),
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"return_index": True,
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"return_inverse": True,
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"return_counts": True,
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"axis": [1],
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"is_sorted": True,
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}
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self.outputs = {
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'Out': unique,
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'Indices': indices,
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"Index": inverse,
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"Counts": counts,
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}
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class TestUniqueOpAxis1FP16Op(TestUniqueOpAxis1):
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def init_dtype(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support the bfloat16",
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)
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class TestUniqueOpAxis1BF16Op(TestUniqueOpAxis1):
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def init_dtype(self):
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self.dtype = np.uint16
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_dygraph=False
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) # unique return sorted data in dygraph
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class TestUniqueAPI(unittest.TestCase):
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def test_dygraph_api_out(self):
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paddle.disable_static()
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x_data = x_data = np.random.randint(0, 10, (120))
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x = paddle.to_tensor(x_data)
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out = paddle.unique(x)
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expected_out = np.unique(x_data)
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self.assertTrue((out.numpy() == expected_out).all(), True)
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def test_dygraph_api_attr(self):
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paddle.disable_static()
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x_data = np.random.random((3, 5, 5)).astype("float32")
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x = paddle.to_tensor(x_data)
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out, index, inverse, counts = paddle.unique(
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x,
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=0,
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)
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np_out, np_index, np_inverse, np_counts = np.unique(
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x_data,
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return_index=True,
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return_inverse=True,
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return_counts=True,
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axis=0,
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)
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if np.lib.NumpyVersion(np.__version__) >= "2.0.0":
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np_inverse = np_inverse.flatten()
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self.assertTrue((out.numpy() == np_out).all(), True)
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self.assertTrue((index.numpy() == np_index).all(), True)
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self.assertTrue((inverse.numpy() == np_inverse).all(), True)
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self.assertTrue((counts.numpy() == np_counts).all(), True)
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def test_dygraph_attr_dtype(self):
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paddle.disable_static()
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x_data = x_data = np.random.randint(0, 10, (120))
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x = paddle.to_tensor(x_data)
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out, indices, inverse, counts = paddle.unique(
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x,
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return_index=True,
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return_inverse=True,
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return_counts=True,
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dtype="int32",
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)
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expected_out, np_indices, np_inverse, np_counts = np.unique(
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x_data, return_index=True, return_inverse=True, return_counts=True
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)
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self.assertTrue((out.numpy() == expected_out).all(), True)
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self.assertTrue((indices.numpy() == np_indices).all(), True)
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self.assertTrue((inverse.numpy() == np_inverse).all(), True)
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self.assertTrue((counts.numpy() == np_counts).all(), True)
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def test_static_graph(self):
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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x = paddle.static.data(name='x', shape=[3, 2], dtype='float64')
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unique, inverse, counts = paddle.unique(
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x, return_inverse=True, return_counts=True, axis=0
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)
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self.assertEqual(unique.dtype, paddle.float64)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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x_np = np.array([[1, 2], [3, 4], [1, 2]]).astype('float64')
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result = exe.run(
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feed={"x": x_np}, fetch_list=[unique, inverse, counts]
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)
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np.testing.assert_array_equal(result[0], np.array([[1, 2], [3, 4]]))
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np.testing.assert_array_equal(result[1], np.array([0, 1, 0]))
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np.testing.assert_array_equal(result[2], np.array([2, 1]))
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def test_static_graph_int64_input(self):
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with (
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paddle_static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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x = paddle.static.data(name='x', shape=[3, 2], dtype='int64')
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unique, inverse, counts = paddle.unique(
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x, return_inverse=True, return_counts=True, axis=0
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)
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self.assertEqual(unique.dtype, paddle.int64)
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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x_np = np.array([[1, 2], [3, 4], [1, 2]]).astype('int64')
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result = exe.run(
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feed={"x": x_np}, fetch_list=[unique, inverse, counts]
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)
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np.testing.assert_array_equal(result[0], np.array([[1, 2], [3, 4]]))
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np.testing.assert_array_equal(result[1], np.array([0, 1, 0]))
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np.testing.assert_array_equal(result[2], np.array([2, 1]))
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class TestUniqueError(unittest.TestCase):
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def test_input_dtype(self):
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def test_x_dtype():
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with paddle_static_guard():
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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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x = paddle.static.data(
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name='x', shape=[10, 10], dtype='int16'
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)
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result = paddle.unique(x)
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self.assertRaises(TypeError, test_x_dtype)
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def test_attr(self):
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with paddle_static_guard():
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x = paddle.static.data(name='x', shape=[10, 10], dtype='float64')
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def test_return_index():
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result = paddle.unique(x, return_index=0)
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self.assertRaises(TypeError, test_return_index)
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def test_return_inverse():
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result = paddle.unique(x, return_inverse='s')
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self.assertRaises(TypeError, test_return_inverse)
|
|
|
|
def test_return_counts():
|
|
result = paddle.unique(x, return_counts=3)
|
|
|
|
self.assertRaises(TypeError, test_return_counts)
|
|
|
|
def test_axis():
|
|
result = paddle.unique(x, axis='12')
|
|
|
|
def test_dtype():
|
|
result = paddle.unique(x, dtype='float64')
|
|
|
|
self.assertRaises(TypeError, test_axis)
|
|
|
|
|
|
class TestUniqueAPI_ZeroSize(unittest.TestCase):
|
|
def test_dygraph_api_out(self):
|
|
paddle.disable_static()
|
|
x_data = np.random.randint(0, 10, (0, 2))
|
|
x = paddle.to_tensor(x_data)
|
|
out = paddle.unique(x)
|
|
expected_out = np.random.random([0, 2])
|
|
np.testing.assert_allclose(out.numpy(), expected_out)
|
|
|
|
|
|
class TestUniqueAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random(size=[3, 5]).astype("float32")
|
|
self.place = (
|
|
core.CUDAPlace(0)
|
|
if core.is_compiled_with_cuda()
|
|
else core.CPUPlace()
|
|
)
|
|
|
|
def test_dygraph(self):
|
|
paddle.disable_static()
|
|
out = paddle.unique(paddle.to_tensor(self.x_np))
|
|
expected_out = np.unique(self.x_np)
|
|
np.testing.assert_allclose(out.numpy(), expected_out)
|
|
|
|
def test_static(self):
|
|
paddle.enable_static()
|
|
x = paddle.static.data(name='x1', shape=[-1, 5], dtype='float32')
|
|
out1 = paddle.unique(x)
|
|
out2 = paddle.unique(x=x)
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(
|
|
feed={
|
|
'x1': self.x_np.reshape(3, 5),
|
|
},
|
|
fetch_list=[out1, out2],
|
|
)
|
|
expected_out = np.unique(self.x_np)
|
|
for result in res:
|
|
np.testing.assert_array_equal(result, expected_out)
|
|
paddle.disable_static()
|
|
|
|
def test_dygraph_sorted(self):
|
|
paddle.disable_static()
|
|
out = paddle.unique(paddle.to_tensor(self.x_np), sorted=True)
|
|
expected_out = np.unique(self.x_np)
|
|
np.testing.assert_allclose(out.numpy(), expected_out)
|
|
|
|
def test_dygraph_axis(self):
|
|
paddle.disable_static()
|
|
out = paddle.unique(paddle.to_tensor(self.x_np), sorted=True, axis=1)
|
|
expected_out = np.unique(self.x_np, axis=1)
|
|
np.testing.assert_allclose(out.numpy(), expected_out)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|