311 lines
11 KiB
311 lines
11 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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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_config()
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def test_check_output(self):
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self.check_output()
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def init_config(self):
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self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype='int64'), }
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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(
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[2, 3, 1, 5], dtype='int64'),
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'Index': np.array(
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[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 = {'X': np.array([2], dtype='int64'), }
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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(
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[2], dtype='int64'),
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'Index': np.array(
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[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='int64')}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
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np_unique, np_index, reverse_index = np.unique(self.inputs['X'], True,
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True)
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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='int64')
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target_index = np.array(
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[list(target_out).index(i) for i in self.inputs['X']],
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dtype='int64')
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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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def test_type():
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fluid.layers.unique([10])
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self.assertRaises(TypeError, test_type)
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def test_dtype():
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data = fluid.data(shape=[10], dtype="float16", name="input")
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fluid.layers.unique(data)
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self.assertRaises(TypeError, test_dtype)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestOneGPU(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.array([2], dtype='int64'), }
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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(
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[2], dtype='int64'),
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'Index': np.array(
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[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():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-5)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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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='int64')}
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self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
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np_unique, np_index, reverse_index = np.unique(self.inputs['X'], True,
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True)
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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='int64')
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target_index = np.array(
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[list(target_out).index(i) for i in self.inputs['X']],
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dtype='int64')
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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():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-5)
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class TestSortedUniqueOp(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype='int64')}
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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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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 TestUniqueOpAxisNone(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.random.random((4, 7, 10)).astype('float64')}
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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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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 TestUniqueOpAxis1(TestUniqueOp):
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def init_config(self):
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self.inputs = {'X': np.random.random((3, 8, 8)).astype('float64')}
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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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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 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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paddle.enable_static()
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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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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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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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paddle.enable_static()
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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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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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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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paddle.enable_static()
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def test_static_graph(self):
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with paddle.static.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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x = paddle.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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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(feed={"x": x_np},
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fetch_list=[unique, inverse, counts])
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np_unique, np_inverse, np_counts = np.unique(
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x_np, return_inverse=True, return_counts=True, axis=0)
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self.assertTrue(np.allclose(result[0], np_unique))
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self.assertTrue(np.allclose(result[1], np_inverse))
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self.assertTrue(np.allclose(result[2], np_counts))
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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.program_guard(paddle.static.Program(),
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paddle.static.Program()):
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x = paddle.data(name='x', shape=[10, 10], dtype='float16')
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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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x = paddle.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)
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def test_return_counts():
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result = paddle.unique(x, return_counts=3)
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self.assertRaises(TypeError, test_return_counts)
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def test_axis():
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result = paddle.unique(x, axis='12')
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def test_dtype():
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result = paddle.unique(x, dtype='float64')
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self.assertRaises(TypeError, test_axis)
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if __name__ == "__main__":
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unittest.main()
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