test_unique.py

#   Copyright (c) 2019 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 eager_op_test import OpTest, paddle_static_guard

import paddle
from paddle.fluid import core


class TestUniqueOp(OpTest):
    def setUp(self):
        self.op_type = "unique"
        self.init_dtype()
        self.init_config()

    def test_check_output(self):
        self.check_output(
            check_dygraph=False
        )  # unique return sorted data in dygraph

    def init_dtype(self):
        self.dtype = np.int64

    def init_config(self):
        self.inputs = {
            'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype),
        }
        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
        self.outputs = {
            'Out': np.array([2, 3, 1, 5], dtype=self.dtype),
            'Index': np.array([0, 1, 1, 2, 3, 1], dtype='int32'),
        }


class TestOne(TestUniqueOp):
    def init_config(self):
        self.inputs = {
            'X': np.array([2], dtype=self.dtype),
        }
        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
        self.outputs = {
            'Out': np.array([2], dtype=self.dtype),
            'Index': np.array([0], dtype='int32'),
        }


class TestRandom(TestUniqueOp):
    def init_config(self):
        self.inputs = {'X': np.random.randint(0, 100, (150,), dtype=self.dtype)}
        self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
        np_unique, np_index, reverse_index = np.unique(
            self.inputs['X'], True, True
        )
        np_tuple = [(np_unique[i], np_index[i]) for i in range(len(np_unique))]
        np_tuple.sort(key=lambda x: x[1])
        target_out = np.array([i[0] for i in np_tuple], dtype=self.dtype)
        target_index = np.array(
            [list(target_out).index(i) for i in self.inputs['X']], dtype='int64'
        )

        self.outputs = {'Out': target_out, 'Index': target_index}


class TestUniqueRaiseError(unittest.TestCase):
    def test_errors(self):
        with paddle_static_guard():

            def test_type():
                paddle.unique([10])

            self.assertRaises(TypeError, test_type)

            def test_dtype():
                data = paddle.static.data(
                    shape=[10], dtype="float16", name="input"
                )
                paddle.unique(data)

            self.assertRaises(TypeError, test_dtype)


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestOneGPU(TestUniqueOp):
    def init_config(self):
        self.inputs = {
            'X': np.array([2], dtype=self.dtype),
        }
        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
        self.outputs = {
            'Out': np.array([2], dtype=self.dtype),
            'Index': np.array([0], dtype='int32'),
        }

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            self.check_output_with_place(
                place, atol=1e-5, check_dygraph=False
            )  # unique return sorted data in dygraph


@unittest.skipIf(
    not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
)
class TestRandomGPU(TestUniqueOp):
    def init_config(self):
        self.inputs = {'X': np.random.randint(0, 100, (150,), dtype=self.dtype)}
        self.attrs = {'dtype': int(core.VarDesc.VarType.INT64)}
        np_unique, np_index, reverse_index = np.unique(
            self.inputs['X'], True, True
        )
        np_tuple = [(np_unique[i], np_index[i]) for i in range(len(np_unique))]
        np_tuple.sort(key=lambda x: x[1])
        target_out = np.array([i[0] for i in np_tuple], dtype=self.dtype)
        target_index = np.array(
            [list(target_out).index(i) for i in self.inputs['X']], dtype='int64'
        )

        self.outputs = {'Out': target_out, 'Index': target_index}

    def test_check_output(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
            self.check_output_with_place(
                place, atol=1e-5, check_dygraph=False
            )  # unique return sorted data in dygraph


class TestSortedUniqueOp(TestUniqueOp):
    def init_dtype(self):
        self.dtype = np.float64

    def init_config(self):
        self.inputs = {'X': np.array([2, 3, 3, 1, 5, 3], dtype=self.dtype)}
        unique, indices, inverse, count = np.unique(
            self.inputs['X'],
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=None,
        )
        self.attrs = {
            'dtype': int(core.VarDesc.VarType.INT32),
            "return_index": True,
            "return_inverse": True,
            "return_counts": True,
            "axis": None,
            "is_sorted": True,
        }
        self.outputs = {
            'Out': unique,
            'Indices': indices,
            "Index": inverse,
            "Counts": count,
        }


class TestSortedUniqueFP16Op(TestSortedUniqueOp):
    def init_dtype(self):
        self.dtype = np.float16


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestSortedUniqueBF16Op(TestSortedUniqueOp):
    def init_dtype(self):
        self.dtype = np.uint16

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(
            place, check_dygraph=False
        )  # unique return sorted data in dygraph


class TestUniqueOpAxisNone(TestUniqueOp):
    def init_dtype(self):
        self.dtype = np.float64

    def init_config(self):
        self.inputs = {
            'X': np.random.randint(0, 100, (4, 7, 10)).astype(self.dtype)
        }
        unique, indices, inverse, counts = np.unique(
            self.inputs['X'],
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=None,
        )
        self.attrs = {
            'dtype': int(core.VarDesc.VarType.INT32),
            "return_index": True,
            "return_inverse": True,
            "return_counts": True,
            "axis": None,
            "is_sorted": True,
        }
        self.outputs = {
            'Out': unique,
            'Indices': indices,
            "Index": inverse,
            "Counts": counts,
        }


class TestUniqueOpAxisNoneFP16Op(TestUniqueOpAxisNone):
    def init_dtype(self):
        self.dtype = np.float16


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestUniqueOpAxisNoneBF16Op(TestUniqueOpAxisNone):
    def init_dtype(self):
        self.dtype = np.uint16

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(
            place, check_dygraph=False
        )  # unique return sorted data in dygraph


class TestUniqueOpAxisNeg(TestUniqueOp):
    def init_dtype(self):
        self.dtype = np.float64

    def init_config(self):
        self.inputs = {
            'X': np.random.randint(0, 100, (6, 1, 8)).astype(self.dtype)
        }
        unique, indices, inverse, counts = np.unique(
            self.inputs['X'],
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=-1,
        )
        self.attrs = {
            'dtype': int(core.VarDesc.VarType.INT32),
            "return_index": True,
            "return_inverse": True,
            "return_counts": True,
            "axis": [-1],
            "is_sorted": True,
        }
        self.outputs = {
            'Out': unique,
            'Indices': indices,
            "Index": inverse,
            "Counts": counts,
        }


class TestUniqueOpAxisNegFP16Op(TestUniqueOpAxisNeg):
    def init_dtype(self):
        self.dtype = np.float16


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestUniqueOpAxisNegBF16Op(TestUniqueOpAxisNeg):
    def init_dtype(self):
        self.dtype = np.uint16

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(
            place, check_dygraph=False
        )  # unique return sorted data in dygraph


class TestUniqueOpAxis1(TestUniqueOp):
    def init_dtype(self):
        self.dtype = np.float64

    def init_config(self):
        self.inputs = {
            'X': np.random.randint(0, 100, (3, 8, 8)).astype(self.dtype)
        }
        unique, indices, inverse, counts = np.unique(
            self.inputs['X'],
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=1,
        )
        self.attrs = {
            'dtype': int(core.VarDesc.VarType.INT32),
            "return_index": True,
            "return_inverse": True,
            "return_counts": True,
            "axis": [1],
            "is_sorted": True,
        }
        self.outputs = {
            'Out': unique,
            'Indices': indices,
            "Index": inverse,
            "Counts": counts,
        }


class TestUniqueOpAxis1FP16Op(TestUniqueOpAxis1):
    def init_dtype(self):
        self.dtype = np.float16


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestUniqueOpAxis1BF16Op(TestUniqueOpAxis1):
    def init_dtype(self):
        self.dtype = np.uint16

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(
            place, check_dygraph=False
        )  # unique return sorted data in dygraph


class TestUniqueAPI(unittest.TestCase):
    def test_dygraph_api_out(self):
        paddle.disable_static()
        x_data = x_data = np.random.randint(0, 10, (120))
        x = paddle.to_tensor(x_data)
        out = paddle.unique(x)
        expected_out = np.unique(x_data)
        self.assertTrue((out.numpy() == expected_out).all(), True)

    def test_dygraph_api_attr(self):
        paddle.disable_static()
        x_data = np.random.random((3, 5, 5)).astype("float32")
        x = paddle.to_tensor(x_data)
        out, index, inverse, counts = paddle.unique(
            x,
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=0,
        )
        np_out, np_index, np_inverse, np_counts = np.unique(
            x_data,
            return_index=True,
            return_inverse=True,
            return_counts=True,
            axis=0,
        )
        self.assertTrue((out.numpy() == np_out).all(), True)
        self.assertTrue((index.numpy() == np_index).all(), True)
        self.assertTrue((inverse.numpy() == np_inverse).all(), True)
        self.assertTrue((counts.numpy() == np_counts).all(), True)

    def test_dygraph_attr_dtype(self):
        paddle.disable_static()
        x_data = x_data = np.random.randint(0, 10, (120))
        x = paddle.to_tensor(x_data)
        out, indices, inverse, counts = paddle.unique(
            x,
            return_index=True,
            return_inverse=True,
            return_counts=True,
            dtype="int32",
        )
        expected_out, np_indices, np_inverse, np_counts = np.unique(
            x_data, return_index=True, return_inverse=True, return_counts=True
        )
        self.assertTrue((out.numpy() == expected_out).all(), True)
        self.assertTrue((indices.numpy() == np_indices).all(), True)
        self.assertTrue((inverse.numpy() == np_inverse).all(), True)
        self.assertTrue((counts.numpy() == np_counts).all(), True)

    def test_static_graph(self):
        with paddle_static_guard():
            with paddle.static.program_guard(
                paddle.static.Program(), paddle.static.Program()
            ):
                x = paddle.static.data(name='x', shape=[3, 2], dtype='float64')
                unique, inverse, counts = paddle.unique(
                    x, return_inverse=True, return_counts=True, axis=0
                )
                place = paddle.CPUPlace()
                exe = paddle.static.Executor(place)
                x_np = np.array([[1, 2], [3, 4], [1, 2]]).astype('float64')
                result = exe.run(
                    feed={"x": x_np}, fetch_list=[unique, inverse, counts]
                )


class TestUniqueError(unittest.TestCase):
    def test_input_dtype(self):
        def test_x_dtype():
            with paddle_static_guard():
                with paddle.static.program_guard(
                    paddle.static.Program(), paddle.static.Program()
                ):
                    x = paddle.static.data(
                        name='x', shape=[10, 10], dtype='float16'
                    )
                    result = paddle.unique(x)

                self.assertRaises(TypeError, test_x_dtype)

    def test_attr(self):
        with paddle_static_guard():
            x = paddle.static.data(name='x', shape=[10, 10], dtype='float64')

            def test_return_index():
                result = paddle.unique(x, return_index=0)

            self.assertRaises(TypeError, test_return_index)

            def test_return_inverse():
                result = paddle.unique(x, return_inverse='s')

            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)


if __name__ == "__main__":
    unittest.main()