wehub-resource-sync
699 lines
24 KiB
Python
699 lines
24 KiB
Python
# Copyright (c) 2020 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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)
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from utils import static_guard
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import paddle
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from paddle import base
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from paddle.base import core
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class TestRollOp(OpTest):
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def setUp(self):
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self.python_api = paddle.roll
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self.op_type = "roll"
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self.public_python_api = paddle.roll
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self.prim_op_type = "prim"
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self.init_dtype_type()
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self.attrs = {'shifts': self.shifts, 'axis': self.axis}
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bf16_ut = self.dtype == np.uint16
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x = np.random.random(self.x_shape).astype(
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np.float32 if bf16_ut else self.dtype
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)
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out = np.roll(x, self.attrs['shifts'], self.attrs['axis'])
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if bf16_ut:
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x = convert_float_to_uint16(x)
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out = convert_float_to_uint16(out)
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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def init_dtype_type(self):
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self.dtype = np.float64
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self.x_shape = (100, 4, 5)
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self.shifts = [101, -1]
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self.axis = [0, -2]
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def test_check_output(self):
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self.check_output(
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check_prim=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad_normal(self):
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self.check_grad(
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['X'], 'Out', check_prim=False, check_pir=True, check_prim_pir=True
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)
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class TestRollOpCase2(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.float32
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self.x_shape = (100, 10, 5)
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self.shifts = [8, -1]
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self.axis = [-1, -2]
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class TestRollOpCase3(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.float32
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self.x_shape = (11, 11)
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self.shifts = [1, 1]
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self.axis = [-1, 1]
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class TestRollFP16OP(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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self.x_shape = (100, 4, 5)
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self.shifts = [101, -1]
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self.axis = [0, -2]
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class TestRollFP16OpCase2(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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self.x_shape = (100, 10, 5)
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self.shifts = [8, -1]
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self.axis = [-1, -2]
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class TestRollFP16OpCase3(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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self.x_shape = (11, 11)
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self.shifts = [1, 1]
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self.axis = [-1, 1]
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class TestRollBollOp(OpTest):
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def setUp(self):
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self.python_api = paddle.roll
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self.op_type = "roll"
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self.public_python_api = paddle.roll
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self.prim_op_type = "prim"
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self.init_dtype_type()
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self.attrs = {'shifts': self.shifts, 'axis': self.axis}
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x = np.random.random(self.x_shape).astype(self.dtype)
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out = np.roll(x, self.attrs['shifts'], self.attrs['axis'])
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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def init_dtype_type(self):
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self.dtype = np.bool_
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self.x_shape = (100, 4, 5)
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self.shifts = [101, -1]
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self.axis = [0, -2]
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def test_check_output(self):
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self.check_output(
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check_prim=True, check_pir=True, check_symbol_infer=True
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)
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class TestRollBoolOpCase2(TestRollBollOp):
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def init_dtype_type(self):
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self.dtype = np.bool_
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self.x_shape = (100, 10, 5)
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self.shifts = [8, -1]
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self.axis = [-1, -2]
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class TestRollBoolOpCase3(TestRollBollOp):
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def init_dtype_type(self):
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self.dtype = np.bool_
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self.x_shape = (11, 11)
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self.shifts = [1, 1]
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self.axis = [-1, 1]
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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 and not support the bfloat16",
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)
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class TestRollBF16OP(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.uint16
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self.x_shape = (10, 4, 5)
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self.shifts = [101, -1]
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self.axis = [0, -2]
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_prim=True, check_pir=True
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)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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self.place, ['X'], 'Out', check_prim=False, check_pir=True
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)
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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 and not support the bfloat16",
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)
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class TestRollBF16OpCase2(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.uint16
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self.x_shape = (10, 5, 5)
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self.shifts = [8, -1]
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self.axis = [-1, -2]
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_prim=True, check_pir=True
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)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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check_prim=False,
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check_pir=True,
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check_prim_pir=True,
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)
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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 and not support the bfloat16",
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)
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class TestRollBF16OpCase3(TestRollOp):
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def init_dtype_type(self):
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self.dtype = np.uint16
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self.x_shape = (11, 11)
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self.shifts = [1, 1]
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self.axis = [-1, 1]
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self.place = get_device_place()
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def test_check_output(self):
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self.check_output_with_place(
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self.place, check_prim=True, check_pir=True
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)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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check_prim=False,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestRollAPI(unittest.TestCase):
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def input_data(self):
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self.data_x = np.array(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
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)
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def test_roll_op_api_case1(self):
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with 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(name='x', shape=[-1, 3], dtype='float32')
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data_x = np.array(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
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).astype('float32')
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z = paddle.roll(x, shifts=1)
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exe = paddle.static.Executor(paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[9.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_roll_op_api_case2(self):
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with 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(name='x', shape=[-1, 3], dtype='float32')
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data_x = np.array(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
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).astype('float32')
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z = paddle.roll(x, shifts=1, axis=0)
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exe = paddle.static.Executor(paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[7.0, 8.0, 9.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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paddle.disable_static()
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def test_dygraph_api(self):
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self.input_data()
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# case 1:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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z = paddle.roll(x, shifts=1)
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np_z = z.numpy()
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expect_out = np.array(
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[[9.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 2:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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z = paddle.roll(x, shifts=1, axis=0)
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np_z = z.numpy()
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expect_out = np.array(
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[[7.0, 8.0, 9.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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def test_roll_op_false(self):
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def test_axis_out_range():
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paddle.enable_static()
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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(name='x', shape=[-1, 3], dtype='float32')
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data_x = np.array(
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[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
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).astype('float32')
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z = paddle.roll(x, shifts=1, axis=10)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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self.assertRaises(ValueError, test_axis_out_range)
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paddle.disable_static()
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def test_shifts_as_tensor_dygraph(self):
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with base.dygraph.guard():
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x = paddle.arange(9).reshape([3, 3])
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shape = paddle.shape(x)
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shifts = shape // 2
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axes = [0, 1]
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out = paddle.roll(x, shifts=shifts, axis=axes).numpy()
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expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
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np.testing.assert_allclose(out, expected_out, rtol=1e-05)
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def test_shifts_as_tensor_static(self):
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paddle.enable_static()
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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.arange(9).reshape([3, 3]).astype('float32')
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shape = paddle.shape(x)
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shifts = shape // 2
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axes = [0, 1]
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out = paddle.roll(x, shifts=shifts, axis=axes)
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expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
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exe = paddle.static.Executor(paddle.CPUPlace())
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[out_np] = exe.run(fetch_list=[out])
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np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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exe = base.Executor(base.CPUPlace())
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[out_np] = exe.run(fetch_list=[out])
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np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)
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paddle.disable_static()
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for bool dtype is not fully supported",
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)
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class TestRollBoolAPI(unittest.TestCase):
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def input_data(self):
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self.data_x_bool = np.array(
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[[True, False, True], [False, True, False], [True, False, True]]
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).astype('bool')
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def test_roll_op_api_case1(self):
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with 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(name='x', shape=[3, 3], dtype='bool')
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data_x = np.array(
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[
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[True, False, True],
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[False, True, False],
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[True, False, True],
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]
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).astype('bool')
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z = paddle.roll(x, shifts=1)
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exe = paddle.static.Executor(paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[
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[True, True, False],
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[True, False, True],
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[False, True, False],
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]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_roll_op_api_case2(self):
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with 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(name='x', shape=[3, 3], dtype='bool')
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data_x = np.array(
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[
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[True, False, True],
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[False, True, False],
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[True, False, True],
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]
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).astype('bool')
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z = paddle.roll(x, shifts=1, axis=0)
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exe = paddle.static.Executor(paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[
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[True, False, True],
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[True, False, True],
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[False, True, False],
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]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_dygraph_api(self):
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self.input_data()
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# case 1:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x_bool)
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z = paddle.roll(x, shifts=1)
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np_z = z.numpy()
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expect_out = np.array(
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[[True, True, False], [True, False, True], [False, True, False]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 2:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x_bool)
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z = paddle.roll(x, shifts=1, axis=0)
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np_z = z.numpy()
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expect_out = np.array(
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[[True, False, True], [True, False, True], [False, True, False]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"Skip XPU for zero size is not fully supported",
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)
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class TestRoll0SizelAPI(unittest.TestCase):
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def input_data(self):
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self.data_x_zero_size1 = np.array([]).reshape(0, 3).astype('float32')
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self.data_x_zero_size2 = np.array([]).reshape(4, 0, 3).astype('float32')
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def test_roll_op_api_case1(self):
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paddle.enable_static()
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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(name='x', shape=[0, 3], dtype='float32')
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data_x = np.array([]).reshape(0, 3).astype('float32')
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z = paddle.roll(x, shifts=1)
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exe = paddle.static.Executor(paddle.CPUPlace())
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(res,) = exe.run(
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paddle.static.default_main_program(),
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feed={'x': data_x},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array([]).reshape(0, 3)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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def test_roll_op_api_case2(self):
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with static_guard():
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[0, 3], dtype='float32')
|
|
data_x = np.array([]).reshape(0, 3).astype('float32')
|
|
z = paddle.roll(x, shifts=1, axis=0)
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
(res,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
expect_out = np.array([]).reshape(0, 3)
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
|
|
def test_roll_op_api_case3(self):
|
|
with static_guard():
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(
|
|
name='x', shape=[4, 0, 3], dtype='float32'
|
|
)
|
|
data_x = np.array([]).reshape(4, 0, 3).astype('float32')
|
|
z = paddle.roll(x, shifts=1)
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
(res,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
expect_out = np.array([]).reshape(4, 0, 3)
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
|
|
def test_roll_op_api_case4(self):
|
|
with static_guard():
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(
|
|
name='x', shape=[4, 0, 3], dtype='float32'
|
|
)
|
|
data_x = np.array([]).reshape(4, 0, 3).astype('float32')
|
|
z = paddle.roll(x, shifts=1, axis=0)
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
(res,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
expect_out = np.array([]).reshape(4, 0, 3)
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
|
|
def test_dygraph_api(self):
|
|
self.input_data()
|
|
# case 1:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x_zero_size1)
|
|
z = paddle.roll(x, shifts=1)
|
|
np_z = z.numpy()
|
|
expect_out = np.array([]).reshape(0, 3)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
# case 2:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x_zero_size1)
|
|
z = paddle.roll(x, shifts=1, axis=0)
|
|
np_z = z.numpy()
|
|
expect_out = np.array([]).reshape(0, 3)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
# case 3:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x_zero_size2)
|
|
z = paddle.roll(x, shifts=1)
|
|
np_z = z.numpy()
|
|
expect_out = np.array([]).reshape(4, 0, 3)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
# case 4:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x_zero_size2)
|
|
z = paddle.roll(x, shifts=1, axis=0)
|
|
np_z = z.numpy()
|
|
expect_out = np.array([]).reshape(4, 0, 3)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
|
|
class TestRollAPI_Compatibility(unittest.TestCase):
|
|
def input_data(self):
|
|
self.data_x = np.array(
|
|
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
|
|
)
|
|
|
|
def test_roll_op_api_case1(self):
|
|
with static_guard():
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
|
|
data_x = np.array(
|
|
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
|
|
).astype('float32')
|
|
z = paddle.roll(input=x, shifts=1)
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
(res,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
expect_out = np.array(
|
|
[[9.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]
|
|
)
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
|
|
def test_roll_op_api_case2(self):
|
|
with static_guard():
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
|
|
data_x = np.array(
|
|
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
|
|
).astype('float32')
|
|
z = paddle.roll(x, 1, dims=0)
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
(res,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
expect_out = np.array(
|
|
[[7.0, 8.0, 9.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
|
|
)
|
|
np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
|
|
paddle.disable_static()
|
|
|
|
def test_dygraph_api(self):
|
|
self.input_data()
|
|
# case 1:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x)
|
|
z = paddle.roll(input=x, shifts=1)
|
|
np_z = z.numpy()
|
|
expect_out = np.array(
|
|
[[9.0, 1.0, 2.0], [3.0, 4.0, 5.0], [6.0, 7.0, 8.0]]
|
|
)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
# case 2:
|
|
with base.dygraph.guard():
|
|
x = paddle.to_tensor(self.data_x)
|
|
z = paddle.roll(input=x, shifts=1, dims=0)
|
|
np_z = z.numpy()
|
|
expect_out = np.array(
|
|
[[7.0, 8.0, 9.0], [1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
|
|
)
|
|
np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
|
|
|
|
def test_roll_op_false(self):
|
|
def test_axis_out_range():
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.static.data(name='x', shape=[-1, 3], dtype='float32')
|
|
data_x = np.array(
|
|
[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
|
|
).astype('float32')
|
|
z = paddle.roll(input=x, shifts=1, dims=10)
|
|
exe = base.Executor(base.CPUPlace())
|
|
(res,) = exe.run(
|
|
feed={'x': data_x},
|
|
fetch_list=[z],
|
|
return_numpy=False,
|
|
)
|
|
|
|
self.assertRaises(ValueError, test_axis_out_range)
|
|
paddle.disable_static()
|
|
|
|
def test_shifts_as_tensor_dygraph(self):
|
|
with base.dygraph.guard():
|
|
x = paddle.arange(9).reshape([3, 3])
|
|
shape = paddle.shape(x)
|
|
shifts = shape // 2
|
|
axes = [0, 1]
|
|
out = paddle.roll(input=x, shifts=shifts, dims=axes).numpy()
|
|
expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
|
|
np.testing.assert_allclose(out, expected_out, rtol=1e-05)
|
|
|
|
def test_shifts_as_tensor_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
x = paddle.arange(9).reshape([3, 3]).astype('float32')
|
|
shape = paddle.shape(x)
|
|
shifts = shape // 2
|
|
axes = [0, 1]
|
|
out = paddle.roll(input=x, shifts=shifts, dims=axes)
|
|
expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
|
|
|
|
exe = paddle.static.Executor(paddle.CPUPlace())
|
|
[out_np] = exe.run(fetch_list=[out])
|
|
np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)
|
|
|
|
if paddle.is_compiled_with_cuda() or is_custom_device():
|
|
exe = base.Executor(base.CPUPlace())
|
|
[out_np] = exe.run(fetch_list=[out])
|
|
np.testing.assert_allclose(out_np, expected_out, rtol=1e-05)
|
|
paddle.disable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|