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226 lines
8.2 KiB
226 lines
8.2 KiB
# Copyright (c) 2018 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 paddle
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import paddle.fluid.layers as layers
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from paddle.fluid.framework import Program, program_guard
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from paddle.fluid.executor import Executor
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from paddle.fluid.optimizer import MomentumOptimizer
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid.layers.control_flow import split_lod_tensor
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from paddle.fluid.layers.control_flow import merge_lod_tensor
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from paddle.fluid.layers.control_flow import ConditionalBlock
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import unittest
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import numpy as np
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class TestMNISTIfElseOp(unittest.TestCase):
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# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
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def not_test_raw_api(self):
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prog = Program()
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startup_prog = Program()
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with program_guard(prog, startup_prog):
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image = layers.data(name='x', shape=[784], dtype='float32')
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label = layers.data(name='y', shape=[1], dtype='int64')
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limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
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cond = layers.less_than(x=label, y=limit)
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true_image, false_image = split_lod_tensor(input=image, mask=cond)
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true_out = layers.create_tensor(dtype='float32')
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true_cond = ConditionalBlock([cond])
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with true_cond.block():
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hidden = layers.fc(input=true_image, size=100, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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layers.assign(input=prob, output=true_out)
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false_out = layers.create_tensor(dtype='float32')
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false_cond = ConditionalBlock([cond])
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with false_cond.block():
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hidden = layers.fc(input=false_image, size=200, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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layers.assign(input=prob, output=false_out)
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prob = merge_lod_tensor(
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in_true=true_out, in_false=false_out, mask=cond, x=image)
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loss = layers.cross_entropy(input=prob, label=label)
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avg_loss = layers.mean(loss)
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optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
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optimizer.minimize(avg_loss, startup_prog)
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=10)
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(startup_prog)
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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x_data = np.array([x[0] for x in data]).astype("float32")
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y_data = np.array([x[1] for x in data]).astype("int64")
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y_data = np.expand_dims(y_data, axis=1)
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outs = exe.run(prog,
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[avg_loss])
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print(outs[0])
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if outs[0] < 1.0:
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return
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self.assertFalse(True)
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# FIXME: https://github.com/PaddlePaddle/Paddle/issues/12245#issuecomment-406462379
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def not_test_ifelse(self):
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prog = Program()
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startup_prog = Program()
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with program_guard(prog, startup_prog):
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image = layers.data(name='x', shape=[784], dtype='float32')
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label = layers.data(name='y', shape=[1], dtype='int64')
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limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
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cond = layers.less_than(x=label, y=limit)
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ie = layers.IfElse(cond)
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with ie.true_block():
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true_image = ie.input(image)
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hidden = layers.fc(input=true_image, size=100, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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ie.output(prob)
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with ie.false_block():
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false_image = ie.input(image)
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hidden = layers.fc(input=false_image, size=200, act='tanh')
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prob = layers.fc(input=hidden, size=10, act='softmax')
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ie.output(prob)
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prob = ie()
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loss = layers.cross_entropy(input=prob[0], label=label)
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avg_loss = layers.mean(loss)
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optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
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optimizer.minimize(avg_loss, startup_prog)
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=200)
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(startup_prog)
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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x_data = np.array([x[0] for x in data]).astype("float32")
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y_data = np.array([x[1] for x in data]).astype("int64")
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y_data = y_data.reshape((y_data.shape[0], 1))
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outs = exe.run(prog,
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feed={'x': x_data,
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'y': y_data},
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fetch_list=[avg_loss])
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print(outs[0])
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if outs[0] < 1.0:
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return
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self.assertFalse(True)
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class TestIfElse(unittest.TestCase):
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def set_test_case(self):
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# condiction is: self.data < self.cond_value
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self.cond_value = 0.5
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self.data = np.random.rand(25, 1).astype(np.float32)
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def numpy_cal(self):
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s1 = self.data[np.where(self.data < self.cond_value)]
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res = np.sum(np.exp(s1))
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s2 = self.data[np.where(self.data >= self.cond_value)]
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res += np.sum(np.tanh(s2))
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return res
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def compare_ifelse_op_and_numpy(self, place):
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self.set_test_case()
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prog = Program()
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startup_prog = Program()
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with program_guard(prog, startup_prog):
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src = layers.data(name='data', shape=[1], dtype='float32')
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cond = layers.fill_constant(
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[1], dtype='float32', value=self.cond_value)
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ifcond = layers.less_than(x=src, y=cond)
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ie = layers.IfElse(ifcond)
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with ie.true_block():
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true_target = ie.input(src)
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true_target = fluid.layers.exp(true_target)
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ie.output(true_target)
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with ie.false_block():
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false_target = ie.input(src)
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false_target = fluid.layers.tanh(false_target)
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ie.output(false_target)
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if_out = ie()
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out = layers.reduce_sum(if_out)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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fetch_list = [out]
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o1, = exe.run(fluid.default_main_program(),
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feed={'data': self.data},
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fetch_list=[out])
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o2 = self.numpy_cal()
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self.assertTrue(
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np.allclose(
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o1, o2, atol=1e-8),
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"IfElse result : " + str(o1) + "\n Numpy result :" + str(o2))
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def test_cpu(self):
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self.compare_ifelse_op_and_numpy(fluid.CPUPlace())
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def test_cuda(self):
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if not core.is_compiled_with_cuda():
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return
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self.compare_ifelse_op_and_numpy(fluid.CUDAPlace(0))
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class TestIfElseTrueBranch(TestIfElse):
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def set_test_case(self):
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# condiction is: self.data < self.cond_value
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self.cond_value = 10.
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self.data = np.random.rand(25, 1).astype(np.float32)
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class TestIfElseFalseBranch(TestIfElse):
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def set_test_case(self):
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# condiction is: self.data < self.cond_value
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self.cond_value = -10.
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self.data = np.random.rand(25, 1).astype(np.float32)
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if __name__ == '__main__':
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unittest.main()
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