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222 lines
8.0 KiB
222 lines
8.0 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 paddle.fluid as fluid
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import numpy as np
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class BackwardNet(object):
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"""
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Abstract Base Class.
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All Net inherited this Class should implement two functions:
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build_model: build net to test the logic of backward
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init_data: fake input data to test all programs.
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"""
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def __init__(self):
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self.stop_gradient_grad_vars = set()
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self.no_grad_vars = set()
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self.params_names = set()
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self.op_path = []
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def build_model(self):
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"""
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Build net to test the logic of backward.
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:return: loss
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"""
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raise NotImplementedError
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def init_data(self):
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"""
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Fake input data to test all programs.
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:return: dict, {'var_name': var_data}
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"""
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raise NotImplementedError
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class TestBackward(unittest.TestCase):
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"""
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All related TestClass should inherit this class,
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and only implement test_backward function.
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"""
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def _check_all(self, net):
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place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = net.build_model()
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self._check_backward(loss, main)
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optimizer = fluid.optimizer.SGD(learning_rate=0.1)
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optimizer.minimize(loss)
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exe.run(startup)
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exe.run(feed=net.init_data())
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def _check_backward(self, loss, main_program):
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global_block_idx = self.global_block_idx
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params_grads = self._check_params_grad(loss)
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# 1.1 get_stop_gradients
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no_grad_dict = self._check_stop_gradient(main_program)
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# 1.2 find_op_path
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op_path, block_no_grad_set = self._check_op_path(
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main_program.block(global_block_idx), [loss], [], no_grad_dict)
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# 1.3 _find_no_grad_vars
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no_grad_vars = self._check_find_no_grad_vars(
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main_program.block(global_block_idx), op_path, [loss],
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block_no_grad_set)
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# update no_grad_dict
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block_no_grad_set.update(no_grad_vars)
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no_grad_dict[global_block_idx].update(
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list(map(fluid.backward._append_grad_suffix_, block_no_grad_set)))
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def _check_params_grad(self, loss, parameter_list=None, no_grad_set=None):
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params_grads = fluid.backward.append_backward(loss, parameter_list,
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no_grad_set)
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params_names = set(
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[param_var.name for (param_var, grad_var) in params_grads])
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self.assertSetEqual(params_names, self.net.params_names)
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return params_grads
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def _check_stop_gradient(self, program):
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no_grad_dict = fluid.backward._get_stop_gradients_(program)
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if no_grad_dict is not None and isinstance(no_grad_dict, dict):
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self.assertSetEqual(no_grad_dict[self.global_block_idx],
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self.net.stop_gradient_grad_vars)
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return no_grad_dict
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def _check_op_path(self, root_block, outputs, inputs=[], no_grad_dict=None):
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if no_grad_dict is None or not isinstance(no_grad_dict, dict):
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block_no_grad_set = None
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else:
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block_no_grad_set = set(
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map(fluid.backward._strip_grad_suffix_, no_grad_dict[
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self.global_block_idx]))
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op_path = fluid.backward._find_op_path_(root_block, outputs, inputs,
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block_no_grad_set)
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op_types = [op.type for op in op_path]
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self.assertListEqual(op_types, self.net.op_path)
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return op_path, block_no_grad_set
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def _check_find_no_grad_vars(self, root_block, op_path, targets,
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block_no_grad_set):
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no_grad_vars = fluid.backward._find_no_grad_vars(
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root_block, op_path, targets, block_no_grad_set)
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self.assertSetEqual(no_grad_vars, self.net.no_grad_vars)
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return no_grad_vars
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class SimpleNet(BackwardNet):
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def __init__(self):
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super(BackwardNet, self).__init__()
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self.stop_gradient_grad_vars = set([
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u'x_no_grad@GRAD', u'x2_no_grad@GRAD', u'x3_no_grad@GRAD',
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u'label_no_grad@GRAD'
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])
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self.no_grad_vars = set()
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self.params_names = set([u'w2v', u'fc_predict.b_0', u'fc_w'])
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self.op_path = [
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u'lookup_table_v2',
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u'lookup_table_v2', # embedding
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u'elementwise_add', # merge
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u'mul',
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u'elementwise_add',
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u'softmax', # fc
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u'elementwise_sub',
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u'square',
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u'mean'
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] # loss
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self.shape = [16, 50]
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def init_data(self):
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assert len(self.shape) == 2
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x = np.random.randint(0, 90, self.shape).astype('int64')
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x2 = np.random.randint(0, 90, self.shape).astype('int64')
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x3 = np.random.randint(0, 90, self.shape).astype('int64')
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label = np.random.random([self.shape[0], 1]).astype('float32')
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return {
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'x_no_grad': x,
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'x2_no_grad': x2,
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'x3_no_grad': x3,
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'label_no_grad': label
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}
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def build_model(self):
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# stop_gradient = True in input
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x = fluid.data(name='x_no_grad', shape=self.shape, dtype='int64')
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x2 = fluid.data(name='x2_no_grad', shape=self.shape, dtype='int64')
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x3 = fluid.data(name='x3_no_grad', shape=self.shape, dtype='int64')
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label = fluid.data(
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name='label_no_grad', shape=[self.shape[0], 1], dtype='float32')
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# shared layer, the grad of 'w2v' will be summed and renamed.
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# To test _addup_repetitive_outputs_
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x_emb = fluid.embedding(
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x, size=[100, 64], param_attr=fluid.ParamAttr(name='w2v'))
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x2_emb = fluid.embedding(
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x2, size=[100, 64], param_attr=fluid.ParamAttr(name='w2v'))
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x3_emb = fluid.embedding(
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x3, size=[100, 64], param_attr=fluid.ParamAttr(name='w2v'))
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# merge layers
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x_merge = fluid.layers.elementwise_add(x_emb, x2_emb, name='x_add_x2')
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x2_merge = fluid.layers.elementwise_add(
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x2_emb, x3_emb, name='x2_add_x3')
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# shared fc_w
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predict = fluid.layers.fc(input=x_merge,
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size=1,
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act='softmax',
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param_attr=fluid.ParamAttr(name='fc_w'),
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name='fc_predict')
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# useless layer for calculating loss
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fc_no_use = fluid.layers.fc(input=x2_merge,
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size=1,
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act='sigmoid',
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param_attr=fluid.ParamAttr(name='fc_w'),
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name='fc_no_use')
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# loss
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cost = fluid.layers.square_error_cost(input=predict, label=label)
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loss = fluid.layers.mean(cost, name='mean_loss')
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return loss
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class TestSimpleNet(TestBackward):
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def test_backward(self):
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"""
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Instantiate each NetClass to test backward.
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"""
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self.global_block_idx = 0
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self.net = SimpleNet()
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self._check_all(self.net)
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# TODO(Aurelius84): add conditional network test
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class ConditionalNet(BackwardNet):
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def __init__(self):
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super(BackwardNet, self).__init__()
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if __name__ == '__main__':
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unittest.main()
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