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85 lines
3.0 KiB
85 lines
3.0 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 numpy as np
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from paddle.fluid.executor import Executor
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from paddle.fluid.backward import append_backward
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from paddle.fluid.layers.control_flow import ConditionalBlock
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class ConditionalBlockTest(unittest.TestCase):
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def test_forward(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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data = layers.data(name='X', shape=[1], dtype='float32')
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data.stop_gradient = False
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cond = ConditionalBlock(inputs=[data])
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out = layers.create_tensor(dtype='float32')
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with cond.block():
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hidden = layers.fc(input=data, size=10)
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layers.assign(hidden, out)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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exe.run(startup_program)
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x = np.random.random(size=(10, 1)).astype('float32')
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outs = exe.run(main_program, feed={'X': x}, fetch_list=[out])[0]
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print(outs)
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loss = layers.mean(out)
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append_backward(loss=loss)
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outs = exe.run(
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main_program,
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feed={'X': x},
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fetch_list=[main_program.block(0).var(data.name + "@GRAD")])[0]
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print(outs)
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class TestConditionalBlockOpInferShape(unittest.TestCase):
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def test_infer_shape(self):
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main_program = fluid.Program()
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startup_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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global_block = main_program.global_block()
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sub_block = main_program._create_block()
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main_program._rollback()
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step_scope = global_block.create_var(
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type=core.VarDesc.VarType.STEP_SCOPES)
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cond_var = layers.fill_constant(
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shape=[1], dtype='bool', value=False)
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op = global_block.append_op(
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type='conditional_block',
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inputs={
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'Cond': [cond_var],
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'Input': [],
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},
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outputs={'Out': [],
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'Scope': [step_scope]},
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attrs={'sub_block': sub_block,
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'is_scalar_condition': True})
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op.desc.infer_shape(global_block.desc)
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if __name__ == '__main__':
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unittest.main()
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