未验证 提交 0677a1c1 编写于 作者: H Huihuang Zheng 提交者: GitHub

Fix That conditional_block_op Doesn't Have InferShape (#21733)

上级 ac666b8a
......@@ -74,6 +74,15 @@ class ConditionalBlockOp : public ConditionalOp {
}
};
class ConditionalBlockInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE_EQ(context->HasInputs(ConditionalOp::kCondition), true,
platform::errors::InvalidArgument(
"conditional_block_op must have condition input"));
}
};
class ConditionalBlockGradOp : public ConditionalOp {
public:
ConditionalBlockGradOp(const std::string &type,
......@@ -278,6 +287,7 @@ class ConditionalBlockGradMaker : public framework::SingleGradOpMaker<T> {
namespace ops = paddle::operators;
REGISTER_OPERATOR(conditional_block, ops::ConditionalBlockOp,
ops::ConditionalBlockInferShape,
ops::ConditionalBlockOpProtoMaker,
ops::ConditionalBlockGradMaker<paddle::framework::OpDesc>);
REGISTER_OPERATOR(conditional_block_grad, ops::ConditionalBlockGradOp,
......
......@@ -14,18 +14,21 @@
from __future__ import print_function
import numpy as np
import unittest
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.core as core
from paddle.fluid.framework import default_startup_program, default_main_program
from paddle.fluid.executor import Executor
from paddle.fluid.backward import append_backward
from paddle.fluid.layers.control_flow import ConditionalBlock
import numpy
class ConditionalBlockTest(unittest.TestCase):
def test_forward(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data = layers.data(name='X', shape=[1], dtype='float32')
data.stop_gradient = False
cond = ConditionalBlock(inputs=[data])
......@@ -36,21 +39,46 @@ class ConditionalBlockTest(unittest.TestCase):
cpu = core.CPUPlace()
exe = Executor(cpu)
exe.run(default_startup_program())
exe.run(startup_program)
x = numpy.random.random(size=(10, 1)).astype('float32')
x = np.random.random(size=(10, 1)).astype('float32')
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
outs = exe.run(main_program, feed={'X': x}, fetch_list=[out])[0]
print(outs)
loss = layers.mean(out)
append_backward(loss=loss)
outs = exe.run(
main_program,
feed={'X': x},
fetch_list=[
default_main_program().block(0).var(data.name + "@GRAD")
])[0]
fetch_list=[main_program.block(0).var(data.name + "@GRAD")])[0]
print(outs)
class TestConditionalBlockOpInferShape(unittest.TestCase):
def test_infer_shape(self):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
global_block = main_program.global_block()
sub_block = main_program._create_block()
main_program._rollback()
step_scope = global_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
cond_var = layers.fill_constant(
shape=[1], dtype='bool', value=False)
op = global_block.append_op(
type='conditional_block',
inputs={
'Cond': [cond_var],
'Input': [],
},
outputs={'Out': [],
'Scope': [step_scope]},
attrs={'sub_block': sub_block,
'is_scalar_condition': True})
op.desc.infer_shape(global_block.desc)
if __name__ == '__main__':
unittest.main()
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