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kukekuke
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Merge pull request #3949 from kuke/reshape_op_dev
Add reshape operator
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‎paddle/operators/reshape_op.cc‎

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/operators/reshape_op.h"
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namespace paddle {
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namespace operators {
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class ReshapeOp : public framework::OperatorWithKernel {
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public:
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ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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// input check
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
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auto shape = ctx.Attr<std::vector<int>>("shape");
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PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
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for (auto dim : shape) {
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PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
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}
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// capacity check
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int64_t capacity =
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std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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auto *in = ctx.Input<framework::Tensor>("X");
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int64_t in_size = framework::product(in->dims());
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PADDLE_ENFORCE_EQ(capacity, in_size,
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"The size of Input(X) mismatches with Attr(shape).");
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// resize output
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
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}
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};
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class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ReshapeOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of reshape operator.");
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AddOutput("Out", "The output tensor of reshape operator.");
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AddAttr<std::vector<int>>("shape", "Target shape of reshape operator.");
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AddComment(R"DOC(Reshape operator
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Reshape Input(X) into the shape specified by Attr(shape).
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An example:
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Given a 2-D tensor X with 2 rows and 2 columns
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[[1, 2], [3, 4]]
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with target shape = [1, 4], the reshape operator will transform
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the tensor X into a 1-D tensor:
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[1, 2, 3, 4]
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)DOC");
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}
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};
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class ReshapeGradOp : public framework::OperatorWithKernel {
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public:
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ReshapeGradOp(const std::string &type,
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const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
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PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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auto dims = ctx.Input<framework::Tensor>("X")->dims();
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auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_in->Resize(dims);
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(reshape, ops::ReshapeOp, ops::ReshapeOpMaker, reshape_grad,
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ops::ReshapeGradOp);
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REGISTER_OP_CPU_KERNEL(reshape,
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ops::ReshapeKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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reshape_grad, ops::ReshapeGradKernel<paddle::platform::CPUPlace, float>);

‎paddle/operators/reshape_op.cu‎

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/operators/reshape_op.h"
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REGISTER_OP_GPU_KERNEL(
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reshape,
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paddle::operators::ReshapeKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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reshape_grad,
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paddle::operators::ReshapeGradKernel<paddle::platform::GPUPlace, float>);

‎paddle/operators/reshape_op.h‎

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class ReshapeKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out = ctx.Output<framework::Tensor>("Out");
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auto* in = ctx.Input<framework::Tensor>("X");
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out->mutable_data<T>(ctx.GetPlace());
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auto shape = ctx.Attr<std::vector<int>>("shape");
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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out->CopyFrom<T>(*in, ctx.GetPlace());
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out->Resize(out_dims);
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}
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};
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template <typename Place, typename T>
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class ReshapeGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_x->mutable_data<T>(ctx.GetPlace());
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auto in_dims = d_x->dims();
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d_x->CopyFrom<T>(*d_out, ctx.GetPlace());
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d_x->Resize(in_dims);
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}
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};
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}
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}

‎paddle/pybind/pybind.cc‎

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USE_OP(top_k);
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USE_OP(squared_l2_distance);
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USE_OP(sum);
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USE_OP(reshape);
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namespace paddle {
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namespace framework {

‎python/paddle/v2/framework/tests/CMakeLists.txt‎

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py_test(mnist SRCS mnist.py)
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py_test(test_concat_op SRCS test_concat_op.py)
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py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
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py_test(test_reshape_op SRCS test_reshape_op.py)

‎python/paddle/v2/framework/tests/test_reshape_op.py‎

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import unittest
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import numpy as np
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from op_test import OpTest
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class TestReshapeOp(OpTest):
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def setUp(self):
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self.op_type = "reshape"
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self.inputs = {'X': np.random.random((10, 20)).astype("float32")}
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self.attrs = {'shape': [10 * 20]}
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self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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if __name__ == '__main__':
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unittest.main()

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